Death by PowerPoint in the Age of AI

Prologue

I still remember reading a paperback called "Death by PowerPoint"—the kind of book you bought in an airport bookstore before “death by” became a meme template. Cherie Kerr’s warning wasn’t subtle. PowerPoint, used badly, doesn’t just bore people. It drains them. It turns smart ideas into dense screens of text that get read aloud by the person standing directly in front of them, as if the audience came for synchronized silent reading.

What sticks with me is how early that critique arrived. This wasn’t written in the age of generative AI. This was the early 2000s, when we still treated slide decks like a professional output rather than a necessary evil. And even then, someone felt compelled to write a whole book begging people to stop killing their audiences with bullet points and visual clutter.

Which is why the current trend is almost perfect in its absurdity. PowerPoint was already widely recognized as a medium that encourages lazy communication. Now we’re adding systems that can generate fluent, confident content at scale—sometimes accurate, sometimes not—and calling it “productivity.” Welcome to the era of automated death by PowerPoint.

Idea to deck in minutes

If you judge the AI-presentation market by its marketing language, you’d think the last remaining inefficiency in modern work is the tragic act of aligning text boxes. The pitch is always the same: paste an outline, drop a document, type a prompt, and receive a “professional” slide deck—structured, visual, on-brand, and ready for the client. Microsoft Copilot promises a head start by drafting slides from a prompt or a file inside PowerPoint.  Google pitches Gemini in Slides as an in-editor assistant that can generate slides and images and work from your Drive context.  Tools like Plus AI position themselves as “AI inside PowerPoint/Google Slides,” with templates aimed at business and consulting use cases.  Beautiful.ai sells “AI-powered” pitch decks with automation that keeps slides “looking great.”

On paper, this is a compelling story: faster drafts, fewer blank-page moments, fewer late-night formatting emergencies. In practice, it collides with two stubborn realities: factual accuracy and design control. And the collision is loud.

Slides are still LLM output

There’s a reason the best vendors keep slipping the same sentence into their documentation: verify the output. Microsoft says it plainly: Copilot in PowerPoint can generate content that is “inaccurate or inappropriate,” and it “can’t understand meaning or evaluate accuracy.”  Microsoft’s broader Copilot guidance repeats the point: review and cross-check because responses can be incorrect.

OpenAI’s own Help Center says the quiet part out loud, too: ChatGPT can produce incorrect or misleading outputs, including “hallucinations,” and it may sound confident while being wrong.  And OpenAI has also published a deeper explanation of why hallucinations happen in language models: they generate plausible statements that can be false, even for straightforward questions.

Once you translate that into “make me a deck,” the risk profile gets worse, not better—because a slide deck compresses nuance. Slides are a forcing function: fewer words, bigger claims. If the model is even slightly wrong, the slide format turns “slightly wrong” into “headline wrong.”

And yes, “hallucination-aware prompting” helps at the margins, but it does not change the underlying issue: these systems optimize for fluent completion, not truth. When you ask for a crisp slide that includes a market size, a growth rate, a timeline, and “credible sources,” you’re basically asking for the perfect conditions under which a model feels rewarded for producing something that looks complete—even when it isn’t.

When “generate” doesn’t mean it works

If you want a concrete signal that this category is still messy, you don’t need to look for courtroom drama. You can look at product support channels.

Users report Copilot generating “unrelated and nonsensical slides” when trying to create a presentation from a Word document—exactly the workflow that is supposed to be the flagship.  Others run into operational constraints that are completely invisible in the marketing pitch: Copilot’s “create from file” behavior depends on where the file lives (for some scenarios, it must be in OneDrive/SharePoint contexts), and users regularly report the feature not recognizing documents or not producing the expected “create from file” experience.  There are also recurring “it said it would generate a file, then nothing happened” complaints—less dramatic than a hallucinated citation, but devastating if you’re under a deadline.

So even before we argue about aesthetics, we already have three layers of fragility: the model can be wrong, the workflow can fail, and the integration can behave inconsistently.

Corporate design is a system

Now to your point—the one every serious consulting team learns the hard way: “make it on brand” is not a vibe. It’s an engineering specification.

PowerPoint branding typically lives in the theme (colors, fonts, effects), the Slide Master, and the layout library. Microsoft’s own guidance makes the mechanics explicit: Slide Master is where you define global consistency—colors, fonts, placeholders, and layout rules.  Microsoft even has specific guidance for keeping a presentation “on brand with Copilot,” which basically translates to: set up the template correctly in Slide Master so the system can inherit it.  And Microsoft’s Designer-related template guidance gets even more granular: you need visually distinct layouts, correctly configured placeholders, and careful margins—because the automation relies on those structures to place content without breaking the slide.

That is the part most AI presentation tools struggle to respect, for predictable reasons. The AI generator wants to “compose.” Corporate templates want to “constrain.”

A real corporate deck template encodes decisions like spacing, grid, typographic hierarchy, icon style, photography treatment, image cropping rules, chart styling, and the social contract about how much text is acceptable on a slide. Most AI generators do not operate as constraint solvers. They operate as plausible-content generators with a light design layer attached.

So what happens? Exactly what you experienced:

The visuals come from mixed sources with mixed style priors. Unless the tool enforces a single icon library and a single illustration or photography treatment, you get the classic “Franken-deck”: one slide looks like stock photography, the next looks like flat illustration, the next looks like something that escaped a 2016 onboarding portal.

The colors drift because “use my brand colors” is often interpreted as “use something in that neighborhood,” not “use this exact palette with strict roles for Accent 1–6 and defined background/text contrast.”

The typography fails because brand fonts may not be available in the tool, not embedded, not licensed for that environment, or not mapped correctly to theme fonts. And when fonts are substituted, spacing changes—so layouts break.

Icons look like cheap emojis because many tools default to generic icon sets. Some tools allow theme-level icon styling, but that still stays inside the tool’s internal design system rather than your actual corporate system.

And when you export back to PowerPoint, you can lose fidelity. Tools built on their own “card” or web layout paradigms can produce PowerPoint files that require extensive reformatting.  This is why the category keeps producing decks that look “template-y.” The AI is not failing at design taste. It’s failing at design governance.

Why “less text, more visuals” breaks

Here’s the cruel irony: humans know that slides should be primarily visual support for spoken narrative, not a document you read to the audience. That view is backed by presentation best-practice literature: presentations are an exercise in listening, not reading, and words should be used sparingly so the audience can process visual and auditory channels effectively.

Edward Tufte’s classic critique of PowerPoint ("The Cognitive Style of PowerPoint") makes a related point from the opposite direction: Slideware routinely impoverishes information density and encourages simplistic, slogan-like text blocks.

So you try to do the “right” thing: less text, more visuals, more story. And AI fails—because generating coherent visual language across slides is much harder than generating fluent bullet points.

Visual coherence requires shared style constraints: consistent illustration technique, consistent photography treatment, consistent iconography, consistent color roles, consistent composition rhythm. Most AI tools can generate “an image,” but they cannot reliably generate “the same art direction, twelve times in a row, under brand constraints, aligned to a narrative arc.” Google Gemini in Slides can generate images and suggest images, but that capability does not automatically imply art-direction consistency across a deck.  The result is a deck that looks like it was assembled by a committee where every member had a different taste level, and none of them spoke to each other.

The medium is the problem

Your instinct—“if you want to share information, write a nicely structured document”—is not contrarian. It’s operationally sane.

There’s a reason the most execution-obsessed companies institutionalized narrative documents over slide decks. Amazon’s internal culture is famous for replacing PowerPoint-style presentations with dense memos that get read at the start of meetings—explicitly to avoid hiding sloppy thinking in bullet points.

If the output is supposed to carry real informational weight—decisions, tradeoffs, risks, financials, legal exposure—then “slides” are a hostile format. Slides strip context. They reward confident compression. They create exactly the environment where hallucinations are hardest to detect and easiest to ship.

In that light, “AI-generated PowerPoints” become the perfect productivity trap: the tool accelerates the production of a format that already encourages overconfidence, and it does so using a technology that also encourages overconfidence.

Where these tools help and where they fail

AI slide generators can be useful when the stakes are low, and the intent is scaffolding: a first outline, a rough structure, a fast internal brainstorm, a disposable draft that a human will rewrite.

They consistently struggle when the stakes are high and the constraints are real: client-facing decks, corporate branding, investor materials, regulated industries, anything involving numbers, claims, or citations.

Even Microsoft’s own Copilot documentation implies a limitation that matters a lot in real work: when you create a presentation by referencing a file, you may not be able to provide additional context in the same prompt, and the output should be reviewed and edited.  That’s another way of saying: the system can draft, but it can’t own your intent.

Stop asking AI to make the deck

If you have to use slides, the safer workflow is to treat AI as a writing and structuring assistant before design ever begins.

Use AI to produce a narrative document first: the argument, the sequence, the evidence, the caveats, the decision points. Then design slides as a visual derivative of that document, with strict template governance, locked brand assets, and an explicit rule that no factual claim enters the deck unless it has been verified from a source you trust. If that sounds like “more work,” it is—because it’s the work that prevents embarrassment.

The real value of AI here is not “final slides.” It’s the acceleration of the thinking artifacts that slides were never good at carrying in the first place.