AI Leadership, Governance, and Talent

This unit prepares manufacturing leaders to sustain and scale AI initiatives by focusing on ROI measurement, governance, culture change, talent development, and ongoing monitoring. Students learn to translate AI vision into operational practices—addressing data quality, legacy integration, costs, ethical deployment, and sector-specific adaptation (e.g., healthcare)—to enable responsible, measurable adoption.

Learning Objectives

• Analyze and quantify return on investment (ROI) and total cost of ownership for AI initiatives by selecting appropriate financial and operational metrics, computing payback periods, and designing ongoing monitoring plans.
• Design a comprehensive AI governance framework and responsible-deployment policies that assign roles, define oversight processes, and incorporate qualitative feedback and human-in-the-loop controls for continuous improvement.
• Evaluate organizational readiness and barriers (culture, legacy systems, talent gaps, and cost constraints) and produce a change-management strategy that turns resistance into engagement through cross-functional collaboration and experimentation.
• Develop a strategic talent plan to recruit, grow, and retain AI capabilities—specifying training pathways, retention incentives, leadership responsibilities, and measurable indicators—and apply it to a manufacturing or healthcare case study.