Leadership, Governance, and AI Scaling

This unit prepares manufacturing leaders and practitioners to move from AI exploration to operational deployment by focusing on leadership readiness, pilot execution, infrastructure selection, scaling strategies, and governance. Learners examine cross-industry case studies, design and evaluate pilots, develop ethical AI governance, and create KPI-driven measurement plans to support scalable, responsible AI adoption.

Learning Objectives

• Analyze organizational processes, stakeholder needs, and operational data to prioritize at least three high-impact AI use cases aligned to manufacturing goals and ROI criteria
• Design and plan a pilot AI project that specifies objectives, success metrics, data requirements, chosen tools/infrastructure, risk mitigations, and an evaluation timeline
• Evaluate infrastructure and tooling options and produce a scalable deployment roadmap addressing data pipelines, integration points, security/privacy controls, and resource requirements
• Develop an AI governance and measurement framework that defines leadership roles, ethical guidelines, KPIs for continuous improvement, and a plan to scale validated pilots across the organization