Executive conversations usually start the same way: “How do we use AI to replace work, automate tasks, generate assets?” Wrong question. And it invariably leads to the wrong outcomes. Let me show you what happens when you flip it.
We recently needed to build an important new feature. Before anyone discussed scope, design, or MVP – before a single document was written – I built a fully functional prototype as a proof of concept.
Not a mockup. Not a slide deck. A working thing. The point wasn’t to ship it. The point was to learn – fast.
- Can AI actually do what we need?
- Which models, in which sequence?
- What context helps, what’s just bloat?
- Can we repeat the value generation reliably?
- Does it hold up when real users hit it?
- Is it meaningfully better than a generic LLM chat?
All of that – answered – before we’d spent a single moment on PRDs or interface design.
Once we knew the core value was real and consistently better than working natively with Claude, Gemini, or ChatGPT, we built a second prototype.
This one didn’t need all the functionality. It just focused on exactly what it would look like to use the feature – every screen, flow, and edge case.
Two prototypes. Two sets of criteria. Both passed before we wrote a line of production code.
‘AI collapses the cost of learning. The path from “I think this might work” to “I know this is worth it” gets radically shorter.’
Is this more efficient? Sure. But efficiency isn’t the point.
The point is that AI collapses the cost of learning. Time to insight drops from weeks to days or even hours. The path from “I think this might work” to “I know this is worth it” gets radically shorter.
You don’t get there by asking “What human tasks can we remove?” You get there by optimising for learning.
Mindset always matters. And it’s never mattered more than now.