• Apply a structured framework to map AI opportunities across the full value chain, from productivity and cost reduction to new revenue streams and business model reinvention.

• Distinguish short-term quick wins from medium-term transformation and long-term strategic repositioning, sequence initiatives for impact and organizational readiness.

• Participants select their organization or industry and begin scoping their AI Global Transformation Playbook.

• Identify where AI-native competitors threaten your core business and where you hold defensible moats, applying a structured disruption-response framework across industries and geographies.

• Determine when to build, partner, acquire, or retreat in the face of AI challengers; map competitive AI dynamics in your sector.

• Explore how AI enables fundamentally new business models, platform economics, AI-as-a-service, data network effects, and intelligent product layers across global markets.

• Redesign pricing structures, incentive models, and value chain relationships for the AI era; build a business model canvas for an AI-first version of your organization.

• Construct rigorous AI investment cases using ROI frameworks, risk-adjusted returns, and stage-gate governance structures that give organizations confidence to commit capital at scale.

• Understand how AI investments are evaluated differently from traditional technology spending and how to position AI initiatives within existing capital allocation processes.

• Develop a working understanding of how today's AI systems are built, including foundation models, large language models, fine-tuning, retrieval-augmented generation (RAG), and agentic architectures, at the level a CAIO needs to ask the right questions of technical teams and vendors.

• Understand AI system limitations, failure modes, hallucinations, bias, and cost structures, the technical realities that determine whether an AI deployment succeeds or fails at scale.

• Learn to evaluate vendor claims, assess build-vs-buy decisions, and interrogate technical proposals with confidence.

• Understand the data architecture decisions that determine AI success, including data lakes, vector databases, data quality, governance pipelines, and the organizational challenge of making enterprise data AI-ready.

• Assess cloud AI infrastructure options, on-premise vs. hybrid deployments, and the cost, latency, and data sovereignty tradeoffs that shape enterprise AI architecture choices across global operating environments.

• Apply a data readiness diagnostic to your organization: what needs to be in place before AI can deliver value at scale.

• Examine the shift from generative AI tools to agentic AI systems, including AI that plans, executes multi-step tasks, and operates across enterprise workflows — and what this means for process redesign, risk, and human oversight.

• Explore live enterprise deployments of agentic AI in operations, finance, supply chain, customer service, and software development, examining what is working, at what scale, and under what governance conditions.

• Build a framework for evaluating which enterprise processes are ready for agentic AI versus those requiring deeper human oversight and intervention design.

• Faculty-moderated panel with technology executives from AI-native companies, global enterprises, and the venture ecosystem to discuss the real frontiers of AI disruption.

• Discussion themes: Where enterprise AI is delivering transformational value today; the biggest organizational and technical barriers to scale; what the CAIO role looks like from the inside; and where AI is headed in the next 18–36 months.

• Build a comprehensive AI risk framework, covering technical failure, regulatory exposure, reputational risk, ethical harm, and competitive obsolescence, with contingency protocols for high-stakes AI failures in complex global operating environments.

• Navigate the evolving global AI regulatory landscape, including EU AI Act, US federal and state frameworks, sector-specific regulation, and translate compliance requirements into practical governance architectures that scale across jurisdictions.

• Examine case studies of AI governance failures and the organizational design lessons they reveal for CAIOs building enterprise-wide accountability structures.

• Architect a balanced portfolio of AI initiatives spanning quick wins, transformation bets, and long-horizon experiments, sequenced for organizational readiness, resource availability, and strategic priority.

• Build a stage-gated experimentation and scaling framework: how to run low-cost AI experiments, capture learning, make kill-or-scale decisions, and move from pilot to enterprise deployment without losing momentum.

• Apply technical evaluation criteria to initiative prioritization: data readiness, integration complexity, vendor dependency, and total cost of deployment at scale.

• Define the CAIO mandate, including scope of authority, reporting structure, and key interfaces with the CEO, CTO, CDO, CIO, and the wider leadership team; benchmark against emerging CAIO archetypes across US, European, and Asia-Pacific organizations.

• Design AI operating models: centralized center of excellence, federated, embedded, or hybrid — and when each is right for your organization's culture, scale, and AI maturity stage.

• Apply Thunderbird's global leadership frameworks to AI adoption across different cultural contexts, diagnosing and overcoming resistance, trust deficits, and adoption barriers in globally distributed enterprises.

• Examine how leading multinationals navigate AI transformation across diverse workforce cultures, regulatory environments, and digital maturity levels across the US, Europe, Asia, and emerging markets.

• Redesign jobs, workflows, and team structures for an AI-augmented workforce, building a strategic augmentation architecture that captures AI value without eroding employee trust and organizational capability.

• Build an AI talent strategy: Attracting AI-literate senior leaders, reskilling existing teams, and creating a culture of continuous AI learning that sustains transformation momentum over time.

• Apply proven change management frameworks to the specific challenges of enterprise AI adoption, managing resistance, building internal coalitions, and creating durable organizational readiness for continuous AI evolution.

• Develop a stakeholder engagement strategy: from frontline employees and middle management to government partners, regulators, and external partners across global operating contexts.

• Faculty-moderated panel bringing together 3–4 senior technology executives from AI-nativecompanies, global enterprises, and the venture ecosystem to discuss the real frontiers of AI disruption.

• Discussion themes: where enterprise AI is delivering transformational value today; the biggest organizational and technical barriers to scale; what the CAIO role looks like from the inside; and where AI is headed in the next 18–36 months.

• Live Q&A with the cohort — participants bring their toughest strategic and technical questions to practitioners shaping the AI landscape.

Format: 90-minute panel. Panelists drawn from Thunderbird's global corporate network and XED's practitioner community. Faculty moderates; cohort submits questions in advance

Participants will develop and refine their AI Global Transformation Playbook with industry experts through three focused small-group working labs designed to sharpen their assumptions, surface strategic blind spots and strengthen the coherence and credibility of their Playbook.

Select participants present their completed AI Global Transformation Playbook to the faculty and cohort covering the full arc from opportunity assessment and competitive strategy to initiative portfolio, governance architecture, change management plan, and risk framework.