AI Strategy and GenAI Implementation: full course guide
A non-code executive track for leaders who must decide where GenAI creates value — and where it burns budget. Build an AI strategy, evaluate build-vs-buy, quantify ROI, run a use-case portfolio, and stand up responsible-AI governance. Framed around board-level decisions, vendor evaluation, and change management, not model internals.
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What is inside this guide
01The GenAI Landscape for Leaders
- What LLMs actually do — capabilities without the math
- Where GenAI creates value vs where it destroys it
- Foundation models, fine-tuning, and RAG in plain terms
- The competitive landscape and vendor ecosystem
- Separating durable advantage from hype cycles
02From Opportunity to Use-Case Portfolio
- Mapping AI opportunities across the value chain
- Scoring use cases on value, feasibility, and risk
- Quick wins vs strategic bets vs moonshots
- Building a prioritised use-case portfolio
- Defining success metrics before you start
03Build, Buy, or Partner
- Build vs buy vs fine-tune vs API — the decision framework
- Total cost of ownership: tokens, infra, talent, maintenance
- Evaluating vendors and avoiding lock-in
- Data readiness and the 'garbage in' problem
- Estimating ROI and constructing the business case
04Responsible AI & Governance
- Key risks: hallucination, bias, IP, privacy, security
- An AI governance operating model and review gates
- Regulation landscape (EU AI Act and beyond) at a leader's level
- Human-in-the-loop and accountability structures
- Measuring and communicating trust and safety
05Execution & Change Management
- Operating model: centre of excellence vs federated teams
- Workforce enablement and reskilling
- Piloting, scaling, and killing projects on evidence
- Communicating AI change to the board and the org
- Capstone: a board-ready AI strategy and roadmap
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