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.
Brand narrative strategy and copy 