Winning with AI: From Strategy to Business Value

Opening: The AI Transformation Challenge

Purpose: 
Establish the strategic stakes, surface participants’ current questions, and create a peer-learning environment.

• Welcome, introductions, and program objectives
• Live participant pulse: ambition, readiness, barriers, and confidence
• The central challenge: moving from experimentation to competitive advantage
• Introduction to the AI Transformation framework

Participant takeaway: 
A baseline view of where their organization stands today and the central AI transformation question they want to address during the program.

Module 1: Set the Strategic Ambition

Where should AI matter to our strategy, and where should it not?
AI is more than a technology issue. It is changing industry structures, sources of competitive advantage, and the boundaries of organizations. This module helps participants move beyond disconnected use cases and consider what role AI should play in their organization’s strategy.

• How AI is reshaping industry structure, strategic positioning, and organizational boundaries
• Moving beyond “use cases”: choosing an AI ambition consistent with business strategy
• Strategic choices: compete, differentiate, transform, partner, or abstain
• Common failure patterns: technology-first pilots, unowned value cases, fragmented investments, and unaddressed adoption barriers
• Selected industry examples and perspectives

Participant takeaways:

• Understand the strategic implications of AI for their organization and industry
• Recognize the forces reshaping sources of competitive advantage
• Clarify where AI should and should not matter to business strategy
• Identify the strategic questions their organization needs to address

Networking Break

Module 2: Create and Capture Value

Where can AI create meaningful business value?
AI creates value not simply by automating existing activities, but by enabling organizations to rethink customer value, economics, business models, and sources of differentiation. This module helps participants distinguish promising opportunities from technology-driven experimentation and consider where AI can create sustainable advantage.

• AI and the sources of advantage: customer value, superior economics, speed, insight, innovation, and ecosystem position
• Reimagining business models and customer journeys, not merely automating existing processes
• Value creation versus value capture: who benefits, what changes, and how returns are measured
• Portfolio logic: balancing quick wins, foundational investments, and strategic bets
• Industry cases selected for the audience mix

Participant takeaways:

• Understand how AI is shifting sources of competitive advantage
• Identify where AI can create meaningful strategic and economic value
• Evaluate opportunities for growth, differentiation, and business model innovation
• Apply a value-based lens to prioritizing AI opportunities

Networking Lunch

Module 3: Build Capabilities for AI Transformation

What capabilities does the organization need to capture AI-enabled value at scale?
Moving from promising opportunities to sustained value requires more than technology investment. Organizations need the right combination of data, talent, operating models, governance, and external partnerships. This module helps participants assess what their organizations already have, what they need to build, and what they may need to access externally.

• Data, technology, and vendor choices as business decisions, not simply IT decisions
• Operating-model alternatives: centralized platform, federated execution, or hybrid models
• The shadow AI challenge: governance as an enabler of speed, responsible use, and trust
• Talent beyond technical specialists: business ownership, product leadership, domain expertise, and workforce learning
• Partnerships and ecosystems: what to own, what to access, and what to co-create

Participant takeaways:

• Understand the capabilities required to move from isolated AI initiatives to scalable value
• Assess their organization’s readiness across critical capability areas
• Identify capability gaps and priorities for investment
• Evaluate choices about what to build internally, acquire, partner for, or access through an ecosystem

Networking Break

Module 4: Lead Organizational Change

What needs to change in the organization to make it happen? 
AI transformation ultimately depends on how people work, make decisions, learn, and adapt. Technology deployment alone does not create organizational change. This module focuses on the leadership, behavioral, and cultural conditions required to move from experimentation and uncertainty to confident, responsible adoption.

• Why workflow redesign and behavior change—not deployment alone—determine realized value
• Trust, transparency, and human accountability in AI-enabled work
• Leading through uncertainty: communication, role clarity, incentives, and visible leadership behavior
• Creating a learning system: safe experimentation, feedback loops, and scaling what works

Participant takeaways:

• Diagnose where AI adoption is most likely to stall in their organization—and why
• Understand how workflows, behaviors, incentives, and culture influence realized value
• Identify leadership actions that can accelerate adoption while preserving trust and accountability
• Translate transformation priorities into visible organizational action

From Insight to Action

What should we do next?
The program concludes by bringing the four elements of the AI Transformation framework together and translating them into practical next steps.

• Revisit the AI Transformation framework
• Reflect on the most important strategic and organizational implications for their own organization
• Identify one priority action and one measure of progress
• Develop a one-page AI Transformation Action Plan

Participant takeaways:
Participants leave with a clearer view of where AI matters to their business, where it can create value, what capabilities and organizational changes are required, and what they can do next to move their organization forward.