Leading AI Adoption and Governance

This unit equips manufacturing leaders to move from AI strategy to practical implementation by assessing business needs, running pilots, selecting infrastructure and tools, and preparing for enterprise-scale deployments. It also addresses leadership roles in ethical AI governance, performance measurement, and translating pilot results into organization-wide change, using industry case studies to ground learning.

Learning Objectives

• Analyze organizational readiness and business needs for Gen AI adoption in a manufacturing context by conducting a stakeholder assessment and prioritizing at least three use cases based on ROI, feasibility, and risk.
• Design an AI pilot project plan that specifies infrastructure requirements, tool selection criteria, data needs, success metrics, and a rollout timeline suitable for a mid-sized manufacturing operation.
• Evaluate ethical, legal, and governance implications of proposed AI initiatives and recommend leadership-driven governance policies and mitigation strategies aligned with regulatory requirements and industry best practices.
• Apply KPI frameworks and continuous measurement methods to develop a scaling strategy that defines metrics, monitoring cadence, and a go/no-go decision matrix for transitioning pilots to enterprise deployments.