Artificial Intelligence is reshaping the conversation in maintenance and reliability, promising predictive accuracy, automated insights, and data-driven transformation. Yet, across industries, a consistent reality is emerging: AI initiatives often falter not because of the technology itself, but because of the organisational, cultural, and infrastructure barriers that surround it. While 72% of asset-intensive organisations are exploring AI applications, more than three-quarters fail to achieve expected returns — revealing a growing gap between business ambition and operational readiness.

Many organisations underestimate the infrastructure required to support AI. From insufficient network capacity to unprepared data environments, implementation frequently exposes foundational weaknesses rather than delivering breakthroughs. Participants describe cases where “intelligent” systems crashed networks or overwhelmed servers, and where data streaming requirements far exceeded what existing IT environments could sustain. The lesson is clear: before AI can transform maintenance, the groundwork — infrastructure, governance, and data integrity — must be solid.

Beyond technology, the greatest obstacles lie in trust, culture, and capability. Maintenance professionals with decades of experience often struggle to accept algorithmic insights that contradict their hard-earned intuition. When AI systems miss obvious faults or produce outputs that technicians cannot interpret, confidence erodes rapidly. These experiences underscore the need for human-AI collaboration models that augment — rather than replace — engineering judgement. As one participant noted, “AI amplifies everything — including your data quality problems.” Without strong foundations and skilled interpretation, AI risks accelerating poor decisions instead of improving them.

This panel will bring together industry experts who have navigated both the promise and pitfalls of AI in maintenance. Panellists will discuss what separates successful implementations — such as drone-based inspections, automated defect detection, and predictive analytics — from those that stall. Attendees will gain insight into the cultural, technical, and ethical dimensions of AI adoption, and how to prepare their organisations for meaningful, trustworthy integration. The session invites an honest conversation about what it truly takes to move from AI hype to operational value.