This unit guides learners in translating Gen AI strategy into practical, scalable solutions for manufacturing organizations. Students learn to assess business needs, design and execute pilot projects, select infrastructure and tools, measure impact with KPIs, and establish leadership-driven governance and ethical practices to support safe, effective AI adoption.
Learning Objectives
- Analyze organizational readiness for Gen AI by conducting a gap analysis of workforce skills, data maturity, and infrastructure to prioritize use cases
- Apply a pilot-design framework to develop a proof-of-concept plan for a selected manufacturing use case, including objectives, timeline, success criteria, and risk mitigations
- Demonstrate the ability to specify infrastructure and tool selections by producing an architecture recommendation (cloud/on-prem/edge), cost-benefit justification, and integration plan
- Evaluate AI governance, ethical considerations, and performance by creating a governance checklist and KPI-aligned measurement plan to guide scaling and continuous refinement
Curriculum
- 1 Section
- 33 Lessons
- 30 hours
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- Unit 2 — Implementing and Governing Gen AI33
- 1.1The Critical Role of Leadership in Building an AI-Ready Workforce20 mins
- 1.2Assessing Business Needs20 mins
- 1.3Conducting AI Pilot Projects20 mins
- 1.4Building the Right Infrastructure and Selecting Tools20 mins
- 1.5Scaling AI Solutions Across the Organization20 mins
- 1.6Continuous Measurement and Refinement20 mins
- 1.7Example: Implementing Gen AI in a Financial Services Firm20 mins
- 1.8Key Takeaways for Leadership20 mins
- 1.9Case Study 1: Enhancing Quality Control in Electronics Manufacturing20 mins
- 1.10Case Study 2: Predictive Maintenance in Automotive Manufacturing20 mins
- 1.11Case Study 3: AI-Augmented Product Design in Automotive Engineering20 mins
- 1.12Case Study 4: AI-Driven Supply Chain Optimization in Beverage Production20 mins
- 1.13Case Study 5: AI-Enabled Energy Optimization in Glass Manufacturing20 mins
- 1.14Agentic AI in Manufacturing: Successes, Challenges, and Failures20 mins
- 1.15Insights for Manufacturing Leaders20 mins
- 1.16Conclusion: Restating the Case for AI in Manufacturing20 mins
- 1.17Advancements in AI Models and Capabilities20 mins
- 1.18Emerging Applications Across Industries20 mins
- 1.19Enhanced Personalization and Customer Engagement20 mins
- 1.20AI-Enhanced Decision-Making and Automation20 mins
- 1.21Ethical and Regulatory Challenges in AI Implementation20 mins
- 1.22Preparing for Quantum AI and Other Emerging Technologies20 mins
- 1.23AI-Driven Transformation20 mins
- 1.24Strategic Considerations for Leadership20 mins
- 1.25The Role of Leadership in AI Governance20 mins
- 1.26The Need for Leadership-Driven AI Governance in Manufacturing20 mins
- 1.27Leadership’s Role in Ethical AI Practices20 mins
- 1.28Leadership Strategies for Ethical AI Governance in Manufacturing20 mins
- 1.29Building an Ethical AI Culture in Manufacturing20 mins
- 1.30Case Study: Leadership in AI Governance at a Global Manufacturing Company20 mins
- 1.31Conclusion: AI Governance as a Competitive Advantage in Manufacturing20 mins
- 1.32Defining Success: Aligning AI Metrics with Business Goals20 mins
- 1.33Key Performance Indicators for Evaluating AI Success20 mins
Instructor
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