AI is rapidly reshaping maintenance and reliability, but most organisations are struggling to translate ambition into measurable results.

Why do so many AI initiatives fail to deliver, and what does this reveal about gaps in infrastructure, data readiness, and governance?

How can organisations strengthen the foundations needed to support AI without disrupting existing operations?

What role do trust, culture, and human judgement play when algorithmic insights challenge decades of experience?

How do we design human–AI collaboration models that enhance, rather than undermine, engineering decision-making?

What practical lessons can be learned from real-world successes and failures to move from experimentation to sustainable value?

This panel brings together industry experts who are navigating both the promise and pitfalls of AI in maintenance, sharing their experiences of case studies, some that succeed and some that stall. Looking at the cultural, technical, and ethical enablers required to make AI work in real operational environments.