Model Online / On-Site
Duration
Modules 6 modules

Course Objective & Scope

Moving beyond scattered proofs of concept (PoCs) to durable business value requires executives to align strategy, capital allocation, operating model and risk governance. This multi-module academy equips senior leaders to evaluate generative AI, agentic workflows and machine learning through a strategic lens—covering value portfolio management and AI unit economics, federated versus hub-and-spoke operating models, architectural trade-offs (buy vs partner vs build) and governance aligned with the EU AI Act and ISO/IEC 42001.

Between modules, participants work on applied institutional assignments with advisory feedback, culminating in an executive AI value thesis, governance charter and 12-month transformation roadmap.

Who Should Attend?

  • C-suite executives, VPs and executive committee members
  • Business unit heads, directors and functional leaders
  • Strategy, digital transformation, technology and data/AI leaders (CIO, CTO, CDO, CAIO)
  • Senior leaders sponsoring enterprise-wide AI transformation initiatives

Key Takeaways

  • Formulate an enterprise AI thesis by matching predictive ML, generative AI and agentic automation to the right business problems
  • Evaluate and sequence AI investments using strategic fit, data readiness, technical feasibility and unit economics (TCO/ROI)
  • Interrogate buy, partner, RAG customisation and model fine-tuning proposals with rigorous executive questions
  • Establish board-level AI risk, compliance and assurance guardrails aligned with the EU AI Act, ISO/IEC 42001 and data privacy laws
  • Design a scalable AI operating model, workforce transition plan and 12-month execution roadmap

Curriculum

The Strategic AI Landscape: From Copilots to the Agentic Enterprise
  • Economic and operational differences between predictive ML, generative AI and agentic systems
  • Moving from individual productivity gains to core process and business-model redesign
Value Portfolio, Investment Sequencing and AI Unit Economics
  • Escaping PoC purgatory: balancing quick wins, workflow redesign and strategic bets
  • Calculating real TCO and ROI across inference costs, integration, maintenance and adoption
Technology, Data and Architectural Decision Frameworks
  • Trade-offs between off-the-shelf SaaS, enterprise RAG, open-weight models and fine-tuning
  • Data readiness, vendor lock-in mitigation and sovereign/hybrid deployment choices
AI Operating Model, Talent and Organisational Change
  • Centralised CoE, federated and hub-and-spoke operating models
  • Workforce task redesign, capability building and executive sponsorship rituals
Responsible AI, Regulation and Board-Level Assurance
  • Executive implications of the EU AI Act, ISO/IEC 42001, NIST AI RMF and data protection laws
  • Stage-gate controls for shadow AI, IP exposure, hallucinations and reputational risk
Capstone: Enterprise AI Transformation Roadmap
  • Presenting a unit or enterprise AI strategy charter and 90/180/360-day roadmap
  • Peer and faculty stress-testing on investment, risk and execution readiness

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