AI roadmaps, data governance, and build-vs-buy decision frameworks.
Build an AI strategy that survives contact with reality — data readiness, use-case prioritisation, build-vs-buy, and a 12-month roadmap you would defend at a board meeting.
CIOs, CDOs, heads of data and AI, and the strategists and consultants advising them.
The guides are numbered — read in order for the curriculum path, or jump straight to the one you need. Each card is self-contained.
What an AI strategy actually is, why 2026 is different, and how to connect AI to real business outcomes.
A one-page framework linking objectives, data, capability, governance, and value.
Five honest stages from ad-hoc experiments to AI-native operations, and how to move up.
Assessing whether your data can actually support AI, and what to fix before you invest.
Quality, lineage, access, privacy, and data contracts, rewritten for LLMs and agents.
Data architecture for decision-makers: why platforms are being rebuilt to serve AI, not just dashboards.
Why the documents, emails, and transcripts you have been ignoring are now the highest-leverage input to AI.
Data products, mesh, and the ownership model that makes AI at scale actually work.
Value vs feasibility scoring and how to build a portfolio that ships wins while placing real bets.
The full spectrum of AI sourcing decisions and the trade-offs that actually matter in practice.
How to choose between frontier proprietary models and open-weights options, and why most teams need both.
A scorecard for cutting through AI-powered pitches and choosing partners who can actually deliver.
Delegation vs augmentation, and where to deploy the first agents that will actually pay off.
The pilot trap, the operating disciplines that get AI into production, and how to cross the chasm.
Turning strategy into a phased, fundable delivery plan that balances early wins with long-term bets.
The common patterns that quietly break AI strategies, and the leadership habits that prevent them.