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A prescriptive, sequential build guide that walks a zero-maturity public-sector team through a complete 10-stage AI deployment cycle — from classification intake through post-deployment monitoring — using only open-source tooling. The playbook branches proportionately for model paradigm, provenance, data sensitivity, and deployment criticality, with blocking security sign-off gates at defined checkpoints. It embodies the Minimal Viable Governance philosophy: impose proportionate gates on day one and mature iteratively, rather than stalling behind enterprise-grade process.

A structured, repeatable process for capturing, evaluating, and prioritizing AI use cases across your organization. Move from scattered ideas to a governed backlog of high-value opportunities.

End-to-end governance for designing, monitoring, and adjusting a GenAI- and LLM-intensive AI budget that is token-savvy, risk-aware, and jointly owned by CFO, AI leader, and CTO. This playbook defines how to move from ad-hoc experiments to a managed AI portfolio with clear AI spend guardrails, scenario bands, and AI FinOps rhythms.

A practical, end-to-end guide for medium-sized government organisations to move from scattered AI experiments to a hybrid AI Centre of Excellence that is a visible pillar of digital transformation. It walks you through defining the CoE’s mandate, operating model, functional structure, core processes, and a phased implementation roadmap aligned to your digital transformation roadmap and public value goals.

A prescriptive 30-day build plan for standing up a usable AI incident response capability with a 2-3 person shared-responsibility team and no existing IR function. Builds a minimal general IR skeleton — declaration, severity tiers, escalation, comms — and layers AI-specific detection, containment, and review on top, delivering one battle-tested containment runbook by day 30. Designed for mid-size professional services and B2B technology companies where brand trust and business continuity, not regulatory compliance, are the primary drivers.

A pre-project diagnostic instrument for AI advisors who must be right, persuasive, and consistent across engagements. Defines six pathology categories, a context-calibrated fatal-vs-tractable decision system, and a reusable scorecard that builds cumulative institutional authority through cross-project benchmarking. The playbook's authority comes from its structure, not from the advisor's position.

The spine of the Sovereign AI Vendor Strategy bundle — a prescriptive, five-layer architecture for classifying AI vendors, enforcing intake gates, calibrating due diligence, codifying contract requirements, and maintaining a living inventory. Designed for a greenfield governance lead with stop-procurement authority who must seize the one-time architectural window before adoption accelerates. Produces two anchor deliverables: a board-endorsable AI vendor strategy document and a vendor inventory and risk dashboard schema ready to operationalize the next day.