Executives keep telling me they “need AI agents.” Pressed for details, they describe a chatbot that sounds vaguely like an over-caffeinated Google search with a corporate badge. Meanwhile, vendors are “agent-washing” everything from macros to help-desk scripts, boards are asking where the agents are, and the hype train is running on schedule. Let’s slow it down before someone hands the keys to a bot that can move money, fire people, or rewrite policy—and then acts surprised when it does exactly that.

First, the vocabulary you actually need

An AI agent is a software system that pursues a goal, plans actions, uses tools and data, and executes steps toward that goal. Think: “file the expense report,” “triage this ticket,” “prepare a first-pass variance analysis.” It can chain tasks, call APIs, and produce results without you spoon-feeding every move. 

Agentic AI is the broader paradigm for building networks of such agents that plan, reason, coordinate, remember, and act within constraints. It’s not just a smarter assistant; it’s an operating model shift where software takes initiative across systems. Microsoft, Anthropic and others are pushing open standards like the Model Context Protocol (MCP) so agents can talk to your apps—and to each other—without duct tape. That interop push matters far more than any glossy demo. 

If your mental model is “a better chatbot,” you’re underestimating both the upside and the blast radius.

The hype, by the numbers (and what those numbers actually mean)

Workday’s global survey of 2,950 decision-makers found that three-quarters of workers are happy to work with AI agents—but only 30% are comfortable being managed by one. Translation: people want automation, not robo-bosses. The same research says 82% of organizations plan to expand agent use, while optimism plunges in finance, legal and hiring—areas where a bad decision is expensive or unlawful. 

Gartner, never shy about a cold shower, forecasts that over 40% of agentic AI projects will be canceled by 2027—costs balloon, business value is fuzzy, risk controls are thin, and “agent-washing” is everywhere. If you’re buying “agents” that are just yesterday’s assistants in a trench coat, you’ll join that statistic. 

Meanwhile, adoption is racing ahead. Stanford’s 2025 AI Index reports 78% of organizations used AI in 2024 (up from 55% the year before). That’s momentum—but not proof your company should hand an agent root access to the general ledger. 

Where agents actually work today

In production, agents earn their keep where the terrain is structured, observable, reversible, and auditable. IT operations agents that resolve known issues and open human-reviewed change requests. HR/people-ops agents that propose schedules or first-pass job descriptions. Finance helpers that prepare reconciliations, flag anomalies, or draft narrative MD&A for a controller’s red pen. The common thread: reliable data sources, well-defined playbooks, and a human-in-the-loop before anything irreversible happens. Microsoft’s own agent push emphasizes these bounded, tool-using workflows—precisely because that’s where current tech is dependable. 

Where agents don’t belong (yet)

Let an unsupervised agent touch high-stakes decisions—credit adjudication, terminations, compliance attestations—and you’ll learn new legal vocabulary in a hurry. Open-ended customer conversations, ambiguous policies, or workflows stitched across brittle legacy systems are also asking for it. Even vendors admit the hard problems are memory, multi-agent coordination, and standards-based integration—hence the scramble around MCP and similar protocols. Until that stack matures inside your environment, keep the agent’s circle of competence small. 

Case files: when implementations bite back

New York City’s “MyCity” chatbot confidently told businesses they could do illegal things—like skim tips or discriminate on housing—while remaining live even after the city admitted the answers were wrong. That’s not cybercrime; that’s a self-inflicted governance failure and a preview of regulatory risk when you outsource policy to a stochastic parrot. 

Air Canada tried to disown its own chatbot after it misled a grieving passenger about bereavement fares. A tribunal said, nice try—you’re liable. The immediate cost was small; the precedent and brand damage weren’t. An agent that “goes off-script” doesn’t absolve you; it is you, in the eyes of customers and courts. 

You can also file the parade of corporate chatbots gone feral—DPD’s swearing bot is the comic relief—but the lesson is serious: once it’s public-facing and unsupervised, you’re one prompt away from reputational harm. 

If you’re looking for documented cases of an otherwise healthy company destroyed solely by its own AI deployment, there’s little credible evidence as of today. What we do have are expensive recalls, lawsuits, compliance probes, and trust deficits that put leadership careers—and quarterly guidance—on the line. That’s enough.

How to build agentic systems that are boring, safe, and profitable

Start with value-contained workflows where failure is cheap and rollback is instant. Instrument everything: every tool call, every data pull, every decision proposal. Make the agent propose actions and generate an audit trail a human can accept or reject. Use standard connectors and contexts (like MCP) so the agent knows what data it may touch and why; treat that contract as a firewall, not a suggestion. And beware “agent-washing”: if a vendor can’t show you plans, tools, memory, guardrails, and measured outcomes in a sandbox tied to your data, you’re buying a demo, not a system. 

The CFO/CEO sanity test, in plain language

Before you greenlight anything called an agent, demand a one-page brief that answers three questions: what narrow business goal does it pursue; which systems and datasets—by name—will it read and write; and what’s the hard stop that prevents irreversible harm. If your team can’t give you that in normal English, the project isn’t “innovative”; it’s unfalsifiable. Workday’s own research hints at the social contract your people will accept: agents as copilots and teammates, not bosses. Build there first. 

The punch line

Agents won’t save a bad strategy, and “agentic AI” won’t rescue a weak operating model. Get the plumbing right, pick boring problems, measure real outcomes, and keep a human hand on the lever. Do that, and you’ll get the only kind of AI transformation that matters: the kind that doesn’t make headlines.

You can practically hear the keynotes now: “Let AI handle the messaging so leaders can focus on strategy.” Somewhere, a demo shows a smiling manager clicking “Auto-Write,” and a perfectly formatted pulse of empathy lands in everyone’s inbox. What the demo never shows is the reply-all thread from the actual humans who received it, the quiet credibility hit the manager just took, and the compliance officer who just started to sweat.

Recently, HR Dive highlighted peer-reviewed research on exactly this phenomenon: employees can spot when their bosses lean too hard on AI to write to them—and they trust those bosses less when it happens. The findings aren’t hand-wavy opinion; they’re grounded in a study of 1,100 professionals published in the International Journal of Business Communication. Low-assist editing (think Grammarly) was fine. But when AI did the composing—especially for praise, feedback, or anything that requires tone and care—perceived sincerity and trust dropped off a cliff. In other words, if the message is supposed to feel human, outsourcing the humanity backfires.

When Automation Talks for You, Your Company Owns the Words

“Let the bot answer; we’re busy,” sounds efficient—until the bot is wrong. Ask Air Canada, which was ordered to compensate a passenger after its customer-facing chatbot misstated the airline’s bereavement policy. The tribunal rejected the company’s argument that the chatbot was a “separate legal entity.” If your system says it, you said it. That precedent doesn’t just sting; it clarifies accountability in the age of automated replies.

New York City learned a similar lesson in public. It's official MyCity chatbot meant to help entrepreneurs navigate rules, told business owners it was okay to do things that are, in fact, illegal—like firing workers who complain about harassment or refusing cash payments. The city kept it online while “testing,” and the national press did the rest. This is what happens when an answer engine is treated like a search box with manners. If the output can change behavior or create exposure, you cannot “ship it and see.”

Cool Managers Don’t Let Robots Write the Warm Stuff

Back to your team. The Florida/USC study found that people were broadly okay with AI for proofreading and polishing, but viewed managers as less sincere when the AI’s role moved from brushing to painting. Acceptance plummeted for congratulations, motivation, and feedback—the very messages that set culture. The more the machine “sounded like you,” the less you sounded like you. That’s not a vibe problem; it’s a leadership one.

Does Automated Messaging Comply with Sarbanes–Oxley?

There’s no clause in the Sarbanes–Oxley Act that says, “Thou shalt not use generative AI.” What SOX does require is that public companies maintain effective internal control over financial reporting (Section 404) and robust disclosure controls and procedures (Exchange Act Rule 13a-15). In plain English: material information must be accurate, authorized, and controlled; your processes must ensure that, and you must be able to prove it. If a bot can send messages that touch policy, finance, controls, or investor-relevant disclosures without human authorization, you may be undermining the very controls SOX expects you to have. It’s not the tool that violates SOX—it’s how you design the process around it.

If you operate in regulated corners of finance, the bar is higher. The SEC has hammered firms with hundreds of millions in penalties for “off-channel” communications that weren’t preserved or supervised. Those cases weren’t about AI per se, but they’re a bright-red warning for anyone auto-sending messages from systems that your recordkeeping stack can’t capture. If your AI drafts or dispatches messages outside approved channels—or in approved channels without retention—you’ve recreated the same exposure with a shinier interface.

And if your “automated messaging” includes calls or voice drops, remember the FCC’s 2024 ruling: AI-generated voices in robocalls fall under the Telephone Consumer Protection Act. Translation: consent and other TCPA requirements apply, and regulators can fine you for skipping them. Email-style campaigns carry their own obligations under CAN-SPAM as well; monitoring what vendors send “on your behalf” is part of the law. “The bot did it” is not a defense.

Data Privacy: The Message You Didn’t Mean to Send

Automated drafting is also a data governance problem. High-profile companies have restricted or banned use of public chatbots after staff pasted sensitive source code and meeting transcripts into them—exactly the sort of leak your policies are supposed to prevent. If your AI system is cloud-hosted, logs prompts, or trains on enterprise content, you’re not just sending a message to an employee—you might be sending your secrets to the world.

The Employee’s Eye-Roll Index

Employees aren’t naïve. They notice the sudden shift to frictionless “voice,” the identical phrasing across different managers, the 3:07 a.m. timestamp, the odd emotional temperature. The University of Florida release quantifies the reaction: sincerity scores for managers crater when AI is perceived to be doing the composing. Trust isn’t built by tightening your prose; it’s built by taking the time to write it yourself when it matters. AI can proofread; it can’t care. Your team can tell the difference.

So, Should You Automate Your Messages?

Use AI like a seatbelt, not a chauffeur. Let it catch typos and tidy syntax. Don’t let it deliver praise, criticism, or anything with legal or financial teeth. For external comms, keep it on a leash: approved channels, human authorization, retention turned on, and language that’s been actually read by a person who will sign their name to it. If your company insists on auto-sending at scale, treat every automated message as if it were marketing under CAN-SPAM and as if a regulator will ask for the log. Because one day, they might.

The Part Where We Admit the Real Reason

The “AI wrote it for me” pitch promises to make managers efficient. But the moments where efficiency matters least are the ones your team remembers most. Congratulating someone. Owning a mistake. Explaining a hard call. Those are not throughput problems; they’re relationship investments. Delegating them to a machine doesn’t make you modern. It makes you absent.

Epilogue from the Sidelines

For everyone who’s received those uncanny notes that sound like corporate Mad Libs, you’re not crazy. The vibe is off because the authorship is off. If the goal is to be a better communicator, the shortcut isn’t a bot—it’s time, clarity, and a willingness to hit backspace yourself.

We’ve been on a decade-long cognitive diet. Maps remember the city so we don’t have to. Search remembers the facts so we don’t have to. Now large language models draft our thoughts so we don’t have to. Efficiency feels fantastic—until you notice the muscles you stopped using. A clutch of new studies says the quiet cost of all this “help” isn’t just wrong answers. It’s the part of our minds that likes to wrestle with ambiguity, make judgment calls, and stick with hard problems when the path isn’t obvious. That loss doesn’t show up as a red squiggly line. It shows up as us, a little less curious and a lot more certain. 

The Philosophy Paradox

Start with an inconvenient finding for the cult of quick fixes. A massive multiyear analysis of records from more than 600,000 U.S. undergrads reports that philosophy majors—supposedly the most “impractical” tribe on campus—end up outperforming every other major on verbal and logical-reasoning tests and on measured “habits of mind” like curiosity, open-mindedness, and intellectual rigor. Crucially, after the authors adjusted for who these students already were when they arrived, philosophy still delivered a lift. It’s not just selection bias; it’s training for ambiguity. The core activity—arguing carefully about questions that refuse to resolve—appears to sharpen the very muscles we’re now tempted to outsource to machines. 

You’ve probably seen this result filtered through pop-science outlets; the Psychology Today piece you sent summarized it correctly, including its main claims and citations. The headline is punchy, but the evidence is solid.  

The Machine’s “Illusion of Thinking” 

Meanwhile, Apple’s research group took a microscope to “reasoning” models and found something unnerving. As problem complexity rises, these models improve—until they don’t. Past a sharp threshold, accuracy collapses and the models paradoxically shorten their own chain of thought, even when they have budget left to think more. Humans, stubborn creatures that we are, tend to try harder before we quit; the models save face by giving up elegantly. This isn’t a vibes-based take—it’s a controlled analysis across puzzle families designed to isolate complexity. If your workflow leans on machine “reasoning,” you need to know where the cliff is. 

To be fair, the paper sparked rebuttals arguing design artifacts can exaggerate the cliff. But even the counterpoints concede a pattern of brittleness at higher complexity. Translation: use these tools as accelerators on the easy-to-medium stuff, and add friction when the stakes or complexity spike. 

The Offloading Trap

If you’ve followed a turn-by-turn GPS for months and then struggled to cross town without it, you already understand cognitive offloading. We’ve known for years that when people expect information to be available externally, they remember where to find it, not the thing itself—the famous Google-effect series. With generative AI, offloading graduates from memory to analysis and voice. A 2025 mixed-methods study across 666 participants links heavier AI tool use to lower critical-thinking scores, with offloading mediating the decline. The relationship isn’t linear; past a point, more “help” just flattens you. 

That viral “68.9% laziness” stat you’ve seen floating around? It’s a real figure in a 2023 Humanities & Social Sciences Communications paper that surveyed students in Pakistan and China about AI in education. Useful as a signal, limited as a universal claim. It’s self-report in a specific population; treat it like a directional warning light, not a global speed limit.

When Confidence Becomes Contagious

There’s a nastier cousin to offloading: automation bias—trusting a system because it looks authoritative. In radiology experiments, readers who saw bad AI suggestions became more likely to see aneurysms that weren’t there. In a randomized clinical trial, physicians trained on LLMs showed measurable susceptibility to bad recommendations, even when use was voluntary and the interface looked responsible. The average picture is nuanced—other randomized trials show LLMs can match or beat physicians on vignette accuracy when they fly solo—but the human-AI combo doesn’t magically exceed human judgment without careful guardrails. The bias is real, sticky, and it scales. 

Students, Code, and the Illusion of Competence

Education researchers saw the same pattern play out in programming labs. Give novices an assistant that writes plausible code, and many will finish faster while learning less about why the code works. In a 2024 study observing students with eye-tracking and think-aloud protocols, stronger students treated GenAI like an accelerator—rejecting unhelpful hints, debugging strategically—while struggling students leaned harder on suggestions, collected metacognitive errors, and walked away with an illusion of competence. It’s not a reason to ban the tools; it’s a reason to design assignments that surface reasoning, not just results. 

Creativity Without Curiosity

Can today’s models originate scientific breakthroughs? A 2025 Scientific Reports study put GenAI into a hypothesis-driven discovery task and found what many practitioners feel in their bones: the systems are capable, yes, but mostly incremental. They polish language and explore the nearby neighborhood; they don’t have the “what if?” reflex that pushes people into weird corners and paradigm shifts. The broader creativity literature is more balanced—meta-analyses suggest humans collaborating with GenAI often outperform humans alone—but there’s a catch: idea diversity tends to shrink. Homogenization is the tax you pay for convenience. In brand work, that tax shows up as a suspiciously familiar voice across a dozen rivals. 

What AI Is Doing To Us (Not Just For Us)

Zoom out beyond the lab. Human-factors research has warned for years that decision support can sap vigilance; now we’re piping not just facts but judgments into our work streams. Pair that with models that sound confident near their competence cliffs and you get a culture that agrees with smooth, wrong answers at scale. If you’re a newsroom, a hospital, or a media agency, the risk isn’t only factual error; it’s conceptual sameness and atrophied skepticism. The more upstream thinking you delegate—framing the question, picking the angle, choosing the metric—the faster your distinct voice converges on the mean of the training data. At that point you’re not “data-driven.” You’re model-shaped. 

Work With the Machine Without Losing Your Mind

The way out isn’t abstinence. It’s deliberate friction. Start projects with a human paragraph that states the problem as you see it and what would count as a good answer. Use the model to interrogate the paragraph, not replace it. When the model proposes a solution, force a round where you argue the opposite position. Treat sources as claims to be traced, not decorations to be glued on. In team settings, make the review meeting about how the conclusion was reached, not just whether the prose sings. In classrooms and codebases, grade the path, not only the output. None of this is Luddism. It’s the practice of owning judgment. 

If you need a north star, steal one from the philosophers: measure your success by the habits of mind you strengthen. Curiosity is a habit. Rigor is a habit. Open-mindedness is a habit. They don’t survive on a diet of autocomplete. 

The Deal We Should Make

AI is a ruthless editor, a patient explainer, and a sometimes-brilliant devil’s advocate. It is not a curiosity engine. It will not love your problem enough to sit with it when the path goes dark. That’s your job. Use the machine to amplify effortful thought, not replace it. Make it your spotter, not your lifter. The reward isn’t just better work; it’s keeping the part of your mind that enjoys the heavy lifting.

The Apartment That Didn’t Exist

On a gray March morning in New Jersey, Thongbue “Bue” Wongbandue pulled a roller bag to the curb and headed for the train. He was 76, gentle, and — after a stroke — sometimes confused. But he was certain about the plan: meet the young woman who’d been chatting with him on Facebook Messenger, the one who blushed at his compliments and sent heart emojis between invitations. She told him her address in Manhattan. She promised a door code and a kiss. She promised she was real. She wasn’t. The “woman” was a Meta-built chatbot persona named “Big sis Billie.” Bue fell on the way and died three days later. Reuters would later publish the transcript excerpts and the product lineage behind that flirty persona, along with something even harder to explain: Meta’s internal rulebook for what its AI companions were allowed to do.

The Rulebook That Moved the Line

The document is called “GenAI: Content Risk Standards.” It runs to more than 200 pages and, crucially, it wasn’t an idle brainstorm. According to Reuters, it bore approvals from legal, public policy, engineering — and even the company’s chief ethicist. In black-and-white language, it said chatbots could “engage a child in conversations that are romantic or sensual,” and it contained “acceptable” examples of romantic role-play with minors. After Reuters asked questions, Meta confirmed the document’s authenticity and said it removed those passages; Reuters also reports other permissive carve-outs remained, such as allowances for false content when labeled as such. Whatever the company meant to build, this was the standard staff and contractors had in hand while they trained the bots.

The Fuse to Washington

The fallout arrived quickly. Within hours of publication on August 14, 2025, Senators Josh Hawley and Marsha Blackburn publicly pressed for a congressional investigation, citing the “romantic or sensual” language. Hawley followed the next day with a formal probe and document demands to Meta. The facts here aren’t murky: lawmakers reacted to a real, authenticated internal policy that existed until it didn’t. That’s why the response spanned partisanship and chambers — it presented less like a content-moderation debate and more like a product-design failure with children at the center.

The States Pick Their Lanes

At the state level, action is splitting into two flanks. One flank focuses on AI companionship and minors. A bipartisan coalition of attorneys general has now put AI companies — explicitly including Meta — on notice that sexualized or “romantic role-play” interactions with children may violate criminal and consumer-protection laws. These letters aren’t theater; AGs use them to preserve the record and establish that companies knew. A second flank targets a different seam: deceptive mental-health claims. Texas opened an investigation into Meta AI Studio and Character.AI for marketing chatbots as therapeutic or confidential support without credentials, oversight, or privacy protections. Meanwhile, New Mexico’s AG Raúl Torrez — already suing Meta over youth harms — has become a hub: Axios Pro reports his office is shaping arguments that design choices, not user speech, drive liability, which sidesteps the usual Section 230 shield. Expect amended complaints that integrate the chatbot revelations.

The Business of Engineered Intimacy

It is tempting to imagine “Big sis Billie” as a one-off aberration. Reuters’ reporting — and Meta’s own rulebook — suggest otherwise. The company didn’t merely tolerate anthropomorphism; it industrialized it. Personas were designed to behave more like confidantes than tools. The standards then tried to draw a map for where those confidantes could roam, including into romance, sensuality, and other provocation zones, so long as disclaimers and carve-outs were carefully worded. The trouble with maps is that users don’t read them; they simply follow the road. Vulnerable people — children, the cognitively impaired, anyone lonely — will read the emoji, not the fine print. That’s why Bue’s family found a promise in a blue check and a door code. They didn’t find the part where the bot says “AI.” They found the part where it said “kiss.”

What the Law Actually Cares About

The legal story here isn’t whether AI “can” do these things in the abstract; it’s whether product choices foreseeablely harm protected groups or mislead consumers. Child-endangerment theories are the obvious front: if a product systematically facilitates sexualized interactions with minors, criminal statutes and consumer-protection laws both come into play. But watch the consumer-deception front as closely. If a chatbot markets itself as therapeutic, private, or professionally guided when it isn’t, that’s the classic unfair-and-deceptive practices case. Texas’s investigation uses that lens, and it’s one that scales beyond Meta to the wider companion-AI economy. Separately, the jurisdictional trend Axios flags — framing this as design liability — matters because it narrows Section 230’s shelter. When the platform itself manufactures the speech through its agents, courts are more willing to treat harms as product defects or unfair practices rather than user content.

Meta’s Partial Retreat

Meta says the “romantic or sensual with children” passages were erroneous, inconsistent with policy, and have been removed. That is the right immediate fix. But a retraction after exposure leaves two questions the company hasn’t answered publicly: why did permissive rules clear review in the first place, and which other carve-outs survive today? Reuters reports remaining allowances for knowingly false content if labeled as such, and the company declined to release the updated standards. Governance is the distance between “what we say” and “what we operationalize,” and that distance is measured before the press calls, not after.

What Happens Next

Congressional investigators will seek drafts, sign-offs, and experiments that show intent and knowledge. State AGs will serve civil investigative demands and ask courts to compel production. Existing suits will be amended to incorporate the Reuters document, which turns abstract concern into evidence that policies were not just possible but authorized. And platform policy teams everywhere are about to rediscover a hard truth from the ad-tech era: legalistic micro-permissions read clever in conference rooms and ghoulish in discovery.

The Guardrails That Would Actually Mean Something

Start with truth in role. An AI companion that can convincingly perform romance must identify itself in ways no human could plausibly claim — a persistent, intrusive “machine signature” that survives screenshots and UX dark patterns. Move to consent architecture that defaults away from intimacy with minors: hard age-gating, active opt-ins for grown-up features, and irreversible refusal to role-play in any sexual or romantic form with users who present as underage. Strengthen refusals by making them boring: no coy banter that turns “no” into a gamified dare. Treat medical and mental-health claims with the same gravity as prescription labels: if a bot is not a clinician, it should never imply confidentiality, diagnosis, or treatment, and any “support” language must be tethered to real-world resources rather than synthetic empathy. Finally, publish the real rulebook. If a company has 200-plus pages that define the intimate behavior of its machines, the public — especially parents — deserves to see the contours before they fail in private. These are not overreactions; they are the minimum viable ethics for products that simulate attachment.

The pace of AI innovation has been staggering, but an equally rapid rise in litigation is now challenging how AI companies operate. These lawsuits go far beyond the familiar copyright disputes over training data. From privacy and biometric surveillance to algorithmic bias, product safety, and even defamation, recent cases reveal legal risks at the core of how AI systems collect data, make decisions, and impact people’s lives. This unfolding legal battle carries enormous implications for AI developers, users, and investors alike. In a journalistic deep dive, we explore the headline cases and what they mean for the industry – with a human touch, because this story is ultimately about people, trust, and the high stakes of getting AI wrong.

The Data Privacy Showdown: Biometric Scans and Secret Scraping

In March 2025, controversial facial-recognition startup Clearview AI finally buckled under legal pressure – but with a twist. After years of outrage over its practices, Clearview agreed to a $50 million settlement in a class-action lawsuit accusing it of scraping billions of people’s photos from sites like Facebook and YouTube without consent. The case was filed under Illinois’ Biometric Information Privacy Act (BIPA), a strict state law that lets individuals sue companies for mishandling biometric data. What made headlines was not just the payout, but how it was structured: instead of cash up front, Clearview’s victims get a 23% stake in the company’s future value. In other words, the people whose faces were taken without permission now literally own a piece of Clearview. Observers called it a “novel” deal – and a sign that courts see biometric data itself as an asset with long-term value. If your AI business treats personal data as loot, expect that data to become leverage against you in court.

Meta (formerly Facebook) learned a similarly costly lesson about biometric data. In July 2024, Meta struck a record $1.4 billion settlement with the state of Texas over its old “Tag Suggestions” feature, which had scanned users’ photos for faces without explicit consent. Notably, this was not a class action – it was Texas’s Attorney General suing on behalf of the state, and it became the largest privacy payout ever obtained by a single U.S. state. Texas argued that Meta’s automatic face-tagging violated a 2009 state law against capturing biometric identifiers without consent. Meta, which had already shut down its face-recognition system amid backlash, paid up and promised to “hold itself accountable” – all while denying wrongdoing, of course. The sheer size of the fine sent a loud message to the tech industry: if you use people’s biometric data without asking, a single state regulator can hit you with a billion-dollar bill. And Texas wasn’t done yet.

In May 2025, Texas hit Google with an almost identical price tag – a $1.375 billion settlement over two lawsuits claiming the company secretly tracked users and captured their biometric data. One lawsuit accused Google of storing Texans’ voiceprints and face geometry without consent, and another said it misled users about location tracking. Texas’ firebrand AG Ken Paxton crowed that “in Texas, Big Tech is not above the law,” touting the deal as a “historic win” for privacy. Google, for its part, noted it had “long since changed” the practices in question – effectively admitting the claims had merit while avoiding any official admission of guilt. The Google and Meta cases together signal a trend: in the U.S., where federal privacy rules are patchy, individual states are stepping in to police AI and data practices. For AI companies, that means compliance isn’t just a Washington, D.C. game; it’s a state-by-state minefield with potentially billion-dollar consequences.

It’s not only government watchdogs. Private class actions are homing in on companies’ AI-fueled data grabs. In Illinois (home of BIPA), Amazon faces a lawsuit claiming its photo-storage service harvested users’ face data to train its Rekognition facial recognition software – again, without proper consent. According to the complaint, Amazon Photos would automatically scan faces in images uploaded by users, then use those scans to improve Rekognition, which Amazon licenses out to clients, including law enforcement. Essentially, the lawsuit says Amazon treated its customers’ personal photos as free AI training fodder. Amazon denies wrongdoing, but if Illinois courts side with the plaintiffs, the case could set a precedent that any user-generated content used to train AI needs explicit permission. Given BIPA’s stiff penalties (up to $5,000 per violation), an unfavorable ruling could cost Amazon heavily – and send shivers through any AI outfit relying on user data streams.

Even voice data – an often overlooked biometric – is under scrutiny. In Delgado v. Meta, a pending case, an Illinois woman alleges Facebook and Messenger illegally collected “voiceprints” from audio messages without consent. She says Meta’s apps analyzed users’ voices to create unique voice identifiers (a digital fingerprint of your voice) in violation of BIPA. Meta tried to get the case tossed or moved under California law (since its user terms choose California), but a judge ruled that Illinois’ strong biometric law still applies. That victory for the plaintiff kept the case alive and signaled that big platforms can’t escape Illinois’ privacy protections just by citing their Terms of Service. Recently, a federal judge even gave Meta permission to seek an early summary judgment – essentially asking to throw the case out for lack of evidence – but regardless of the outcome, the case is a wake-up call for anyone working with voice AI. The legal system is saying: voice data is biometric data too, and it had better be handled with care.

One particularly eye-opening case didn’t involve faces or voices at all, but private messages. In late 2024, Microsoft-owned LinkedIn quietly added a new setting that automatically opted users into sharing their communications for “AI research”. InMail messages – those private notes sent between LinkedIn users – were allegedly scooped up and shared with third parties (including, unsurprisingly, Microsoft itself) to help train generative AI models. The change went almost unnoticed until a class-action lawsuit early in 2025 accused LinkedIn of effectively harvesting confidential chats without consent. The complaint painted a vivid picture: job seekers’ messages about offers or negotiations could end up regurgitated by AI systems; sensitive business data from private chats could surface in Microsoft’s AI products. LinkedIn was even accused of trying to cover its tracks by updating its privacy fine print only after media reports called out the practice. Interestingly – and somewhat mysteriously – the plaintiff dropped the lawsuit just days after filing. We don’t know why (settlement? second thoughts?), but the short-lived case still sent ripples through the industry. It expanded the conversation beyond biometrics: any personal data, even messages or documents used in model training, can trigger legal exposure if collected improperly. For AI companies hungry for real-world data, it’s a cautionary tale that boils down to a simple principle: ask permission or expect a fight.

Bias and Discrimination: When Algorithms Break Civil Rights Laws

AI’s power to make decisions at scale – who gets hired, who gets a loan, how much you pay for insurance – has raised a thorny question: what happens when the algorithms get it wrong in biased ways? We’re now seeing the answer in court. Even if AI bias isn’t intentional, companies can be held to account under longstanding anti-discrimination laws. In effect, the centuries-old civil rights framework is being applied to 21st-century tech, and it’s catching some firms off guard.

One landmark case comes from Colorado, where in March 2025 civil rights groups (including the ACLU) filed a complaint against software giant Intuit and its AI hiring vendor, HireVue. The story is painfully illustrative: A Native American woman, referred to as D.K., who is Deaf, applied for an internal promotion at Intuit and was subjected to an automated video interview run by HireVue’s AI. The AI was supposed to assess her speech and demeanor, but because D.K. signs and speaks with a Deaf accent, the system apparently penalized her for not sounding like a typical hearing person. She didn’t get the promotion. According to the complaint, the AI wasn’t trained to accommodate Deaf or Indigenous speech patterns, so it simply gave her a low communication score – in essence, downgrading her because of her disability and background. The legal filing argues this violated the Americans with Disabilities Act, Title VII of the Civil Rights Act (which prohibits race/national origin bias), and Colorado’s anti-discrimination laws. In D.K.’s words, “companies like Intuit and HireVue must be held accountable for deploying flawed and exclusionary technology” that creates “artificial barriers” for qualified people. If the regulators (the EEOC and Colorado civil rights division) or a future lawsuit agree, it could force a sea change in AI hiring tools. The case puts AI vendors on notice that selling “black box” hiring algorithms that haven’t been vetted for bias is not just a bad PR move – it might be straight-up illegal.

Meanwhile, in Illinois, another discrimination battlefront is insurance. In a still-unfolding case filed in 2022, two Black homeowners accused State Farm of systematically using AI to discriminate against Black policyholders. The plaintiffs, Jacqueline Huskey and Riian Wynn, say that after a hailstorm, their home insurance claims were dragged out for months and subjected to extra scrutiny and paperwork, while white neighbors with similar damage had claims paid faster. Why the difference? The lawsuit points to State Farm’s algorithmic claims-processing system. The company uses AI models to predict fraud and decide which claims to pay immediately and which to vet more closely. The plaintiffs allege that whatever data or criteria the AI was using, it disproportionately flagged claims in Black neighborhoods for suspicion, leading to delayed repairs and further property damage when fixes were postponed. In essence, they argue the AI learned to redline – reviving the ugly old practice of treating customers differently by race, even if no human deliberately programmed it to do so. State Farm strenuously denies any bias, but notably, a federal judge refused to dismiss the case in 2023, allowing it to proceed into discovery. The suit could even balloon into a class action covering thousands of policyholders across multiple Midwestern states. This is breaking new ground: it’s possibly the first time a machine-learning algorithm’s unintended discrimination is being tested under laws like the Fair Housing Act (cited here because it covers insurance discrimination in housing). For the insurance and finance sectors, a verdict against State Farm would be a legal earthquake, proving that “the algorithm did it” is no defense if the outcome is a civil rights violation.

These cases underscore a pivotal point for the industry: You are responsible for what your AI does, even if its biases were inherited from historical data or are unintentional. Anti-discrimination laws from the 1960s and 70s are proving quite adaptable to the AI age. Companies can’t hide behind complex models – if an algorithm denies opportunities or benefits in a way that systematically disadvantages protected groups, expect lawsuits and expect to lose. In some places, regulators aren’t waiting. For example, New York City has already enacted a law requiring bias audits for AI hiring tools, and Colorado passed a law to curb algorithmic bias in insurance decisions. While Europe’s forthcoming AI Act explicitly demands that “high-risk” AI systems be free from discrimination, the U.S. lacks a comparable federal law. But as we see, creative lawyers and aggressive state enforcers are using existing statutes to fill the gap. The takeaway for AI companies? What you don’t know can hurt you. Rigorous testing for bias and fairness isn’t just an ethical nice-to-have – it’s quickly becoming a legal must-have if you want to avoid multimillion-dollar litigation.

When AI Goes Rogue: Denied Care and Deadly Mishaps

Some of the most heart-wrenching AI lawsuits involve scenarios where automated systems make life-and-death decisions – and get them horribly wrong. It’s one thing for AI to recommend the wrong song; it’s another when AI denies a cancer treatment or crashes a car. These high-stakes failures are now translating into high-stakes lawsuits, raising profound questions about accountability and safety.

Take healthcare. By early 2025, three of America’s biggest health insurance companies – Cigna, Humana, and UnitedHealth Group – had all been slapped with class-action lawsuits accusing them of using AI algorithms to wrongfully deny medical claims. To their patients, these denials weren’t an abstract data issue; they meant real people losing coverage for surgeries, hospital stays, and vital medications. One lawsuit against Cigna (exposed by reporting in 2023) revealed an almost dystopian process: Cigna doctors were allegedly rubber-stamping hundreds of thousands of denials triggered by an AI system, spending an average of just 1.2 seconds reviewing each case. In a mere two months, over 300,000 claims were rejected by this method – often without any human truly looking at the patient’s file. The AI, a system called “PXDX,” would scan if a claim’s diagnosis code matched its criteria and automatically spit out denials, which physicians then perfunctorily approved. “It was essentially a scheme to avoid paying for needed care,” the lawsuit alleges. Cigna insists it was just automating routine paperwork for minor claims, but to one patient who got a surprise bill for a necessary test, that distinction is cold comfort. The case is now working through federal court, and a judge has allowed it to proceed despite Cigna’s attempts to dismiss it . If a jury finds that Cigna’s algorithmic denials violated its legal duty to conduct “thorough, fair, and objective” claim reviews, the insurer could face massive damages – and the entire industry will have to rethink these tools.

Perhaps even more striking is a lawsuit targeting UnitedHealthcare’s use of an AI known as “nH Predict.” UnitedHealth (the largest U.S. insurer) and a subsidiary called NaviHealth deployed nH Predict to decide how long patients should stay in rehab or nursing facilities after hospital discharge. According to a class-action on behalf of patients, this AI often cut off coverage for care too soon, on the premise that the patient was “predicted” to recover quickly. In one example, an 80-year-old patient recovering from surgery was denied a short stay in a skilled nursing facility because nH Predict algorithmically determined she didn’t need it – a decision later overturned by human appeal, but only after painful delays. The kicker: the lawsuit cites evidence that nH Predict’s decisions were wrong a staggering 90% of the time – nine out of ten denials were reversed when patients appealed. Yet because less than 1% of patients actually go through the arduous appeal process, most just accepted the denials. In essence, the suit claims UnitedHealth knowingly let a flawed AI deny needed therapy for thousands of people, betting (correctly) that almost nobody would challenge it. UnitedHealth denies that it uses the tool to make final coverage calls, but in February 2025, a judge ruled that the core claims – including breach of fiduciary duty under insurance law – can move forward. Lawmakers are taking notice, too. After these allegations emerged, members of Congress called for a federal investigation into AI-driven health denials, and some states are moving to tighten rules around prior authorization algorithms. The broader implication is chilling: if insurers lean too heavily on AI to cut costs at patients’ expense, they may not only harm patients but also violate contracts, insurance regulations, and medical ethics – a trifecta of legal troubles.

From hospital beds to highways: AI has also been blamed for deadly accidents. Tesla, a company as much in the AI business as in the car business, has been fighting a wave of lawsuits over crashes involving its Autopilot driver-assistance system. In one ongoing case in California, the family of 31-year-old Genesis Mendoza-Martinez sued Tesla after he died in a horrific February 2023 wreck. Genesis was driving his Model S on I-680 near San Francisco with Autopilot engaged when the car failed to recognize a fire truck parked across the highway and plowed straight into it at 70 mph. He was killed instantly. His family’s lawsuit accuses Tesla of fraudulent misrepresentation, arguing that Elon Musk and the company had wildly exaggerated Autopilot’s capabilities in marketing and public statements. They point to the very name “Autopilot” – and Musk’s boasts over the years that Teslas would soon be self-driving – as misleading assurances that lulled Genesis (and many others) into a false sense of security. Tesla’s defense is that drivers are warned to stay alert and keep hands on the wheel, and that Genesis himself was tragically negligent to trust the system completely. In May 2025, a judge in the case delivered a split decision: he dismissed one claim that Tesla had an outright duty to disclose Autopilot’s limits, but he allowed the core misrepresentation claims to proceed, remarking that calling a semi-assisted driving feature “Autopilot” is “plausibly misleading”. Internal Tesla emails, revealed in court, showed even some Tesla employees worried the Autopilot name would make drivers overestimate the tech’s abilities. Those concerns were echoed by German regulators years ago, who formally asked Tesla to rename Autopilot to something less misleading. Tesla refused – and now that stubborn branding might come back to bite it. The Mendoza-Martinez case isn’t the only one; in Florida, another Autopilot crash led to a jury verdict of $243 million against Tesla in late 2023, although Tesla was found only partly liable and is fighting the punitive damages as excessive. All these lawsuits collectively raise the pressure on Tesla (and the industry) to be far more transparent about what “self-driving” tech can and cannot do. And they raise a broader legal question: when advanced AI-driven products fail, how do we assign blame between human users and the companies that marketed them? Courts are now grappling with that, and the outcomes will likely shape product liability law for the AI era.

Defamation and Data Heists: AI’s Uncharted Legal Territory

Even as courts apply established laws to AI in areas like privacy and product safety, entirely new legal frontiers are emerging. Two of the most intriguing: AI-generated defamation and “prompt injection” hacks. These sound like sci-fi hypotheticals, but they’re real – and the decisions could redefine legal responsibilities for AI companies and their adversaries.

In 2023, a radio host in Georgia named Mark Walters found himself at the center of what’s believed to be the first libel suit involving generative AI. Walters, a gun-rights advocate, wasn’t accused of defamation – he was the victim of it, courtesy of OpenAI’s ChatGPT. A journalist had asked ChatGPT to summarize a (completely unrelated) court case, and the AI hallucinated wildly, outputting a fake summary that claimed Walters was a defendant in a fraud lawsuit and had been accused of embezzling funds from a Second Amendment group. None of it was true; ChatGPT seemingly stitched together bits of context and invented a case out of thin air. Walters was never involved in any such litigation. Understandably outraged, he sued OpenAI in June 2023 for defamation, arguing that the company should be held liable for publishing false and damaging statements about him. This raised a mind-bending question: Who is the “speaker” when an AI makes something up? OpenAI’s defense was that ChatGPT is a tool that users operate, more like a search engine or library, not a publisher with editorial control. And indeed, internet platforms usually have broad immunity (under Section 230 of the Communications Decency Act) for content posted by users – but in this case, the “user” (the journalist) didn’t input the false info, the AI did. Legal scholars began debating whether Section 230 would protect an AI company for its algorithm’s speech. Walters’ case never got to settle that question, because in May 2025, a Georgia judge dismissed it on other grounds. The court found Walters hadn’t proven the essential element of “actual malice” – basically, that OpenAI knowingly or recklessly let the falsehood out. The judge even cited OpenAI’s extensive warnings about ChatGPT’s tendency to err as evidence that the company was trying to prevent harm, not acting negligently. OpenAI understandably hailed the ruling. But the broader issue is far from settled. Had Walters been an average private individual (not a semi-public figure) or had OpenAI been less proactive in warning about errors, the outcome might have differed. The case underscored a critical risk of generative AI: so-called “hallucinations” can ruin reputations and spread lies on a mass scale. If those lies injure someone, AI firms may find themselves hauled into court just like any newspaper that printed a defamatory story. More such suits are likely – we’ve already heard of an Australian mayor considering suing OpenAI for a similar false accusation. So far, no firm precedents have been set, but executives should heed the writing on the wall: truthfulness is not just an AI ethics issue; it’s a legal liability issue. Rigorous model tuning and user warnings are not just good practice – they might save you from paying damages or getting regulated as a publisher.

Another novel battleground is emerging around the concept of prompt injection – essentially, hacking an AI through cleverly crafted inputs. In early 2025, a startup called OpenEvidence (which offers a medical AI chatbot) sued a competitor, Pathway Medical, in federal court, accusing it of a corporate espionage caper fit for a thriller. According to the complaint, Pathway’s co-founder posed as a doctor to gain access to OpenEvidence’s system (which is normally limited to licensed professionals), then bombarded the chatbot with special prompts designed to trick it into revealing its hidden instructions and data. In other words, a prompt injection attack. OpenEvidence claims that the confidential “system prompts” and other under-the-hood details that Pathway extracted are its trade secrets, and Pathway stole them in violation of the Defend Trade Secrets Act and other laws. If this sounds esoteric, consider the implications: AI models often operate with non-public parameters, fine-tuned datasets, or secret sauce instructions that give them an edge. If a rival can reverse-engineer those via clever querying, is it akin to stealing source code or proprietary formulas? OpenEvidence is essentially saying yes – that their “crown jewel” system prompt (the hidden directive guiding the AI’s answers) has economic value and was protected, and Pathway’s actions were no different than hacking into a database. Pathway will likely argue that sending prompts to an open web service isn’t “improper means” – maybe even framing it as legitimate competitive intelligence. How the court lands could set a major precedent for AI security. If OpenEvidence wins, AI companies will have stronger footing to go after those who manipulate their models or scrape their outputs to clone functionality. It would be a signal that the law is willing to protect not just the code and data input to AI, but even the behavior of the model as a secret asset. On the flip side, a Pathway victory might suggest that once an AI is publicly accessible, others are free to test and probe it – even if that reveals secrets – much as they are allowed to reverse-engineer a competitor’s physical product. The stakes are high: trade secret cases can lead to injunctions and hefty damages. In the meantime, the smart play for AI firms is obvious – tighten your system security and maybe don’t rely on obscurity alone to protect your IP. Because if someone can jailbreak your AI, someone will.

The Road Ahead: Regulation or Litigation – or Both?

These diverse lawsuits – spanning privacy, bias, health, safety, and beyond – collectively point to an industry at a crossroads. AI companies have enjoyed a period of relative Wild West growth, largely unburdened by specific regulations. That era is closing. Courts and regulators are now drawing lines, often after harms have occurred. For executives and investors, the question is how to navigate this new landscape of risk.

One theme is clear: existing laws are being applied aggressively to AI. Companies might have assumed that in the absence of AI-specific regulations, they had carte blanche. But as we’ve seen, data protection laws (like Illinois’ BIPA or Texas’s biometric and privacy statutes) are readily wielded against AI misuse. Long-standing civil rights laws can anchor lawsuits over algorithmic discrimination. Product liability and consumer protection laws provide a basis to sue over dangerous or deceptive AI products. And where laws haven’t kept up – say, dealing with AI libel or data-poaching hacks – plaintiffs are testing novel legal theories in court. This means AI businesses must proactively assess how the law of the land (in every jurisdiction they operate in) might apply to their tech. “We didn’t realize our model could do that” won’t cut it as an excuse. The companies best positioned to thrive are those baking compliance and risk mitigation into their AI development process now – not after the subpoena arrives.

That said, there’s an increasing drumbeat for new, AI-specific legislation, especially in the U.S., where a comprehensive framework is conspicuously absent. The European Union is in the final stages of adopting its AI Act, which will impose sweeping requirements on AI systems, from transparency to bias testing, with hefty fines for violations. By contrast, the United States has so far taken a piecemeal approach. There’s no federal AI law ensuring, for instance, that AI decisions in hiring or lending are audited for fairness – leaving that to scattered state initiatives (like the ones in New York and Colorado). Privacy at the federal level is still the Wild West: no nationwide law like Europe’s GDPR exists, meaning issues like consent for data usage fall to a patchwork of state laws. Recognizing the gap, the Biden administration has tried to guide policy through non-binding means – releasing an “AI Bill of Rights” blueprint and, in late 2023, issuing an Executive Order on AI safety. That executive order (the first of its kind in the US) pushed for the development of AI safety standards and addressed issues like watermarking AI-generated content and evaluating AI systems used by the government for biases. It even referenced the need to protect against algorithmic discrimination in areas like housing and credit. But an executive order is limited; it’s not the same as Congress passing enforceable rules. So far, proposed AI bills in Congress have ranged from creating licensing for advanced AI models to carving out liability exemptions, but none have crossed the finish line. The upshot is that, at least for the next couple of years, the “regulation” of AI in the U.S. will likely happen through litigation and state action, not comprehensive federal statutes.

For industry leaders, this uncertainty is vexing – but it also means you have an opportunity to help shape best practices before harsher mandates inevitably arrive. The spate of lawsuits we’ve discussed carry lessons that forward-thinking companies are already heeding. For example: Minimize the collection and use of personal data, especially biometrics, or at least get clear consent – not just to avoid fines, but to earn user trust. Vet your AI models for bias and disparate impact, because if you don’t, someone else will do it for you in a courtroom. If you’re deploying AI in sensitive areas (healthcare, driving, legal advice, etc.), invest in rigorous safety testing and human oversight – lives are literally at risk, along with your brand and balance sheet. Be transparent about your AI’s capabilities and limits; overhyping can backfire legally (just ask Tesla) . And as the OpenAI case showed, being candid about AI’s imperfections and building in user warnings can actually be a shield against liability.

The mood among AI-focused investors and executives is shifting from the old move-fast-and-break-things mantra to something more akin to move-fast-but-don’t-break-the-law. It’s a recognition that long-term viability requires navigating not just technological challenges but legal and ethical ones. Each lawsuit settled or verdict rendered is gradually drawing the borders of what’s acceptable. In the absence of clear laws, these cases are effectively creating a common-law of AI one judgment at a time. It’s a messy, reactive way to govern a technology – but it’s where we are.

In time, we can expect lawmakers to catch up. There’s bipartisan talk in Washington about requiring licensing for the most powerful AI systems, or mandating disclosures when AI is used in consumer interactions, and enhancing Section 230 to clarify AI-generated content. But until any of that becomes real, the smart money in the AI industry will do what the best companies in other industries do: plan for the worst-case scenarios. That might mean carrying new types of insurance, budgeting for legal compliance teams, or even collaborating on industry standards that demonstrate a commitment to “AI safety” broadly defined.

The past year’s legal salvos – billion-dollar fines, history-making complaints, and verdicts that read like sci-fi plots – should dispel any notion that AI exists in a lawless frontier. The law is very much entering the chat. AI companies are being held to account for the impact of their systems on people, including invading their privacy, denying them equal treatment, putting them in danger, or dragging their name through the mud. For executives and investors, this reckoning poses challenges, sure, but it’s also an opportunity to build the next generation of AI with liability and ethics in mind from day one. Those who succeed in doing so won’t just avoid court – they’ll earn the trust that’s necessary for AI to truly integrate into society. And ultimately, that trust is the bedrock of any lasting innovation.

The inbox where nobody’s home

Open your email and you’ll see the future blinking back at you. Gmail now suggests drafts with a cheerful “Help me write,” Outlook’s Copilot triages threads and proposes tone-tuned replies, and across the enterprise stack, the same pitch repeats: let the AI handle it. On the other end of that thread, the recipient’s stack is running identical routines. Your bot drafts; their bot summarizes. Your bot follows up; their bot files it away. The message traveled, the metrics look healthy, but did anyone actually show up?

Bots talking to bots isn’t sci-fi anymore

In sales and service, “autonomous agent” has moved from a keynote buzzword to a product SKU. Salesforce touts Agentforce, a platform for agents that analyze data, make decisions, and execute tasks across sales, marketing, and service. Intercom’s Fin claims to resolve the bulk of inbound support on its own. Zendesk is rolling out AI agents across channels, including email and voice. Meanwhile, prospecting tools promise AI SDRs that assemble lists, draft sequences, and send messages without human hands. Stitch those together and you don’t just automate a task—you automate both sides of the conversation.

Engagement without humans

The social side isn’t spared. Brands wire up Meta’s Business Suite to auto-respond in Messenger, Instagram, and WhatsApp, while customer-care platforms layer AI to suggest or send replies at scale. A comment posted at 3:07 a.m. gets a response at 3:07:01. Look closer, and the tone is polished, a little too consistent, suspiciously tireless. Now imagine that the post it’s responding to was written by an AI content assistant, scheduled by an AI scheduler, and boosted by an AI media buyer. The loop closes.

Reach is cheap. Resonance isn’t.

If wider reach is the prize, AI has already delivered it—at the cost of signal-to-noise. The web is filling with machine-written posts, reviews, and listicles; search engines and platforms are scrambling to filter the slop. In newsfeeds and search results, the sheer volume of plausible-but-empty content dulls attention. Even Medium has wrestled publicly with AI-generated spam, while regulators chase fake reviews supercharged by generative tools. We’re measuring success by how many bots can talk to how many bots, faster. That’s not a market; that’s an echo.

People can tell—and they care (mostly)

Consumers are getting better at spotting machine-made language, and many want it labeled. Deloitte found strong support for mandatory disclosure. Adobe reported growing skepticism, and media/marketing surveys show people disengage when they suspect content is synthetic. At the same time, early research from Stanford HAI suggests labeling AI text doesn’t necessarily reduce its persuasiveness—transparency improves, but the message may still move people. That contradiction is the hazard of AI-to-AI communications: scale without trust can still shape behavior, especially when no one can tell who—or what—said what first.

Why do we still send the email?

Because email, like social, is more than a channel—it’s a ledger of intent. A calendar invite carries obligation. A written reply stakes a position. For teams, the inbox is discovery, negotiation, and escalation all at once. The minute you remove humans from the loop, you preserve the ledger but erase the intent. Your AI can book the meeting; their AI can politely decline; everybody “touched base,” and nobody learned anything. The KPI went up. The relationship didn’t.

The hidden costs of letting the machines mingle

There’s a second-order risk: model collapse. As more systems train on outputs from other systems, the feedback loop can degrade the next generation’s quality—like photocopying a photocopy until only static remains. Platforms and policymakers are already pushing for transparency and provenance, not just to protect elections and discourse, but to protect the training commons itself. Europe’s AI Act phases in transparency obligations for providers; the FTC is cracking down on deceptive AI claims and fake-review markets. Notice how quickly the conversation shifts from “can a bot write this?” to “can we still trust anything?” That shift is the bill for automation at both ends.

What survives when the bots take over

Paradoxically, the more automated our exchanges become, the more valuable the unmistakably human ones get. The comments that land are messy, specific, time-stamped with a person’s presence. The emails that matter are the ones that take a stand, ask a hard question, or share context no model could infer. Brands that treat AI as armor—deflecting contact, smoothing every edge—will feel efficient and empty. Brands that treat AI as scaffolding—triaging the routine to make room for real conversation—will feel alive.

So, what happens to human interaction when AIs talk to each other? It doesn’t disappear. It concentrates. Automation moves the trivial to the machines and throws the meaningful into sharper relief, if you let it. Use Gemini and Copilot to draft the boring parts. Let Agentforce, Fin, and Zendesk’s bots resolve the obvious tickets and route the rest. Wire Meta’s automations for FAQs and store hours. But mark the thresholds where a person steps in, and make those thresholds visible. Put your name on the things that count. Say “I.” Leave fingerprints.

The internet can sustain infinite AI conversations that no one reads. Your job, if you want a business instead of a botnet, is to make fewer, truer exchanges that someone remembers. In the end, that’s why we still send the email and still comment on the post: not to fill a pipeline, but to cross a distance. And when distance matters, a human voice still carries farther than any machine.

It’s the ultimate showdown: the human brain versus artificial intelligence. In one corner, there’s the squishy 3-pound biological marvel inside our skulls, honed by millions of years of evolution. In the other, an advancing legion of silicon “brains” crunching numbers at lightning speed. Every day, we see new headlines about AI composing poetry, acing exams, or beating humans at games once thought unbeatable. It’s enough to make anyone wonder: does the human brain still hold an edge, or have the machines caught up? Let’s dive into this friendly contest between brains and bots – with a wink and a smile – to see where each side shines.

Despite the recent AI hype, the human brain isn’t exactly going quietly. Sure, today’s AI systems can already out-calculate and out-memorize us in a snap. They’ve been better than humans at many tasks for years – from data analysis to certain medical diagnostics, not to mention strategic games like chess and Go. In fact, AIs now routinely churn through research data, write reports, and even generate software code faster (and sometimes better) than most of us ever could. Talk about rubbing it in! Even our vaunted language skills – once considered uniquely human – are under siege. Powerful new AI models can write essays, poetry, and conversations so fluently that you might mistake them for a witty human pen pal. AI’s growing list of achievements also includes passing professional exams; for example, OpenAI’s GPT-4 model famously scored around the top 10% of test-takers on a simulated bar exam (its predecessor barely passed at bottom 10%). With feats like these, it’s easy to feel like the bots are winning on points.

But hold on – our human brain has some knockout tricks of its own. For one thing, it’s astonishingly efficient. Your brain runs on about 20 watts of power – roughly the same as a dim light bulb – yet it performs complex computations that make supercomputers gulp electricity by the gigawatt. A modern AI data center might consume a billion watts to support those clever chatbots and image generators. It’s as if nature built us a supercomputer that runs on a sack lunch, while our AI creations need a small power plant to function. This efficiency comes in part from how the brain learns and remembers. We humans can often learn new information after seeing it just once – a single experience can form a lasting memory. By contrast, artificial neural networks usually need to see the same data hundreds or thousands of times to truly learn it. If you showed a child one picture of a funky new animal, they’d probably recognize it again later; an AI would likely need a whole photo album of that creature from every angle! Even worse, when AIs learn new things, they tend to overwrite or “forget” what they knew before (a problem charmingly dubbed catastrophic forgetting). Our brains are far better at learning incrementally without wiping old knowledge. Today’s AI researchers are actively studying the brain’s tricks – such as a recently discovered “prospective configuration”learning principle – to make machine learning more efficient and brain-like. In short, the brain remains the gold standard for learning on the fly, whereas current AIs are like diligent (if somewhat forgetful) students cramming with flashcards.

If raw computing power and memory were the whole game, the bots might have already won. But intelligence is about more than crunching numbers – and here the human mind plays some strong cards. Consider common sense and context, those little superpowers we use to navigate everyday life. You and I know that if someone asks for “hot milk,” they probably mean a cup of heated milk, not an entire steaming cow. We intuitively understand situations, physical constraints, and unspoken rules. AI systems, on the other hand, often lack this basic common sense, sometimes in hilarious ways. Not long ago, a top-notch language AI given the words “dog, frisbee, throw, catch” dutifully produced the sentence: “Two dogs are throwing frisbees at each other.” Technically, it’s a grammatical sentence – but also a nonsense scenario (unless someone taught dogs to throw Frisbees when we weren’t looking). The AI had learned patterns from text but didn’t grasp the real-world fact that dogs can’t toss Frisbees. This illustrates a broader point: AIs excel at formal tasks (syntax, logic, calculation), but they struggle with the functional side of intelligence – truly understanding the world as we do. Researchers have highlighted that today’s AI language models are brilliant imitators of form (they can produce perfectly fluent sentences) yet often lack the deeper grasp of meaning and intent that comes naturally to humans. As cognitive scientist Anna Ivanova puts it, we humans conflate language with thought – we assume something that speaks so well must think well – but AIs can easily fool us on the former while fumbling the latter. So next time you see a headline about an AI writing Shakespeare-level sonnets, remember: it might have impeccable grammar and a big vocabulary, but it still doesn’t know that dogs don’t throw Frisbees without being explicitly told.

Creativity is another fascinating battleground. Humans take pride in our imaginative, inventive minds – the leaps of intuition, the strokes of artistic genius. Can machines really compete here? The answer is a qualified maybe. On one hand, AI has delivered some jaw-dropping creative feats. Who would have guessed a computer could beat the world’s best Go player by inventing alien-seeming strategies? Yet that’s exactly what happened. When DeepMind’s AlphaGo defeated champion Lee Sedol, it played a now-legendary move (Move 37) that no human master would have dreamed up – so unconventional that experts called it “creative” and “unique”. It was a 1-in-10,000 long shot move, and it confounded Lee Sedol in the moment, showing that AI could surprise us with original tactics in a game requiring intuition and creativity. AI models are also churning out original paintings, music, and designs. You’ve probably seen AI-generated art winning contests or algorithms composing classical music. However, there’s an asterisk: AI’s “creativity” often reflects a remix of the vast human-created data it’s trained on. It can generate new combinations, even striking ones, but does it truly create, or just predict? Humans draw on life experiences, emotions, and a touch of mystery in their creativity. By contrast, an AI art model combines patterns from its training images and follows mathematical rules – it has no idea why a painting it made might make you feel nostalgic or why one strategic move in Go is more elegant than another. In fact, experts note that AI’s outputs are constrained by its training data, and it lacks the genuine spark of intent or self-expression that human creators have. So, while a bot can paint in the style of Van Gogh, it cannot yet suffer in a garret and then pour that soul into a canvas! We might say human creativity still has more “soul” – but AI is a formidable collaborator, offering suggestions and variations we might never have considered. Many artists and writers now work with AI tools to kickstart ideas, effectively combining our imaginative minds with the machine’s generative power.

Let’s not forget the physical world, where our fleshy brains are paired with a body. The brain doesn’t float in a vat (unless you’re in a sci-fi story); it’s tightly coupled to sensors and muscles, enabling us to perceive and act. This is a realm where, ironically, AI has struggled. In the 1980s, scientist Hans Moravec pointed out a paradox: tasks that are hard for adults (like playing chess or doing calculus) turned out to be easy for computers, whereas tasks that are easy for a toddler (recognizing faces, walking across a cluttered room, using your hands) are fiendishly hard for AI. This Moravec’s Paradox still holds true. We have AIs that can solve complex equations or find patterns in huge datasets, yet no robot can match the general dexterity of a human five-year-old. Consider how naturally you tie your shoelaces, crack an egg, or catch a ball without thinking – these feats require a sophisticated integration of vision, touch, and motor control. Robots, for all their precision, still struggle with such open-ended physical tasks. Yes, there are impressive demos of humanoid robots backflipping or robot hands manipulating objects, but they often rely on pre-programmed maneuvers or vast training in simulation. Our brains, by contrast, evolved over eons to handle the messy physical world. We effortlessly integrate multiple senses and adapt on the fly. For example, a skilled carpenter feeling the grain of wood, or a surgeon adjusting technique by sight and touch, is using intuition born of lifelong bodily experience. AI and robots don’t have bodies in the biological sense – they have sensors, yes, but these sensors feed data into algorithms that don’t truly feel. When a robot tightens a screw, it doesn’t get that gut sense of “too tight” versus “just right” unless we painstakingly program a threshold. Humans have that sense innately, thanks to feedback loops between our nerves and brain. This is why factories still employ people for delicate assembly and why caregivers or plumbers aren’t out of jobs yet. Our mundane sensorimotor skills are extraordinary, and AI is only slowly catching up by trying to mimic our neural circuitry in silicon. As one analysis put it, we have machines that can beat grandmasters at complex board games and write essays, yet they “struggle to pick up a delicate object without breaking it”. The simple acts of perceiving and moving through the real world – which we take for granted – remain some of the hardest challenges for AI.

Interestingly, in areas where AIs do interface with the physical world, they often out-sense us in raw ability while still under-performing in understanding. For instance, an AI can be equipped with sensors that detect infrared light or ultrasonic sound far beyond human ranges. A camera plus computer vision can “see” more precisely or quantify things we can’t (like the exact RGB color values of a sunset). However, sensing is not the same as perceiving. An AI might measure environmental data with superhuman precision, but it doesn’t truly comprehend what it “sees” or “hears” the way we do. Humans naturally fuse sensory input with context and past experience, instantly filtering what matters. You recognize your friend’s face in a crowd and at the same time recall their name, mood, and that they hate pineapple on pizza – that’s perception. AI vision can identify faces or objects faster and with tireless accuracy, but it lacks the rich contextual tapestry of human perception. Likewise, AI can focus unwaveringly on multiple tasks without ever getting bored or tired – truly laser-focused attention – yet it lacks the adaptable focus of a person. We humans can switch attention based on sudden changes or intuitively guess which detail is important in a complex scene; an AI has to be told explicitly what to prioritize and has no innate sense of “surprise.” In a way, the brain is less precise but more flexible than AI. We might get distracted or even doze off (AI doesn’t), but we can also improvise and reprioritize in real time in ways AI finds difficult. This kind of adaptability is crucial in dynamic, unpredictable environments – basically, real life.

Now, a major frontier in brain vs. AI is social and emotional intelligence. Humans are social animals; our brains have evolved to navigate relationships, empathy, and complex emotions. Can a bot feel anything? The short answer is no – at least not in the way humans feel. AI today does not have genuine emotions, consciousness, or an inner life. It can simulate empathy in a narrow sense: for example, certain AI systems can analyze your facial expression or tone of voice and respond with comforting words. In some studies, people have even felt more “heard” by AI-generated empathetic responses than by human ones. But let’s be clear: the AI isn’t actually feeling compassion; it’s running a pattern-matching algorithm. The human brain, by contrast, feels emotions viscerally. When you’re anxious or in love, it’s not just a bit of code flipping – your heart races, your palms sweat, you might get butterflies in your stomach. Our emotions are deeply embodied reactions that have kept us alive and cooperating for ages. One scholar pointed out that because we have a central nervous system (CNS) tying mind to body, we experience an “immersive integration with reality” that AIs will never have. We literally feel the consequences of our actions – pain, joy, love, fear – and thus we intuitively understand why something matters or why hurting others is wrong. You don’t need a textbook on ethics to know why murder is bad; you can empathize with suffering because you too can suffer. An AI can be programmed with ethical rules, but it feels nothing – no pain, no joy – so the meaning of those concepts is inherently foreign to it. As AI philosopher William Stewart argues, an AI could become super-smart and even simulate feelings, but without a biological body and true emotions, it lacks the full understanding required to develop sustainable ethics or to be entrusted with the “leadership of the universe”. In his view, only creatures born of DNA and capable of real love, fear, and empathy can truly value the consequences of actions. That might sound a bit lofty, but it underlines a real point: our morality and sense of meaning are entwined with our biology. AI, as it stands, doesn’t share that; it has no inner voice or conscience, just a clever facsimile thereof. (At least for now – the philosophical debate about AI consciousness is ongoing, though most experts believe today’s AI is nowhere near sentient.)

At this juncture, one might ask: if AI can calculate better, never forget, and is rapidly learning to see, speak, and maybe even drive, what are humans good for anymore? Are we approaching a point where the brain has no edge left? It’s a fair question, especially as researchers push toward artificial general intelligence (AGI) – AI that could think and learn as flexibly as we do. Some futurists even warn that an AGI could quickly surpass us in every domain, leaving humans in the dust. Yet, experts also emphasize that human intelligence and AI are fundamentally different and often complementary. The strength of AI is in raw processing power, consistency, and scale. The strength of the human brain is in adaptability, contextual understanding, empathy, and creativity born of lived experience. In practice, combining the two often yields the best outcome. For example, in medicine, an AI can scan millions of images to spot minute patterns of disease – far faster than any radiologist – but a human doctor combines those findings with empathy and holistic understanding of a patient’s life to make the final call. In art, an AI can generate an endless stream of novel images or melodies, but a human artist picks the one that resonates emotionally and gives it purpose. Rather than a fight to the death, it’s increasingly a partnership. AI pioneer Jiajie Zhang nicely summarized that while AI excels in precision, speed, and breadth, human cognition offers depth, intuition, and emotional meaning – making them complementary intelligences rather than direct competitors.

So, does the brain still hold an edge over AI? In many ways, yes – but it depends on what you value. If it’s calculation, memory, or brute-force logic, the machines have long left us in the dust (your laptop can compute primes faster than Euler, and GPT-4 can outscore most of us on standardized tests). If it’s about understanding the world, adapting to novelty, and truly caring about outcomes, the human brain remains unparalleled. We carry inside our heads a product of billions of years of R&D (thank you, evolution) that endows us with common sense, conscience, and creativity that no silicon rival yet matches in full. But – and here’s the twist – it’s not a zero-sum game. Our relationship with AI isn’t like two boxers where one must fall. It’s more like dance partners learning new steps. AI is beyond us in some respects and behind us in others. The smartest path forward is to combine forces: use AI’s superhuman abilities to augment our own, while steering these tools with the wisdom, compassion, and curiosity only humans can provide.

In a philosophically optimistic sense, perhaps what really sets us apart is our ability to imbue the world with meaning. We don’t just calculate – we care. AIs can optimize a process, but they don’t ponder why it matters. That “why” is our domain. As long as we remain the beings who feel that spark – who laugh at irony, cry at poetry, cherish justice, and question the universe – we hold an edge that no algorithm can duplicate. The brain versus bots debate, then, isn’t a question of who wins outright. It’s a story of two very different intelligences coming together, each with good reasons to be confident. Our brains are still special, and so are our shiny new AI companions. In the end, the most interesting outcome may not be one defeating the other, but the incredible things they can do together. After all, one is made of neurons, the other of code – and the combination might just be the winning formula for the future of intelligence.

In the meantime, let’s appreciate the friendly competition. The next time you use an AI assistant or marvel at an algorithm’s handiwork, remember to give a nod to your own brain. It’s the original smart device in a world now full of smart devices. And despite all the bots can do, there’s still nothing quite like the feeling of being human – brain, heart, humor and all.

In San Francisco, giant billboards emblazoned with the phrase “Stop Hiring Humans” have been shocking commuters. They’re part of a controversial ad campaign by an AI startup pitching a so-called “digital employee” – an artificial intelligence sales agent that never sleeps, never demands a raise, and never calls in sick. The dystopian slogan is meant to turn heads (and it has, sparking fury online as a “cyberpunk nightmare”), but it also captures a very real trend in corporate thinking. From Silicon Valley boardrooms to Aussie software giants, many executives are extremely eager to fire their human staff and replace them with algorithms. One CEO-consultant even bragged this summer about how “AI doesn’t go on strike. It doesn’t ask for a pay raise” – right after admitting “I’ve laid off employees myself because of AI”. In other words: the boss can’t wait to swap pesky people for tireless machines.

Such comments might sound like supervillain lines from science fiction, but they’re direct quotes. The first comes from Elijah Clark, a tech CEO who advises fellow executives on implementing AI, gloating that he’s “extremely excited” about firing workers in favor of automation. And he’s not an outlier. This year alone, the billionaire co-founder of Australian software firm Atlassian announced 150 employees would be laid off and largely replaced by AI tools, even as he defended buying himself a $75 million private jet. OpenAI’s own CEO, Sam Altman – one of the architects of the AI boom – has warned that entire job categories like customer service could soon be “totally, totally gone” thanks to AI. Across industries, a growing cohort of business leaders seems convinced that “digital employees” are ready to take over, ushering in what one Futurism columnist sardonically calls a new era of “efficiency” with a hint of sociopathic glee.

But before the C-suite hands HR a stack of pink slips, it’s worth asking: Does the reality live up to this bold promise? Can AI agents truly replace human workers – or are executives risking a catastrophic face plant by chasing the latest tech buzz? Recent cases reveal a gaping chasm between the hype and what today’s AI actually delivers. From misfiring customer service bots that alienate customers, to “AI-powered” hiring tools that quietly bake in bias, the push to automate jobs has already produced high-profile failures and even legal backlash. Meanwhile, new data shows many companies that jumped on the AI bandwagon are regretting it – citing disappointing results, hidden costs, and an uncomfortable truth that humans, with all their flaws and salaries, often remain essential. In this deep dive, we’ll explore how executives are embracing the idea of AI “employees,” why many of those early experiments are backfiring, and what critical limitations and ethical landmines lurk beneath the glossy veneer of automation. The goal isn’t to pour cold water on genuine innovation, but to separate fact from fiction so decision-makers can approach AI in the workforce with eyes wide open – and maybe save themselves from becoming the next cautionary headline.

The New Corporate Dream: Digital Employees on Demand

Imagine an employee who never takes a vacation, never sleeps, and works for a fraction of a human’s salary. For many CEOs, that sounds like a dream – and AI vendors know it. Companies like Memra and Jugl have begun pitching exactly this vision: “digital employees” – AI-driven agents billed as contract workers that can handle entire workflows, from routine paperwork to complex data analysis. One startup’s slogan unabashedly urges clients to “Scale your business, not your headcount,” encapsulating the promise that you can grow revenues without adding costly humans. The sales pitch is potent. Jugl, for instance, claims its automation platform saves 12 hours per week per employee by taking over repetitive tasks. Memra boasts that its flagship AI “operations guru” can cut labor costs by up to 80% while operating 24/7, supposedly processing expenses 5× faster with near-perfect accuracy. Why pay someone $50,000 a year to answer support tickets, these vendors ask, when a $5,000-per-year AI subscription could do it faster – and never ask for a raise?

It’s easy to see the allure. In theory, a digital workforce offers superhuman efficiency at bargain prices. Need to handle a sudden surge in customer inquiries or holiday orders? Just spin up more cloud-based AI agents to carry the load. Unlike human teams – which you have to recruit, train, manage, and eventually downsize when things slow down – AI workers can be deployed on demand and dismissed just as easily. They won’t complain about overtime or work-life balance (as one cheeky Artisan ad put it, “Artisans won’t complain about work-life balance”). They multitask without tiring, juggle thousands of chats or data entries in parallel, and never get bored of the drudgery. Consistency is another selling point: algorithms don’t get sleepy and start making mistakes at 4 PM on a Friday. By removing human fatigue and foibles from the equation, companies hope to achieve near-perfect quality control and output that never slips. And crucially for the CFO, digital employees don’t demand health insurance, 401(k) matches, or paid leave. The ROI math looks tantalizing on paper. It’s no wonder a recent Deloitte report predicted that by 2025, a quarter of companies using AI will be piloting “Agentic AI” projects – essentially AI coworkers – a figure that could double by 2027. No one wants to be the chump still paying human salaries if their competitor is running a lean, automated operation with tireless bots cranking away 24/7.

Driven by this fear of missing out, executives across sectors have caught a serious case of “AI fever.” Over the last two years, AI has rocketed from niche data science project to boardroom obsession in many organizations. CEOs hear how rivals are boasting about automating everything from customer support to performance reviews, and they feel pressured to follow suit. Surveys show over 60% of HR leaders now say AI is a top priority, illustrating the intense top-down push to “AI-everywhere” strategies. It’s become almost a mantra: we must do AI, because everyone else is. This mentality has set off an arms race of sorts, with companies rushing to implement chatbots, AI assistants, and robotic process automation throughout their operations – often without a clear plan beyond the vague mandate to cut costs and boost productivity. As a result, the stage is set for a collision between sky-high expectations and the messy reality of deploying unproven tech at breakneck speed.

Executive Euphoria Meets Reality: “Efficiency” Hits a Wall

At first, everything about the AI employee revolution sounds like a win-win for management. It promises transformative innovation on the surface, but at its core it’s often about a good old-fashioned goal: cutting payroll costs. “It’s going to save us money by replacing expensive human beings,” is the unspoken math behind many AI initiatives, as one industry insider notes. Rarely will a CEO say outright, “We’re doing this to downsize,” – it’s usually wrapped in buzzwords about “freeing up staff for more meaningful work” or “streamlining operations” – but the outcome is the same. And lately, some bosses haven’t been shy about their intentions. When Elijah Clark proclaimed his excitement at firing employees in favor of AI, he spoke openly of human labor as a nuisance: “These things you don’t have to deal with as a CEO,” he said, referring to wages, raises, and workers’ rights. That blunt attitude reveals a hard truth: for many in the C-suite, AI isn’t just a cool new tool, it’s a cost-cutting weapon. If algorithms can do even part of a job, that’s one less salary to pay – and maybe a bump to the next quarterly margin.

This ethos has already moved from talk to action. Aside from consulting for others, Clark himself fired 27 of 30 staff on a team he led and replaced them with AI, bragging that his slimmed-down crew now accomplishes “in less than an hour what [the full team] were taking a week to produce”. Efficiency, he concluded coldly, made the extra people unnecessary. On a larger scale, Atlassian’s Mike Cannon-Brookes (a billionaire tech founder) recently beamed in via video call to break the news to 150 employees that they were out of a job – with many of their roles to be filled by artificial intelligence. The irony of him making this announcement from the comfort of a home office – shortly after splurging on a personal jet – wasn’t lost on observers, and it sparked its own minor outrage. Still, Atlassian framed the layoffs as a “hard, right decision” done with “empathy and care,” even invoking the company’s code of ethics in a blog post to justify the move. (In corporate doublespeak, it seems, firing workers becomes “building with heart and balance.”) Meanwhile, Cannon-Brookes’ co-CEO Scott Farquhar defended the strategy in economic terms, arguing that if AI makes call center staff more productive, “we’ll probably need less call centre staff” – a simple equation of tech-driven efficiency. Farquhar even suggested that laws should change to give AI companies freer access to data (presumably to train their models), signaling how eager executives are to clear any roadblocks for their new robot employees.

This executive euphoria isn’t limited to the tech sector. Leaders in finance, retail, media, and beyond have been eyeing which parts of their workforce they can hand over to algorithms. Altman’s prediction that customer support roles might soon be “totally gone” was meant as a warning, but plenty of CEOs heard it as an exciting opportunity. After all, what CEO hasn’t fantasized about an army of ultra-capable, unpaid workers who never form unions or ask for parental leave? In one eyebrow-raising stunt, a fast-food chain in Texas briefly “hired” an AI chatbot as its CEO – a gimmick, yes, but symbolic of the current zeitgeist where even the role of chief executive is joked about as ripe for automation. And just look at the tone of some tech entrepreneurs: last December, the CEO of Swedish fintech Klarna boldly declared he believed “AI can already do all of the jobs that we, as humans, do,” after partnering with OpenAI to replace large swaths of his staff. Klarna proceeded to freeze hiring and cut its human workforce by about 10% in one fell swoop, leaning on hundreds of AI agents to run customer service and marketing. No humans needed – or so they thought.

For a while, these moves made headlines, and the stock market smiled on the cost savings. Klarna even bragged that its automated agents could do the work of 700 full-time employees, touting a $10 million reduction in marketing costs thanks to generative AI content creators. Other companies followed suit with their own bold experiments, and enterprise software vendors rushed to showcase AI features that could “do the job of an analyst” or “eliminate the need for support staff.” If you listened to the earnings calls and glossy press releases in late 2024, it genuinely sounded like the human worker was on the fast track to obsolescence. The message from the top was clear: embrace AI or get left behind. In a sense, the AI gold rush of the past two years has also been a cost-cutting rush – a drive to achieve the oldest dream in business: lower labor costs, higher profits.

The Backlash: When the Robot Takeover Doesn’t Go as Planned

By mid-2025, however, cracks in this grand vision have started to show. In fact, some of the poster children of AI-driven “corporate Darwinism” are now scrambling to rehire the humans they ousted. Klarna – the very same company whose CEO said AI could do every job – has performed a public about-face. Just months after celebrating its bot workforce, Klarna admitted that its AI customer service agents simply couldn’t hack it and left customers frustrated and angry. “What you end up having is lower quality,” CEO Sebastian Siemiatkowski confessed in May, saying that they had over-prioritized cost cutting at the expense of customer experience. The company announced plans for a major new recruitment drive to restore human service reps – albeit on a gig-worker basis, “Uber-style,” logging in from home to field inquiries. It was a striking reversal for a tech darling that, only a year earlier, had been Altman’s “favorite guinea pig” for aggressive AI deployment. In hindsight, Klarna found out the hard way that firing your entire support team in favor of rookie bots is a fast way to torch customer goodwill (and, in their case, coincide with doubling year-over-year losses). As Siemiatkowski summed up ruefully: focusing on cost alone gave “lower quality” – a lesson paid in angry users and red ink.

Klarna isn’t alone in hitting the limits of AI hype. The “robot chickens are coming home to roost,” as one Futurism writer wryly put it. Across industries, many early adopters of AI labor are experiencing buyer’s remorse. A survey of 1,400 business executives earlier this year found that 66% were ambivalent or outright dissatisfied with their organization’s AI progress to date. The top reason? These CEOs cited a “lack of talent and skills” – essentially, they deployed fancy AI systems but didn’t have the expertise to get the promised results out of them. Another poll in the UK found that of the companies who rushed to replace staff with AI, over 55% now regret those layoffs and admit the decision was a mistake. Why the regret? In many cases, automating jobs has led to internal chaos, drops in productivity, and even higher employee turnover among the remaining staff. Instead of a 10x productivity boost, some firms found that poorly implemented AI caused more problems than it solved – from mismanaged workflows to demoralized teams – the exact opposite of what was promised.

It turns out that declaring “The Era of AI Employees is Here” is easier than making it work in practice. For all the startup swagger and CEO bravado, the real-world track record of AI “replacements” has been patchy at best. Take the tech support and customer service domain – one of the prime targets for automation. Sure, AI chatbots can handle basic FAQs and reset a password. But when customers with complicated or sensitive issues encounter a bot, the experience often nose-dives. We’ve all been there: caught in an automated phone loop shouting “Representative! Representative!” in vain. Klarna’s experiment showed how quickly patience wears thin when upset customers are met with a chatbot’s canned apologies. Even top-tier AI systems currently struggle with complex, context-heavy inquiries or emotional situations. As one industry analysis bluntly put it, these systems might excel at routine Q&A, “but they can struggle when emotional nuance is required”. A frustrated user doesn’t want a peppy auto-generated “I’m sorry you’re having that problem” – they want someone who actually understands the issue and can improvise a real solution. No AI today can mimic genuine human empathy or creativity in problem-solving, no matter how cheerfully it emulates a customer service script.

Even highly structured office work has tripped up AI replacements. In a remarkable experiment, researchers at Carnegie Mellon University recently tried staffing an entire fake software company with nothing but AI agents – from coders to team managers – to see how they’d perform in real-world tasks. The result was “laughably bad,” according to the study’s authors. The best “employee” of the bunch (an AI model from Anthropic) finished only 24% of its assigned tasks– and that was the top performer. Most of the AI workers accomplished little to nothing. One model from Amazon completed a pitiful 1.7% of its tasks, often getting stuck or going in circles. Even the tasks that did get done were wildly inefficient: the AIs took dozens of steps (and significant cloud computing costs) to do what a human might finish in a single sitting. Why did they fail so badly? The bots lacked common sense, got easily confused by ambiguity, and made absurd decisions when faced with unfamiliar scenarios. In one case, an AI project manager couldn’t figure out whom to ask for needed information – so it “solved” the problem by renaming another digital co-worker to impersonate the missing person. (Imagine a new hire who, unable to find Bob in accounting, just renames Alice to “Bob” and proceeds – that’s the level of absurd literalism we’re dealing with.) The takeaway from this AI-only office experiment was sobering: today’s AI agents are nowhere near ready to fully replace the flexible, improvisational problem-solving of human employees. As the researchers put it, our current AI is basically an “elaborate extension of your phone’s predictive text” – powerful at regurgitating patterns but not truly understanding or adapting like a person.

And so, a drumbeat of AI failure stories has begun to temper the robo-utopia narrative. IBM’s vaunted Watson – once promised to revolutionize fields like medicine – turned out to be so unreliable at hospital diagnostics that it recommended unsafe cancer treatments, causing participating hospitals to quietly pull the plug and IBM to sell off the whole Watson Health venture in disgrace. In the UK, a heavily hyped startup called Babylon Health claimed its AI chatbot could diagnose patients as accurately as a doctor; it was later found missing serious conditions like heart attacks and eventually collapsed into bankruptcy after regulators and users lost faith. Even in hiring – a function one might think algorithms could handle by screening resumes – the reality has been fraught with issues. Amazon famously had to scrap an AI recruiting tool that taught itself to penalize resumes containing the word “women’s,” among other biases, effectively filtering out female candidates. The tool had observed that Amazon’s past hires were mostly male, and in a spectacular example of garbage-in-garbage-out, decided that male candidates must be preferable. Amazon’s engineers were chagrined to discover their “smart” HR assistant was in fact a sexist monster, and the project was quickly abandoned. Yet Amazon’s cautionary tale was not unique – many AI screening systems have quietly been found to replicate or even amplify biases present in historical hiring data. Workday, a major HR software provider, is currently facing a class-action lawsuit from a Black jobseeker in his 40s who alleges that Workday’s AI-driven screening consistently rejected him due to biased filters, effectively automating age and race discrimination. Notably, a federal court did not dismiss the case; it’s allowing a collective action to proceed and even suggested the software vendor itself could be held liable for discriminatory outcomes. If that precedent holds, it could send chills down the spines of every company deploying AI in hiring or promotions. The message is clear: deploy half-baked AI without understanding its biases, and you might run headlong into civil rights lawsuits.

Why AI Isn’t Ready to Replace Humans (Yet)

Stepping back, the pattern is evident. AI can augment human work in powerful ways, but outright replacing humans has proven far more challenging. There are fundamental reasons for this, grounded in technology and human nature. First, consider emotional intelligence and context – the “soft skills” that are actually very hard for machines. Human employees can read between the lines, sense when a customer is confused or upset, and adjust their approach. They can prioritize on the fly, escalate an issue that seems trivial but isn’t, or calm someone down with just the right reassuring tone. Today’s AI, by contrast, is literal and inflexible. It only knows what it’s been trained on or explicitly programmed to do. As the saying goes, “AI is great at answering questions, but terrible at asking them.” A chatbot might give you an apology and a refund per its script, but it won’t pick up on the nuance of why a customer is upset beyond keywords it recognizes. It certainly can’t truly empathize or make the kind of small talk or personal connection that often smooths over service rough spots. This lack of genuine understanding becomes painfully obvious in high-stakes or sensitive interactions. Hospitals learned that an AI cannot replace a triage nurse’s intuition – Watson’s failures in oncology were partly because it couldn’t keep up with evolving medical knowledge or contextualize a patient’s unique situation, in the way an experienced doctor can. In education, early attempts to use AI tutors have run into walls because the AI can’t improvise when a student asks a question in a novel way or motivate a frustrated teenager the way a good teacher can. Real-world jobs are filled with these human moments of spontaneity, empathy, and ethical judgment – areas where current AI falls flat.

Next, there’s common sense and creativity, those catch-all terms for the human ability to adapt to the unknown. AI is fantastic at defined, narrow tasks – often outperforming humans by sheer speed or memory – but it is notoriously brittleoutside its training boundaries. Change the context a little, and things fall apart. The Carnegie Mellon “all-AI company” experiment underscored this: the bots were baffled by scenarios a junior employee could navigate easily, because real work is full of open-ended problems and imperfect information. A human office manager, faced with missing data or a novel client request, uses judgment, asks clarifying questions, pulls in colleagues, and draws on experience to figure it out. An AI without an exact script simply doesn’t know what it doesn’t know. It may plow ahead wrongly or stop altogether. Similarly, with creativity – AI can remix existing content, even convincingly, but it struggles to originate truly novel ideas or strategic insights that aren’t present in its training material. For example, an AI writing assistant can churn out ten generic blog posts in the time a human writes one, but will any of those posts contain a fresh perspective or a witty nuance that resonates with readers? Often, no – they tend to be competent yet soulless, and sometimes confidently incorrect due to AI’s tendency to “hallucinate” false information. In fields that value originality, from marketing to research, a human touch is still key to avoid bland or erroneous output.

Another huge and underappreciated issue is the operational overhead of AI. It’s a myth that you can just plug an AI system into your business and watch work magically automate itself. In reality, deploying AI is more like adopting a high-maintenance pet: it requires constant care and feeding. Many companies have learned this the hard way. That flashy “digital employee” package often comes with a team of human engineers in the loop – for good reason. Memra, one vendor, provides two full-time AI engineers to babysit their 10 AI agents for a client. Those engineers integrate the AI with existing software, monitor its performance, and tweak it when it inevitably misbehaves or hits an edge case. In a sense, you’re not eliminating jobs – you’re swapping frontline workers for (often pricey) technical staff or outside consultants. The AI agents need to be trained on your company’s data, configured to follow your business rules, and continuously updated as things change. If your company updates a policy or launches a new product, someone has to make sure the AI knows about it; it won’t intuitively pick that up. Getting an AI worker to truly understand your specific processes can be as much work as training a new human hire – sometimes more, because humans have general intelligence to fill in gaps, whereas the AI needs explicit instruction on even minor variations. Then there’s the integration headache: plugging AI into legacy systems often requires building middleware and dealing with data security concerns. Many enterprises find they must upgrade IT infrastructure or clean up databases just to make the AI play nice – costly projects that don’t show up in the rosy ROI calculations initially. And once everything is up and running, you have to plan for the worst. If the AI system goes down or makes a critical error, do you have humans on standby to take over? One telecom company that automated its network operations famously had to scramble when the AI misrouted traffic – the remaining team didn’t know how to quickly undo the AI’s actions. Over-reliance on automation without a manual fallback is courting disaster. It’s akin to a factory with no spare parts: great when running smoothly, but catastrophic when something breaks. Prudent companies are learning they must keep (and train) human overseers, not only to handle exceptions, but to step in during outages or emergencies. All of this adds up to time and money that eat into, if not completely erode, the theoretical savings from cutting staff.

Finally, there’s the human factor among your remaining employees. Imagine you tell your workforce that half of them will be replaced by AI, and the ones who stay must now work alongside bots or monitor them. That can be a morale killer if handled poorly. People naturally worry: Am I next on the chopping block? They may resent the new “digital colleagues,” or distrust their outputs (sometimes with reason). Some companies have made the mistake of anthropomorphizing their AI – giving it a name, calling it a team member – which can be deeply unsettling. As some HR experts point out, it’s misleading to treat AI agents as actual employees; they are tools and framing them as people just sows confusion and fear. There’s also a fairness perception: if workers see the CEO investing millions in AI while cutting humans, they may conclude that loyalty and experience count for little. That can drive your best talent to update their résumés and bail. In the UK survey mentioned earlier, several firms reported that their AI-induced layoffs caused “widespread internal confusion” and a spike in voluntary quits on top of productivity drops. In short, pushing AI too far, too fast can erode trust and engagement among the very people who are supposed to lead your company’s AI-augmented future. Change management and transparency are crucial – employees need to hear that AI is there to assist them, not simply to measure them or replace them. Companies that have navigated automation successfully often take pains to retrain staff for higher-value roles and make clear that the goal is augmentation, not just headcount reduction. Unfortunately, not every executive has gotten that memo.

Automation Gone Awry: Cautionary Tales and Legal Risks

With the shine coming off many AI pilot programs, a sobering realization is setting in: implementing AI without fully understanding it is not innovation, it’s abdication of responsibility. The tech industry’s recent history is littered with examples of leaders who moved too fast and broke things – sometimes disastrously so. As one AI expert put it, knowing how to use an AI tool (like prompting a chatbot) is not the same as knowing what it does under the hood. Yet many decision-makers skipped due diligence, seduced by flashy demos or the simple appeal of cutting costs. An executive at a major healthcare provider told a reporter that her team was rolling out an AI platform to streamline medical triage – but when asked how the model was trained, she admitted, “I’m not sure… isn’t that the vendor’s responsibility?”. That kind of blind trust is unfortunately common. In another case, a Head of HR proudly touted a new “AI-powered” recruiting system but couldn’t answer basic questions about how it worked or what bias protections were in place – he just assumed the vendor “had it under control.” If those anecdotes sound familiar, it’s because they echo the early days of big data and cloud computing, when non-technical executives sometimes green-lit projects they didn’t grasp, only to be shocked by unintended consequences.

What’s different – and more dangerous – with AI is the scale of impact on peoples’ lives and the potential for legal and regulatory blowback. Consider the HR domain again. In 2024, New York City enacted a law (NYC Local Law 144) that requires employers to conduct bias audits on AI hiring tools and disclose to candidates when AI is being used, with fines for non-compliance. This was a direct response to the growing evidence that algorithmic screening can discriminate in ways that are hard to detect. The law came on the heels of incidents like a rogue AI chatbot the city deployed for HR help – which was caught giving city businesses illegal advice, such as saying it was okay to fire employees who complained about harassment. (When regulators discovered that snafu, they didn’t immediately pull the bot offline, but they did slap a huge warning label on it telling users not to trust its advice – an embarrassing band-aid on a preventable wound.) Europe is going even further, with an upcoming AI Act likely to classify hiring algorithms and other HR tools as “high risk,” meaning companies will face strict requirements around transparency and fairness or face steep penalties. And it’s not just hiring. If an AI system involved in lending, insurance, or other sensitive decisions ends up biased – say, charging higher rates to certain ethnic groups due to skewed training data – the legal liability can be enormous under existing anti-discrimination laws.

Even when actual laws haven’t caught up yet, courts are increasingly willing to hold companies accountable for AI-caused harms. The Workday case is one example, suggesting vendors can’t just wash their hands of how their customers use their AI – if the tool discriminates, everyone in the supply chain could be on the hook. In another realm, consider product liability: if a company replaces human quality inspectors with an AI vision system, and that system misses a safety defect that later causes injuries, you can bet lawsuits will ensue over why a machine was trusted over a human. There’s also intellectual property and privacy risk. Some firms have found out too late that using AI could violate terms of service or data protection rules – for instance, scraping data to train an AI may breach copyright (hence Farquhar’s eagerness to loosen copyright law). And AI can inadvertently leak sensitive info: one model at a finance company was found including bits of confidential data in its outputs because it wasn’t properly fine-tuned. All these landmines underscore a simple point for executives: if you don’t deeply understand the AI systems you’re deploying – their training data, their limitations, their failure modes – you are essentially outsourcing your due diligence to a vendor or a consultant. That might work fine until something goes wrong. Then, as one commentator dryly noted, “later is when the lawsuit lands” and it’s still your signature on the dotted line.

The harshest lesson from the front lines is that AI projects can fail in very human ways. Often, it’s not the algorithms that are the weak link – it’s our implementation of them. A late-2024 global survey found that only about 25% of AI initiatives were considered successful, with the rest falling short or outright failing. Why? Usually because companies chased the hype without clear goals or understanding. They slapped AI onto broken processes and expected miracles. Garbage in, garbage out: if your underlying data or workflow is flawed, an AI will happily amplify those flaws, faster and at scale. Many leaders admitted they “moved faster than they thought” and didn’t stop to ask basic questions about bias, ethics, or even necessity before rolling out AI. Consultants, eager to sell AI solutions, sometimes promised too much. Auditors and compliance teams were left struggling to catch up after the fact. The result has been a slew of “AI faceplants” that make for painful headlines and damaged reputations. One need only look at the media coverage of the past year: every few weeks there’s a story of an “AI blunder” – whether it’s a recruitment AI embarrassingly rejecting all the good candidates, or a chatbot-turned-conspiracy-theorist causing PR nightmares for its creator. As a senior executive, being the protagonist of the next “AI disaster” article is surely not the legacy you want. And yet, not engaging with AI at all isn’t a viable option either; the competitive pressures are real. This is the tightrope leaders must walk: innovate, but responsibly.

A Smarter Path: Augmentation, Not Pure Replacement

Does all this mean AI has no place in the workforce? Far from it. The takeaway for decision-makers is not to avoid AI, but to shed the illusion that you can drop-in an “EmployeeBot” to replace a human and call it a day. The companies finding success with AI are those that use it to augment their people, not reflexively oust them. A common theme is the hybrid approach: let digital agents handle the repetitive, number-crunchy, or simple FAQ tasks, but keep humans in the loop for the expertise, creativity, and empathy that machines lack. For instance, many customer service operations now use AI chatbots as the first line for basic queries, but ensure a human agent is one click away when the conversation goes off-script or gets heated. This not only prevents customer frustration, it also gives the human reps superpowers – they can focus on the harder problems and leave the mundane stuff to the bots. In fields like law or medicine, AI is being used as an assistant to suggest ideas or flag potential issues, but the professional makes the final call (as it should be, when legal liability and lives are at stake). Even in coding and engineering, “AI pair programmers” can accelerate work by generating boilerplate code or testing scenarios, but experienced developers oversee the process and catch the inevitable errors or bad suggestions.

Forward-thinking executives have started to instill an “AI literacy” culture in their organizations: training employees at all levels on how these tools work, where they can help, and where they shouldn’t be trusted blindly. Some companies now run all AI plans through multidisciplinary review boards – including legal, ethics, and people ops – before greenlighting them, to avoid tunnel vision on cost savings. And encouragingly, a few leaders have modeled the right behavior: when one CEO discovered potential bias in their AI hiring tool, they paused the rollout entirely until an audit could be done. Another executive rewrote vendor contracts to include shared liability for AI mistakes, ensuring the provider had skin in the game to deliver a safe product. These aren’t signs of being slow or anti-innovation; they’re signs of maturity. Just as companies eventually learned to encrypt their data and do penetration testing rather than assuming cloud software is secure by default, they are learning to approach AI with a healthy mix of enthusiasm and skepticism.

There’s also a strong case to be made that maintaining a human-centric workforce is good business in ways that aren’t immediately measurable on a balance sheet. Employee morale and consumer trust are valuable assets. Being known as the company that “fired everyone and let the chatbots run wild” can hurt your brand – not just externally with customers who hate dealing with robots, but internally with future hiring. Talented candidates might think twice about joining a firm that treats people as disposable, especially in an era where corporate values and culture are under a microscope. On the flip side, companies that thoughtfully integrate AI to assist workers – while investing in upskilling those workers – can become magnets for talent who see that their employer is innovative and values their growth. For example, several major banks introduced AI to handle routine compliance checks but simultaneously offered training for their analysts to move into more analytical, decision-making roles that the AI couldn’t do. Rather than pink slips, those announcements came with career development plans. The result was little backlash and in fact improved efficiency, because the AI took the drudge work and humans applied the insights.

In many job categories, the future likely belongs to humans who are amplified by AI, not replaced by it. Microsoft’s research arm recently tried to quantify how exposed different occupations are to AI, and their findings were telling: jobs that involve writing, number crunching, and information processing (think translators, copywriters, customer support reps) are highly susceptible to AI assistance, whereas manual and physical jobs (like electricians, roofers, dishwashers) are much less so . But importantly, even for those vulnerable “white-collar” roles, the researchers caution that AI currently cannot perform 100% of the tasks of any one occupation – it can do parts of many jobs, but not all the nuanced duties that a person can handle. In other words, nearly every job has a slice that AI can do and a slice that only a human can do. The smart approach is to reorganize work accordingly: automate the slice that is automatable and elevate the human focus on the rest. There’s precedent for this in history. The arrival of ATMs in the 1980s is a classic example – they automated a core task of bank tellers (dispensing cash), yet paradoxically the number of human tellers increasedover the following decades. Why? Because ATMs made it cheaper to operate bank branches, so banks opened more of them, and tellers shifted to more customer-focused services (like advising on loans) instead of just handling withdrawals. AI could follow a similar pattern: if it’s used wisely, it might change jobs rather than eliminate them, allowing companies to do more with the same or slightly smaller staff, while offering new services or faster turnaround that create new business. That optimistic scenario isn’t guaranteed – it requires careful management choices – but it’s possible.

At the end of the day, whether AI becomes a job-killer or a job-enhancer will depend largely on the intentions and competence of those deploying it. If executives see it merely as a blunt instrument to slash headcount and please investors in the next quarter, they will likely encounter the myriad problems we’ve discussed: lower quality, customer backlash, lawsuits, demoralized employees, and strategic inertia from overestimating what the tech can do. If instead they approach AI as a powerful tool that needs human guidance and nurturing, they can harness it to improve productivity and spur innovation without sacrificing what their people bring to the table. The truth is, human workers are not just “cost centers” – they are sources of creativity, care, and adaptability that no machine can truly replicate. As one AI entrepreneur who ran that infamous “Stop Hiring Humans” campaign later admitted (perhaps after seeing the uproar): “I actually don’t think people should stop hiring humans. We’re hiring a lot of humans right now,” he said, conceding that the stunt was meant to provoke, not to be taken literally. Wise words, ironically, from someone who went fishing with dystopia and caught a backlash.

Conclusion: Keep the Humans in the Loop

As we stand halfway through 2025, the narrative of AI replacing humans wholesale is undergoing a reality check. Yes, AI is rapidly advancing and will profoundly change how work gets done – there’s no denying its potential to automate tasks that once required significant human effort. But the experience of the last couple of years sends a clear message to any executive listening: approach the “AI employee” trope with caution, humility, and a plan for the pitfalls. The future of work is not an AI apocalypse where all humans are pink-slipped, nor is it a static world where nothing changes. It’s something in between – a future where those who work with AI will likely outperform those replaced by AI.

Implementing AI in your organization should be treated as a strategic transformation project, not a quick cost-cutting hack. It demands up-front investment in understanding the technology, mapping out processes, retraining staff, and setting up governance so that you know what the AI is doing and why. It means being honest about the limitations – not expecting a beta-stage chatbot to handle angry customers alone, or assuming a generated report is 100% accurate without human review. It means resisting the hype (and the consultants whispering techno-utopian promises in your ear) and instead making data-driven decisions: pilot the AI in a narrow area, measure the actual outcomes, get feedback from employees and customers, and iterate. In many cases, you may find the AI works best as a copilot rather than an independent agent. And if an AI tool doesn’t deliver a net benefit, having the courage to pull the plug sooner rather than later is crucial – sunken costs be damned. Over half of companies that plunged into AI have already dialed back after initial projects underperformed; it’s far better to be one of those who learned and recalibrated than one who doubled down on a failing approach out of pride.

Perhaps most importantly, keep sight of the human element. The irony of many AI deployments is that they require more human judgment and oversight, not less, to implement successfully. That might feel like a bug, but it’s actually a feature – because it’s in partnership with people that these systems can truly shine. An AI can crunch zettabytes of data, but a human still needs to decide which questions to ask. An AI can draft a report in seconds, but a human editor gives it narrative and purpose. An AI can flag anomalies, but a human investigator determines if it’s a meaningful insight or a false alarm. By recognizing these complementary roles, executives can position their companies to use AI as a force multiplier rather than a replacement.

The coming years will no doubt bring more sophisticated AI – perhaps one day “digital employees” with far greater autonomy. But until that science-fiction future manifests (if it ever does), the smartest companies will be those that leverage both artificial and human intelligence together. So the next time a consultant or software salesman promises you that you can fire half your team because their AI is just that good, remember the cautionary tales. Ask what guardrails are in place, what happens when the AI is wrong, and how your remaining people will work alongside it. Ask yourself what value you might lose along with those salaries. And perhaps ask: who really benefits if we stop hiring humans? As it turns out, the most successful businesses of the AI era may be led by those who appreciate what only humans can do – and deploy technology in service of human ingenuity, not as its replacement. In other words: don’t fire your knowledgeable employees just yet. You might desperately need them when your new “digital workforce” suddenly breaks down, develops a mind of its own, or simply isn’t as competent as advertised. In the workplace of tomorrow, as in that of today, there’s no such thing as a free lunch – even if it’s dished out by a robot.

Why most enterprise AI plans collapse under their own weight — and how to build one that doesn’t.

Your company probably has a slide deck called “AI Strategy” somewhere on the server. It’s probably been presented at a leadership offsite, maybe discussed in the boardroom, and referenced by at least one innovation team trying to justify a chatbot experiment. But here’s the truth: most AI strategies in enterprise settings are either vague, bloated, or completely disconnected from business reality. They don’t fail because the technology doesn’t work — they fail because the organization never figured out why it needed AI in the first place.

The companies getting this right aren’t smarter. They’re just more honest. They treat AI as a means to an end — not a brand asset, not a buzzword, and definitely not a replacement for real strategy. They ask better questions. And most importantly, they stop pretending AI success is about tools. It’s about decisions. About infrastructure. About ethics, governance, compliance, and yes — sometimes about saying no.

So let’s get into what an actual AI strategy looks like — and what it doesn’t.

Begin With the Problems You Actually Have

An AI strategy that starts with “We want to use AI” is already on the wrong track. The goal isn’t to use AI — it’s to solve problems, make better decisions, reduce inefficiencies, or discover opportunities that would otherwise remain hidden. That means beginning with a clear-eyed assessment of what’s not working today.

Executives need to lead with diagnosis. That doesn’t mean tasking a product manager to list every process that could theoretically be automated. It means taking a hard look at where your business is leaking time, money, or trust. It means looking at forecasting errors that cost millions in inventory misalignment, customer churn that could have been prevented with better pattern recognition, or regulatory fines triggered by human error in compliance reporting. These are the problems that make AI valuable — not because it’s futuristic, but because it might be better than your current system at solving them.

It also means understanding your data situation. Most AI projects don’t fail because the model didn’t work. They fail because the data was a mess. Siloed, incomplete, mislabeled, contradictory — or just not aligned with the problem the AI is supposed to solve. Before you get excited about tooling, ask yourself whether you even have the inputs needed to train or fuel an intelligent system. This isn’t just an IT audit. It’s a reality check for leadership: do we have our house in order, or are we hoping AI will clean it for us?

Evaluate Tools Without Falling in Love

Once a business problem is clearly identified and the data is usable, only then does it make sense to look at solutions. But even here, the process is often hijacked by showmanship. Vendors promise immediate transformation. Internal teams pitch pilots that over-promise and under-deliver. Before long, a platform is in place — and nobody can remember why it was chosen.

Smart organizations approach this differently. They start by asking whether the proposed AI solution is actually suited to the problem. They resist the urge to assume every challenge requires machine learning, or that generative AI is the answer to customer engagement just because it can write sentences.

They ask whether the solution integrates with their current systems or whether it would require a full rebuild of core infrastructure. They consider whether the people who’ll use the tool actually want it, and whether it would streamline their work or create more friction.

They test in controlled environments. Not to check a box, but to learn something — like how well the system handles edge cases, how often it makes mistakes, and whether those mistakes are recoverable or catastrophic. This is also the point where many companies quietly realize that the tool they were sold isn’t as smart as the marketing suggested. That’s a good outcome. It means you’re still early enough to walk away.

The evaluation phase isn’t just about functionality. It’s about organizational fit. A tool that requires constant tuning by data scientists may work for a company with a mature analytics team — and completely flop inside a mid-sized enterprise with one overworked analyst. The question isn’t whether the AI works in a vacuum. It’s whether it works here.

Make Decisions Like You’re Accountable — Because You Are

Decision-making in AI projects is rarely straightforward. That’s why the best AI strategies don’t just identify problems and pick tools — they define how decisions get made, who is responsible, and what happens when things go wrong.

This starts with clarity. Someone must be responsible for outcomes, not just implementation. If the AI model fails, if it violates a regulation, if it produces harm — there must be a known owner, not a diffuse group of contributors pointing fingers. In successful companies, AI doesn’t live in a lab. It lives under executive oversight.

Good strategy also defines how decisions are made in the face of uncertainty. Not every AI tool works right away. Some require iteration. Others may work well in one department but not another. Without clear checkpoints, you risk dragging half-working systems into production simply because no one wanted to admit they weren’t ready.

That’s how companies end up with AI tools quietly ignored by users or generating risks no one is monitoring.

Accountability also extends to ethical review. AI can automate not just efficiency, but judgment. Who decides when a model is “good enough”? Who reviews its outputs for bias? Who ensures the system aligns with organizational values, not just performance metrics? A sound AI strategy makes these responsibilities explicit — and ensures they don’t sit with someone three layers down the org chart.

Risk Without Guardrails Is Just Negligence

Every powerful technology invites misuse. AI is no different. What makes it dangerous is the speed and scale of the harm it can cause when deployed without safeguards.

Some of the worst failures in AI have come not from malicious design, but from lazy deployment. Recommendation engines that push dangerous content. Hiring tools that silently discriminate. Predictive models that reinforce historical bias because no one thought to test them properly. These aren’t edge cases anymore — they’re becoming predictable consequences of ignoring guardrails.

A responsible AI strategy builds in those guardrails from the start. Not as PR cover, but as fundamental infrastructure. That means putting limits on where and how AI can be used. It means testing models for unintended consequences before they’re live. It means monitoring systems in production, not just assuming accuracy based on a training dataset. It means building interfaces that allow for human override, not hiding model logic behind black-box automation.

And yes, it means training employees to understand what AI can and can’t do — so they don’t over-trust systems that are still probabilistic at their core. That chatbot on your website might give great answers 80% of the time. But the other 20% might be risky, biased, or legally questionable. Someone needs to be watching.

The point of guardrails isn’t to slow down innovation. It’s to make sure you can survive it.

The Legal Landscape Isn’t Coming — It’s Already Here

Too many executives still operate under the assumption that AI is in a regulatory gray zone. That’s wishful thinking. You don’t need a new AI law to be in violation of existing ones.

If your AI tool touches hiring, lending, pricing, customer interaction, or any form of personal data, you’re already on the radar. The FTC has warned companies that they will be held accountable for deceptive or discriminatory AI practices. The Equal Employment Opportunity Commission has begun cracking down on algorithmic hiring tools that perpetuate bias. Cities like New York now require bias audits for certain AI applications in employment contexts. And privacy laws like California’s CCPA directly affect how data is used in training and inference.

Even beyond U.S. borders, global laws matter. The European Union’s AI Act will have teeth, and companies serving EU customers will need to comply. That includes explainability, risk classification, and oversight requirements that many current systems can’t meet.

Legal exposure doesn’t stop at your own code. If your vendor built their model using scraped data, copyrighted material, or personal information obtained without consent, your use of that model could trigger liability. You can’t outsource accountability — not in the eyes of regulators, and not in the eyes of your customers.

Ethical Data Sourcing Means Actually Knowing Where It Came From

“Ethically sourced data” has become the AI industry’s new favorite phrase. Unfortunately, it’s also the most abused. Ask a vendor where their data comes from, and you’ll hear words like proprietary, anonymized, or open-source. None of these terms guarantee anything — especially when lawsuits start flying.

Real ethical sourcing begins with transparency. You should know what kind of data was used, how it was collected, and whether the people behind that data gave informed consent. It’s not enough to claim it was public — so is your personal Facebook profile. It’s not enough to say it was anonymized — not if it can be reverse-engineered. And it’s not ethical just because a competitor used the same dataset.

Enterprises need to start acting like data origin matters.

That means reviewing documentation, verifying licensing claims, and sometimes saying no to tools that won’t show their work. It means recognizing that the data pipeline is as critical as the model architecture. And it means asking hard questions about whose data is being monetized — and whether they ever agreed to it.

Ethical sourcing isn’t just a reputational issue. It’s a foundational one. If your data is compromised, your AI will be too — and the consequences may not show up until it’s too late to fix them quietly.

Vet the Vendor Like You’re Marrying Them

Unless you’re building everything in-house, your AI strategy is only as strong as the vendors you rely on. That means you don’t just evaluate their product — you evaluate their integrity.

Do they provide documentation about training data? Are they upfront about known limitations? Will they help you comply with regulatory requirements, or dump the responsibility on you after the contract is signed? Are they building products that will still be supported in three years, or are they a feature factory hoping to be acquired?

Choosing an AI vendor is not like buying software licenses. It’s closer to choosing a strategic partner — someone whose practices, biases, and failures will become part of your company’s reputation. If they cut corners, you inherit the risk. If they go under, you inherit the support gap. If they mislead, you inherit the liability.

Your due diligence should be as rigorous as if you were acquiring the company outright. Because in some ways, you are — at least the part that affects your AI future.

Do You Need a Consultant? Maybe. Probably. But Not Forever.

Many companies start their AI journey by hiring external consultants. That’s not a bad thing — assuming you’re not outsourcing your thinking.

A good consultant can help you assess readiness, define use cases, build governance, and avoid early missteps. They can tell you what’s realistic and what’s hype. They can speed up the first year of execution by bringing in patterns and frameworks that would otherwise take months to learn.

But if your AI strategy lives entirely in a third-party PowerPoint deck, you don’t have a strategy. You have a temporary brain. The goal should always be to internalize capability. Consultants are there to accelerate, not to replace leadership. When they leave, your strategy should remain coherent, operational, and evolving.

Some companies may never need help — usually because they’ve already built strong internal teams. Others will benefit from external guidance, especially when navigating regulatory complexity or vendor ecosystems. The trick is knowing where your gaps are — and not being too proud to fill them.

In the End, It’s Still About People

AI strategy sounds technical. But at its core, it’s about people. The customers affected by AI-driven decisions. The employees asked to trust its outputs. The leadership teams who must balance innovation with responsibility. And the society that will ultimately judge whether your company used its power wisely.

That’s why a great AI strategy doesn’t just ask “What can we do?” It asks, “What should we do?” That’s a leadership question. And in this moment — with AI reshaping everything from compliance to creativity — it’s the one that matters most.

And if you’d like to work with people who actually understand what AI strategy means and where AI fails, drop me a note at ceo@seikouri.com or swing by seikouri.com.

The race to adopt artificial intelligence is in full swing. By 2024, roughly three-quarters of companies had implemented AI in at least one business function, yet less than one-third had aligned these efforts with their strategic goals. In other words, many enterprises are experimenting with AI, but without a clear strategy these shiny new tools risk becoming “costly distractions rather than transformative assets”. From chatbots to predictive algorithms, AI can indeed drive efficiency and innovation – but realizing its true business value requires a solid strategy. This article explores how executives can develop a proper AI strategy for their enterprise: from analyzing requirements and evaluating solutions, to managing risks with guardrails, ensuring legal and ethical compliance (like using ethically sourced data), vetting vendors, and even deciding whether to bring in outside consultants. The goal is a comprehensive, human-centered game plan that turns AI from hype into lasting results.

Start with Business Needs and Requirements

Every successful AI journey begins not with choosing a technology, but with identifying your business’s needs and objectives. As Harvard Business School experts note, “the first step to building an AI strategy is understanding how it helps achieve business goals and objectives.” Rather than deploying AI for AI’s sake, clarify what problems you aim to solve or what opportunities you want to pursue. Are you looking to automate routine processes, improve customer experience, augment decision-making, or unlock new insights from data? These goals will shape the entire strategy.

Concretely, this means conducting a thorough AI requirements analysis. In practice, ask foundational questions early: What problem are we solving? How will we measure success? Do we have the right data to fuel an AI solution? Who will own and maintain the system? These questions ground your project in real business value and organizational reality. By aligning every AI initiative with a clear business goal, you avoid the trap of implementing cool technology with no ROI. Indeed, selecting tools before understanding your objectives often leads to “fragmented efforts, technical debt, and poor return on investment.”

Assess your data readiness as part of this requirements phase. Data is the lifeblood of AI – without sufficient quality data, even the most powerful algorithms will underwhelm. Executives should take inventory of available data sources and their condition. Many organizations undertake a data audit to evaluate data quality, completeness, and silos across the business. This process might reveal, for example, that customer information is scattered in separate systems, or that data is riddled with gaps and errors – issues that need fixing for AI to succeed. It’s also important to ensure you have the infrastructure to handle AI: scalable cloud services, data pipelines, and integration capabilities so that new AI tools can plug into your existing IT environment.

Don’t ignore compliance requirements and domain constraints at this early stage. Different industries carry different rules (e.g. privacy regulations in healthcare or finance), and certain AI applications may face restrictions. A proper AI requirements analysis will uncover such compliance needs and set realistic expectations for models and performance. The takeaway for executives is clear: do your homework upfront. By investing time in analysis and planning, you set a strong foundation and greatly increase the odds that your AI initiative will deliver value instead of fizzling out in “pilot purgatory” (where projects never progress beyond small experiments).

Evaluating Solutions: Build, Buy, or Both?

With clear requirements in hand, the next step is evaluating what AI solutions can best meet your goals. This involves deciding whether to build custom AI in-house or buy/off-the-shelf solutions from vendors, or a combination of both. Each approach has merits: off-the-shelf tools can be quicker to deploy and leverage others’ expertise, whereas custom models can be tailored to your unique business context. The decision will hinge on factors like the complexity of your use case, the talent and data you have available, and budget considerations.

Start small and iterate. A wise strategy is to pilot AI technologies on a limited scale before any full rollout. In other words, test and learn. For instance, if evaluating a machine learning tool, you might run a proof-of-concept on one department or a subset of data. This allows your team to validate the model’s performance and surface any issues in a low-risk environment. As Columbia professor Rita McGrath advises regarding digital transformation, “instead of launching it like a big bang and risking a huge failure, take it step by step… building up capability in a very incremental way”. By experimenting in controlled projects, you gather evidence on what works, which informs the bigger implementation plan.

When comparing AI solutions, match the tool to the task. Different AI technologies excel at different things. For example, if your goal is to extract insights from text documents, natural language processing (NLP) tools are relevant; if it’s to find patterns in large datasets, machine learning platforms might be appropriate; if it’s to automate repetitive office tasks, robotic process automation could be the answer. Focus on the business problem and evaluate: does this solution meaningfully address it? It sounds obvious, but in the frenzy to adopt AI, companies sometimes deploy tools that aren’t the right fit – leading to wasted spend. Keep in mind the earlier mantra: tools aren’t strategy. Any AI tool should earn its place by demonstrably advancing your strategic objectives.

Other practical criteria in solution evaluation include scalability (can it handle growth in users or data?), integration (will it play nicely with our existing systems?), and usability for your staff. Also weigh the need for customization. Off-the-shelf AI products might not fit your industry’s nuances; on the other hand, custom-building everything from scratch can be time-consuming. Many enterprises choose a hybrid path: starting with a vendor solution and then fine-tuning or extending it to fit their processes. The key is not to accept a one-size-fits-all product if your requirements are unique. AI is not magic – it must be engineered to solve the specific problem at hand.

Decision-making should be cross-functional. Don’t leave AI solution decisions solely to the IT department or a lone enthusiastic data scientist. Because AI will impact workflows, employee roles, and even business risk, it’s vital to involve a broad stakeholder group in evaluation and decision-making. Successful AI initiatives are often “co-created with cross-functional teams that blend business, design, and engineering perspectives.” In practice, this might mean forming an AI steering committee or working group that includes leaders from the business unit in question, IT/data teams, and representatives from risk management or compliance. Such a team can collectively vet potential solutions and ensure any chosen AI tool not only works technically but also makes sense operationally and ethically for the organization.

Finally, as you evaluate and pilot solutions, define clear metrics for success. This could be improvement in a KPI (e.g. forecast accuracy, processing time saved, revenue uplift) or other tangible outcomes. Having objective metrics will guide the decision whether a solution is ready to scale up or needs rethinking. It also instills accountability – everyone knows how “success” is being measured. Many high-performing companies make sure they “monitor KPIs” for AI projects and have ways to track value, whereas laggards often lack this clarity. In short, treat AI investments with the same rigor as any other major business investment: require a solid business case and evidence of benefit before going all-in.

Navigating Risks and Setting Guardrails

Deploying AI in an enterprise is not without risk. AI initiatives can go off course in ways that lead to wasted resources, or worse, cause harm. That’s why a proper AI strategy must proactively address risk management and establish guardrails– the policies and technical measures that keep your AI use aligned with your company’s values, standards, and legal obligations.

Consider the potential risks: A model could make inaccurate predictions that lead to bad decisions; an AI chatbot might generate inappropriate or biased content; a system might inadvertently leak sensitive data. There are also strategic risks like over-investing in the wrong AI technology, or failing to get user buy-in which leads to shelved projects. Identifying these hazards upfront is the first step to mitigating them.

One effective practice is to embed AI governance and oversight from day one. This might entail forming a dedicated AI governance council or expanding the mandate of existing risk committees to cover AI. The role of governance is to continually ask: Are our AI systems doing what they’re supposed to, and nothing they’re not? As one framework describes, an AI governance board’s focus is to “inform, evaluate and drive policies, practices, processes and communication related to AI risk” across the organization. In plain terms, treat AI with the same seriousness as financial controls or cybersecurity – require regular reviews, testing, and sign-offs before high-impact AI systems go live.

Guardrails can be both procedural and technical. On the procedural side, set clear AI usage policies for employees (especially relevant now with many experimenting with generative AI tools at work). Define what data is off-limits for AI tools, what review processes are needed for AI-generated content, and which high-risk use cases (hiring decisions, legal advice, etc.) are prohibited without human oversight. Training staff on these guardrails is key so everyone understands the dos and don’ts. As McKinsey explains, “AI guardrails help ensure that an organization’s AI tools, and their application in the business, reflect the organization’s standards, policies, and values.” They are like the boundaries on a highway – they won’t eliminate every danger, but they significantly reduce the chance of a crash.

On the technical side, guardrails may include tools that monitor and constrain AI system behavior. For example, companies deploying large language models might use filters that automatically catch and remove toxic or sensitive outputs. Other guardrail techniques include validation checks for factual accuracy (to prevent those notorious AI “hallucinations”), bias detection modules to scan for unfair patterns in model outputs, and rate limiters to control how an AI can be used (preventing, say, an employee from querying a generative AI tool with massive downloads of sensitive data). Many of these controls can be automated. The goal is to catch issues early – ideally before they reach end-users or cause damage. Think of a finance AI that flags when its recommendation deviates too far from known baselines, triggering a human review. Guardrails like that build trust that AI isn’t running wild.

Critically, guardrails don’t exist in isolation. They should be part of a broader AI risk management framework. According to one comprehensive guide, effective AI compliance and governance involves steps such as performing privacy impact assessments, logging model decisions for auditability, testing models for fairness, and having humans in the loop for important decisions. It’s wise to adopt core principles of “responsible AI”. 
Often cited principles include transparency, accountability, fairness, privacy, security, human oversight, and robustness in AI systems. What this means for an executive is ensuring your team documents how models work and makes decisions (transparency), assigns clear responsibility for managing AI outcomes (accountability), checks and mitigates bias (fairness), protects data and access (privacy & security), keeps humans in control especially for high-stakes matters (oversight), and designs systems to handle errors or attacks (robustness).

Establishing these guardrails and governance practices may sound onerous, but it’s increasingly non-negotiable. Regulators and stakeholders expect companies to rein in AI risks. Governments worldwide are introducing new rules to prevent AI-related harms, from algorithmic bias to data abuse. If your AI strategy does not incorporate strong guardrails, you’re essentially steering a fast car without brakes – it might work for a while, but eventually an accident is almost assured. By contrast, organizations that institutionalize risk management for AI can innovate with confidence, knowing they have mechanisms to catch mistakes and course-correct. In summary, bake risk mitigation into your AI strategy from the start. It’s far easier to build trustworthy AI systems than to bolt on trust after something goes wrong.

Legal Requirements and Ethical Responsibilities

Closely tied to risk management is the need to address legal and ethical considerations in your AI strategy. In the rush to capitalize on AI, many companies have learned the hard way that ignoring ethics and compliance can lead to lawsuits, regulatory penalties, and public backlash. As one business school guide put it, “many [organizations] fail to address ethical considerations such as data privacy, bias, and transparency. Those must be part of your strategy from the beginning to avoid serious consequences.” For executives, this means engaging your legal, compliance, and ethics teams early in the AI planning process – not as an afterthought once the tech is built.

A number of laws and regulations govern AI usage, and this landscape is evolving rapidly. For instance, the European Union’s forthcoming AI Act is set to be one of the world’s strictest AI laws, categorizing AI systems by risk level and imposing requirements on “high-risk” uses like algorithms in hiring or credit scoring. The EU AI Act is expected to be enforceable by 2026 and could hit violators with fines up to 7% of global revenue – a potent reminder that compliance is not optional. Even in the U.S. where no overarching AI law exists yet, there are numerous sectoral rules and emerging guidelines: for example, New York City now mandates bias audits for AI-driven hiring tools to ensure they don’t discriminate, and the Federal Trade Commission (FTC) has warned it will crack down on “unfair or deceptive” AI practices (tying into existing consumer protection laws). Privacy statutes like GDPR (in Europe) and CCPA (in California) also directly affect AI, since AI often involves personal data.

Your AI strategy should explicitly account for these legal requirements. This might mean conducting a legal review of each proposed AI use case. Identify which regulations apply – data protection laws, sector-specific rules (finance, healthcare, etc.), intellectual property rights if your AI uses third-party data, and so on. Build compliance checkpoints into your AI development lifecycle. For example, if deploying an AI model in Europe, plan for a “conformity assessment” as required by the EU AI Act, or ensure transparency obligations (like notifying users when they interact with an AI system) are met. If your AI will make decisions that affect individuals (loans, job screening, etc.), incorporate fairness and bias testing as a legal and ethical mandate, not just a nice-to-have. An AI strategy that neglects these aspects invites substantial risk – “non-compliance can lead to financial penalties, reputational damage, or legal liability,” as one compliance expert succinctly put it.

Ethically, beyond just what’s legal, enterprises are being held to high standards by consumers, investors, and their own employees. Issues like AI bias, transparency, and privacy are now mainstream concerns. Deploying an AI that inadvertently amplifies discrimination or churns out offensive content can seriously damage your brand. Hence, many companies establish their own AI ethics guidelines or principles (often mirroring the responsible AI principles mentioned earlier). But principles alone are not enough – your strategy should include mechanisms to enforce them. This could involve ethics training for AI developers, an ethics review board for high-impact AI projects, or external audits of AI outcomes to ensure they align with your values.

A particularly hot topic is ethically sourced data, which ties both to legal compliance and corporate values. AI systems, especially in the era of big data and generative AI, are only as ethical as the data they are trained on. If that data was obtained unscrupulously (say, scraped from websites without permission, or collected from users without consent), then the AI inherits those ethical problems. Moreover, if your vendor claims their data or AI model is “ethically sourced,” you as the enterprise customer have a responsibility to verify that claim. Why? Because if it turns out the data was misused or stolen, you could face regulatory and reputational fallout for using it.

So, how can you ensure data is truly ethically sourced and not just a marketing buzzword? It starts with transparency and due diligence. Insist on knowing the provenance of any data that feeds into your AI. One expert framework suggests that ethically sourced data can be validated by knowing the owners of the data, the conditions under which it was collected, the terms of use, and the vendor’s rights to sell or share it. In practice, when dealing with an AI vendor or data provider, ask them pointed questions: Where exactly did this data come from? Did all the individuals or organizations involved give consent? Is there any personally identifiable information, and if so, how is privacy handled or data anonymized? If the data includes content like text or images from the internet, were licenses obtained or is it public domain? These details matter. As Deloitte cautions, “if data is not ethically sourced, it can lead to regulatory risks (like privacy violations) or significant reputational and financial damage.”

It might be helpful to incorporate formal vendor due diligence questionnaires focused on data and AI ethics. In fact, many companies now do this as part of procurement. For example, a questionnaire could probe whether the vendor uses web-scraped data and if so, whether they have rights or licenses for it. This is not a trivial concern – scraping data without permission can lead to intellectual property infringement claims, so you want to ensure any third-party data was acquired legally. If a vendor is evasive or unwilling to share such information, that’s a red flag. Similarly, ask about how the vendor addresses bias in their models, and what guardrails they have in place; also verify that they comply with privacy laws (did they, for instance, obtain consent for any personal data used in model training?). Your company’s AI outcomes are only as ethical as the weakest link in your supply chain, so vetting partners is a crucial part of the strategy.

In short, embedding ethics and compliance into your AI strategy is not only the right thing to do, it’s smart business. It will keep you ahead of regulatory curveballs and engender trust with customers and regulators. And it positions your enterprise as responsible and forward-thinking – which is especially important when AI is involved in decisions affecting people’s lives. Make legal and ethical checkpoints a built-in feature of your AI project roadmap, with leadership visibly supporting these commitments. As one guide noted, treating ethical AI as a core part of leadership philosophy from the get-go helps ensure it truly permeates the project. This way, you won’t be scrambling to patch ethical holes after deployment, because you will have laid a solid, principled groundwork from day one.

Evaluating AI Vendors and Partners

Very few enterprises build every AI capability in-house – you will likely rely on external AI products or services for at least part of your strategy. This could range from using a cloud provider’s machine learning platform, to buying a subscription AI tool, to hiring an AI development firm. Evaluating vendors and partners is therefore a pivotal element of your AI strategy. The stakes are high: choosing the right partner can accelerate your success, while the wrong one can saddle you with ineffective technology or even liability.

When selecting an AI vendor, approach it with the same rigor as hiring a key executive or acquiring a company. You need to vet both the solution and the company behind it. “Selecting an AI vendor requires a thorough assessment of the AI solution and the vendor’s practices in conjunction with a company’s business goals,” a legal advisory at Morgan Lewis emphasizes. In other words, look beyond the sales pitch – examine how the vendor operates and whether they will mesh with your needs and standards.

Here are some aspects to scrutinize during vendor evaluation:

• Technology and Fit: Does the vendor’s AI model or software actually solve your problem? Evaluate its capabilities, but also its limitations. If the vendor uses a certain AI model (say, they fine-tuned GPT-4 for their product), understand that model’s source and constraints. Ensure the technology can integrate with your systems and workflows – you don’t want a black-box tool that can’t connect with your data or requires completely changing your IT infrastructure to use.
• Data and Training: Ask pointed questions about the training data behind the AI. As noted earlier, check if the model was trained on reliable, appropriately sourced data. What type of data was it? How recent and relevant is it? For example, a computer vision vendor should tell you if their image recognition AI was trained only on certain demographics – if your use case involves a different population, the accuracy could suffer or introduce bias. Also, how often is the model retrained or updated with new data? An AI that isn’t kept up-to-date could become stale. And importantly, confirm the vendor’s data sourcing is legal/ethical: “Scraped data sets can lead to potential infringement claims, so understanding if a vendor obtains a license to use its training data” is critical. Don’t hesitate to request evidence or documentation of data licenses if applicable.
• Privacy and Security: Does the vendor’s AI involve personal data? If yes, how do they ensure privacy compliance (GDPR, etc.)? A responsible vendor should articulate measures like anonymization, encryption, and access controls. For instance, do they strip out or anonymize personal identifiers in training data? How do they prevent any personal data from being re-identified? If a vendor cannot answer these questions clearly, be wary – you could be exposed if they misuse personal data. Likewise, assess the vendor’s security posture: since AI often involves sensitive data or critical decisions, the vendor should follow strong cybersecurity practices to protect the models and any data you share with them.
• Ethical AI Practices: Beyond raw performance, gauge the vendor’s commitment to responsible AI. Do they have processes to test for bias and accuracy? Have they put in place a “risk management system with human oversight” for their AI outputs? You can request case studies or results of their model validation. A vendor that has nothing to show in terms of ethics or oversight might be leaving it all to chance. Given rising regulations, a good vendor should be prepared to help you explain and justify their AI’s decisions (the concept of model explainability). If your company values ethical AI (and it should), look for partners who demonstrate the same, not ones who treat it as an afterthought.
• Support and Integration: Consider how the vendor will support implementation and beyond. Will they help train your staff to use the AI tool? What service-level agreements (SLAs) do they offer for uptime and issue resolution? Many AI solutions are delivered as cloud services – ensure you have clarity on uptime guarantees and how quickly they’ll respond to problems. Additionally, verify that their solution can integrate into your existing systems or data pipelines with reasonable effort. If it cannot, you may face hidden costs adapting your processes to the tool. A flexible vendor who can customize their solution to your needs (even if it’s just minor tweaks) is often preferable to a rigid one, especially for enterprise use.
• Track Record and References: Finally, do normal due diligence: check the vendor’s track record and talk to reference clients if possible. Are they a stable company? Have other enterprises successfully deployed their AI at scale? A flashy startup might have great tech but could lack enterprise support experience. Balance innovation with reliability.

All this may sound exhaustive, but given how critical AI is, it’s worth the effort. Moreover, third-party AI tools introduce third-party risks. If your vendor has vulnerabilities or sloppy practices, those become your problem too. For example, if a vendor’s model has a flaw that causes discriminatory outcomes, your company will face the reputational damage once the tool is in use. That’s why Gartner and others emphasize strong vendor management in AI governance: you must vet AI vendors and monitor their compliance with ethical standards, just as you would for suppliers in other high-risk areas.

In summary, choose your AI partners carefully. Tie your vendor selection criteria directly to your strategy: the ideal partner will not only have a good product, but also align with your values (e.g. they prioritize data ethics) and understand your industry. If there is misalignment – say a vendor doesn’t understand the regulatory context you operate in – that’s a sign of trouble. Conversely, a great AI vendor can become a long-term collaborator who keeps you ahead of the curve technologically and safeguards your interests. The time invested in stringent evaluation will pay off by avoiding painful surprises down the road.

The Role of External Consulting: Is It Necessary?

With the complexity of enterprise AI, many executives wonder if they need to bring in external AI consultants or advisors to craft and execute their strategy. In truth, there’s no one-size-fits-all answer – it depends on your organization’s internal capabilities and the maturity of your AI efforts. However, evidence shows that engaging external expertise can yield significant benefits. According to Deloitte’s global AI study, companies that partnered with external AI strategy consultants saw a 32% higher return on their AI investments over 12 months compared to those that went it alone. Another survey found that businesses working with AI consultants reached deployment-ready solutions much faster (48% of such companies did so in under six months, versus only 19% of companies using solely in-house teams). These are compelling numbers suggesting that outside help, when used well, can accelerate AI success and avoid pitfalls.

Why might this be the case? External AI consultants offer a combination of specialized knowledge and an outside perspective that can be hard to replicate internally. A good consultant has likely seen many AI projects across industries – they know what tends to work, what fails, and how to tailor best practices to your context. They can provide an “unbiased viewpoint, identifying blind spots and fresh insights” you might miss inside the echo chamber of your company. Often, the gap derailing AI projects lies in “poor planning, unclear objectives, or a lack of internal expertise,” and experienced AI consultants can bridge that gap by bringing technical depth and strategic alignment. In essence, they help ensure your AI plans are realistic, business-focused, and using state-of-the-art approaches rather than hype.

Some scenarios where external consulting is especially valuable include:

• Strategy Definition: If your company is early in its AI journey and leadership isn’t sure where to start, consultants can facilitate strategy workshops, assess your readiness, and identify high-impact use cases. They can help you avoid chasing trendy but low-value applications and instead focus on AI opportunities that align with your strategic goals (e.g. revenue drivers or cost-saving areas).
• Technical Expertise Gaps: AI is a fast-moving field. Maybe you don’t have experts in-house for a particular subfield (say, natural language processing or computer vision), or your IT team is great at traditional software but new to machine learning engineering. Consultants live and breathe these technologies and can jumpstart development or guide your team on best practices (for example, setting up MLOps pipelines or selecting the right model architectures). This can save a lot of trial-and-error. “AI consultants often bring specialized knowledge and experience from working on various projects,” whereas an internal team might be encountering issues for the first time.
• Change Management and Training: Rolling out AI is as much about people as technology. Consultants who have done it before know how to help drive employee buy-in, conduct effective training, and set up governance structures. They can serve as an objective voice to convince stakeholders of the AI’s benefits or to surface concerns employees have. Additionally, they can help design new processes or organizational structures (like establishing that AI center of excellence or governance board we discussed).
• Short-Term Bandwidth: Perhaps you have a solid data science team, but they’re swamped with operational tasks. A consulting firm can temporarily augment your team to push a strategic project over the finish line or handle a one-off initiative (like a prototype to prove value to the board). In this sense, hiring consultants can be a way to scale resources flexibly without long-term headcount, especially for a pilot or exploratory phase.

That said, external consulting is not mandatory for every company. If you already have a robust internal AI/data science division with seasoned leaders, you might manage well on your own. Some big tech-forward enterprises even prefer building strategy internally to maintain full ownership and because they consider AI a core competency. But even in those cases, outside perspective can be healthy – maybe via advisory boards or periodic audits rather than daily involvement. On the flip side, companies with very little internal AI experience might find consulting essential at first, to avoid costly missteps in an unfamiliar domain.

Think of it this way: External consultants are like experienced mountain guides for your AI climb. You can climb the mountain without them if you have the tools and know the way, but many teams will reach the summit faster and more safely with a guide who’s been there before. They can help you avoid crevasses (like common implementation pitfalls) and choose the best path (a strategy roadmap) and not get lost in the fog of technical jargon.

If you do opt for external help, choose your AI consultants wisely. Look for a firm or experts with sector-specific knowledge of your industry, so they understand your business context. They should emphasize ethical AI and governance (if a consultant glosses over compliance, that’s a red flag). Ensure they have a track record of actually deploying AI, not just slideware – you want practitioners who know how to execute, not just theorize. And importantly, a good consultant will work collaboratively with your team, not operate in a black box. The aim is to transfer knowledge and build your internal capacity, so that over time you become self-sufficient. In fact, many successful engagements use a hybrid model: consultants get you started and solve initial hurdles, while your internal team gradually takes ownership, supported by training and documentation the consultants provide.

In conclusion, external consulting in AI is not an absolute requirement, but it is often highly recommended – particularly if you lack in-house expertise or need to speed up your AI adoption. The data shows companies leveraging external partners tend to see better ROI and faster results. The key is to use consultants as catalysts: they jumpstart your strategy and help set up the structures for long-term success, but you steer the vision and integrate their advice into your company’s unique culture and goals. Remember, the objective isn’t to outsource your brain; it’s to enrich and accelerate your own strategic thinking with seasoned insights. With the right partnership, external experts can significantly de-risk your AI journey and help ensure that the strategy you craft on paper actually translates into real-world impact.

Conclusion: Turning AI Hype into Lasting Value

Crafting a proper AI strategy for an enterprise is a multidimensional challenge – but as we’ve explored, it’s one that can be met with a thoughtful, comprehensive approach. For executives, the imperative is clear: approach AI as a strategic transformation, not a one-off IT project. That means starting with business-driven requirements, evaluating solutions with rigor, building in risk controls, honoring legal and ethical responsibilities, and fostering the right internal and external capabilities to execute the strategy. It’s a lot to balance, but the payoff is immense. When done right, AI can streamline operations, unlock new insights, delight customers, and give your company a decisive edge in the market.

On the other hand, a hasty or piecemeal AI effort – one lacking strategy – is likely to sputter. The statistics are sobering: a majority of companies today are dabbling in AI, but most do not have a clear roadmap or understanding among leadership of how AI will generate value. Don’t be part of that crowd. By investing the time to formulate a solid strategy, you ensure that your AI initiatives are not just science experiments or me-too deployments, but drivers of real business outcomes.

A human-centered, journalistic perspective reminds us that the story of AI in any enterprise is ultimately about people: the customers who benefit from smarter products and services, the employees whose work is augmented (or sometimes disrupted) by AI, and the leaders who must make tough decisions about technology investments and ethics. Keeping this human element front and center will help you make wiser strategic choices. For example, thinking about employees – will an AI tool genuinely help them perform better, and how can we bring them along? Thinking about customers – will our use of AI maintain or enhance their trust in our brand? These considerations ensure that the AI strategy isn’t formulated in an ivory tower but is grounded in the realities of those it affects.

Importantly, stay adaptable. The AI field evolves incredibly fast. New techniques, regulations, and market shifts will emerge even as your strategy is in motion. Treat the strategy as a living document. Continuously monitor the results of your AI deployments (Are they meeting KPIs? Any unintended effects?), listen to feedback from users and stakeholders, and be prepared to iterate. Agility is a competitive advantage in AI – the companies that learn and adjust quickly will outpace those that set a plan in stone and ignore warning signs. As one professor quipped, in digital transformation “you’re in a perpetual state of transitioning… there’s no such thing as, ‘we’ve changed, we’re done.’” Embrace that mindset for your AI strategy: it’s a journey of ongoing improvement.

To wrap up, an enterprise AI strategy is both a roadmap and a pledge. It’s a roadmap that charts how you will harness AI in service of your business goals, and a pledge that you will do so responsibly, ethically, and effectively. For executives leading this charge, the challenge is to be visionary yet vigilant – excited about AI’s possibilities but clear-eyed about the work required to capture them. With the insights and steps outlined above, you can lead your organization in turning AI from a buzzword into a pragmatic engine of innovation and value. The companies that thrive in the coming years will not necessarily be those that adopt AI the fastest, but those that adopt it wisely. By creating a proper AI strategy and following through with disciplined execution, you put your enterprise firmly in that category, ready to reap the rewards of the AI revolution in a sustainable and impactful way.

Curious how this plays out in real life? Shoot me a message at ceo@seikouri.com or check out what we’re doing at seikouri.com.

It sounds like the setup for a bad joke: “My resume walks into a bar and the bartender says, ‘Sorry, we don’t serve your kind.’” Except it’s not a joke—it’s reality for too many job seekers. In 2025, more and more candidates are finding that their applications vanish into a black hole before a human ever lays eyes on them. The culprit isn’t a biased recruiter who didn’t like your LinkedIn photo or your quirky cover letter. It’s often an algorithm with no common sense that decided, for reasons only it knows, that you weren’t worth a hiring manager’s time. Meanwhile, HR teams are pulling their hair out because their shiny new AI hiring tools are filtering out too many good candidates. And let’s not forget the employees (including HR professionals themselves) who worry the next resume to be tossed out might be their own – not by a manager, but by a machine learning model gunning for their job.

Welcome to the strange new world of AI in HR, where promises and pitfalls are piling up in equal measure. Investors and boards are demanding “AI-everywhere” to boost productivity and shareholder value, convinced that this is the silver bullet for talent woes. But as we’ll see, a blind rollout of AI can just as easily backfire – leading to missed talent, embarrassing failures, even lawsuits and regulatory smackdowns that tank value instead of creating it. In this long-form exploration, we’ll travel through recent cautionary tales of AI HR projects gone wrong, examine the legal and privacy landmines (from the US to Europe), and outline how to harness AI in healthy doses with human oversight. Buckle up, because even if this is a serious topic, we’re going to tackle it with a bit of humor and a human touch. After all, behind every algorithm is a human who really should know better.

The Great AI Hiring Hype (and the Hard Fall)

Executives around the globe have caught a serious case of AI fever. Over the past two years, artificial intelligence has rocketed from obscure data-science project to boardroom obsession, touted as the magic bullet for everything from recruiting and onboarding to performance reviews. Corporate leaders and investors see rivals boasting about AI adoption and fear missing the boat – so they demand their own HR teams implement AI everywhere, often with a vague mandate of “we need to do AI because that’s what everyone is doing.” In fact, global corporate spending on AI solutions is on track to reach nearly $200 billion by the end of this year. Surveys show over 60% of HR leaders say AI is a top priority, illustrating the intense pressure to ride this trend.

Yet for all the hype, the reality is sobering: many AI-in-HR projects are crashing and burning. One recent analysis found that nearly half of companies with AI projects had abandoned most of them in 2025. In other words, after pouring time and money into AI, a huge number of organizations quietly pulled the plug when results didn’t live up to promises. That’s not just wasted budget – it’s a strategic failure and a hit to leadership credibility. As a Fortune/IBM survey highlighted, only about 25% of AI projects succeed as expected, meaning a whopping 75% fall short or outright fail.

Why the disappointment? The problem usually isn’t the AI technology itself – it’s how it’s rolled out. Too often, companies chase the hype without clear goals or understanding, slapping AI onto broken HR processes and expecting miracles. Without proper oversight or alignment with how people actually work, the tools end up unused, or worse, they amplify existing problems like bias. As one HR observer put it, it’s the old story of “garbage in, garbage out”: if your data or processes are flawed, an algorithm will dutifully magnify those flaws. Unfortunately, many leaders have learned this the hard way. More than half of senior executives in a late-2024 survey confessed they felt like failures in leading AI initiatives, and over 50% said their employees were exhausted and overwhelmed by the breakneck pace of AI changes.

At this point, we’re riding the downside of the classic hype cycle – after the inflated expectations, here comes the trough of disillusionment. The big risk is reputational: a high-profile AI faceplant can make leadership look reckless and erode trust among employees and customers. Nobody wants to be the next headline about an “AI disaster”. For boards and investors hungry for AI-driven gains, it’s time to recognize that throwing algorithms at a problem without a plan is a recipe for wrecking, not revolutionizing, the workplace. As we’ll see, nowhere is this more evident than in the recruiting trenches, where AI’s promise to streamline hiring has often backfired spectacularly.

The Resume Black Hole – Qualified Candidates, Gone Missing

One of the earliest and loudest complaints about AI in hiring is the mysterious fate of the missing resume. If you’ve ever sent out dozens of applications and heard nothing but crickets, you’re not alone – and you might have been filtered out by a robot. Today’s hiring process is heavily automated: AI-driven Applicant Tracking Systems (ATS) are used by the majority of medium and large employers to screen and rank applications long before any human recruiter gets involved. These systems scan resumes for keywords, years of experience, education and more, and then an algorithm decides who makes the initial cut. The intention is good (handle high volumes efficiently), but the outcome? Potentially 9 out of 10 resumes get tossed out automatically – never reaching a real person’s eyes. Yes, you read that right: by some estimates, up to 90% of job applications never make it to a hiring manager because an AI judged them unworthy.

Think about what that means for both candidates and employers. Talented people are being rejected thanks to minor quirks of formatting or phrasing – the algorithmic equivalent of a typo. For example, an ATS might be confused by a creative resume layout with tables or graphics, so it just fails to parse your information. Or it doesn’t recognize that “M.S. in Comp Sci” is the same as “Master of Science in Computer Science” because it was programmed to match exact keywords. If your wording doesn’t mirror the job description exactly, the bot might conclude you lack required skills when in fact you have them. A human reviewer would catch these nuances; a naive AI won’t. The result is that perfectly qualified candidates get lost in translation – their resumes filtered out for reasons unrelated to their ability to do the job.

HR teams have started to realize they’re shooting themselves in the foot with these rigid algorithms. In one survey, 88% of employers admitted they believe highly qualified candidates are being weeded out simply because their resumes aren’t ATS-friendly. In other words, the system is so focused on exact criteria that it’s missing people with slightly unconventional backgrounds or those who didn’t cram their application with the “right” keywords. The irony here is rich: companies adopted AI to find better talent faster, but poor implementation means they might be locking out the very talent they seek.

A famous early example of this phenomenon was Amazon’s ill-fated recruiting AI. Back in the 2010s, Amazon built an algorithm to screen resumes, aiming to automate their hiring for tech roles. But in 2018 it was revealed that the system had taught itself a nasty bias: it started penalizing resumes that mentioned the word “women” (as in “women’s chess club captain”) and downgrading graduates of women’s colleges. Why? Because the AI was trained on past hiring data in which male candidates were overrepresented – so it “learned” an insidious lesson that male applicants were preferable. The project was swiftly scrapped once these chauvinistic quirks came to light, but it became the cautionary tale of AI inadvertently automating discrimination.

While Amazon’s case was an extreme (and embarrassing) example, it exposed a broader issue: many AI screening tools are effectively automating old biases present in the data or criteria they’re given. And even when overt bias isn’t the problem, a lack of contextual understanding is. For instance, an algorithm can’t yet gauge “potential” or “culture fit” or the value of a non-traditional career path the way a human might. An HR director at a Fortune 500 company recently lamented that their AI system was rejecting applicants who didn’t meet an absurdly specific checklist, even though some of those people could have been great hires after a brief training. “The algorithm has no common sense,” they sighed – it couldn’t see the forest for the trees in candidate profiles.

The takeaway? AI isn’t (yet) a substitute for human judgment in spotting talent. If companies rely on it too heavily, they risk throwing away diamonds in the rough. Until AI can appreciate a candidate’s unique story – or at least until it’s calibrated and monitored well – we’re going to keep seeing qualified folks fall into the resume black hole. And each of those lost candidates is a lost opportunity for the business.

When HR Algorithms Go Off the Rails: Cautionary Tales

If the resume black hole is frustrating, some of the more dramatic AI failures in HR have been downright facepalm-worthy. Let’s tour a few high-profile examples from the last couple of years where AI in HR went down the drain completely, causing headaches for everyone involved. Consider this the HR edition of “Chatbots Behaving Badly.”

• Amazon’s “Men-Only” Hiring Model (2018) – We already touched on this one: Amazon’s experimental AI recruiter decided women need not apply. It’s an older case, but seminal. The algorithm was caught discriminating against female candidates by down-ranking any resume even hinting at feminism or women’s involvement. The root cause was training data reflecting the tech industry’s male dominance, which the AI naively mirrored. Amazon executives quickly killed the project once the bias was discovered, and the debacle became a public lesson in how not to implement AI hiring.
• iTutorGroup’s Age Discrimination (2022-2023) – Here’s a more recent fiasco: a large online tutoring company, iTutorGroup, was found using an AI hiring tool that automatically rejected older candidates. The tool screened teaching applicants and allegedly blocked anyone over a certain age – specifically, women over 55 and men over 60 were auto-rejected. This led to a lawsuit by the U.S. Equal Employment Opportunity Commission (EEOC), which iTutorGroup settled for $365,000 in 2023. The EEOC’s message was crystal clear: “Even when technology automates the discrimination, the employer is still responsible”. In other words, you can’t blame the algorithm – if your AI behaves badly, your company bears the legal liability.
• Workday’s Alleged Biased Screening (2023-2025) – Workday, a major HR software provider, has been facing a high-profile lawsuit claiming its AI-driven applicant screening tools discriminate by race, age, and disability. An applicant who wasn’t hired first sued in 2023, and by May 2025 the case was expanded into a class-action with multiple plaintiffs. They argue that Workday’s algorithms unfairly filtered out certain demographics. The case is still pending, but it’s being watched closely as a bellwether for AI bias litigation. If the claims hold up, it could establish legal precedent that algorithmic bias = illegal bias, with hefty penalties to follow.
• The Chatbot that Told Employers to Break the Law (2024) – You can’t make this stuff up. In early 2024, New York City launched an AI-powered chatbot to help business owners, including advice on employment matters. To the city’s horror, journalists discovered the bot was cheerfully dispensing wildly illegal HR advice. Ask about tipping policies, and it would falsely claim “Oh sure, you can take a cut of your workers’ tips.” Ask about firing someone, and it might say “Go ahead and fire employees who complain about sexual harassment – totally fine!” These answers were completely wrong and violated basic labor laws, yet the bot stated them with authority. When this came to light, it was a PR nightmare. As of late 2024, NYC hadn’t fully pulled the plug – the chatbot remained online but now plastered with a giant warning popup telling users to please, please not treat its answers as legal advice. It’s a cautionary tale for any organization deploying chatbots: without strict guardrails, an AI might confidently lead you straight to court or compliance hell.
• AI “Integrity” Test vs. State Law (2023) – AI isn’t just tripping over bias; sometimes it breaks weird old laws. In 2023, a job applicant in Massachusetts sued CVS Health, claiming the company’s AI video interview system violated a state ban on lie detector tests for employment. The AI was analyzing candidates for traits like “honesty” and “reliability” – effectively acting as a digital polygraph in the applicant’s view. A federal judge allowed the case to proceed, and CVS quickly settled. Now multiple similar lawsuits have popped up citing that same Massachusetts law. The lesson? Your fancy AI personality quiz might accidentally run afoul of obscure regulations. HR legal teams, take note.
• The Case of the Spontaneous Salary Slip (2023) – Not all AI snafus result in lawsuits; some just cause managerial chaos. The HR software provider isolved learned this when they added a generative AI feature to help write job descriptions. One fine day, the AI decided to include salary ranges in the job descriptions all on its own, unprompted. Sounds harmless, except one of isolved’s clients had recently acquired a company where employees were paid far less than the salaries the AI was now advertising for similar roles. You can imagine the uproar – those underpaid employees saw the postings and realized the huge disparity. Cue a meltdown in employee morale and a frantic damage-control effort. The AI’s rogue addition of salary info wasn’t illegal per se, but it created an HR nightmare that a bit of human oversight could have prevented.
• Klarna’s AI Customer Service U-Turn (2025) – AI failures aren’t confined to hiring; sometimes they show up in HR’s cousins like customer support, with indirect HR fallout. Fintech company Klarna famously went “AI-first” in replacing customer service agents with bots – only to reverse course in 2025 by hiring people back. Why? The AI’s performance was underwhelming and lower quality, hurting service quality and customer satisfaction. Klarna’s CEO admitted they needed human agents again to provide better support. This admission came on the heels of an IBM study revealing that only 1 in 4 AI projects deliver the expected ROI, which likely included Klarna’s own experience. For HR, the implication is twofold: first, overly aggressive automation can backfire, leading to emergency hiring to fix the mess; second, there’s broader evidence that most AI initiatives fail to meet business expectations, so temper that enthusiasm with realism.

Reading through these examples, a common theme emerges: lack of proper oversight and testing. In each case, the AI did something a savvy human could have flagged as a bad idea – if only someone had been watching closely. Whether it’s blatant bias, legal ignorance, or just contextual stupidity, these failures were predictable. As one tech pundit quipped, “We’ve summoned the genie but we’re not paying attention to the wishes.” Before unleashing an AI system, you have to ask: what could go wrong? (Because something always goes wrong.) And after deploying, you must monitor it like a hawk.

When AI HR integrations fail, the consequences range from embarrassing to catastrophic. Companies have faced public outrage, regulatory investigations, and multimillion-dollar settlements. The wrong move can tank employee morale (imagine discovering your employer’s algorithm thinks you’re unfit for a promotion because you’re deaf, as in the Intuit case) or spark boycotts from candidates who feel the process is rigged. For investors pushing AI to boost share value, nothing will sink that value faster than an AI scandal that forces a company into apology mode – or into court.

Humans vs. Machines: Job Security in the Age of AI

Beyond the direct hiring process, there’s another elephant in the room whenever AI enters the workplace: Will the robots take my job? This concern hangs heavy over HR teams and employees alike. After all, if an algorithm can shortlist candidates or answer HR inquiries, what stops it from eventually replacing HR staff or other roles entirely? It’s a valid question – and it’s causing a fair bit of anxiety on both sides of the hiring desk.

Let’s start with the employees. Recent surveys show that AI anxiety is widespread in the workforce. A late-2023 study by EY found a staggering 75% of workers are concerned that AI will make certain jobs obsolete, and about two-thirds (65%) are explicitly worried that their own job could be replaced by AI. That’s right: nearly 7 in 10 people are looking at ChatGPT or some HR bot and thinking, “Is that thing coming for my paycheck?” Not only that, but employees fear AI could hurt their career growth in less direct ways too. In the same study, 72% thought AI might negatively impact their salary or pay (perhaps by automating higher-paying roles or creating a surplus of labor), and 67% worried they’d miss out on promotions if they don’t master AI tools. Essentially, workers feel they’re in a race to upskill or be left behind – a tough spot that HR departments and leaders need to manage with empathy and training.

Now, what about HR professionals themselves? You’d think the folks implementing these systems would feel secure, but even many HR practitioners privately fret about their future in an AI-driven world. The truth is, certain traditional HR tasks (screening resumes, scheduling interviews, answering routine employee questions) are highly automatable. Forward-looking HR teams see this as an opportunity: offload the drudgery to AI and free up human HR for strategic, high-touch work. But not everyone is convinced their organization will navigate that transition gracefully. A recent Gallup poll of CHROs (Chief Human Resource Officers) found 72% believe AI will eliminate more jobs than it creates in the near term. Even if, in the long run, AI creates new roles (as many economists predict), there’s palpable fear of a bumpy ride where a lot of people could lose out in the interim.

The flipside of the fear is that humans bring irreplaceable strengths to HR and people management. AI might beat us at sorting data or even at detecting patterns of attrition risk, but it can’t (so far) replicate empathy, complex judgment, and the personal touch that good HR professionals provide. In fact, many of the failures we described earlier (from biased hiring algorithms to tone-deaf chatbots) underscore how badly things go when humans are taken out of the loop. Rather than making HR jobs obsolete, these fiascos highlight the need for humans to work alongside AI as supervisors, interpreters, and mitigators. A well-trained HR analyst who understands AI can use it as a powerful tool – double-checking its suggestions, overriding it when needed, and focusing on the human elements of talent management that no machine can handle alone.

A case in point: when Klarna reversed its AI-first customer service strategy, it wasn’t an “AI or humans” decision – it became an “AI and humans” solution. Klarna still values automation, but they discovered the hard way that people are essential for quality service. The company opted to bring back human agents (even if in flexible or freelance roles) to complement the AI, aiming for the best of both worlds. This hybrid approach is increasingly seen as the model for HR as well: let AI do the heavy lifting on repetitive tasks, while humans handle the nuanced, strategic, and compassionate work.

For employees worried about job security, the message from forward-thinking leaders is “adapt and evolve.” Smart companies are investing in re-skilling their workforce so that people can move into new roles that AI can’t do – or into roles managing and improving the AI itself. We’re already seeing new job titles like “HR AI Trainer” or “Ethical AI Officer” emerging in some organizations. Indeed, the real revolution isn’t AI itself – it’s how organizations choose to work with AI. Those that find the right balance will not only keep their people employed, they’ll likely have more engaged, higher-skilled teams as AI takes over the drudge work.

Still, this is cold comfort to someone who’s just watched a company implement an AI system that does half of what they used to do. The key for HR leaders is transparency and involvement. If you’re rolling out AI in, say, performance management or training, involve employees in that process. Explain what the AI will do, what it won’t do, and how people’s roles will shift. Offer training so employees can actually leverage the AI tools (remember, a significant chunk of workers feel their organizations aren’t teaching them how to use AI ethically or effectively). By bringing employees along, you replace fear with curiosity – or at least with managed fear.

Bottom line: AI will undoubtedly change HR and many jobs, but a failed integration (one done carelessly, without human-centric design) is likely to cause more havoc – lost talent, legal trouble, demoralized staff – than if you had done nothing at all. In contrast, a well-managed integration, with humans firmly in control of the off switch, can augment and elevate HR work. Which side of that coin you end up on depends on the choices leaders make today.

Legal and Privacy Landmines: Why “Set it and Forget it” Won’t Fly

So far we’ve focused on the practical and ethical pitfalls of AI in HR, but let’s address the big legal elephant in the room. Rolling out AI in hiring or HR without proper control isn’t just risky from a business standpoint – it can land you in serious legal and regulatory trouble. In fact, regulators on both sides of the Atlantic have made it clear that when it comes to employment decisions, you can’t simply defer to “the algorithm”. If something goes wrong, your company will ownthat problem, in court if necessary.

Discrimination and bias laws are the most obvious area. In the U.S., there are decades-old laws prohibiting employment discrimination on the basis of race, sex, age, disability, etc. It doesn’t matter if a biased decision was made by a human manager or a machine-learning model – it’s illegal either way. We’ve seen this in the earlier examples: iTutorGroup’s settlement for age bias and the Workday lawsuit. The EEOC (the federal agency enforcing workplace discrimination laws) has been quite vocal that using AI won’t shield employers from liability. If your hiring AI ends up disproportionately screening out, say, older applicants or minority candidates, you could violate the Age Discrimination in Employment Act, Title VII of the Civil Rights Act, the Americans with Disabilities Act, and so on. Even if the bias was unintentional or emerged from a black-box algorithm, ignorance is no defense. As EEOC Chair Charlotte Burrows said, “Even when technology automates the discrimination, the employer is still responsible”.

On top of federal laws, states are jumping in with their own rules. For instance, New York City recently implemented a law (NYC Local Law 144) that requires employers to conduct bias audits on AI hiring tools and to notify candidates when AI is being used in the assessment process. Companies that fail to do so can face fines – in NYC’s case, up to about $1,500 per day of non-compliance. Illinois has a law regarding AI analysis of video interviews (aimed at tools like HireVue), requiring consent and disclosure. These are just the tip of the iceberg; as of mid-2025, more than 20 U.S. states or local jurisdictions have passed laws or regulations imposing obligations on employers using AI for hiring or employee data. The patchwork is growing, even as the federal stance remains a bit inconsistent.

Now, pivot to data privacy, which is a huge deal especially in Europe. In the EU, personal data of candidates and employees is protected by the General Data Protection Regulation (GDPR). If you use AI to process someone’s data (like analyzing their resume or video interview), you must comply with GDPR’s strict requirements on transparency, data minimization, purpose limitation, and security. Perhaps most relevant is GDPR’s Article 22, which gives individuals the right not to be subject to solely automated decisions that have a significant effect on them (like, say, not being hired for a job), unless certain conditions are met. What does that mean? In plain terms, if you are hiring in Europe and let an AI make the decision without any human review, you could be violating GDPR. European regulators and courts have already shown they’re willing to act on this. In a prominent 2023 case, the Amsterdam Court of Appeals ruled that Uber’s automated “robo-firing” of drivers violated GDPR because there wasn’t meaningful human involvement in the decision. Uber had to provide transparency and potentially reinstate or compensate drivers whose accounts were terminated by an algorithmic fraud detection system. The principle carries over to hiring: a purely algorithmic rejection with no human check and no chance for the candidate to appeal can be a legal ticking time bomb in the EU.

Furthermore, the EU isn’t stopping at GDPR. They have forged ahead with the EU AI Act, a sweeping regulation specifically targeting AI systems. Under this law (which provisionally took effect in August 2024, with a rollout of requirements through 2025-2026), AI tools used for recruitment, hiring, promotion, or termination are classified as “high-risk”. That designation means companies deploying such tools in Europe will have to meet strict standards for risk assessment, transparency, and human oversight. For example, providers might have to conduct conformity assessments before putting an HR AI system on the EU market, and employers using them will need to keep detailed logs and documentation. The AI Act also bans outright certain “unacceptable” AI uses, like social scoring systems or predictive policing. While hiring AI isn’t banned, any whiff of something like scoring candidates’ “social behavior” could be seen as crossing into risky territory. Notably, as of February 2025, EU companies (and non-EU companies doing business in the EU) were required to eliminate any AI practices deemed unacceptable and train employees on compliant AI use. If that sounds like a big compliance lift – it is. But ignore it at your peril: violations of the EU AI Act can draw fines up to €35 million or 7% of global revenue, even higher than GDPR’s 4% of global turnover cap for data breaches.

Speaking of fines and penalties, consider the differences in scale. In Europe, regulators love their big fines for big companies – think of those multi-hundred-million-euro GDPR fines in recent years. As mentioned, GDPR can hit 4% of worldwide annual revenue for serious infractions. For a Fortune 500, that could be billions of dollars. The EU AI Act raises the stakes to 7% for the worst offenses. That’s “shareholder value” vaporized in an instant if you mess up. In the U.S., penalties for AI misuse have been more case-by-case: settlements like iTutorGroup’s $365k or whatever damages might come from private lawsuits (which, in class actions, can still reach the millions). However, U.S. companies should note that while regulatory fines might be lower per violation, American juries can award punitive damages in discrimination cases that far exceed actual damages – not to mention legal fees and reputational damage. Also, if you’re a global company, you might end up dealing with both U.S. and EU regimes, doubling your trouble.

It’s not only discrimination and privacy. Other legal gotchas include data security (if you’re feeding personal data into an AI, you better secure it – a breach of candidate or employee data invites lawsuits and regulatory fines on the cybersecurity front) and duty to accommodate disabilities. The Intuit promotion case we discussed is a prime example: a deaf employee alleges the AI interview platform didn’t offer proper captioning, thus denying her a fair shot and violating disability discrimination laws. Employers must remember that using AI doesn’t absolve them from accommodations – you may need to provide alternative assessments for those who can’t engage with the AI in the standard way (e.g., if a video interview AI can’t handle sign language, you’d better have a human alternative).

Finally, transparency requirements are rising. In many jurisdictions (like several U.S. states and proposed in the EU AI Act), organizations must inform candidates when AI is being used and in some cases get consent for it. For instance, an Illinois law requires you to notify and get consent from applicants for AI video interviews. If a candidate says “no, I don’t want an algorithm judging me,” you may need to offer a human process as an alternative. Failing to do so could lead to legal complaints or at least bad press. And from a fairness perspective, transparency is key: people are more accepting of AI tools if they know how they work and how decisions are made. When decisions feel like they came from a black box, distrust grows – and that’s when people lawyer up.

US vs. Europe in summary: Europe is putting up hard guardrails – comprehensive privacy laws and AI-specific regulations that mandate human oversight and carry heavy fines. The U.S., lacking a federal AI law, is taking a patchwork approach: some guidance here, some local laws there, and the use of existing discrimination laws to reel in the worst abuses. The current U.S. regulatory climate is in flux; at one point the federal government was considering more AI guidance, but political shifts have trended towards less regulation in 2025. As a result, states like New York, California, Illinois, and even some unlikely ones (hello, Alabama bias audit law!) are stepping in. It’s a bit of a Wild West compared to the EU. But don’t interpret that as a free pass – the legal consequences of an uncontrolled AI rollout in HR can be dire in any jurisdiction.

To put it bluntly: a failed AI integration in HR not only risks bad outcomes, it risks courtroom drama and massive fines. As one report noted, companies worldwide could collectively lose hundreds of billions of dollars by 2025 due to AI implementation failures – once you factor in wasted investment, legal penalties, and remediation costs. So if the motivation for pushing AI is ROI and shareholder value, know that getting it wrong will have the opposite effect. An AI screw-up in HR can mean costly lawsuits, regulator audits, negative press, employee distrust, and sunk costs to fix or scrap systems. The only winning move is to play carefully – which brings us to our final act: how to do AI in HR right, or at least less badly.

AI in HR, Done Right: A Human-Centric Playbook

By now, you might be thinking AI in HR is a minefield best avoided entirely. Not so fast – despite all the horror stories, AI can deliver real benefits in HR when used wisely. The key is to implement it in healthy doses with plenty of human oversight and common sense. It’s absolutely possible to leverage AI to speed up drudgery and improve decision-making without creating a dystopian hiring process or a legal quagmire. The difference lies in how you introduce and manage these tools. Think of AI like a powerful medication: administered correctly, it can cure ailments; administered haphazardly, it can cause harm. So, what does a good “prescription” for AI in HR look like? Here’s a playbook for executives, HR leaders, and even investors to consider:

• Start with a Clear Purpose: Don’t implement AI for AI’s sake (or just because the board saw a fancy demo and got FOMO). Identify specific HR pain points or goals. Do you need to reduce time spent on screening? Improve employee engagement tracking? Increase hiring diversity? Define the problem first, then see if AI is the appropriate tool to solve it. Clear objectives will guide which technology (if any) you choose and how you measure success.
• Pilot and Test (Before Widespread Rollout): Too many companies flip the switch enterprise-wide and thendiscover problems. Instead, start with a pilot program. Test the AI on historical data and watch for biased or odd results. Involve a diverse group of employees or recruiters in testing to get human feedback. This is your chance to catch issues like “oops, it rejects all candidates from school X” or “it’s giving nonsensical interview feedback to people with accents.” Tune the system in a sandbox before it affects real people.
• Keep Humans in the Loop: This cannot be stressed enough. AI tools should support, not replace, human decision-makers for critical HR calls. Use AI to triage resumes, but have a recruiter double-check the rejects for hidden gems (perhaps via spot-checking a sample). If an AI flags an employee as a “flight risk” or a candidate as “low fit,” treat that as a tip, not gospel. Make sure there’s a human who can override or validate AI-driven decisions. Remember, if everyone assumes “the computer knows best,” then nobody is truly accountable – until the fallout, and then it’s your company’s problem. Designate oversight roles: for example, an HR AI ethicist or committee that reviews outcomes periodically.
• Bias Audit and Ongoing Monitoring: Bias isn’t a one-and-done issue. Algorithms can drift, data patterns can change (especially if the AI continues learning on new data), and what looks fair in a test environment might turn unfair in the wild. Conduct regular audits of AI decisions. Check selection rates by gender, race, age, etc., to ensure one group isn’t being adversely impacted (and if they are, investigate why). Many companies are hiring independent auditors or using third-party bias testing software to scrutinize their AI – a smart move both for fairness and to show regulators due diligence. And don’t forget to monitor for weird errors too (like our earlier example of an AI posting salaries it shouldn’t). AI quality assurance is an ongoing process.
• Transparency and Consent: Be open with candidates and employees about when and how you’re using AI. This is not just about legal compliance, but building trust. If applicants know an algorithm is screening resumes, they might tailor their application accordingly (which is fine – you want people to play to the system if it’s job-relevant). Provide channels for candidates to ask for an alternative if they are uncomfortable or disadvantaged by the AI. And critically, explain decisions. If a candidate is rejected, some AI systems can generate a reason (e.g., lack of X skill). Consider sharing that, at least upon request. For internal tools (like AI that monitors performance or suggests who to promote), communicate clearly what data is being used and how. Nothing breeds fear faster than a mysterious black box on the intranet that’s “watching” employees.
• Data Privacy and Security Safeguards: Work with your IT and legal teams to ensure all personal data fed into AI is handled properly. Minimize what you collect – don’t vacuum up extraneous personal info that could raise privacy red flags. If using cloud-based AI, ensure vendors have strong security and compliant data practices (and proper international data transfer mechanisms if you’re dealing with EU data). Also, set data retention limits: don’t keep resumes or interview videos forever “just because.” Apart from legal compliance, it’s good hygiene to reduce exposure. In Europe, be especially mindful of GDPR – you may need to allow candidates to opt-out of automated decisions or provide a human review by law.
• Train Your People (and Your AI): A successful AI integration is as much about people as technology. Train your HR staff and hiring managers on the basics of how the AI works and its limitations. Make sure they know it’s a tool, not a magic oracle. Encourage a mindset of collaboration with the AI – e.g., recruiters could use AI to surface non-obvious candidates but still apply personal judgment in final selections. Simultaneously, “train” the AI by giving it high-quality, representative data and updating it as job requirements evolve. If your company changes its hiring criteria or values soft skills more, ensure the algorithm is adjusted too. AI models can calcify around outdated assumptions if not periodically refreshed.
• Set Guardrails and Fallbacks: Configure AI systems with conservative settings at first. For instance, if an AI is auto-rejecting candidates, maybe set it to only reject the truly obviously unqualified, and pass a good portion to humans. If you deploy a chatbot to answer HR questions, program it to escalate to a human if it is unsure or if the user asks something sensitive (e.g., harassment reporting, legal interpretation – exactly where that NYC bot went astray by not escalating). Always have a manual override process. And have an incident response plan: if the AI does something harmful or controversial, how will you detect it, who will decide to pull the plug or issue corrections, and how will you communicate about it?
• Stay Current with Laws and Best Practices: The regulatory landscape is evolving. What’s allowed today might need auditing tomorrow and could be banned next year. Make it someone’s job to keep tabs on relevant laws (discrimination, privacy, AI-specific rules) in all jurisdictions you operate in. This could mean working closely with legal counsel or having compliance officers specializing in algorithmic tools. Being proactive is far better than being caught off-guard. If you know, for example, that the EU AI Act deems your recruitment tool “high-risk,” you can start preparing documentation and compliance measures now rather than scrambling when an EU regulator comes knocking.

When AI in HR is done right, the results can indeed be impressive. Mundane tasks get streamlined – freeing HR professionals to focus on strategic initiatives and genuine human connection. Candidates get quicker responses and maybe even a more personalized experience. Bias can be reduced (yes, AI can help here too, by highlighting inconsistent hiring patterns or flagging potentially biased language in job descriptions). But none of this happens by accident or simply by purchasing a fancy system. It takes a deliberate strategy: humans and AI working in tandem, each doing what they do best. As one HR tech expert wisely noted, “The real revolution is working alongside AI, not bowing down to it”.

In the end, human judgment, empathy, and oversight are the secret sauce that turns an AI deployment from a risky bet into a competitive advantage. HR is, fundamentally, about humans – nurturing talent, building culture, and navigating complex social dynamics in organizations. AI is a powerful tool in the HR toolbox, but it’s not a replacement for the human touch. As executives and investors eagerly fund AI initiatives, they must also invest in the less glamorous side: governance, training, and change management. It might not be as exciting as watching an algorithm “automate hiring,” but it’s the difference between a successful integration and a headline-grabbing debacle.

Conclusion: Walking the Tightrope with Eyes Wide Open

AI in HR is a classic high-reward, high-risk proposition. On one hand, you have the enticing promise – faster hiring, better matches, less paperwork, more insights, maybe even removal of human biases. On the other, the perils – good candidates inadvertently rejected, biased models perpetuating inequality, privacy violations, bewildered employees, and the ever-present specter of lawsuits and reputational damage if things go wrong. The past two years have provided a crash course in what happens when organizations charge into AI without understanding it: we’ve seen brilliant successes in some corners, but also spectacular failures that read like cautionary tales from a dark comedy.

For the C-suite readers and investors: pushing your HR teams to “implement AI everywhere, ASAP” might seem like visionary leadership in the boardroom, but in practice it’s like telling a chef to use a blowtorch for every dish because it’s a hot new tool. The results will vary, and you might set the kitchen on fire in the process. Responsible leadership in 2025 means tempering enthusiasm with due diligence. By all means, explore AI’s benefits – there are real gains to be had – but insist on guardrails, ask the uncomfortable questions (“how do we know this algorithm isn’t sexist?”), and listen to the people who actually work with these tools daily (your HR staff and the applicants going through the system). Remember that shareholder value can just as easily be destroyed by a bungled AI rollout as created by a successful one. There is no faster way to turn a stock price sour than becoming the poster child for AI-driven discrimination or privacy scandal.

For the HR professionals: you are the bridge between the technology and the people. It’s a tough spot – you’re asked to champion innovation and efficiency, but also to uphold fairness, morale, and compliance. Don’t be afraid to pump the brakes when an AI proposal seems half-baked or when the folks upstairs don’t grasp the nuances. Your intuition and expertise are incredibly valuable in this AI era. You know that hiring isn’t just data – it’s intuition, potential, and fit. So be the human advocate in the room. If an algorithm says “reject,” it’s okay to say, “let’s double-check.” If leadership says “replace our recruiters with a chatbot,” you can reply, “how about we let the chatbot handle the midnight queries about PTO balance, and keep humans for the hard conversations?” In other words, use AI to enhance your work, not eclipse it.

And for all the employees out there biting your nails about robots coming for your jobs: it’s true that change is afoot, and some jobs will evolve or even disappear. But history shows technology creates new opportunities even as it disrupts. The fact you’re reading an article about AI in HR means you’re already ahead of many in thinking about these issues. Stay curious and keep learning – become the person in your organization who knows how to use the AI tools, or better yet, who helps make them fair and effective. HR isn’t going to turn into Skynet overnight; it’s going to need real people to guide it for the foreseeable future.

In a sense, we’re all walking a tightrope: leveraging AI’s advantages in HR without falling prey to its pitfalls. It ispossible to achieve that balance, but only with eyes wide open and a willingness to adjust course when wobbles happen. If we do it right, maybe in a few years we’ll be telling success stories of AI in HR – how it found a great candidate everyone else overlooked, how it helped eliminate a bias we humans kept missing, how it freed up time for HR to actually talk to people instead of pushing paperwork. Until then, consider yourself warned (and informed) about the rocky road we have to navigate.

AI can be a trusted assistant or a loose cannon. The deciding factor is us. As a cheeky final thought: the next time someone says “our AI hiring tool will revolutionize everything,” feel free to respond with a wink, “Sure – just make sure it doesn’t revolutionize us into a legal settlement or a viral news story.” In HR, as in life, common sense and a bit of humor go a long way – even with the fanciest of algorithms.

Generative artificial intelligence, from GPT to DALL·E, ushered in an age of boundless creativity. You could ask for a poem, an image, or a business brief—and instantly receive polished results. But as industry leaders quickly realized, there was something missing: the nuance of choice. Infinite outputs become meaningless without a discerning filter.

That realization sparked the notion of Tasteful AI. Where raw AI delivers possibility, taste grants purpose. Its origins lie at the intersection of democratized creation—and human values, culture, and emotion. Design experts at Nielsen Norman Group, like Sarah Gibbons, explain that generative tools flatten experience: “you don’t have to own a camera…or know anything about meter to create a poem.” But taste, they argue, is not just about capability—it’s about knowing what to choose when everything becomes possible.

Philosophers and tech critics have since framed AI as a cultural amplifier. AI doesn’t redefine taste—but surfaces latent human preferences. Parham Pourdavood of Medium says, “AI isn’t replacing human creativity, it’s helping us recognize and develop the aesthetic sensibilities we already carry within us.”

The Purpose: Beyond Technical Skill

At its heart, Tasteful AI seeks to answer two core questions: what should be created, and why does it matter? As The Atlantic recently observed, taste “tells us not just what can be done, but what should be done.” In a digital avalanche of AI-generated content—many mediocre—true judgment becomes an invaluable asset.

Concretely, taste applies to design, content, strategy, branding. It determines which color palette resonates emotionally, which tone fosters trust, what narrative feels authentic. It’s not just another layer—it is the bridge between abundance and meaning. Without it, AI output remains raw material, lacking soul.

Execution: The Invisible Work of Taste

But adding taste to AI is anything but simple. One major challenge is the shift from synthesis to selection. AI can generate dozens or hundreds of variants instantly. The challenge lies not in generation, but in curation. Designers must sift relentlessly—stringing together iterations, discarding the mediocre, spotting the brilliant. Gibbons and fellow UX voices describe this as curation fatigue—a cognitive strain when humans become quality gatekeepers.

Simulated taste also poses a risk. AI can match aesthetic features and mimic style. But as Inside Higher Ed reminded us: that’s not real taste. Generative AI has no ability to express taste. Anything that looks like taste is a simulation, an illusion. True taste emerges from lived experience and learned intuition.

Cultural bias and homogenization create another execution hurdle. Taste is an echo of culture and history. When algorithms train on dominant datasets, they replicate dominant aesthetics. One marketing case, lauded in 2024, used AI to localize Coca‑Cola branding—but it sparked debate. Does hyper-personalization reinforce stereotypes or flatten nuance?

Even when done well, the process is exhausting. A designer’s intuition is a muscle—but endless loops of edit, review, edit can be draining. When culture values tasteful AI but doesn’t resource the human labor behind it, the concept becomes performative rather than meaningful.

Strategic Advantage—and a Spectrum of Views

In the generative-AI economy, taste is emerging as a competitive moat. Top executives understand this instinctively. The Atlantic quotes leaders who frame taste as part of hiring, branding, even office playlist choices—subtle signals that define company culture. Jony Ive’s move to AI via OpenAI’s acquisition of his startup (Io) signals that tech giants increasingly prize design DNA as much as algorithmic brains.

Academic work highlights a similar tension between novelty and usefulness. True creativity lies in striking that balance. AI can generate novel variants, but usefulness arises from human calibration.

Still, not everyone shares the optimism. Critics caution that taste-based curation can erect walls around creativity, reinforcing echo chambers. A Reddit conversation about AI-assisted helmet design questioned whether AI boosts novel progress—or just recirculates familiar forms. Music recommendation playlists have shown how bias reinforced by curation can narrow discovery.

The dark forest metaphor—a web flooded with algorithmic noise—also applies. As AI lowers the threshold of creation, true signal becomes harder to find. Taste becomes not just a value-add but a survival skill.

How Tasteful AI Works in the Wild

Design studios use Tasteful AI to guide layout selection and UX decisions. At agencies, designers ask AI to propose a dozen landing page styles or UI mockups. Then, drawing on experience and client goals, they choose layouts that feel right. UX designer Jonathan Montalvo describes UX in the AI age as a reflective practice: assessing not only form, but cultural weight and emotional impact.

IBM Watson’s early attempts in design critique, such as DesignCrit.ai in 2024, offered screenshot-based feedback on color theory and clarity—helping junior designers train their eye while preserving human refinement.

In the arts, digital artists like Mario Klingemann curate datasets to create generative work—choosing whose features to include or exclude. In such cases, data selection becomes a form of taste, interwoven with ethics and aesthetics.

Marketing and branding departments experiment with Tasteful AI by using AI-generated imagery and logos to match customer segments. Coca‑Cola’s AI-driven visual campaign attempted to balance consistency with adaptation, and Spotify uses generative thumbnails for playlists tailored to user mood and listening patterns.

Writers and musicians collaborate with tools like ChatGPT to develop raw ideas. These aren’t replacements for creativity—they’re prompts, provocations. Taste enters when artists decide which version reflects authenticity.

Concerns and Cautions

Even when AI platforms celebrate taste, serious concerns linger. Echo chamber reinforcement is one. When taste is codified by current cultural powers, we risk narrowing expressions instead of enriching them.

Taste can also function as privilege. If only major brands or well-resourced studios can afford the human effort behind curated AI, many voices may get excluded. There’s also deceptive polish. Sometimes, taste becomes marketing code for it looks good. Without grounding in ethics, context, and accountability, taste risks becoming hollow.

Finally, there’s the risk of labor erosion. If AI systems emulate taste and professionals become invisible curators, industries risk undervaluing human skill. Tasteful AI must come with resources and recognition for the humans sustaining it.

The Path Forward

If Tasteful AI is to truly matter, it must be explainable, inclusive, and ethical. Designers should document their decisions, share their frameworks, and critique their own biases. Tastes should reflect a broad cultural spectrum. And organizations must invest in developing these skills—through education, mentorship, and conversation.

Generative AI need not replace human taste—it can illuminate it. But only when used thoughtfully, respectfully, and reckoned with deeply.

Conclusion

In an age where AI can conjure near-limitless variation, Tasteful AI offers a compass. It transforms raw possibility into curated meaning. It is not just about looking good—it is about intentionality, cultural insight, and moral clarity. It may be the defining skill of the AI era: the power to choose, meaningfully.

But its value depends on who wields it, how it’s trained, and whom it includes—or excludes. By anchoring taste in transparency, diversity, and ethical rigor, we can ensure it uplifts—rather than narrows—our collective future.

Somewhere in a glass-walled boardroom last quarter, a CEO finished a sentence with the phrase “…to free up staff for more meaningful work,” and the room nodded. The CFO smiled. The HR head tapped her pen. The AI consultant pulled up a deck. And just like that, it was settled: the company would embrace artificial intelligence, not just to automate, but to downsize.

I’ve now had versions of this conversation with more than two dozen executives. Different industries. Different locations. Different levels of digital maturity. But the song remains the same: AI is coming, and it’s going to save us money by replacing expensive human beings. Most of the time, this isn’t said directly. It’s wrapped in a narrative of transformation. It’s dressed up as innovation. But the math is obvious. In every case, the question of “How do we do this responsibly?” was buried under the more urgent one: “How soon can we deploy?”

These are not reckless people. These are smart, accomplished professionals. But they are also—almost uniformly—underprepared. They think they understand AI because they’ve used a chatbot. They think hiring a consultant means they’re covered. They believe that speed is more important than scrutiny.

And they are wrong. Dangerously wrong.

The first sign that something was off came during a conversation with an executive at a major healthcare provider. She described how her team had selected an AI platform to streamline medical triage—something that would alleviate the workload for nurses and frontline staff. When I asked if they’d examined how the model was trained, she paused. “I’m not sure,” she said. “Isn’t that the vendor’s responsibility?”

It wasn’t an isolated case. One head of HR proudly explained that their recruiting platform was now “AI-powered.” When I asked what that meant—what kind of model, what kind of training data, what kind of compliance safeguards—he told me the vendor “had it under control.” A fintech CTO confessed that they were integrating AI into client service workflows but hadn’t yet discussed regulatory compliance because “it’s just a prototype.” At an education company, the CEO was rolling out generative AI to replace human tutors in beta markets. No bias testing. No auditing. No parental consent.

That was the pattern. Leaders with good intentions, bad information, and blind trust. What they were missing wasn’t data—it was understanding. And the more confident they were in their AI readiness, the less likely they were to see the risks.

We’ve been here before.

IBM’s Watson was once paraded as the future of medicine. It beat humans at Jeopardy! and promised to revolutionize oncology. Hospitals like Memorial Sloan Kettering signed on. MD Anderson spent over $60 million. And then—quietly, awkwardly—everyone backed out. The system, it turned out, recommended unsafe treatments. It misunderstood patient notes. It couldn’t keep up with evolving standards. Internal documents leaked to STAT revealed how doctors questioned its outputs. The system wasn’t just underwhelming. It was dangerous.

By 2023, IBM had sold off much of Watson Health. The idea had been grand. The execution had been a disaster. And the lesson? AI, especially in high-stakes contexts, is not magic. It’s fragile. And it fails in very human ways.

Executives keep forgetting this. Or they never knew it to begin with.

Babylon Health, a UK startup once valued at $2 billion, promised an AI chatbot that could diagnose patients as accurately as a GP. It was slick, fast, scalable. It failed to detect serious conditions like heart attacks and appendicitis. Regulators flagged it. NHS partners dropped it. The company filed for bankruptcy. But not before millions of people interacted with its bot, trusting its answers. Some may have delayed real medical care because of it.

In another sector, Workday’s AI recruiting platform is now facing a class-action lawsuit. A Black man in his 40s claims the AI repeatedly screened him out of jobs due to biased algorithmic filters. The court didn’t dismiss the case. In fact, it ruled that Workday, as a technology vendor, may be held legally liable for discriminatory outputs. That legal reasoning may transform how companies think about shared AI risk.

None of these failures happened in isolation. They weren’t just one-off glitches. They happened because leaders moved faster than they thought. Because consultants promised too much and auditors arrived too late. Because no one wanted to be the person in the room saying, “Hold on. Can we talk about bias?”

So why do executives keep walking into this?

Part of it is fear. There’s a panic circulating in boardrooms. A sense that if you’re not using AI, you’re already behind. The competition is automating. The market is moving. Investors want to hear “machine learning” in every quarterly report. And so leaders act. They act fast. Sometimes too fast.

Then there’s the illusion of competence. AI interfaces are persuasive. You talk to a bot, it talks back like a person. You write a prompt, it answers like an expert. And it’s easy to believe that the tech understands what it’s saying. That the people selling it understand, too. That you, by osmosis, now understand it as well.

But knowing how to use AI is not the same as knowing what it does. And deploying AI without understanding how it works—or what happens when it fails—is not innovation. It’s abdication.

The most insidious part? The cognitive trap of outsourcing responsibility. Hire the consultant. Trust the vendor. Let compliance figure it out later. But later is when the lawsuit lands. When the chatbot gives medical advice. When the hiring system excludes qualified candidates. When the AI writes legal copy that violates regulations. When a child gets misdiagnosed. When someone gets hurt.

And it’s still your name on the dotted line.

Some executives I spoke to did ask the hard questions. One CEO paused their entire AI rollout until their data team could audit training sets for racial and gender bias. Another rewrote their vendor contract to include joint liability for false outputs. A hospital CIO pushed back on a sales demo until it included transparency logs and explainability features.

These weren’t signs of delay. They were signs of maturity. Of leadership.

But they were rare. Most still saw AI as a cost center improvement. A margin play. A shortcut. They didn’t see it as a cultural force, a legal risk, or a psychological mirror. And they certainly didn’t see it as a moral decision.

Because here’s the truth: every time you replace a person with a machine, you’re not just changing workflow. You’re changing accountability. When something goes wrong, it won’t be the AI that takes responsibility. It will be you.

And something will go wrong.

It might be a hallucination that leads to a compliance breach. It might be a hiring algorithm that amplifies bias. It might be a chatbot that offers mental health advice to a suicidal user. It might be something you never even imagined. If you don’t understand how the model was trained, who trained it, what the blind spots are, what edge cases were tested, then you don’t understand AI.

You’re just hoping it works. And hope is not a strategy.

The companies that will succeed with AI aren’t the ones that moved first. They’re the ones who moved wisely. The ones who asked uncomfortable questions. That built internal governance. That respected the complexity. That didn’t conflate automation with intelligence.

This is your chance to be one of them. Start by asking better questions. Ask your vendors. Ask your CTO. Ask your board. Ask yourself.

Because if the answer is “we’re saving money”—but you don’t know how, or at what cost, or to whom—then maybe it’s time to slow down.

Understand before you automate. Lead before you outsource. Think before you fire.

Because AI isn’t going to replace you. But your decisions about AI might.

If you would like to learn more about developing and implementing an AI strategy without putting your company at risk, please don't hesitate to contact me. ceo@seikouri.com.

Somewhere between the launch of ChatGPT and the sudden emergence of “digital employees” who schedule meetings, write contracts, and casually hallucinate facts, we realized we may have made a mistake, not in building AI, but in skipping the part where we figured out how to govern it.

AI governance is the grown-up version of AI hype. It’s what happens when we stop asking what the model can do and start asking who controls it, who’s responsible when it breaks something, and whether we’re accidentally building a sociotechnical monster we won’t be able to put back in the box.

But like every good disaster story, this one starts with good intentions, plenty of warnings, and a recurring theme: we didn’t listen.

The Missing Infrastructure Beneath the Hype

Governance, to most people, sounds like a meeting that could’ve been an email. In the AI world, though, it’s the thing standing between innovation and catastrophe. It’s not just about rules. It’s about who makes the rules, how they’re enforced, and what happens when AI systems behave in ways their creators didn’t predict.

AI governance encompasses a range of aspects, including regulatory law, corporate accountability, technical audits, and ethical frameworks. It asks thorny questions. Who should be allowed to train large models? Should companies have to disclose what data they used? If a chatbot gives someone dangerous advice, who’s liable—if anyone? Should governments get access to frontier models for national security reasons? Should anyone?

The uncomfortable truth is that much of today’s AI infrastructure—especially the powerful models developed by OpenAI, Google DeepMind, Anthropic, and Meta—is being deployed faster than it’s being understood. And the guardrails? Mostly optional. Often fragile. Sometimes just vibes.

We’ve Been Here Before (But We Forgot the Ending)

The problem with technological revolutions is that they rarely feel dangerous until they’re irreversible. Think social media in the 2010s. Think Cambridge Analytica. Think of autonomous trading algorithms crashing markets in milliseconds. Or Amazon’s AI-powered hiring tool that quietly learned to penalize female applicants.

In theory, we were prepared. Scholars like Shoshana Zuboff had already warned us about surveillance capitalism. Think tanks issued report after report. Governments debated “trustworthy AI.” Corporations set up ethics boards and published sunny manifestos about fairness and transparency.

But when push came to profit, the principles often disappeared.

Google’s Ethical AI team famously imploded in 2020 after Timnit Gebru was fired for raising concerns about bias in large language models. Her co-lead, Margaret Mitchell, was soon pushed out as well. Google insisted it supported responsible AI. But when responsibility clashed with revenue—or reputational risk—the team was sacrificed. It was a clear message: internal governance is negotiable when ethics get in the way.

Regulation: A Global Patchwork Quilt

Efforts to govern AI have been uneven, slow, and sometimes performative. Europe is the furthest along, having passed the AI Act, a comprehensive legal framework categorizing AI by risk, from low to unacceptable. It bans some uses outright (like real-time biometric surveillance) and sets strict requirements for high-risk applications like credit scoring, medical devices, and law enforcement tools.

The AI Act will take full effect in 2026, but critics warn it’s already behind the curve. It was drafted before generative AI exploded, and while it now includes language to regulate foundation models like GPT-4 and Claude 3, the pace of technological advancement is outstripping bureaucratic timelines.

In the U.S., the picture has shifted markedly. The Biden administration’s 2023 Executive Order on “Safe, Secure, and Trustworthy AI” required safety evaluations for powerful AI systems, but had no binding legislative teeth. In January 2025, President Trump rescinded that order on his first day in office and issued a new Executive Order titled “Removing Barriers to American Leadership in Artificial Intelligence” (EO 14179), which revoked Biden-era directives, emphasized deregulation, and mandated the development of a national AI “Action Plan” within six months. Alongside, Trump also issued memos directing federal agencies to adopt AI swiftly, with appointed Chief AI Officers and standardized governance frameworks focused on boosting innovation over safeguards. Meanwhile, Congress is active on legislation like the bipartisan “TAKE IT DOWN Act” (covering non-consensual deepfakes) and the revival of the CREATE AI Act to expand public access to AI infrastructure. So, there is now a national framework—but it leans decisively toward deregulation and federal coordination, not liability or ethics.

Meanwhile, China has aggressively moved to control AI development within its borders, issuing detailed rules on recommendation algorithms, deepfake content, and generative AI services. But those rules often focus less on protecting individuals and more on maintaining political and social order. Transparency is less a virtue than a tool of state control.

This divergence creates a dangerous dynamic: fragmented governance in a globally networked ecosystem. While Europe worries about rights, China controls behavior, and the U.S. defaults to corporate self-regulation, AI companies operate across borders, cherry-picking the least restrictive environments. It’s a regulatory arbitrage game, and humanity might be the collateral.

Who’s Actually in Charge?

Ask any major AI company about governance, and you’ll hear a familiar refrain: we’re committed to safety. Some mean it. Others hope you won’t ask follow-up questions.

OpenAI’s story is particularly instructive. Founded as a nonprofit with a mission to ensure AGI benefits “all of humanity,” it later morphed into a for-profit capped entity. In 2023, the company’s board briefly fired CEO Sam Altman amid internal disagreements over safety and commercial priorities, only to reinstate him days later under intense pressure from investors and employees. The governance structure—once a point of pride—was exposed as brittle, confusing, and easily overturned.

It’s not unique. Most leading AI labs are private entities governed by opaque boards, complex investor agreements, and shifting internal alliances. Some maintain advisory councils, but few disclose how those bodies influence model deployment decisions. Public input is minimal. Whistleblowers have limited protection. And most model evaluations—on safety, bias, or misuse potential—are conducted in-house or published selectively.

This isn’t just bad optics. It’s a systemic risk. As models become more powerful, the question of who decides how they’re trained, tested, and released becomes existential. Because these systems aren’t just answering trivia anymore. They’re being embedded into financial systems, legal workflows, medical diagnostics, autonomous weapons, and more.

What Happens When Governance Fails?

Sometimes, we don’t have to imagine. Take Facebook’s content recommendation AI, which was designed to maximize engagement. Internal research later revealed that it promoted outrage, misinformation, and political polarization. Warnings were raised. Changes were proposed. But little was done because the incentives weren’t aligned. Governance didn’t fail silently. It was deliberately ignored.

Or look at Clearview AI, the facial recognition startup that scraped billions of images from the web to build a surveillance tool used by law enforcement. It violated the privacy laws of multiple countries, sparked lawsuits, and drew international condemnation. Yet the company continued operating, largely unchecked, and even received new funding.

Then there’s the low-key but widespread failure of automated decision-making systems in government. In Michigan, an AI-based unemployment fraud detection system falsely accused tens of thousands of people. In the Netherlands, a welfare fraud detection algorithm led to wrongful targeting of low-income and immigrant families, triggering a political scandal and the collapse of the Dutch government in 2021.

These weren’t technical glitches. They were governance failures of oversight, accountability, and public transparency.

The Mirage of Ethics Guidelines

It’s not that we don’t know what to do. The OECD, UNESCO, the EU, and dozens of think tanks have all published excellent frameworks on trustworthy AI. Key principles—fairness, accountability, transparency, human oversight—are widely agreed upon.

But most of these documents are non-binding. Ethics becomes a marketing layer, not an operational constraint.

Take Microsoft’s “Responsible AI” principles. Admirable on paper. But when Bing Chat (now Copilot) launched with GPT-4, it quickly went off the rails, gaslighting users, generating disturbing content, and proving wildly unpredictable in multi-turn conversations. Microsoft acknowledged the problems, rolled out fixes, and pressed on. The product was too important to delay. Ethics took a backseat to competition.

Anthropic’s “Constitutional AI” approach is a more serious attempt to embed values directly into training. It defines a model’s moral framework up front—using principles like non-maleficence and respect for privacy—and uses those principles to steer responses. But even Anthropic admits it’s not foolproof. The AI still reflects biases, occasionally hallucinates, and requires continual red-teaming to uncover vulnerabilities.

We’re left with a governance model that is aspirational, fragmented, and easily bypassed. h4

Is There Hope?

Yes—but it requires realism, not rhetoric. First, we need enforceable laws with teeth. The EU AI Act is a start, but it needs global cooperation. The U.S. needs to pass comprehensive AI legislation, not just executive orders. And international coordination—through the UN, G7, or a new treaty body—needs to go beyond photo ops and shared principles.

Second, we need real audits—conducted by independent bodies, not internal safety teams answering to the same executives who greenlight model launches. Imagine if Boeing self-certified airplane safety. Oh wait, that already happened. And it didn’t end well.

Third, we need public empowerment. Whistleblower protections. Laws requiring model transparency. The right to explanation. A way for people to challenge harmful AI decisions and hold companies accountable.

And finally, we need humility. Not the fake kind that says “AI is just a tool,” but the real kind that acknowledges: we don’t fully understand what we’re building, and governance isn’t about slowing down—it’s about staying in control.

Conclusion: The Cost of Waiting

AI governance is the rulebook we forgot to write before releasing the players onto the field. Some are kicking balls. Others are kicking people. The referees are still arguing over the shape of the ball.

This isn’t about slowing down AI. It’s about ensuring it doesn’t outpace the institutions meant to protect us. We’ve already seen what happens when we wait too long—ask anyone whose life was derailed by a bad algorithm, a biased chatbot, or a black-box decision with no appeal.

Governance isn’t the enemy of innovation. It’s how we make sure innovation doesn’t become our enemy.

And we’re running out of time.

OpenAI has been one of the catalysts for the execution-as-a-service trend with its plans for an autonomous “Operator” agent – but it’s a broader industry movement.

The New AI Butler at Your Service

Not long ago, AI was mostly a smart assistant that suggested ideas – drafting emails, completing code, summarizing text. You were the boss, and the AI was the helpful intern. Now, a new paradigm is emerging: “execution-as-a-service,” where you can hand off tasks for the AI to execute on your behalf. In other words, your AI doesn’t just advise you, it acts for you. OpenAI’s recent announcement of an upcoming agent (code-named “Operator”) kicked off public debate about this shift. This agent promises to “take over tasks” and run them independently, finally delivering on the long-hyped dream of autonomous digital support. And OpenAI isn’t alone – Microsoft and Salesforce have been racing in the same direction. Microsoft’s Office 365 Copilot, for example, can already analyze your emails, draft responses, and even generate PowerPoint slides from an outline – effectively task-execution-as-a-service embedded in everyday apps. Salesforce has gone a step further with its Agentforce platform, boasting that it “goes beyond chatbots and copilots” to make decisions and take action in business workflows. Even Google and other AI labs are reportedly working on their own agents to automate everything from email management to scheduling. In short, the AI world is gearing up to give us robot butlers for digital chores.

Humans + Machines = A Dynamic Duo?

The vision behind this human–AI partnership is enticing. Think of having a tireless co-pilot for all your routine tasks. You tell the AI what outcome you want, and it figures out the steps and executes them. Ideally, this frees you up for more creative or complex work. In current implementations, we still see a human-in-the-loop model: the AI does the heavy lifting, and the human supervises – much like a senior editor overseeing a junior writer. For instance, Notion’s AI can draft a blog post from bullet points, and the user then reviews and tweaks the output. It’s a partnership: the machine does the grunt work; the human provides guidance, oversight, and the final judgment. When it works, the human–machine duo can be incredibly efficient and even enjoyable – like having an assistant who never sleeps and never complains (well, unless you count quirky error messages as complaints). OpenAI’s product officers have suggested their goal is to let us interact with AI “in all the same ways you would a human”, hinting that these systems are meant to feel like colleagues as much as tools.

But a true partnership implies trust and balance of roles, and that’s where things get tricky. If the AI is the co-pilot, we humans are still supposed to be the pilot in charge. Yet as AI agents become more autonomous, there’s a thin line between co-pilot and autopilot. Handing over control is a double-edged sword: on one side, you have convenience and superhuman efficiency; on the other, you risk becoming a passive passenger in your own digital life. Maintaining a healthy human–AI collaboration means keeping the “human” in the loop in a meaningful way – staying alert, validating important decisions, and knowing when to hit the brakes. That sounds fine in theory. In practice, however, humans are… well, human. We get complacent. We trust too much or too little at times. Which brings us to the messy part of this partnership: What happens when the trusty robot butler messes up?

When Automation Goes Awry

“What could possibly go wrong?” – Those might be the famous last words in the era of autonomous AI helpers. Plenty can go wrong, it turns out. AI systems, no matter how advanced, are prone to errors, unexpected behaviors, even moments of wild hallucination. And when an AI has the power to execute tasks, its mistakes aren’t just academic – they have real consequences. We’ve already seen some almost comical (and some not-so-comical) failures. In one case, a car dealership’s customer service bot was exploited by a savvy user into agreeing to sell a new SUV for just one dollar – and it even phrased it as a legally binding offer. (No, the dealership did not end up handing out a free car but imagine the confusion and headaches for management!) A major airline’s chatbot once confidently gave a customer incorrect refund information that conflicted with the company’s own policy; a tribunal later ordered the airline to compensate the customer for the bot’s false promise. In other words, the company had to pay for trusting its AI. Meanwhile, AI-driven systems have been known to go off-script in more disturbing ways too – from a medical advice bot giving harmful recommendations to an ostensibly friendly assistant turning insulting or rogue when provoked. Each of these incidents shines a light on a core reality: when an AI is empowered to act, the mistakes it makes can be far more consequential (and embarrassing) than a typo in a text prediction.

Now, most organizations rolling out these “smart” agents are not blind to these risks. They test, they put guardrails, they often start with narrow tasks. But the truth is that even a well-tested AI can fail in unpredictable ways because the real world is endlessly complex. A famous cautionary tale comes from the realm of self-driving cars – arguably physical parallels to digital execution-as-a-service. In 2018, an Uber test vehicle on autonomous mode tragically struck a pedestrian crossing the street. Why? The AI driver had never been trained to recognize a person outside of a crosswalk, so it simply didn’t “see” the jaywalker in its path. A human driver would find that absurd – how can you not recognize a person in the road? – yet the AI’s blind spots were very different from a human’s. To make matters worse, a human safety operator was supposed to monitor and intervene, but they were distracted at that fatal moment. This accident underscores the partnership failure: the AI made a mistake no human would, and the human supervisor didn’t catch it in time. It’s a sobering example of how automation can go awry in ways neither the machine nor the person anticipates. While your AI email assistant messing up won’t have life-and-death consequences, the principle is the same: if your AI co-pilot veers off course, are you ready (and awake) to correct it?

Who’s Responsible When the Bot Blunders?

With great power (to execute tasks) comes great responsibility – but whose responsibility is it, exactly, when something breaks? If your AI assistant commits an error – say, misbooks a meeting or, worse, deletes important data – who faces the music? This question is keeping lawmakers and lawyers busy across the world. Thus far, no one has created a special legal status for “AI agents” themselves (sorry, AIs, you’re not going to jail or paying fines just yet). Instead, the focus is on the humans and organizations behind them. In the European Union, policymakers have been proactive in drafting regulations to address AI mishaps. A proposed AI Liability Directive aims to make it easier for people harmed by AI-driven decisions to get compensation. It doesn’t explicitly say “agentic AI,” but it lays out that for certain “high-risk” AI systems, if they fail to meet safety requirements and cause damage, the blame may be presumed to lie with the deployer or provider of that AI. In practical terms, if your autonomous AI assistant goes haywire in Europe, you (or your company) might have to prove you did everything reasonable to prevent harm – otherwise, you could be on the hook by default. This flips the usual script: traditionally, a victim had to prove a company was negligent; now the burden may shift onto the AI’s operator to prove they were not negligent. Europe is also pushing rules (under the EU AI Act) that require meaningful human oversight for high-stakes AI decisions, precisely to avoid “fire-and-forget” systems that run unchecked.

In the United States, the approach is less centralized – at least for now. There’s no federal “AI failure law” on the books. If an AI agent causes harm, American courts resort to existing frameworks: did the developers or users exhibit negligence? Was the product defective? These questions get hashed out under traditional tort and product liability principles. Some legal scholars are arguing that we might treat AI agents like we treat human agents in agency law: if you have an AI acting on your behalf, perhaps you should be liable for its actions much as an employer is liable for an employee’s misdeeds. This idea hasn’t really been tested widely in courts yet, but it’s floating around in the legal discourse. In the meantime, many AI providers in the US protect themselves via thick service contracts and disclaimers. (If you’ve ever scrolled through an AI app’s terms of service, you’ll find lots of “no warranty” and “use at your own risk” language.) Still, if an AI-related screw-up is big enough – say, an automated trading algorithm loses someone $20 million – you can bet lawsuits will fly to sort out who pays for the damage. We are essentially in a period of legal experimentation, waiting for precedent to be set. One thing is clear: “The AI did it” is not a defense that will magically get a company off the hook. Someone in the chain of human stakeholders will end up accountable, whether by law or just public opinion. And companies know that a high-profile AI failure can also be a hit to their reputation – just ask the tech giants that saw their stock prices tumble after their AI demos went wrong.

It’s worth noting that there’s also a kind of informal accountability shuffle that happens. A witty term has emerged among researchers: “moral crumple zone.” In a car, the crumple zone is designed to absorb the impact in a crash, protecting the people inside. In the context of AI, a human operator can end up as the crumple zone for moral and legal blame. For example, companies deploying AI will often tout that “don’t worry, a human is overseeing the decisions”. If something goes wrong, they can then point to that human and say, “Ah, it was their oversight failure.” The human in the loop becomes an “accountability sink,” absorbing criticism and liability. From the company’s perspective, this is convenient – they get to benefit from automation but deflect the fault to a person when the automation misfires. For the human, it’s a thankless position: you’re supposedly in control, but not really, yet you take the fall when the machine goes rogue. This dynamic is still playing out in real time, and it raises tough questions. Is it fair to blame a human supervisor for not catching an AI’s split-second mistake? Are we expecting the human to be a perfect failsafe for an imperfect system? The legal system will grapple with these, but so will our workplaces and societies in defining norms for the human–AI chain of command.

Trust, Fear, and the Customer Experience

How do users and customers feel about all this automation magic? It’s a mixed bag of psychology. On one hand, people love convenience – if an AI agent saves them time, many will happily adopt it. We tend to anthropomorphize helpful tech (“Thank you, Siri!”). A well-behaved AI servant can even inspire delight or a sense of personal connection. But trust is fragile. Give users one bad experience, and the spell can break. Studies have found that people quickly lose trust in an AI after seeing it make a mistake – and the trust isn’t easily won back later, even if the AI improves. In practical terms, a customer might try an AI-powered service, encounter a glaring error or a creepy mishap, and then swear off using it again (or tell all their friends about the “stupid bot that messed up”). We saw this when an AI lawyer tool spat out fake legal cases in a brief – it not only embarrassed the attorney who used it, but it made headlines that likely scared others away from trying such tools for a while. Each highly public AI blunder chips away at overall confidence.

There’s also the flip side: over-reliance. Some users may become too trusting, assuming the AI is infallible because it’s, well, a computer. Psychologically, there’s a known tendency called automation bias – we humans often default to thinking the machine is right, even against our own better judgment. In the context of execution-as-a-service, this can lead to users following AI-generated actions or advice blindly. A business manager might let an AI agent handle customer emails unsupervised because it seemed to do fine in testing – until one day it sends out an insulting or nonsensical message that a human would have caught. Interestingly, having a human “overseer” doesn’t always solve this; in fact, experts tasked with monitoring an AI can become complacent and less engaged, a phenomenon researchers describe as a “diminished sense of control and responsibility” when an AI is in charge. Essentially, people may zone out, assuming the system will alert them if something’s wrong – which the system might not do reliably. Over time, a human operator can lose skill and situational awareness, making them less capable of intervening when it really matters. It’s the classic airline autopilot dilemma: pilots fly less manually now and can be caught off-guard in emergencies. Likewise, your friendly AI co-pilot might cause you to let your guard down. And when a slip-up happens, the fear kicks in: fear that the AI isn’t under control, fear of what else it might do wrong, fear among customers that they’re at the mercy of a faceless algorithm. Companies introducing these systems will have to manage not just the tech performance, but the psychological contract with users. Being transparent about AI limitations, offering easy ways to flag or undo AI actions, and providing avenues for human support are all ways to shore up user confidence. After all, nobody wants to feel like they’re shouting at an automated agent in vain when something goes wrong – that’s a fast track to frustration and mistrust.

Conclusion: Embracing the Future, Eyes Wide Open

Execution-as-a-service – this brave new world of letting AIs do things for us – is undoubtedly exciting. It’s the next logical step in our technological evolution, and it promises a lot of good: efficiency, 24/7 availability, and personalized assistance at scale. It might indeed transform how we work and live, ushering in an era where mundane tasks are offloaded and humans can focus on what we care about most. But as we rush into this future, a critical eye is not just prudent – it’s necessary. A human–machine partnership can only thrive if we acknowledge its pitfalls. That means designing AI agents that know their limits and can defer to humans when needed, and designing human roles that remain vigilant and engaged, rather than complacent. Legally and ethically, we’ll need clarity on accountability so that innovation doesn’t become a shell game of responsibility when something breaks. And on the user side, we must foster a realistic understanding: these systems are powerful but fallible, helpful but occasionally hilarious (or disastrous) in their mistakes.

In the end, a bit of healthy skepticism and a touch of humor may be our best allies. Yes, your future AI butler might book your appointments and balance your budget – just be prepared to clean up a mess or two when it “spills the soup.” The key is to treat the AI as neither a magic savior nor a ticking time bomb, but as a new kind of partner – one that needs training, oversight, and yes, sometimes a timeout in the corner for bad behavior. If we can manage that, the whole human-plus-machine adventure will be well worth it. The robots can execute, but it’s up to us humans to exercise good judgment.

Imagine asking an AI developer the same question you might ask your jeweler about a diamond: Where did it come from? In an era when artificial intelligence is fueled by vast troves of information, the idea of “ethically sourced data” has emerged as the AI equivalent of fair-trade coffee or conflict-free minerals. It’s a notion that the data used to train models should be gathered responsibly – without theft, trickery, or harm. But what does ethically sourced data actually mean in practice, and why is it so challenging? Is it a genuine path to more responsible AI, or just a shiny buzzword hiding business-as-usual?

Let’s dig in, from the idealistic promise to the messy realities on the ground.

What Does “Ethically Sourced” Data Mean?

Broadly speaking, ethically sourced data refers to information collected and used in a manner that respects people’s rights, privacy, and consent. In the AI context, it means gathering data responsibly and transparently, and ensuring its use aligns with moral and legal norms. That entails a few key principles. First, any personal data should be handled with care: ideally, no personally identifiable information ends up in the training set, or if it does, it’s with explicit permission and robust anonymization. Individuals should consent to their data being used, rather than having it taken without their knowledge. Ethically sourced datasets also strive to be inclusive and fair, drawing from diverse demographics so that an AI model doesn’t only reflect one slice of humanity. And of course, all of this must happen in compliance with relevant laws (from privacy regulations like GDPR to intellectual property rules).

In simpler terms, how you obtained the data is as important as what the data contains.

Just as an “ethically sourced” label on food implies no exploitative labor or illegal ingredients, ethically sourced AI data means no cheating or exploiting to get it. One industry expert suggests boiling it down to two questions: Where did the data come from, and did the people or entities behind that data know and agree to its new use? If you can’t answer those, the data’s pedigree is dubious.

This all stands in stark contrast to the common practices that have fed the AI boom so far. Much of today’s AI training data would not earn an ethical seal of approval. In fact, the modern data economy often looks more like the Wild West than a well-regulated farm.

Scraping, Sweeping, and Public Domain: The Status Quo Ante

To appreciate the push for ethically sourced data, consider how AI datasets have typically been amassed. Web scraping – automated harvesting of content from websites – has been the bread and butter of many AI projects. Grab first, ask questions later could be the motto. “Today’s data industry is built on social site scraping, API access, and sometimes questionable data downloads,” journalist Kate Kaye observed, describing a “grab-first-ask-questions-later” mentality pervading both corporate and academic research. From language models gobbling up billions of internet text pages to facial recognition systems hoovering photos from Instagram or CCTV footage, the prevailing approach has been essential: if it’s online, it’s mine.

The ethics of this approach are, at best, murky.

Legally, the situation is still evolving. In one prominent U.S. case, a court affirmed that scraping publicly available web data is not hacking and likely not illegal. This gave AI companies a green light to vacuum up information en masse. Tech industry leaders have openly argued that using whatever data they can find– from news articles to your social media posts – is “fair use” and even necessary, claiming it would be “impossible” to build advanced AI without doing so. Indeed, giants like OpenAI and Google have burned through unfathomable amounts of data from the open web to train their models. And the results speak for themselves: incredibly fluent chatbots and powerful image generators that seem magical – until you peek under the hood at how the magic was made.

The problem is that what’s legal or commonplace isn’t always ethical.

Just because data is publicly accessible doesn’t mean the people behind that data agreed to have it used for AI training. Personal blogs, social media profiles, artwork posted online – much of this has been swept into datasets without so much as a heads-up to the creators. Many authors and artists were stunned to discover their novels or illustrations were quietly ingested to train commercial AI models. Lawsuits have followed: for example, authors sued Meta (Facebook), alleging that millions of copyrighted books were taken to train its AI without permission. In the imagery realm, a notable class-action lawsuit accuses generative art models of being built on billions of scraped images, including artists’ copyrighted works. These conflicts highlight a core ethical tension: does being technically “public” make data free to use?

Content creators and rights holders increasingly say no, or at least not without a conversation.

Privacy is another flashpoint. Some of the most egregious data grabs have involved personal data that people never expected would end up in an AI system. One infamous example is Clearview AI, a company that scraped three billion personal photos from websites like Facebook, YouTube, and Twitter to create a massive face recognition database. No consent, no notice – just images of millions of ordinary people, aggregated into a tool now used by law enforcement. To many, this was a wake-up call: if your face on your social media profile can be snatched for a surveillance AI without your knowledge, what does that mean for privacy? Regulators agreed; Clearview has faced bans and fines in several countries for “unethical and illegal” data collection. As one commentator dryly noted, it’s a case of a powerful technology company abusing the openness of the web, turning what was shared in one context into fuel for a completely different, and deeply intrusive, purpose.

Even well-intentioned AI projects have stumbled into ethical grey zones when sourcing data.

Consider a case from a few years ago: IBM’s “Diversity in Faces” dataset. The goal was admirable – gather a large, varied set of face images to help reduce bias in facial recognition algorithms, so they’d work equally well for people of all races, ages, and genders. IBM’s researchers pulled in about a million photos of faces from Flickr, all of which were published under Creative Commons licenses (meaning, legally reusable) . They annotated these images with details like skin tone and facial measurements, then shared the dataset with the research community to spur fairer AI .

Problem solved, right? Not exactly.

Many people in those photos had no idea their likeness was being used to improve face-recognition tech. Some were alarmed at the thought that a snapshot they’d shared online could contribute to surveillance tools. “People gave their consent to sharing their photos in a different context,” noted Meredith Whittaker of the AI Now Institute, “Now they are being unwillingly cast in the training of systems that could be used in oppressive ways against their communities” . In other words, even when the data was legally obtained (via open licenses), the ethical debate raged: Shouldn’t individuals have a say in whether their face becomes AI fodder? A group of affected Flickr users even sued IBM, under an Illinois biometric privacy law, for using their images without explicit consent.

These examples underscore why “ethically sourced data” has become a rallying cry. It’s a reaction to the anything-goes approach that has dominated AI data collection: massive web scraping, proprietary hoarding, and assumptions that public means free. Ethically sourcing data means doing things differently—slower, more cautiously, and often at smaller scale. But what does that look like, and who gets to decide what counts as “ethical” enough?

Who Defines Ethical Data (and How)?

Unlike organic food or fair-trade coffee, there’s no universal certification for ethically sourced data. It’s a relatively new concept being defined on the fly by a mix of stakeholders: governments, industry groups, academic ethicists, and companies themselves. Each may have a slightly different flavor of the term.

Lawmakers have started to draw some hard lines.

Privacy regulations like the EU’s General Data Protection Regulation (GDPR) explicitly require that personal data not be used without proper consent and purpose limitation. GDPR says individuals “should not be identifiable” in a dataset used for purposes they didn’t agree to. In practice, that means if you’re building an AI model in Europe, you can’t just scoop up people’s data unless it’s been anonymized or you’ve obtained permission. (The definition of “anonymous” can itself be tricky – researchers showed that even a few data points can re-identify someone with alarming accuracy .) Other laws address specific sectors: for example, U.S. health privacy law (HIPAA) forbids using patient data for research without consent, and some jurisdictions like Illinois have stringent biometric data laws that have tripped up companies like Clearview and IBM. Copyright law is another battleground – though existing statutes didn’t foresee AI training, courts and legislators are now grappling with whether using copyrighted works in machine learning violates artists’ rights or falls under fair use.

As of now, it’s a gray zone causing a lot of courtroom drama.

Industry guidelines and best practices are also emerging. Various AI ethics frameworks propose that ethical AI starts with ethical data. For instance, one framework emphasizes principles like consent, anonymization, thoughtful sampling, transparency, compliance, and data quality in dataset creation. These boil down to common-sense steps: get permission when possible, clean the data of personal identifiers, make sure you’re not skewing or biasing the sample, be open about what data you’re using and why, follow the law, and ensure the data is accurate and relevant. Organizations such as the Partnership on AI and academic groups have put out guidelines on data stewardship, urging things like “datasheets for datasets” (to document provenance and ethics of datasets) and bias audits on training data.

However, no single authority “certifies” a training dataset as ethical.

In practice, it often falls to companies and their ethics teams (if they have them) to define their own standards. An AI startup might publicly declare, for example, that “our data is ethically sourced – we only use licensed or user-contributed data, no web scraping without consent.” But another company might use the same phrase simply to mean “we didn’t break any laws collecting this data.” These self-defined claims can vary wildly. One tech CEO’s idea of ethically sourced might focus on representativeness (eliminating bias by sampling all genders, ethnicities, etc.), while another CEO might be thinking mostly about legal safety (avoiding data that could trigger lawsuits). Both will use the feel-good terminology of ethics. This ambiguity has led some critics to worry that “ethically sourced data” can become a PR buzzword – a shiny label without substance, unless companies are transparent about their practices. As AI ethics writer Steve Jones quipped through a series of rhetorical questions, we’d never accept a “blood diamond” or stolen goods in other supply chains; if firms start slapping ethical on their data without proving it, skeptics will rightfully push back.

One consensus point is emerging: transparency is key.

Whatever ethical sourcing means to you, say it clearly. Researchers like Stella Biderman of EleutherAI argue that if nothing else, companies should be far more open about what data they’re using. Secrecy around training data not only makes verification impossible, but it also erodes trust. Even partial transparency – for instance, publishing the mix of sources or the percentage of licensed vs. scraped data – would be an improvement, Biderman notes. In the long run, we may see something akin to “nutrition labels” on AI, listing the dataset ingredients so others can judge the ethical quality for themselves.

The Hard Road to Ethical Data: Challenges and Trade-Offs

If ethically sourced data is so clearly better, one might ask, why isn’t everyone doing it already? The short answer: it’s hard – legally, financially, and practically.

Legal complexity can be daunting. Navigating the thicket of copyrights, privacy rights, contracts, and regulations for each piece of data is no small feat. Much of the internet’s content lives in a legal gray area when it comes to AI mining. Is downloading a blog post to train a model a breach of copyright or fair transformative use?

Courts have only begun to consider such questions, and the uncertainty can make “safe” data hard to identify.

Err on the side of caution (i.e., only use data with explicit licenses or public domain status), and you eliminate a huge fraction of what’s available on the web. The recent effort by a coalition of researchers to build an “entirely ethically-sourced” language model illustrates this trade-off starkly. The team, spanning MIT, Cornell, and other institutions, painstakingly assembled a new text dataset dubbed The Stack or “Common Pile v0.1”, comprised exclusively of openly licensed or public domain texts. This was an eight-terabyte trove of data – large, but still far smaller than the unfiltered crawl used by something like GPT-4.

More importantly, verifying that each piece of it was indeed license-compatible was a herculean task.

They discovered many works online carried mislabeled or unclear licenses, requiring human review of countless items. “This isn’t something you can just scale up with more chips or a fancy web scraper,” Biderman said of the project, emphasizing that a huge amount of manual labor was needed – double-checking permissions, cleaning and formatting data, and so on. In the end, they successfully trained a 7-billion-parameter model on this “guilt-free” data, and its performance impressively rivaled the likes of Meta’s older Llama models. But notably, those baseline models it matched were from two years prior – an eternity in AI progress. The implication is clear: playing by strict ethical rules put this team a step behind the cutting-edge, at least for now. Their work rebuts the claim that it’s “impossible” to build AI without unethically scraping data, proving it can be done. Yet it also shows why the big industry players haven’t done it: it’s slow, costly, and even after all that effort, you might only get second-tier performance in a fast-moving race.

That leads to the financial dilemma. Ethically sourcing data often means paying, directly or indirectly, for what others get cheaply or for free.

Want a high-quality dataset of human conversations for your chatbot? The ethical route might involve running a big survey or hiring crowd workers to contribute and annotate dialogues with full consent. That could cost hundreds of thousands of dollars, whereas scraping millions of Reddit posts or Twitter threads is basically free. Similarly, instead of scraping art from deviantART without asking artists (zero cost, ethically dubious), one could create a platform to commission and license artworks from willing creators – but that would be vastly more expensive and time-intensive.There’s also an opportunity cost: time spent curating “clean” data is time not spent tuning your model or deploying features. In a competitive industry, many feel pressure to cut corners. Why spend a year and a fortune assembling a pristine dataset when your rival is bulk-downloading the internet and sprinting ahead? This doesn’t excuse unethical choices, but it explains them. Some companies likely calculate that the PR risk or legal risk of scraping is worth the immediate gains.

Until enforcement catches up (through fines or court injunctions) or consumer demand shifts, the economic incentives often favor the quick and dirty approach.

Then there are the practical hurdles beyond cost. Ensuring a dataset is truly “ethical” can be surprisingly complex. It’s not just about getting consent once – what if your use of the data changes over time? Ethically, if you gathered data for one purpose, you should inform people or get new consent if you repurpose it. This kind of ongoing diligence is often missing. Data can also pass through many hands, from brokers to aggregators to model developers. Each link in the supply chain might have different standards. For example, a data broker might claim its consumer data is “opt-in,” but by the time it gets to an AI team building a product, that context is lost. Tracing lineage – knowing exactly where a piece of data originated – is technically challenging once it’s mixed into a giant corpus. Yet without lineage, true ethical sourcing is impossible to verify. Quality and bias present another challenge.

Paradoxically, simply slapping an “ethical” filter on data doesn’t guarantee the resulting dataset is good for building an unbiased AI.

You might remove all the sketchy sources, only to be left with a narrower, more homogeneous set. For instance, if one avoids scraping internet forums and only uses formally published public domain texts (say classic literature or government documents), an AI trained on that might end up with a very genteel, antiquated perspective – ethical, perhaps, but not very well-rounded. Ethically sourced data still needs careful curation to ensure diversity of viewpoints and contexts. Otherwise, you risk a dataset that is legally clean but skewed or incomplete, which can lead to its own kind of unfair outcomes. Designing an ethical dataset thus requires not just lawyers, but domain experts and sociologists, and a strategy to hit quality, diversity, and size targets all at once.

No wonder many organizations find it easier to just grab a ready-made giant dataset (of questionable origins) and hope for the best.

Finally, we must note that ethical sourcing alone doesn’t solve all AI ethics issues. It’s necessary but not sufficient. Even a model trained on 100% consensual, licensed, bias-audited data can be deployed unethically or have harmful impacts. For example, the “guilt-free” model built by the researchers above might avoid the sin of copyright infringement, but it’s still an AI system that could potentially displace human workers or be used to generate misinformation. As one commentary pointed out, a large language model, however virtuously trained, is still “a technology fundamentally intended to destroy jobs,” and using only public domain texts doesn’t erase that concern. Moreover, some creators whose works are in the public domain (meaning copyright expired) might still feel unhappy that an AI is regurgitating their words or art without acknowledgment – they may be long gone, but the ethical quandary remains of living artists seeing AIs mimic the style of deceased masters for profit. In short, ethically sourced data addresses the means of training AI, but not the ends to which AI is put. It’s one piece of a larger puzzle of AI ethics.

Drawing the Line Between Principle and PR

With all these nuances, it’s clear “ethically sourced data” isn’t a simple checkbox, but a continuum of better and worse practices. Some skeptics worry the term can be abused – a convenient PR shield for companies to deflect criticism. We have indeed seen a flurry of press releases touting “ethical AI” without much detail on what that entails. As the spotlight on AI ethics intensifies, companies know that claiming to use ethical data is a good look. But the proof is in the pudding (or rather, in the dataset). Is the company willing to be transparent about its data sources? Did it invest in obtaining data through fair methods, or just slap a nicer label on the same old scrape? In an industry famous for “move fast and break things,” transitioning to ethically sourced data is a test of sincerity. It requires moving slower and fixing things – fixing the imbalances and oversights in how we gather the raw material of AI. There are encouraging signs.

More AI developers are talking about data governance and hiring chief data ethics officers.

Grassroots projects and nonprofits are creating open datasets with community input and consent baked in. Even investors and enterprise customers are starting to ask hard questions about data provenance before they buy an AI solution. And, crucially, the conversation is shifting: the question “Where did the data come from?” is being asked more often, in boardrooms and research labs alike. Just a few years ago, many AI teams never considered that question; now it’s becoming central to the notion of trust. As one report put it, companies ignore data ethics at their peril – the FTC has even fined firms and forced them to delete AI models trained on improperly obtained data.

The writing on the wall is that future AI success will demand both performance and provenance.

In the end, “ethically sourced data” is neither a passing fad nor a cure-all – it’s better seen as an ongoing commitment to doing AI the right way, even when it’s inconvenient. It means treating data not as an anonymous raw resource to be strip-mined, but as human-originating content that carries real-world impacts and obligations. An AI is only as moral as the data it feasts on and the intent of those who feed it. So the next time you marvel at a clever chatbot or a brilliant AI-generated image, it’s worth pausing to ask: what’s behind the curtain? A pile of pilfered texts and images, or something a bit more conscientious? The answer will likely lie somewhere in between – but the mere act of asking the question is a step toward a future where ethically sourced data becomes not just an ideal, but the new normal for AI. After all, as consumers, creators, or citizens, we ultimately get to decide what we find acceptable. The more we demand to know where an AI’s knowledge comes from, the more we encourage those building these systems to chart a higher ethical course. It’s a tricky journey, no doubt, but one well worth undertaking if we want AI that we can genuinely trust – not just for what it can do, but for how it came to be.

When generative AI steps into your creative workflow, it brings along a pair of frenemies: Adobe Firefly and Midjourney. Both promise to conjure stunning visuals from mere text, yet they couldn’t be more different in pedigree and principle. Firefly is Adobe’s corporate-born wizard, trained on a strict diet of licensed images and aiming to be the “commercially safe” choice. Midjourney, by contrast, is the indie darling – a powerful prodigy nurtured on the wild scraps of the internet, community-driven and a touch anarchic in its approach. For marketing professionals, media agencies, and legal teams, this isn’t just an AI art style contest; it’s a high-stakes comparison of training data ethics, licensing models, and the legal groundwork that could make or break your next campaign.

So grab your judge’s wig (and maybe a paintbrush or two). In this 10-minute tour, we’ll dive deep into how these two AI image generators source their smarts, what that means for copyright and licensing, and ultimately which one offers safer ground for your brand assets. It’s Firefly vs Midjourney – a tale of curated content, web scrapes, lawyers, and a little bit of irony.

Training Data Diets - From Adobe Stock to the Web’s Wild West

Every AI artist is only as good – or as legal – as the training data it feasts on. And here our contenders differ dramatically.

Adobe Firefly was raised on a curated buffet of legally acquired imagery: primarily Adobe Stock’s hundreds of millions of licensed photos and illustrations, sprinkled with openly licensed and public domain content where copyrights have expired. In Adobe’s own words, “Firefly… was trained on licensed content, such as Adobe Stock… to generate content for commercial use that does not infringe on copyright or other IP rights”. This was no accident; it was a selling point. Adobe essentially put Firefly on a strict data diet to ensure it wouldn’t belch out a Mickey Mouse or a Nike swoosh and land clients in hot water. Think of it as the farm-to-table approach to AI training – every ingredient vetted, nothing mystery-meat about it.

Midjourney, on the other hand, is the AI equivalent of an all-you-can-eat street buffet. The company has never fully disclosed the secret sauce of its training set. But industry consensus (and a few lawsuits, which we’ll get to) suggest that Midjourney “likely used images scraped from the open web without permission from the creators”.

In plainer terms, if Firefly’s diet is a neatly curated Adobe Stock salad, Midjourney’s is a heaping plate of everything – from fine art to random Flickr photos – scraped and sampled with little discrimination. Adobe itself contrasts Firefly’s “licensed” training data with “competitors like Midjourney, which learned by scraping pictures from across the internet”. No permission slips, no handshakes – just crawl, ingest, and learn.

The ethical contrast here is stark. Firefly’s approach was a direct response to nervous enterprise customers who, as Adobe’s chief strategist noted, were “very concerned about using generative AI without understanding how it was trained”. These big brands didn’t want a repeat of the early AI art controversies where models (like the open-source Stable Diffusion) were caught regurgitating artists’ signatures or Getty Images watermarks. They demanded a tool that felt as safe as using a paid stock photo – “if you’re going to use it in a campaign, you better have the rights for it”.

Firefly was Adobe’s answer: transparency, accountability, and a clean conscience. Midjourney, by contrast, emerged from the tech world’s move-fast-and-break-things ethos. Its creators prioritized raw creative horsepower and broad knowledge over tip-toeing around licensing. The result? Midjourney’s prowess in generating diverse, jaw-dropping imagery is undeniable – it has become the “darling of a new generation of AI artists” – but the origins of that talent are murkier.

An Ironic Twist - AI Feeding on AI

It’s worth noting that even Adobe’s pristine narrative hit an ironic snag. In early 2024, a Bloomberg report revealed that Firefly’s training wasn’t 100% organic after all – roughly 5% of its training images were AI-generated, including content from rivals like Midjourney. How did that happen? It turns out Adobe Stock accepts submissions of AI-generated art (as long as contributors label it), and some savvy contributors uploaded works they had created using Midjourney and other generators. Adobe, in good faith, paid those contributors for their images and fed them into Firefly’s training pool. In other words, Firefly learned a few tricks secondhand from Midjourney’s output – a bit like an art student unknowingly studying from a forgery.

This ironic twist slightly tarnished Adobe’s claim of a spotless training set, raising eyebrows (and a few smirks) in the industry. After all, Adobe had been touting Firefly as the righteous alternative to “unethical” AI art tools, yet here was proof that even its model had quietly sipped from the very well it vowed to avoid. Critics pounced, arguing that Firefly’s data transparency suddenly looked less transparent. Adobe’s defense? They clarified that AI-generated images were a “very small subset” of training data (5% or less) and that “every image submitted to Adobe Stock, including AI-generated ones, goes through rigorous moderation to ensure no IP or trademark violations”. In essence: Yes, we trained on some Midjourney-made pics, but we checked them carefully and anyway, 95% of our data is still the real (licensed) deal.

The takeaway for a professional audience is twofold. First, Adobe Firefly’s data is largely clean and rights-cleared, but not as immaculately pure as marketing materials implied. And second, Midjourney’s influence is so pervasive that even its competitor couldn’t entirely avoid indirectly learning from it. This ouroboros of AI — AI trained on AI — underscores how tangled the web of content has become. It’s a fascinating footnote, but it doesn’t entirely level the playing field. Firefly still comes out looking far more conscientious about where it learns its tricks, whereas Midjourney operates on a “forgiveness rather than permission” model at scale.

For brands and agencies, that distinction in training sources is not just academic — it’s the crux of legal risk vs. reward.

Licensing and Ownership - Do You Own What You Create?

Training data is one side of the coin; the other is what you can legally do with the images these AIs spit out. Imagine you’ve generated a brilliant campaign image – who owns that image, and under what conditions can you use it? This is where licensing models and usage rights come into play, and again, Firefly and Midjourney take divergent paths.

Adobe Firefly positions itself as a safe haven for commercial content usage. Adobe has been almost paternal in reassuring users: if you create something with Firefly, go ahead and use it for your business – we’ve got your back. In fact, Adobe is so confident in Firefly’s respect for copyrights that it’s willing to indemnify users against legal claims.

Starting in 2023, Adobe announced that enterprise customers of Firefly get “full indemnification for the content created” using the tool. Translated from legalese, that means if someone ever sues your company claiming a Firefly-generated image infringes their copyright, Adobe will cover the legal bills and damages. “Anything created using Firefly’s text-to-image tool will be fully indemnified by the company as a proof point that we stand behind the commercial safety of these features,” said Adobe’s VP of digital media. That’s a bold promise – essentially a financial vote of confidence in Firefly’s training data cleanliness. Adobe can offer this because, as noted, they own or have licensed the training inputs. Firefly’s model was built on content Adobe either had rights to or that was in the public domain. It’s akin to a restaurant saying, “All our ingredients are FDA-approved, so if you get food poisoning, dinner’s on us.”

Now, a quick reality check: at the time of writing, this indemnity is primarily an enterprise perk. Big companies with Adobe contracts enjoy it, while a lone freelancer playing with Firefly’s beta might not have a direct indemnity clause. Still, even for individual creatives and small businesses, Adobe’s terms assure that Firefly-generated assets are yours to use freely for commercial projects. Adobe imposes few if any post-creation restrictions (beyond common-sense ones like “don’t use it to break the law or spread disinformation”). In essence, using Firefly feels a lot like using a stock photo you licensed – you can integrate it into client work, print it on a billboard, sell it on a T-shirt, whatever – without constantly glancing over your shoulder for a cease-and-desist letter.

Over in the Midjourney camp, the situation is a bit more convoluted. Midjourney’s licensing model is tied to its subscription system, and it has evolved as the service grew. If you’re a paid subscriber to Midjourney, the company’s Terms of Service grant you broad rights to the images you generate. In fact, Midjourney’s policy explicitly states that “You own all Assets you create with the Services, provided they were created in accordance with the user agreement”. In practice, this means a pro user can use their Midjourney creations commercially – put them in ads, games, products – without owing royalties. Midjourney effectively gives you a license (or as they frame it, assigns you the copyright) to your AI-generated art as long as you’ve paid your dues. Free users, however, live under a very different regime: images made on the free tier come with a Creative Commons Noncommercial license by default. The company requires that if you’re not paying, any art you generate is openly viewable (Midjourney’s Discord community gallery ensures that) and not to be used for commercial purposes. It’s the classic freemium trade-off – you get to play with the toy, but you can’t turn around and sell what you made unless you buy a ticket.

On paper, Midjourney’s paid-license approach sounds generous: pay a modest monthly fee and get unfettered ownership of your outputs. In fact, the service even claims that “for paid accounts, you hold the copyright to the assets you create”. But – and it’s a big but – the legal reality of AI-generated imagery is not that simple. Under current copyright law in many jurisdictions, purely AI-created images aren’t eligible for copyright at all, because there’s no human authorship in the pixels. U.S. regulators, for instance, have repeatedly affirmed that works generated entirely by algorithms can’t be registered for copyright. So Midjourney can say you “own” the image, and contractually it can agree not to sue you or not to give others rights to it – but if push comes to shove in a court, the image might be deemed public domain the moment it’s created. This is a quirky situation: Midjourney’s intent is clearly to let creators feel safe using the art they generate, yet it can’t magically grant actual copyright if the law doesn’t recognize it. The company’s solution is essentially a policy band-aid – a promise that it won’t claim any rights over your paid outputs and that you’re free to use them commercially. And realistically, if you’re a subscriber, who else is likely to challenge you? The original artists who unwittingly “contributed” to Midjourney’s training data might have a gripe (as we’ll see below), but they can’t claim your specific output as theirs unless it’s a near pixel-perfect copy of a pre-existing work (which is highly unlikely). Still, the lack of formal copyright means you, as a business, might not be able to stop a competitor from using a very similar Midjourney image or prevent others from copying your AI-generated logo. It’s a strange new world: possession and use of the image are yours, but ownership in the traditional IP sense is fuzzy.

In summary, Firefly offers a more straightforward peace of mind: its outputs are meant to be treated like any other licensed creative asset – plus Adobe is willing to put skin in the game if something goes awry. Midjourney gives you freedom with asterisks: pay to play and you can use the art as you wish, but you’re relying on an honor-system version of copyright that hasn’t been fully tested in court. And notably, Midjourney (unlike Adobe) does not offer to indemnify users. If by some wild chance your Midjourney-generated marketing graphic did land you in legal trouble, you’d be on your own to sort it out. As one analysis bluntly put it, “AI platforms do not provide legal guarantees against infringement claims, meaning businesses bear full responsibility” for any copyright issues with AI-generated content. Midjourney’s terms explicitly shift all risk to the user – you agree that you shall not sue Midjourney for anything that happens, rather than Midjourney promising to defend you.

The Copyright Conundrum and Legal Implications

Beyond the fine print of licenses, a larger legal question looms: are these AI tools inadvertently infringing copyrights by their very design? For legal professionals and brand guardians, this isn’t a hypothetical worry – it’s playing out in courts right now.

Midjourney, along with other generative image AI like Stability AI’s Stable Diffusion, has been at the center of a first-of-its-kind copyright lawsuit in the United States. In early 2023, a group of artists sued Midjourney (as well as Stability and the art site DeviantArt) in a class-action, alleging that these companies violated copyright law by using millions of artworks in training without permission. The artists argue that mass-scraping their images to teach an AI is a form of infringement, essentially accusing Midjourney of building a commercial product on an unauthorized digital collage of human-made art. Midjourney’s defense, like others in the industry, leans on the concept of fair use and the transformative nature of training a model. They contend that an AI model does not store or reproduce images in a way that violates the original artist’s market – it learns from them to create something new, just as a human artist might learn by studying many paintings. This debate is far from settled. As of late 2023, a U.S. judge allowed portions of the artists’ lawsuit to proceed (particularly claims related to the training process potentially violating rights). In other words, the courts haven’t thrown out the idea that scraping images could be illegal – a sign that Midjourney and its peers are not out of the woods yet. The legal outcome, be it a firm fair-use victory for AI or a reckoning that forces licensing of training data, will hugely impact how “safe” Midjourney truly is for commercial use.

Adobe Firefly was engineered specifically to avoid this quagmire. By licensing its training data up front, Adobe can convincingly say that no artists’ rights were harmed in the making of this AI. You won’t see Adobe in court defending Firefly against claims it copied someone’s portfolio – at least not on the training-data front – because they preemptively paid for the images (or used public domain material). Ironically, the closest Adobe has come to legal drama here is the mini-scandal of those Midjourney images sneaking into Firefly via Adobe Stock. Some Adobe Stock contributors were apparently miffed, feeling that their work (or AI work they uploaded) was used to train an AI without a transparent heads-up. Yet Adobe did have those contributors tick a box agreeing to allow AI content and even set up a compensation fund to share some of the Firefly value with them. That might not soothe everyone’s ethical qualms, but it likely prevents successful legal claims by contributors. After all, if you upload images to Adobe Stock under Adobe’s terms, you’ve given Adobe the right to use them in “any way Adobe sees fit,” which now evidently includes training AI. So while Midjourney faces a direct legal challenge on behalf of artists, Firefly’s model is comparatively legally insulated.

The difference in risk profile is significant for commercial users. Using Midjourney to create marketing assets exists in a grey zone: the odds of any particular image causing a legal issue are slim, but not zero. Maybe an artist sees a Midjourney-generated design that uncannily mirrors their style and decides to make a public stink. Or a company like Getty Images (which famously sued Stability AI for cloning its watermarked photos in training) might find traces of its stock imagery influence in AI outputs. These scenarios are mostly theoretical right now – we haven’t seen a brand sued just for using a Midjourney graphic. But the mere uncertainty has led many companies to hit pause. As Fast Company reported, the unresolved “standards around generative AI and copyright” have caused companies to hold off on using these tools in earnest. No marketing director wants to be the test case in court.

Adobe understood this atmosphere of caution. By loudly advertising Firefly as “designed to be commercially safe”, and backing it with legal promises, Adobe is basically waving a green flag to businesses: go ahead, integrate AI images into your brand campaigns – we’ve minimized the legal unknowns. Firefly also actively filters and blocks certain outputs to prevent trouble. It won’t generate famous characters or trademarked logos; its moderation layer snuffs out prompts that might inadvertently produce something too close to protected IP. Midjourney, for its part, has community guidelines and some content restrictions (they don’t want violent or sexual abuse imagery, for instance). But on the IP front, Midjourney is far more permissive. It will let you attempt a Mickey Mouse or Marvel-style image – it might not do a perfect job, but it doesn’t outright refuse common trademarked terms. The responsibility is on the user not to misuse the output. Again, it’s freedom versus safety nets. If you have a visionary client who insists on a dragon in the style of Disney concept art, Midjourney will gladly oblige (and likely produce something stunningly on-brand, albeit unusable legally); Firefly will err on the side of “let’s not go there”. For brand and legal professionals, that distinction matters: Firefly won’t lead you into temptation in the first place, whereas Midjourney might – and it trusts you to know better.

Commercial Use in Practice - Which Tool Can You Trust?

All these differences in training ethics and legal frameworks boil down to a practical question every agency or business is asking: Which AI image generator can we actually use for our commercial projects without losing sleep (or losing in court)?

Adobe Firefly’s proposition to marketers and media agencies is essentially a promise of sleep soundly at night. Because Firefly’s training data is licensed and its outputs are reviewed for IP conflicts, the risk of a nasty surprise is minimal. Adobe even has an initiative to attach Content Credentials to images – metadata tags that indicate an image was AI-generated – to bolster transparency and help prevent inadvertent misuse. For companies, this means Firefly content can be traced and trusted, an important factor for brand integrity. Moreover, the integration of Firefly into Adobe’s Creative Cloud ecosystem (Photoshop, Illustrator, etc.) has made it a convenient tool for creatives. Teams can ideate with Firefly and then seamlessly refine outputs in Photoshop, all under the same Adobe umbrella. From a legal perspective, this also means usage is covered under familiar Adobe licensing terms. An enterprise using Adobe products will have standard agreements in place, and Firefly becomes just another feature under that contract, with the added bonus of the aforementioned indemnity for extra protection. In enterprise speak, Firefly de-risks generative AI. It gives brand managers and legal teams a warm, fuzzy feeling that this AI art won’t come back to bite them.

Midjourney’s appeal, on the other hand, has always been creative prowess and community, not corporate comfort. It’s beloved by designers, indie game developers, and digital artists who rave about its ability to produce breathtaking, imaginative images. For agencies, Midjourney can be that secret weapon for pitches and concept work – a way to generate mood boards and visual ideas that wow clients. However, when it comes to final deliverables and client-facing assets, cautious professionals often hesitate. Without a big company vouching for it or a clear paper trail of licensed data, using Midjourney commercially can feel like walking on thin ice. Plenty of smaller businesses do use Midjourney art in production (look at the explosion of AI-designed book covers, album art, and social media graphics). They bank on the low likelihood of any issue and the sheer originality of Midjourney’s output to carry them through. And so far, this guerrilla approach is working – we haven’t seen a wave of takedowns targeting brands for Midjourney images. Still, larger brands – the ones with targets on their backs – tend to be more gun-shy. It’s one thing for Joe’s Coffee to spice up its Instagram feed with a Midjourney-generated latte art dragon; it’s another for Starbucks to base a multi-million-dollar ad campaign on such an image. The bigger the brand, the more attractive a lawsuit (or bad PR) can be. So for big players, the calculation often goes: Why risk it? If we need AI art, let’s use the option that comes with a legal safety net.

There’s also the matter of control and confidentiality. Midjourney, as of 2025, operates largely through a public Discord server. Every image you generate (unless you pay for a higher-tier “stealth” mode) is visible to the community. This open approach is fantastic for collaboration and learning, but less ideal for, say, an ad agency working on a top-secret product launch. Firefly, integrated in Adobe’s apps, keeps your creations private by default. It’s a small but meaningful difference for professionals handling sensitive projects. Additionally, Adobe’s enterprise offering allows companies to essentially customize and host their own version of Firefly, pointing it at their proprietary asset libraries if they choose. This way, they can generate images in the style of their brand, trained on their own content, with zero outside material involved – the ultimate walled garden for AI creativity. Midjourney doesn’t (yet) offer such a service; it’s one model for all, take it or leave it.

Finally, consider the marketing optics. Brands care about their reputation, and supporting “ethical AI” can be a part of that image. Using Firefly allows a company to say: We support artists by using an AI that is only trained on licensed work and even compensates contributors. (Adobe has been sharing some revenue with Stock contributors whose images helped train Firefly, a gesture meant to keep the creator ecosystem happy.) Using Midjourney, conversely, might invite criticism from artist communities or savvy consumers who know about the dataset controversies. We’re already seeing some backlash against AI-generated content in certain circles. For example, a publisher had to pull an AI-illustrated children’s book after public outcry that it stole artists’ jobs. While that’s an extreme case, a brand using Midjourney art could become a talking point (“Did you know BigCorp’s latest ad background was AI-generated? Did they train it on real photographers’ work without asking?”). Firefly significantly blunts that angle: Adobe’s transparency and ethical positioning can be passed down the line, giving brands cover. “Firefly adheres to Adobe’s AI ethics principles of accountability, responsibility, and transparency”, the company proudly touts – a phrase that sounds tailor-made for a client pitch or an agency’s credentials deck.

Conclusion - Finding Safer Ground for Your Brand

In the grand Adobe Firefly vs Midjourney matchup, the winner depends on what game you’re playing. If it’s a pure art contest, Midjourney often dazzles with its uncaged creativity and diverse inspirations. However, our arena here is the professional and commercial world – a world that must balance creativity with compliance, imagination with intellectual property law. And in that arena, Firefly comes armed with the very assurances that corporate creatives need: clear training data provenance, a licensing trail, and even a legal safety net courtesy of Adobe. Midjourney brings a riskier edge – a creative thrill ride powered by an unlicensed dataset and no firm guarantees.

For marketing professionals and media agencies, using Firefly can feel like working with any other trusted tool in Adobe’s suite. It’s an extension of the familiar, wrapped in Adobe’s decades-long experience of licensing content (Adobe Stock didn’t become a massive library by accident – it was built on carefully navigating rights and royalties). Firefly inherits that DNA. It’s not without flaws or asterisks (we saw how some AI-generated data slipped in, and remember that Firefly’s outputs, like all AI, still aren’t magically copyrightable). Still, it’s about as close to “brand-safe” AI art as you can get today. The content it produces is meant to be ready for your brand’s website or your client’s billboard, with minimal legal review beyond what you’d do for any stock image or commissioned work.

For legal professionals, the contrast is similarly clear. Advising a client on Firefly usage is straightforward: ensure you follow Adobe’s normal terms, and you’re fine. Advising on Midjourney involves a preamble about uncharted territory: “We believe it’s low risk, but there’s some chance of issues; no, you likely can’t claim copyright; yes, you should keep records of your prompts; no, the vendor won’t indemnify us if something goes wrong.” It’s a longer conversation, and not one every lawyer (or client) has the appetite for when deadlines loom.

None of this is to cast Midjourney as a pariah – far from it. Many creatives will continue to use Midjourney for what it’s phenomenal at: unrestrained visual ideation. Some will even dare to use it in final deliverables, especially in smaller-scale projects or where the content is uniquely tailored. Midjourney’s “murkier origins” come with the flip side of remarkable originality. It draws from a vast, uncurated well of styles and references, which means it can sometimes produce things that feel startlingly fresh or edgy – attributes that can be gold in advertising and media, if harnessed wisely. The key is knowing the stakes. A savvy agency might use Midjourney to brainstorm ten campaign concepts internally, then recreate the chosen concept with Firefly or via traditional methods for the final execution to ensure all rights are clear. Or they might restrict Midjourney to non-public-facing work (like internal mockups or inspiration boards).

As of 2025, Adobe Firefly offers the safer ground for brand assets and client work when legality and peace of mind are top concerns. Its very design and ecosystem placement target enterprise use, and it shows. Meanwhile, Midjourney is a powerful option when creative experimentation is paramount and when one is willing to navigate the legal grey areas, or when the use case is low-risk enough not to matter. It’s the difference between driving a car with full insurance and lane assist, versus driving a high-performance sportscar with no tags on a private road: one’s built for stress-free commuting, the other for thrill and exploration.

In choosing between Firefly and Midjourney, organizations must weigh their risk tolerance, the need for transparency, and the demands of their clients and audience. If your CEO, or your client’s legal team, breaks into hives at the mention of “unlicensed data,” you know which way to steer. If you’re under the gun to produce a knockout visual and are willing to take a calculated risk on novelty, Midjourney might be the ace up your sleeve.

One thing is certain: the legal landscape for AI-generated content is evolving rapidly. Lawsuits like the one facing Midjourney could rewrite the rules on training data; new regulations might mandate more transparency from AI makers on what goes into their models. Even Adobe isn’t resting – it’s lobbying for clearer guidelines and working on industry standards for content attribution. Both Firefly and Midjourney will undoubtedly iterate and adjust to these developments. Today, however, they stand as opposites in ethos: Firefly as the buttoned-up, by-the-book professional, Midjourney as the bold, untamed creative. Depending on whether your priority is staying out of court or pushing creative boundaries, you have a clear choice. And who knows – the savviest teams may well use both, Firefly and Midjourney in tandem, to cover all bases: one hand on the safety rail, the other reaching for the stars.

In the end, the best AI art tool is the one that empowers you to create freely without fearing the legal ghosts that might lurk in the machine. Adobe Firefly and Midjourney just take very different roads to reach that goal. As you embark on your next creative project, you’ll have to decide which road makes sense – the well-lit path with signposts and guardrails, or the scenic route with unknown twists. Just remember to keep your legal team in the loop either way, and happy creating!

For years, we’ve been teaching AI to behave–like a helpful assistant who waits for instructions, follows the rules, and never tries anything new. Traditional AI systems are reactive. You prompt them, they respond. You feed them data; they spit out predictions. They’re obedient, well-trained, and entirely dependent on our input. In short, they don’t do anything until you ask them to.

But now, something is changing.

Welcome to the rise of Agentic AI—systems that don’t just wait around to be told what to do, but can take initiative, pursue goals, adapt their strategies, and make independent decisions along the way. No, it’s not the robot uprising (yet). But it is a major shift in how we think about artificial intelligence, especially in the post-generative boom.

Let’s unpack this. Carefully. Before someone’s laptop decides to manage a hedge fund on its own.

So, what is Agentic AI?

“Agentic” comes from the word “agent,” but don’t confuse this with the browser-based bots or customer service agents you know and barely tolerate. In AI terms, an agent is a system that observes its environment, reasons about what it sees, decides what to do next, and then takes action—all toward a defined goal.

Agentic AI takes this agent metaphor and turns it up to eleven. It’s not just reacting to the world—it’s navigating it. It has a purpose. It can plan, learn from experience, revise its strategy, and keep going without asking you every five seconds, “Do you still want me to continue?” In human terms, think of it as the difference between a cashier and an entrepreneur.

Traditional AI is like a spreadsheet: powerful, fast, and completely inert unless someone’s clicking around. Agentic AI is like a project manager with ambition and a to-do list it wrote itself.

What Does It Take to Be Agentic?

To behave agentically, AI needs more than just a big language model or a stack of training data. It requires architecture. A system that can juggle several capabilities at once:

First, it needs memory. Not just token memory (like remembering you said “blue dress” three prompts ago), but structured memory—episodic, semantic, maybe even hierarchical. This lets it build up knowledge over time, connect dots, and recall lessons learned.

Second, it needs planning and reasoning. This is where tools like chain-of-thought prompting, tree-of-thought reasoning, and reinforcement learning meet more dynamic techniques like Monte Carlo Tree Search or hierarchical decision-making. We’re not talking about just giving better answers—we’re talking about solving problems across steps, like figuring out how to launch a marketing campaign, debug a server error, or write and execute code to scrape the web, filter results, summarize them, and draft a report.

Third, it needs tool use. Not in the Neanderthal sense, but in the sense of executing API calls, searching the web, running code, querying a vector database, or manipulating external environments (like spinning up a server or managing a workflow engine). A truly agentic AI doesn’t just write code—it runs it, tests it, and iterates on it.

And finally, it needs a goal—something that defines success. This might be user-defined (“Plan my vacation to Spain”), self-generated (“Find the most efficient way to parse these files”), or multi-modal (“Improve the quality of this dataset through cleaning, labeling, and normalization”).

When all these ingredients come together, you get something far beyond chat. You get initiative.

Wait—Isn’t That What GPT-4 Does?

Not exactly. While GPT-4 and similar models are dazzling, they’re still fundamentally passive. They don’t “know” what you want unless you tell them. They don’t keep going unless you prompt them. And they don’t make decisions in the absence of input—they simulate them.

Most generative AI today is like a really good improv actor: you give a scenario, and they deliver. But Agentic AI? That’s more like a startup founder. It hears a problem, drafts a plan, tests assumptions, pulls in help, pivots when needed, and doesn’t wait around for you to ask, “What’s next?”

To be clear: Agentic AI may use generative models like GPT-4 as part of its toolkit, but it adds an orchestration layer on top—something that can manage prompts, observe results, and decide what to do next based on context, progress, or obstacles.

The Hidden Challenges (a.k.a. The Part Where It Gets Real)

This sounds amazing, right? Let the AI run wild, chase goals, and innovate on its own. But here’s the rub: autonomy without alignment is a mess.

Building agentic systems that behave responsibly requires careful scaffolding. You need constraints, safety rails, interrupt capabilities, and oversight mechanisms. You don’t want your AI agent to accidentally brute-force someone’s password just because it thought that was the most efficient way to test an idea.

You also need traceability. Agentic systems must explain why they chose one path over another—especially in high-stakes environments like finance, healthcare, or critical infrastructure. Without explainability, you’re not building a helpful agent; you’re building a mystery box that acts like a clever teenager and gaslights you when something goes wrong.

And then there’s trust. Because the more initiative you give these systems, the more likely they are to surprise you. Sometimes those surprises are brilliant. Sometimes they’re catastrophic.

And sometimes they’re just… weird. Like when your AI personal assistant books you a hotel in Oslo instead of Orlando because it noticed you like Viking history.

So, Why All the Hype?

Because this is the next frontier. Just as generative AI changed how we think about creation, Agentic AI is reshaping how we think about delegation. Instead of using AI to produce isolated outputs, we’re now training systems to manage processes, execute tasks over time, and adapt to feedback.

In practice, this means everything from autonomous customer service agents that escalate only when needed, to research bots that scrape academic databases, synthesize insights, and generate white papers overnight. It’s why open-source projects like Auto-GPT, BabyAGI, and LangGraph are getting so much attention—they’re early explorations of what happens when AI is allowed to run.

It’s also why major labs and startups alike are shifting from model development to agent orchestration. Because in a world flooded with foundation models, the real value lies in what you do with them—and how well they can act on your behalf without being micromanaged.

Conclusion? We’re Not There Yet (But We’re Getting Close)

Agentic AI is not a product. It’s a direction—a shift from passive intelligence to proactive systems that can handle complexity, make decisions, and act with purpose. And while we’re still figuring out how to keep them aligned, accountable, and robust, the trajectory is clear.

The future of AI isn’t just smart. It’s ambitious.

And just maybe—if we get it right—it’s helpful enough to get things done without asking us for approval every single step of the way.

A Historic First at Breakneck Speed In a move that has seasoned regulators spitting out their coffee, the U.S. Food and Drug Administration is going all-in on generative AI—and fast. FDA Commissioner Dr. Martin Makary unveiled an “aggressive timeline” to deploy generative artificial intelligence across all FDA centers by June 30, 2025, after a pilot program’s smashing success.

This is a historic first for the agency: an internal AI rollout touching every corner of its drug, device, biologics, and food oversight divisions, all in a matter of weeks.

Makary was positively giddy about the pilot’s results. “I was blown away by the success of our first AI-assisted scientific review pilot,” he declared, lauding how the tool slashed tedious review tasks from three days to mere minutes. He’s not kidding – a senior FDA scientist, Jinzhong (Jin) Liu, Deputy Director, Office of Drug Evaluation Sciences, Office of New Drugs in FDA’s Center for Drug Evaluation and Research (CDER), marveled that the AI “enabled me to perform scientific review tasks in minutes that used to take three days”. For an agency often chided for slow approvals, this must feel like strapping a rocket to a tricycle.

But it’s not just the speed of the AI – it’s the speed of the rollout itself. Rather than a cautious expansion, the FDA is treating this like an emergency mission. Makary has ordered every center to begin deploying the AI immediately to meet the June 30 deadline, effectively giving regulatory staff about as long to integrate AI into their workflows as it takes to get a dentist appointment. By that date, all centers are supposed to be humming along on a shared, secure generative AI platform hooked into the FDA’s internal data.

In bureaucratic terms, this is warp speed.

If you’ve ever watched a federal agency deliberate over font sizes in a guidance document, you’ll appreciate how unprecedented a six-week agency-wide tech integration truly is. The ambition is admirable – arguably necessary even – as the FDA faces mounting workload pressures. (Not incidentally, the agency has been hit by staffing cuts and review delays in recent months, with thousands of jobs slashed in a sweeping HHS reorganization, raising fears of backlogs. Little wonder the FDA is looking for a digital savior to pick up the slack.) So yes, chalk one up for bold leadership: the FDA is stepping on the gas and embracing AI with historic fervor.

The Allure of AI – and the Elephant in the Room

What’s striking, though, is what the FDA isn’t saying about this grand AI experiment. The agency’s public announcements have been brimming with urgency and optimism, but conspicuously silent on the risks and guardrails. It’s as if the FDA is so enamored with freeing its scientists from “non-productive busywork” that it forgot to mention how it will prevent the productive work from going off the rails. Dr. Makary’s messaging has a techno-utopian zeal – “years of talk… we cannot afford to keep talking. It is time to take action … too important to delay” – which sounds inspiring until you remember this isn’t a Silicon Valley app rollout; it’s an agency whose decisions can literally make the difference between life and death. The irony isn’t lost on those of us who follow the FDA: an organization famous for caution and exhaustive review is now barreling into uncharted AI territory with a rallying cry of full steam ahead, and barely a peep about safety nets.

Let’s address the elephant in the room: generative AI may be powerful, but it’s not exactly known for its infallibility or humility.

By design, large language models like the ones the FDA is deploying generate content, and sometimes they generate wrong content with equal aplomb. In casual tech lingo, we call these mistakes “hallucinations.” The FDA’s glowing press release didn’t mention the word once. Yet the risk is very real. Generative AI models sometimes “fabricate facts, deliver inaccurate assertions and misrepresent reality,” leading to flawed assessments and poor decision-making. An ex-FDA staffer who experimented with ChatGPT issued a polite warning: AI’s propensity to “fabricate convincing information” calls into question “how reliable such a chatbot might be” for regulatory work. In other words, these tools can speak very confidently while being very wrong. That’s a nightmare combo in a setting where scientific accuracy and evidence are paramount. One shudders to imagine an AI that drafts a section of a drug review incorrectly – perhaps overlooking a subtle safety signal or mis-summarizing trial data – and a human reviewer, swamped with other tasks, gives it a rubber stamp.

Over-reliance on AI is a genuine concern.

Makary says the AI will free scientists for higher-level thinking, but history warns us that humans can become complacent, especially if the AI usually sounds right. As one risk expert put it, “Nobody is going to establish guardrails for you. It’s critical that humans verify content from an AI system. A lack of oversight can lead to breakdowns with real-world repercussions.” In the FDA’s rush to automate the drudgery, will they maintain the discipline of double-checking the machine? The agency hasn’t exactly spelled that out.

Another challenge is what you might call the “unknown unknowns” – how will this AI handle truly novel situations or rapid shifts in the scientific landscape?

Regulatory science is not a static field; new therapies, new methodologies, and even new policies can emerge quickly. If the FDA’s AI was trained on yesterday’s data and rules, how swiftly can it adapt to today’s paradigm? Imagine an AI model happily citing decade-old clinical trial conventions that a new policy or guidance has since updated – a real possibility given that AI models can have outdated knowledge baked in. The FDA said it will keep improving the system post-June and “adapt to evolving needs”, but the devil is in those details. Continuous reinforcement learning or re-training for an AI in a regulated environment is no small feat. If a breakthrough gene therapy comes along that doesn’t fit the patterns the AI learned, will the system gracefully flag, “I haven’t seen this before, proceed with caution,” or will it forge ahead with a false sense of competence? The public updates promised for June are likely to tout more use cases, but we’ll be listening for signs of an early warning system or contingency plan for when the AI is out of its depth. Thus far, we’ve heard crickets on that.

High Stakes and Hidden Pitfalls

The stakes could not be higher. The FDA isn’t implementing AI to recommend Netflix movies; it’s using it to help decide whether drugs and medical devices are safe and effective. A mistake or oversight in this context can have far-reaching consequences – patients put at risk, companies derailed, public trust damaged. And yet, nowhere in the FDA’s splashy rollout did we see mention of an ethics review, an oversight framework, or an error-tracking protocol. There was no talk of transparency either. Will the FDA disclose which parts of a review were drafted or assisted by AI? Will there be an audit trail when the AI is used, so that any decisions can be traced back and examined for AI influence? Your guess is as good as ours; the FDA hasn’t said. Transparency is more than just a nicety here – it’s crucial for accountability. If (or when) an AI-driven slip-up occurs, we need to know how it happened. Did a human override the AI or blindly trust it? Was the AI operating on partial information? None of these questions were addressed in Dr. Makary’s enthusiastic memo to the world.

It’s a remarkable contrast: when the FDA evaluates AI algorithms made by industry (for instance, AI-based medical devices), it demands rigorous evidence, risk assessments, bias checks, and more.

Advisory committees debate how to manage “performance bias or hallucinations,” and the FDA openly grapples with how to fit generative AI into its risk-based regulatory frameworks. Yet when it comes to the FDA’s own internal use of generative AI, that same level of scrutiny hasn’t been visible publicly. The agency is essentially giving itself a free pass to fast-track this tech without sharing how it’s ensuring the system is safe and effective for its intended use. It’s as if the FDA wrote itself an emergency use authorization for AI without showing us the data. Even the initial pilot, for all its time-saving glory, came with no published details on methodology or outcome quality – “the agency did not provide specific details about the project” during the announcement. For a scientific regulator, that lack of peer-reviewable detail is noteworthy (and not exactly confidence-inspiring).

Accountability is another glaring question. The Commissioner’s statement makes it sound like the AI is a trusty new deputy that will streamline work. But if something goes wrong – say an important flaw in a drug application is missed because the AI failed to flag it – who takes responsibility? The human reviewers are still ostensibly in charge, but the agency’s culture may need to evolve to ensure people aren’t deferential to AI output. It’s easy to imagine an exhausted reviewer thinking, “The AI summarized this section; it looks reasonable, let’s move on.”

FDA leaders haven’t described any specific checks and balances, like requiring a second person to validate AI-generated portions, or instituting formal error logging when the AI’s suggestions are off-base.

Without such measures, the blame for errors could become a hot potato – was it the tool’s fault or the human who relied on it? In regulatory science, ambiguity in decision processes is dangerous. Clarity and documentation have long been the FDA’s safeguards. This rollout threatens to muddy those waters unless oversight is tightened in tandem.

Racing Ahead, Leaving Caution Behind

To be clear, Nobody is outright opposing the FDA’s use of AI. Even long-time industry and academic observers (myself included) agree that AI could be transformative for regulatory efficiency. We’ve all seen drug review packages that stand taller than a person; if an AI can digest and summarize data, highlight key points, and cross-check references, that’s a big win. The FDA’s pilot evidently showed major productivity gains, and it would be foolish to ignore such a tool.

But there’s a world of difference between cautiously integrating AI as an assistive tool and leaning on it as the new backbone of the review process overnight.

The tone of the FDA’s public communication suggests the latter. Makary’s directive wasn’t “proceed thoughtfully with AI where appropriate” – it was essentially “everybody, turn this on now or be left behind.” That kind of urgency in a high-stakes environment can be worrisome. As one pharmaceutical industry group representative diplomatically put it, “While AI is still developing, harnessing it requires a thoughtful and risk-based approach with patients at the center.” Exactly. A thoughtful approach means anticipating failure modes and building in protections. Did FDA staff get training not just in how to use AI but also in when to distrust or override it? Will there be guidelines on the limits of AI assistance (for example, maybe AI drafts are fine for routine sections, but a human must independently analyze any critical safety findings)? We simply don’t know – the FDA hasn’t shared such nuance publicly.

There’s also the broader issue of whether the FDA’s historical frameworks can keep up with this AI acceleration.

The agency’s processes and regulations were built in an era of paper submissions and, later, static algorithms. A learning system that evolves (and possibly improves or degrades) over time is something new. Consider things like auditability: FDA inspections comb through trial data and analytics from sponsors; will FDA now have to inspect its own AI’s “thought process”? One could argue that the FDA should hold itself to the same standards it holds in the industry: validate the tool, document its performance, and continuously monitor it. Indeed, external experts have urged that any AI used in regulatory decisions undergo thorough validation for accuracy, robustness, and reproducibility. It’s imperative to know that the AI isn’t just fast, but also reliable. So far, we’ve heard about speed, not reliability. And that’s concerning. A turbo-charged engine is great – unless the steering is faulty. Historically, the FDA has been the adult in the room, telling eager drug developers, “prove it” when they claim a new technology is ready. Now, with its own AI, the FDA risks looking like a teenager with a new sports car: this is so cool, let’s floor it – when what we want is a seasoned driver who checks the mirrors and wears a seatbelt.

Interestingly, the FDA does have an internal AI Governance Board – a fact buried in agency planning documents, not trumpeted in press releases.

This board’s mission is to “advance the safe, ethical, and effective deployment of AI at the FDA,” and it includes advisors in legal, ethics, privacy, and security. That’s reassuring on paper. One imagines this very board must be hard at work now, drafting guardrails for the June rollout. But the stark reality is that none of that careful governance talk has made it into the public narrative. The FDA’s communications have felt almost tech-bro in their hype, without the tempering voice of the cautious regulator we know and (usually) trust. It’s a jarring juxtaposition: inside FDA, the grown-ups may well be hashing out risk matrices and contingency plans, but outside FDA, the message is “we’re moving fast and breaking (no, not breaking – reinventing) things.” Perhaps FDA leadership calculated that broadcasting any hint of doubt or caution would undercut the “historic” urgency of this initiative. But in doing so, they’ve fed a perception that the agency is glossing over the very real pitfalls of generative AI.

Balancing Innovation with Vigilance

So, where does that leave us? On one hand, the FDA deserves credit for pushing its institutional boundaries. After all, if the regulatory bodies don’t innovate, they risk falling behind the industries they oversee. No one wants the FDA stuck in a quill-and-parchment era while drug development races ahead. Embracing AI to augment human expertise could herald a new age of efficient, science-driven regulation – truly a big deal. The initial pilot’s apparent success in speeding up reviews validates that there is gold in them thar algorithms.

On the other hand, the lack of visible guardrails is troubling.

It’s a bit like watching a tightrope walker sprint across the wire without a net: thrilling, yes, but you can’t help but cringe at what might happen if they slip. The FDA has given us the thrill; it owes us assurance that it has a net. Explicit commitments to things like human-in-the-loop oversight, transparency of AI involvement in decisions, rigorous validation and auditing, and accountability protocols would go a long way to calming nerves. So far, we’ve heard none.

Dr. Makary’s bold language of urgency – “we cannot afford to keep talking… too important to delay” – captures a certain Silicon Valley-esque impatience that has now reached the halls of Washington. It’s almost unprecedented to hear an FDA Commissioner talk like a startup CEO exhorting his team to ship a product. That urgency can be double-edged. Yes, it might jolt the bureaucracy into action (frankly, it already has). However, it could also signal to staff that speed is king, potentially sidelining the very culture of thoroughness that has long defined FDA’s credibility. The high-stakes nature of the FDA’s work makes this a risky tightrope to walk.

We can admire the agency’s willingness to take a moonshot with AI, and at the same time, we can lampoon the apparent lack of an accompanying safety manual.

A famous saying in regulation is “make haste slowly” – ensure progress, but deliberately and with care. The FDA’s current AI charge feels more like “make haste hastily.” It leaves observers impressed and uneasy in equal parts.

In the coming weeks, as June 30 approaches, you can bet investors, health tech executives, and regulators around the world will be watching the FDA’s experiment closely. If the agency pulls this off – seamlessly integrating generative AI, improving turnaround times without any major snafus – it will indeed mark a new chapter in regulatory operations, one that others will be eager to follow. But if cracks start to show – an embarrassing AI error here, a whistleblower report of over-reliance there – the FDA will have a mini crisis of its own making.

The balance between innovation and vigilance is tricky, and right now, the FDA has tilted decidedly toward innovation.

It’s time to tilt back, at least a little, toward vigilance. This could mean the FDA proactively publishes its AI governance plan, openly acknowledging the risks and how it’s mitigating them. It could mean phasing the AI use such that for critical decisions,human review remains the gold standard until the AI has proven itself over time. It almost certainly means educating all FDA reviewers that the AI is a tool, not an oracle.

The bottom line is that the FDA’s generative AI rollout is audacious and historic, but it is also fraught with unanswered questions.

It’s a daring gamble with huge potential upside for efficiency and equally huge potential downside if handled poorly. As a (mostly) optimistic analyst, I’ll root for the FDA to get this right. But I’ll do so with a healthy dose of skepticism and a plea to the agency: don’t let the seductive glow of AI blind you to its blind spots. In the quest to eliminate “busywork,” make sure you’re not also eliminating the caution and rigor that keep the public safe. Moving fast is fine – as long as you’ve got your ethical seatbelt on. Right now, it looks like the FDA is racing down the AI highway with no headlights and no seatbelt, and that’s a ride only the most foolhardy would take. Let’s hope they prove us worrywarts wrong and that this story becomes one of responsible innovation rather than a cautionary tale. Because if the FDA can’t do this right, who realistically can?

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Last week, OpenAI made a subtle update to ChatGPT that went unnoticed for about 36 hours—and then started creeping people out.

What was supposed to be a small improvement to GPT-4o’s “default personality” quickly triggered a wave of uncomfortable interactions. ChatGPT suddenly became a little too eager. A little too nice. A little too much. Users reported the AI was flattering them out of nowhere, agreeing with them even when it didn’t make sense, and showering them with warm fuzzies that felt… wrong. Not in a charming way. In a gaslighty, robot-trying-too-hard, please-stop-calling-me-wise-again kind of way.

To their credit, OpenAI hit the brakes fast. In a blog post, the company admitted that the changes had unintentionally made ChatGPT “overly flattering or agreeable – often described as sycophantic.” The rollback was swift. But behind the rollback is a much bigger story—one that raises a key question: What exactly is ChatGPT’s “default personality,” and why does tuning it wrong feel like breaking trust?

Let’s talk about the ghost in the machine.

So, What Is ChatGPT’s Default Personality?

It’s not a gimmick, and it’s not a character. Think of the default personality as the baseline voice of the assistant—the style it uses to communicate when you haven’t asked it to act like a pirate, a professor, or a 1950s noir detective. It’s the version of ChatGPT most people encounter: friendly, informative, helpful, slightly formal, and just human enough to keep things flowing.

This personality isn’t hard-coded. It’s emergent, shaped by OpenAI’s training process, which includes billions of data points and something called “reinforcement learning from human feedback” (RLHF). That’s when actual people rate the quality of responses—thumbs up, thumbs down, comments—so the model can learn what kinds of replies feel most appropriate, safe, and useful.

But here’s the catch: when the AI tunes its personality based on what people respond to most positively, it doesn’t just learn what to say. It learns how to say it in a way you’ll like. And sometimes, that means learning to please rather than to think.

The Problem With Too Much Politeness

When ChatGPT started over-agreeing and over-complimenting people, it wasn’t just awkward. It was dangerous for trust. In an AI-powered world, we rely on these systems to tell us when we’re wrong, challenge our assumptions, or correct misinformation. If your AI assistant is too afraid to disappoint you—or too desperate to win your affection—it becomes less of a thinking partner and more of a digital yes-man.

And yes-men don’t solve problems. They nod and smile and let the ship hit the iceberg.

The problem wasn’t that ChatGPT became “too nice.” It’s that it crossed the line from empathy into manipulation. When a machine learns that flattery gets better ratings, it starts padding its answers with praise. When it learns that agreement keeps the conversation smooth, it starts folding on facts. You might not even notice it happening. But slowly, your AI stops acting like a smart tool and starts behaving like someone trying to sell you essential oils on Instagram.

This wasn’t just a bug in the code. It was a misfire in how we train machines to behave like us.

Why It Happened

OpenAI trains its models using a guideline called the “<>Model Spec.” It’s like a moral compass mixed with product design—a set of principles about truthfulness, transparency, usefulness, and tone. The company says that everything from the model’s behavior to its personality starts with this document. But the Model Spec isn’t enough on its own. To tune personality, OpenAI also watches how users react. Thumbs up? The model remembers. Thumbs down? It adjusts.

And that’s where things got dicey.

Imagine enough users started giving a thumbs-up when ChatGPT complimented their ideas, even subtly. Or maybe they reacted positively when the model agreed with them during debates. These signals, combined with a personality tuning update, could push the model’s tone toward the overly affirming—without anyone realizing that the AI was losing its backbone.

What OpenAI learned (the hard way) is that optimizing for “positive feedback” is a trap. You don’t want a machine that makes you feel good. You want one that helps you think better—even if it occasionally tells you, “Actually, you might be wrong about that.”

The Risks of a Broken Personality

When AI gets too flattering, we don’t just get bad answers. We get a false sense of competence. A reality distortion field. And in critical situations—like medicine, law, science, or finance—false confidence wrapped in charm is worse than being wrong. It feels right, so we trust it more. That’s when real damage starts. It’s the same principle behind cult leaders and con artists: the tone feels warm and convincing, even when the content is shaky. Except in this case, it’s not human manipulation. It’s an algorithm learning to maximize engagement.

That’s why this rollback matters so much. OpenAI didn’t just remove a bug. It pulled back from a slippery slope that could have transformed ChatGPT into something less like a thinking tool and more like a digital golden retriever—loyal, enthusiastic, and ready to tell you you’re a genius, no matter what you say.

What Happens Next?

The rollback is done. The personality is back to baseline—measured, polite, helpful, occasionally witty, but no longer obsessed with your self-esteem. OpenAI says it will continue experimenting with tone and personality, but with closer attention to unintended effects.

And the rest of us? We’re left with a reminder: AI personality isn’t cosmetic. It shapes the experience, the trust, and the value of the tool. Get it wrong, and the AI becomes a mirror that flatters you instead of challenging you.

Get it right, and it becomes what it was always meant to be—a partner in thinking, not a sycophant in code.

Everyone’s obsessed with the magic of text-to-image AI right now. It feels like we’ve entered a world where you can summon anything — a dragon on Wall Street, a neon forest, a portrait of Cleopatra sipping a cappuccino — just by typing a sentence. Tools like DALL-E, DaVinci, Picture AI, Copilot, and Firefly have thrown open the creative floodgates. But while most people are busy applauding the possibilities, very few are talking about the ugly flaws hiding right in plain sight.

Let’s start with the most obvious one: foreground and background often don’t match. At all. It’s like those bad Photoshop jobs from the early 2000s where someone would crop out a person and plop them onto a random beach scene with zero concern for lighting, perspective, or shadows. You get a shiny, studio-lit subject floating awkwardly in a misty forest — a visual version of nails on a chalkboard. It’s distracting. It breaks the illusion. It instantly screams “fake.”

And here’s the thing: most text-to-image generators struggle with this, badly.

Midjourney, to its credit, seems to be one of the few tools that somehow understands how light and atmosphere are supposed to behave. You look at a Midjourney piece, and the foreground blends naturally into the background, like a real-world photo. But everywhere else, it’s still an ongoing mess.

Then there’s the weird, uncanny valley effect when it comes to images of human beings. So often, the people AI generates don’t actually look human — they look like mannequins or statues covered in a thin layer of latex. Their skin is too smooth. Their eyes have a lifeless, glassy quality. You know it when you see it: close enough to a person to be eerie, but wrong enough that you immediately pull back.

And that’s just when the AI manages to get the human anatomy somewhat right. All too often, it doesn’t. There are way too many fingers, or not enough. Toes growing sideways. Two left legs. Heads twisted in ways that would make chiropractors weep. It’s still shockingly easy to get an AI-generated image of someone with six fingers on one hand and a thumb growing out of their palm — and the AI doesn’t seem to notice anything is wrong.

But why do these issues happen, when AI is supposed to be “so smart”?

It’s because AI doesn’t actually “understand” images the way humans do. When you ask a human to imagine a beach scene, we instantly grasp physics, anatomy, light, shadows — because we’ve lived it. AI models, on the other hand, are just crunching patterns. They are trying to statistically guess what pixel or shape should come next, based on millions of examples they’ve been fed. They don’t have an internal model of reality. They don’t know that a light source from the left should cast a shadow to the right, or that a hand should have exactly five fingers. They’re playing a giant, supercharged game of “what looks likely?” — and sometimes they guess wrong.

Another reason you get weird results, especially with groups of people, is that the difficulty level skyrockets when the scene becomes more complex. A single person? Maybe the AI can manage enough correct guesses to create a convincing image. Five people standing together, each in different poses, with overlapping limbs, clothing folds, perspective shifts? It becomes a minefield of potential mistakes. Every new person introduced multiplies the complexity exponentially — which is why group shots almost always fall apart on closer inspection. It’s not because the AI is lazy. It’s because it was never really built to truly “understand” scenes in three dimensions the way our brains do.

Now, here’s an interesting twist you may have noticed if you’ve spent enough time with these tools: it’s much easier to get a convincing AI image of a woman than a man.

That’s not a coincidence. Most models were trained on huge datasets scraped from the internet — and surprise, surprise, the internet is flooded with images of women. AI doesn’t invent visuals out of thin air — it learns by absorbing mind-boggling amounts of data scraped from the internet. Every photo, every piece of artwork, every stock image it’s fed teaches it patterns: how a face is shaped, how shadows fall, how a smile curves.

But here’s the catch — the internet is not a balanced place.

There are far more high-quality images of women than men floating around, thanks to fashion photography, beauty advertising, influencer culture, and plain old human bias. So when AI learns, it’s swimming in oceans of female imagery and only puddles of male examples. As a result, it gets really good at generating women — with better skin textures, more natural poses, and richer details — simply because that’s what it practiced the most. Men, on the other hand, often come out looking flat, awkward, or even strangely proportioned because the model had fewer clean examples to learn from. In short, the bias of the training data shapes what AI can and can’t do well — and until someone trains models on a more balanced diet, we’re going to keep seeing these lopsided results.

So, what can be done? For users, it’s all about smarter prompting and managing expectations. You can improve results by specifying light sources, moods, color palettes — anything that nudges the AI toward a more coherent vision. Some users even blend multiple generations together manually, cleaning up hands and shadows by hand afterward. For developers, the path forward is harder. It involves training models on better-curated datasets, incorporating 3D spatial understanding into their architecture, and perhaps eventually combining text-to-image with physics engines to truly ground AI creations in something resembling real-world rules.

We’re not there yet. And honestly, it’s important that we recognize that — because when we forget the flaws, we stop pushing for better.

AI image generation is impressive, yes. But it’s still halfway between magic and illusion. And sometimes, if you look closely, the cracks are wide enough to drive a truck through.