Core question
How can we tell when an AI project is going off track?
Short answer
Watch for drift rather than failure. Success measures that were never defined or keep changing. Users building workarounds. Progress reports about features rather than outcomes. Costs climbing without explanation. When nobody can plainly say what improved, the project has lost its connection to the problem.
Projects rarely announce failure. The signals accumulate quietly, and each one is individually explainable.
People stop using the tool but nobody reports it, because nobody was asked. Employees build spreadsheets around the system to make it usable, which looks like resourcefulness and is actually a symptom. The team talks more about capability and less about the outcome, which is the clearest early tell. Timelines slip for reasonable-sounding reasons. Nobody can explain in one sentence whether value is being produced.
The corrective is a scheduled return to the original assumptions rather than a rescue effort. What problem were we solving? What did we expect to improve, and by how much? What evidence do we have? What has changed since we decided?
The reason this needs to be on a calendar is that the alternative is worse. I watched a client mandate a platform for a use case it was never designed to handle, and the person responsible for enforcing adoption held the line because the platform had AI built in. The executive's own assessment was that it was delivering results about twenty-five percent useful for the purpose it had been forced to serve. Seventy-five percent waste, defended as commitment to a decision.
The most dangerous leadership behavior in this environment is not ignorance. It is the confident, principled defense of a decision that has already failed. Reversing a call at month three costs a fraction of reversing it at month twelve, and the window is shorter now than it used to be.
When did you last reverse a call you knew was wrong?