
Why Predictive Maintenance Pilots Rarely Make It to the Factory Floor
The model is rarely the problem. Almost half of maintenance AI ideas are hard to deliver at scale because of what has to happen between the alert and the repair.
Six executive dilemmas, one for each readiness domain. You see your score straight away.
Ask whether you have an AI strategy and almost everyone says yes. A dilemma asks how you would handle a real situation, such as dozens of AI proposals arriving at once. The answer says much more about how ready you are.
The wording differs per dilemma; these are the general levels.
The Snapshot is a quick, personal view. The full scan is done together by your leadership team in the platform and linked to your AI portfolio. On the right: Technology Readiness for a demo portfolio, with its biggest gap.

The model is rarely the problem. Almost half of maintenance AI ideas are hard to deliver at scale because of what has to happen between the alert and the repair.

In every manufacturing sector, 38 to 41 percent of AI use cases are hard to deliver, mostly because of the decisions they support rather than the data.

Go-live is where AI risk changes shape. Library data shows which kinds of AI are hardest to observe, and what a practical monitoring routine looks like.