AI adoption in manufacturing is accelerating — but most failures are not caused by algorithms.
They are caused by weak data foundations, unclear ownership, and missing governance at the shopfloor level.

This session explores practical AI governance models tailored for manufacturing environments, where legacy equipment, operator-driven processes, and safety-critical systems dominate.

Instead of abstract ethics or compliance checklists, the talk focuses on operational governance:
how data is generated, validated, interpreted, and acted upon across production, maintenance, and engineering teams.

Participants will learn how to establish governance that enables AI to deliver value in areas such as quality, availability, and throughput — without relying on autonomy or black-box decision-making.

Audience: Plant, production & maintenance leaders; industrial IT/OT

Duration: 30–45 min

Focus: Data quality, sensor calibration, signal drift, ownership, operator–engineer feedback loops

Approach: Practical cases, no AI hype, no data science required