Most enterprise AI initiatives stall not because the technology doesn’t work, but because of how they’re run.
Start with the problem, not the technology
The most common failure mode is a team that has decided to “do AI” before they’ve identified a specific problem worth solving. They run workshops, evaluate vendors, and build proofs-of-concept — but without a clear, measurable outcome to optimize for, nothing ships.
The discipline is forcing the question early: what is the specific decision or task we’re trying to improve, how do we currently measure it, and what would success look like in six months?
Keep the first project boring
The first AI project in an organization sets expectations, builds trust, and teaches the team how to ship AI. It should have a high probability of success and a clear before/after comparison.
This argues strongly for narrow scope: one use case, one user group, one metric. The companies that try to transform everything at once typically deliver nothing.
Separate exploration from delivery
Exploration — trying new models, evaluating vendors, running experiments — is inherently unpredictable. Delivery — getting something working into the hands of users — requires predictability.
Running both in the same team with the same cadence creates constant conflict. The exploration work always feels urgent; the delivery work always slips.
Explicitly separate these activities, even if it’s just two people working in different modes.
Measure outcomes, not activity
AI initiatives tend to accumulate impressive-sounding activity metrics: models evaluated, experiments run, accuracy percentages achieved in test conditions. None of this matters if the system isn’t being used or isn’t producing better outcomes for users.
Define the outcome metric before you start. Measure it in production. Report on it honestly.
