AI projects almost never fail on the technology. They fail because they solved a problem nobody actually felt. Point AI at problems people know and feel — bottlenecks, constraints — not abstractions.
Real problems generate momentum.
Aim AI at a felt constraint and the improvement is real enough that people won’t go back — and real improvement is exactly what pulls the next project. That’s the momentum engine.
Abstractions get abandoned.
Aim it at “we should use AI for X,” and even a working tool gets dropped. Using AI costs effort — learning it, changing the workflow — and if the payoff doesn’t clearly beat that cost, people revert to how they’ve always done it. It’s the kaizen hangover: improvements that don’t stick because the change cost more than it returned.
So the real bar isn’t “does it work.”
It’s whether the improvement is worth more than the change it costs. A working tool on a problem nobody felt still loses to the old way.
So what, now what.
So what: an AI project fails on problem selection, not technology. Now what: before you start, name the felt constraint it removes and be honest about the effort it adds. If the improvement doesn’t clearly beat the cost of changing, pick a different problem.
Leave a comment