You have more asset data than ever. But does it improve your asset reliability?

AI can process vast amounts of this data, detect patterns, and flag anomalies faster than any engineer. But what happens when the algorithm meets the reality of your assets and operating conditions?

We will explore which measurements really matter, how to separate useful insights from noise, and how to turn data into maintenance decisions that create value.

Do you want to understand how AI genuinely improves maintenance decisions, where human expertise remains indispensable, and why success comes from combining the strengths of both?  Then this is a session for you.

Because a successful predictive maintenance program starts with a clear purpose, not with collecting more data.

3 Key Learnings:

1. Why digitizing predictive maintenance is not only about installing more sensors.
2. Recognize the pitfalls of an AI driven approach without “learning it the hard way”.
3. How digitalization will bring benefit to the full maintenance and reliability community in the long run.