Nick Haase

Manufacturers are accelerating AI adoption, yet many initiatives stall because the operational foundation is not ready: work is not fully digitized, execution data is inconsistent, and machine signals are disconnected from frontline action. This session outlines a practical, real world path to AI that begins with standardized work and trustworthy data, then builds toward connected assets and automated decision flows.

• Establish a reliable operating dataset by digitizing and standardizing frontline work across assets, shifts, and sites

• Strengthen asset context and governance to ensure machine, maintenance, and execution data can be trusted for AI driven decisions

• Connect PLC, SCADA, and sensor data to frontline workflows to close the loop between equipment signals and real operational action

• Automate prioritization and response by activating AI to turn alerts into targeted work that improves uptime, throughput, and labor efficiency

• Accelerate progress with a clear sequencing model and early use cases that build credibility while avoiding multi quarter integration delays.