AI pilots often fail—not because the technology doesn’t work, but because the problem doesn’t matter.
Too many first attempts are designed to look good, not do good. They chase low-hanging fruit, abstract demos, or polished proofs of concept that live in a deck but never reach the people doing the work.
It’s easy to fall into that trap. You want something small and safe. But if it’s too disconnected from real operations—or too easy to ignore—it won’t build confidence, buy-in, or clarity. It becomes just another innovation effort that doesn’t land.
That’s why your first win shouldn’t just be small. It should be messy—and meaningful.
A good first win sits at the intersection of three things:
It solves a real, felt problem. Something a team is already struggling with. Not a hypothetical improvement.
It’s imperfect but useful. The data may be noisy. The automation may only cover part of the process. That’s fine—as long as it helps.
It’s visible to stakeholders. The impact shows up in the work, not just the metrics.
This kind of win may not be elegant, but it’s credible. And credibility is what earns you the right to do more.
Vanity pilots often sound strategic—but they don’t affect real work. They’re optimized for presentation value, not business value.
Here’s how you know you’re drifting into that zone:
The problem isn’t clearly owned by a business team.
Success is measured by completion, not outcomes.
No one’s asking, “What happens if this works?”
In contrast, valuable pilots solve something someone already cares about. They create new clarity. They free up time, reduce uncertainty, or improve prioritization in a tangible way.
And maybe most importantly—they lead to follow-up questions.
A strong first win doesn’t close the conversation. It opens a new one: “Could we also apply this here?” or “What if we connected this to X?” That’s where the real momentum starts.
Identify one process that’s frustrating, repetitive, or slow—and already under pressure.
Then ask:
If we made this 10–20% better, who would notice?
Could we improve this using prediction, classification, or automation—even partially?
Who would benefit immediately if it worked?
Don’t optimize for flash. Optimize for usefulness.
If the win feels small but meaningful—and if someone outside the pilot team would miss it if it went away—you’re on the right track.