As organisations face mounting pressure to improve uptime, meet sustainability goals, and optimise operational costs, traditional maintenance models are no longer sufficient. Artificial Intelligence is fundamentally reshaping Enterprise Asset Management (EAM) and Asset Performance Management (APM) by driving a structural shift away from reactive and routine preventive schedules toward predictive, data-driven, and increasingly autonomous asset operations.

This interactive roundtable explores how AI technologies are transforming the entire asset lifecycle from end to end. Participants will examine how machine learning models, natural language processing, and digital twins move asset management beyond basic work order execution toward intelligent monitoring, automated failure detection, and dynamic decision support.

The session provides a practical forum to discuss real-world applications, emerging use cases, and tangible business outcomes. Attendees will explore actionable roadmaps for embedding AI directly into existing enterprise asset management strategies, helping maintenance and operational leaders reduce unplanned downtime, enhance reliability, and unlock the full value of intelligent assets.

Key discussion areas
• Transitioning from traditional preventive maintenance schedules to predictive and autonomous operational models
• Transforming asset monitoring and failure detection through AI, machine learning, and digital twins
• Automating work prioritisation, scheduling, and decision support to drive intelligent automation and workforce optimisation
• Implementing practical roadmaps to integrate AI capabilities into existing enterprise asset management frameworks for measurable ROI