Class 1: The AI Imperative – Why Innovate Now? 

Why leaders must consider AI now—economic pressures, technical potential, and the risks of delay. 

• Identify external pressures that make AI transformation urgent. 

• Articulate AI’s value in addressing strategic, operational, and workforce challenges. 

• Frame innovation with AI as a necessity, not a luxury. 

Class 2: Choosing the Right Problem to Solve 

Pinpointing high-value, high-friction workflows for AI intervention. 

• Map friction points across core workflows to reveal AI opportunities. 

• Distinguish between tractable and intractable AI use cases. 

• Prioritize problems where AI can deliver measurable impact quickly. 

Class 3: Analytical vs Generative AI – Making the Right Bet 

Matching AI type to problem structure and organizational data realities. 

• Learn the distinctions between analytical and generative AI. 

• Evaluate which form of AI fits specific organizational tasks and data. 

• Avoid misalignment between AI capabilities and intended outcomes. 

Class 4: AI Maturity vs Organizational Readiness 

Assessing internal capabilities and AI system maturity. 

• Conduct a readiness assessment: data, talent, process, and leadership alignment. 

• Evaluate market offerings for AI maturity and stability. 

• Decide whether to lead, lag, or leap based on capability and risk. 

Class 5: Designing AI Experiments: Lessons from Uber 

How to iterate with MVPs and structured experimentation. 

• Learn how to define and test minimum viable AI products (MVPs). 

• Apply experimental discipline to avoid premature scaling. 

• Integrate feedback loops to iterate intelligently. 

Class 6: Building Institutional Legitimacy for AI 

Creating the internal and external ecosystems needed for innovation to stick. 

• Identify stakeholders whose trust is essential for AI adoption. 

• Learn soft power strategies for building legitimacy and reducing resistance. 

• Apply lessons from institutional voids to AI rollout in complex systems. 

Class 7: Customer Fit and Trust in AI-Enabled Services 

Ensuring new AI tools build—not erode—client confidence and satisfaction. 

• Evaluate customer readiness for AI interaction. 

• Anticipate friction and failure points in customer-facing AI. 

• Design human-in-the-loop and escalation pathways. 

Class 8: Cost-Benefit-Risk Calculus 

Calculating trade-offs: efficiency, headcount, opportunity, and downside risks. 

• Quantify expected value from AI implementation. 

• Map direct and hidden risks—including reputational ones. 

• Build business cases that balance speed, risk, and strategic gain. 

Class 9: AI and Workforce Impact – From Reductions to Redesign 

Navigating talent displacement, redesign, and new skill demands. 

• Distinguish between automation and augmentation opportunities. 

• Explore workforce redesign strategies for inclusion and upskilling. 

• Recast the AI transformation as a talent reinvestment story. 

Class 10: Managing Implementation Risks 

Bias, hallucination, failure modes, overtrust—how to lead resilient deployment. 

• Identify technical and ethical failure risks in AI systems. 

• Create escalation, monitoring, and fallback mechanisms. 

• Implement human-centered governance practices. 

Class 11: Scaling What Works 

From pilot to platform—principles for sustainable and replicable expansion. 

• Translate pilot learnings into systemwide rollouts. 

• Build platform infrastructure and change management support. 

• Measure and adapt performance at scale. 

Class 12: Strategic Alignment – AI as a Brand and Capability 

Aligning AI with mission, brand promise, and competitive position. 

• Assess how AI investments reinforce or disrupt “brand” identity. 

• Ensure coherence between AI activities and enterprise strategy. 

• Frame AI as a differentiating capability, not a tech side project. 

Capstone: Class 13 – The New Leadership Imperatives: How AI Changes Everything 

AI shifts the foundation of leadership—decisions come faster, agency is diffused, power is anticipatory, and legitimacy must be earned in new ways. 

• Shift from reactive to anticipatory leadership in AI-powered organizations. 

• Embrace platform building, orchestration, and distributed agency. 

• Lead ethically by promoting transparency, collaboration, and shared legitimacy.