Tag: chatgpt

Why AI-generated marketing drifts (and why prompts aren’t enough)

AI is remarkably good at producing marketing content.
No debate there.

The problem shows up after the second post, the third variation, or the fifth campaign asset. Everything still sounds fine, but something starts to slip. The language becomes familiar. The emphasis shifts. The work no longer feels like it’s coming from a single, coherent point of view.

This is what I mean by drift.

Drift doesn’t mean the outputs are wrong. In fact, each individual piece may be just fine on its own. Drift happens when those outputs no longer add up to something intentional over time. The marketing outputs don’t feel like they’re coming from the same brand, or even talking about the same things.

Why drift happens

AI doesn’t generate content by understanding intent or judging priorities. It generates content by predicting plausible continuations based on patterns it has seen before.

When the context it’s given is incomplete — which is often the case — AI has to fill in the gaps. And it fills them differently each time.

Prompts help. They tell the system what to do in a given moment. But prompts are inherently reactive. They describe tasks, not priorities. They correct after the fact rather than establishing a stable frame of reference upfront.

Right now, the most common response is to add more instruction:

  • longer prompts
  • more detailed prompts
  • stricter prompts

This can improve individual outputs. But it doesn’t solve the underlying problem.

Why “better prompts” still aren’t enough

Prompt engineering is often treated as the fix for AI inconsistency. That assumes prompts build on each other.

They don’t.

Each prompt is a fresh brief. Without a settled point of view behind it, the system has to decide — again — what matters most.

In marketing circles, that thinking happens upstream, before the brief gets written. With AI, it ends up pushed downstream into the prompts themselves.

That’s why outputs drift. Not because the tool fails, but because the thinking keeps getting renegotiated.

What actually prevents drift

Drift isn’t prevented by more control. It’s prevented by reducing ambiguity.

Specifically: establishing a clear point of view about what the brand stands for, the problem it’s really addressing, and what matters most when choices aren’t obvious.

When AI knows what matters, it no longer has to guess. It can vary language, structure, and format while holding onto the same underlying intent.

That’s when outputs start to feel coherent again — not because they’re identical, but because they’re connected.

Why this matters now

AI is increasingly being used not just to scale campaigns, but to replace individual acts of writing altogether. In that context, drift isn’t a theoretical concern — it’s an operational one.

The more content AI produces, the more important it becomes to give it something stable to work from.

Not just instructions or prompts—a point of view.

Without it, AI becomes a very good reflector of doubt and indecision — and a pretty weak foundation for ongoing brand communication.

AI feels like it’s always starting over—because it is

Imagine going to work every day, say in a kitchen. You clocked in, changed, prepped, took breaks and went through the ups and downs of service, cleaned up, changed and went home.

Now imagine going to work the next day—but without having any idea about yesterday’s shift. You have all the same equipment and recipes. But you’d gained no experience—every shift is a brand new one that doesn’t build on the familiarity of the ones that came before it.

That’s AI. It knows the rules. It’s seen the examples, and it can imitate the patterns. But it doesn’t accumulate experience the way we do. So every prompt is a new first shift in the kitchen. It has all the tools to be the greatest chef on the planet right this minute, but none of the familiarity with its situation—experience—that a human chef builds naturally over time.

How safe becomes default

What would you do if every decision had to be made consciously—if you couldn’t rely on what you implicitly know and take for granted to skip the small decisions, and focus on the important ones?

You’d do what AI does—take the safest option possible. No risks, or “pushing it.” You’d make every decision based on what’s most likely to work — and never try anything new or out of the ordinary. You’d become boring: effective, but dull.

For AI, every prompt is a clean slate. Past work is a reference, not an experience. It knows all the rules, better than you. But it’s missing a sense of why it’s doing the work in the first place. What kind of decisions are expected. Or what usually matters most when things compete. Nothing from its previous work carries over, nothing becomes instinct and second-nature, nothing becomes familiar enough to make assumptions about.

This is why AI-generated work so often comes across as fast, professional, competent—and ever so slightly “off.” The system isn’t failing. It’s behaving exactly as you’d expect when nothing ever becomes assumed.

And safety, at scale, looks like blandness.

Why we need to move past prompting

Adding more reference material—guidelines, decks, examples—won’t fix the problem.

Because reference assumes something crucial already exists: background judgment. Humans bring that automatically. AI doesn’t — and isn’t designed to. We will always have to compensate for its lack of situational awareness—the kind that only comes from familiarity.

This is where a narrative approach can add value. Not as a story to be told — but as a way of structuring judgment: what follows from what, what usually matters most, and how trade-offs are handled when things aren’t obvious.

When that narrative thinking is made explicit, it acts as the “experience” AI can fall back on—the unwritten rules that guide its work in the same way instinct might for us.

So when your new prompt appears, it can be approached as a task with the context already resolved—it’s no longer approached from zero. It won’t be perfect—human experience isn’t mimicable entirely—but there’s at least something to work from.

Not inspiration like it might be for us, but orientation for what to say, and when, that doesn’t drift off into jargon and platitudes when you prompt it for more.

The AI mirage we keep chasing

We assume that the versions of AI we use in business and marketing today will develop the capacity to “experience” as people do.

It’s an honest mistake. We’ve all seen how powerful of a tool it can be—especially how fast AI is. It’s hard not to be impressed by the game-changing burst of pure efficiency that everyone suddenly has access to.

Which also makes it easy to assume AI already has — or will eventually develop — the background humans rely on without thinking.

It won’t.

And until we accept that difference and design around it, AI output will keep delivering the outputs that impress with speed and competence—while quietly letting us down in ways that are hard to point to, but easy to feel.