2.4 Maintenance Maintenance today focuses on preventing failure rather than assuring sustained asset performance. Time-based servicing and manual diagnostics respond after degradation has already occurred, increasing downtime, energy waste and total cost of ownership as complexity rises. The future is lifecycle-driven maintenance performance systems where continuous sensing, prediction and self-optimization sustain reliability, efficiency and low-carbon operations across the asset lifecycle. Agentic maintenance ecosystems THEME 1 Evolutions of themes AI agents coordinate maintenance across OEMs. Suppliers and plants create a self- optimizing network that improves uptime, reduces cost and lowers carbon footprint. Unplanned downtime Maintenance cost From reactive to predictive maintenance — IoT provides visibility into asset performance, energy consumption and component wear. — Predictive AI forecasts failures enabling condition-based maintenance. — Digital twins capture asset behaviour and lifecycle patterns, laying the foundation for intelligent orchestration.Collaborative, AI-orchestrated maintenance — Focus shifts from asset level optimization to network-wide orchestration across plants and partners. — Agentic AI coordinates maintenance windows, spare parts and technician assignments across plants and suppliers through shared digital twins. — Human supervisors oversee AI recommendations as systems learn from disruptions to reduce downtime and optimize resource across sites.Autonomous, self-healing maintenance networks — Federated AI agents optimize decisions across OEMs, suppliers and sites forming a self-learning, ecosystem-wide maintenance network. — They autonomously predict, prioritize and execute actions across partners reconfiguring systems before issues arise. — Human intervention shifts to governance and resilience strategy, as systems aim for zero unplanned downtime.NOW (0-2 years) NEAR (3-5 years) NEXT (5+ years) Objectives MAINTENANCE Intelligent Industrial Operations Outlook 2026 22