The Shift from Systems of Record to Action

For decades, asset-intensive industries (like manufacturing and infrastructure) have relied on "systems of record" to track asset details, work orders, and inventory. While these systems effectively document the past—what changed, when, and by whom—they fail to scale because they rely on manual human intervention to turn data into decisions. Agentic AI acts as a layer on top of these records, enabling "systems of intelligent action" that reason, plan, and execute tasks with operational context.

Automating the Maintenance Lifecycle

Agentic AI transforms the maintenance workflow by offloading cognitive and administrative burdens from human workers:

• Pre-work Planning: Instead of manual scheduling, an AI agent can analyze sensor data, identify potential failures, and automatically generate work orders complete with necessary parts, tools, and diagnostic guidance. The human role shifts from manual creation to high-level approval.
• Real-time Field Assistance: During repairs, agents provide hands-free, multimodal support. By processing verbal observations and visual input from cameras or smart glasses, the agent overlays procedural guidance in real-time, helping technicians diagnose issues on the spot.
• Closing the Compliance Gap: A major source of rework and compliance failure is incomplete documentation. Intelligent systems ensure the work is truly finished by prompting technicians to record parts, complete compliance steps, and schedule follow-up inspections before the task is closed out in the system of record.

By integrating these capabilities, organizations can move beyond simple analysis to systems that proactively manage asset health, reducing the high costs associated with unplanned outages.