Dan Byrne

Many industrial organizations are laying the groundwork for AI-powered maintenance by transforming the underlying processes, systems, and data that support day-to-day operations. With a focus on data quality, system integration, and process consistency, these initiatives are enabling scalable AI use cases across the full maintenance lifecycle, from identifying work through planning, scheduling, execution, and closeout. As many organizations have discovered, success with AI depends less on the technology itself and more on the quality of the operational and data foundations that support it.

This session explores how manufacturers are building the digital infrastructure needed to ensure AI delivers measurable operational value, while evaluating emerging use cases that support predictive maintenance, intelligent scheduling, and frontline decision-making.

• Unifying maintenance processes across sites and business units to create a consistent data layer that supports AI integration and long-term operational visibility
• How AI agents can support complex planning and scheduling decisions by factoring in asset criticality, workforce availability, geospatial constraints, and material readiness
• Preparing for future use cases, including predictive modelling for asset health, computer vision for anomaly detection, and intelligent assistants that support troubleshooting and parts management
• Generating a rich dataset across the full maintenance lifecycle to enable continuous improvement and reshape how operational excellence is measured and achieved

This session will provide practical insights into the foundational capabilities required to move AI initiatives from pilot programs to sustainable business outcomes.