Enterprise AI pilots succeed. Enterprise AI deployments stall. The gap between the two is almost always a management problem, not a technology problem.
There's a pattern in enterprise AI adoption that I've watched repeat across organizations of different sizes, industries, and geographies.
Month one: high enthusiasm. The pilot group shows impressive results. Leadership is optimistic. The vendor is engaged and responsive. Productivity metrics, where they exist, show improvement.
Month three: the numbers flatten. Adoption outside the pilot group is slower than projected. Some early adopters have quietly reverted to their previous workflows. The engineering team is managing more exceptions and edge cases than expected. The business case is starting to look less certain.
Month six: the deployment is technically still active, but it's no longer receiving executive attention. It's running on momentum rather than intention. The question "is this working" is no longer being asked, partly because nobody wants to hear the answer.
