The Challenge
A global manufacturing firm with 12 plants across North America had invested heavily in AI-powered predictive maintenance and quality control tools — but adoption was stalling at 15%. Floor engineers didn’t trust the AI recommendations, data teams couldn’t communicate with operations, and leadership had no framework to measure AI competency.
Our Approach
We designed and delivered a comprehensive Training & Enablement program calibrated to three distinct audiences:
Executive AI Literacy: Two-day intensive for the C-suite and plant directors — demystifying AI capabilities, establishing realistic expectations, and building an AI governance framework they could champion.
Engineering Prompt Design & Agent Interaction: Hands-on workshops for 500+ floor engineers and maintenance technicians — teaching them to effectively work with AI recommendations, validate outputs, and provide feedback that improved model accuracy.
Data Team Upskilling: Deep-dive sessions for the central data science team on agent architecture, knowledge graph design, and production ML operations — transitioning them from ad-hoc model building to systematic AI engineering.
The Results
- 500+ engineers trained: Across all 12 plants in a 90-day rollout.
- AI adoption jumped to 78%: From 15% pre-training, as engineers gained confidence in interpreting and acting on AI recommendations.
- 35% reduction in unplanned downtime: Predictive maintenance recommendations were finally being acted on consistently.
- $22M annual savings: Higher AI adoption directly translated to fewer breakdowns, less scrap, and optimized maintenance schedules.