The Operations Bridge

Point ten in the AI Strategy Points series. Last time was about applying “what, so what, now what” to AI itself. This post is about the single most common reason I’ve seen AI projects quietly fail.

Failure rarely announces itself

AI projects need to solve real problems for real team members to avoid the “fail” status. Very few AI initiatives fail with a dramatic, obvious collapse. Most fail quietly — usage drifts downward, the tool gets mentioned less in meetings, and eventually everyone just stops bringing it up. Nobody declares it dead. It simply stops mattering.

When I trace that pattern back to its root, the technology is rarely the actual cause. The far more common root cause is that the project was never solving a problem a real person actually had. It was solving a problem that sounded good in a strategy deck.

The gap between an executive problem and a floor problem

“Improve operational visibility” is a real strategic goal, but it isn’t a real problem for any specific person until it’s translated into something like “I spend forty minutes every morning manually reconstructing yesterday’s downtime, and I’m often still wrong.” The second version has an owner, a felt cost, and a way to know if it’s fixed. The first version doesn’t.

Too many AI initiatives get built against the executive-level version of the problem and never get translated down to the floor-level version. The tool that results is technically impressive and genuinely unused, because it was never built against anyone’s actual Tuesday.

Finding the real problem

Before scoping an AI project now, I go looking for the complaint that already exists — the thing people are already frustrated about, already working around, already mentioning in hallway conversations. That complaint is worth more than any strategic brainstorm, because it comes with a built-in owner who will actually notice if the fix works.

This connects directly to point two in this series, about defining value before you build. A real problem for a real person is the clearest possible value definition available — it’s specific, it’s felt, and it’s falsifiable. If the fix doesn’t land, the person affected will tell you immediately, without you needing a formal adoption metric to find out.

A question for leaders

For your next AI initiative, can you name the specific person who is currently frustrated by the problem you’re solving, in their own words, before you write a single line of the project plan?

Next: why the art of the prompt depends entirely on the integrity of the underlying data — a theme that connects back to point six in this series.