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Classify your AI system into the appropriate risk tier based on purpose, data sensitivity, autonomy, and potential harm. Helps determine governance requirements and regulatory obligations under frameworks like the EU AI Act.

A structured decision tool that walks teams through business and technical questions to determine whether a proposed use case qualifies as AI and, if so, classifies it as classic, generative, agentic, or hybrid AI.

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.

Week 1 intake form for the shared-responsibility team to define the incident taxonomy, declaration process, role assignments, and stakeholder map. Completed during Day 2-7 of the 30-day AI IR build and reviewed by the executive sponsor.

A practitioner worksheet to design AI spend guardrails, usage caps, quotas, and risk contingencies for your AI budget envelope, grounded in your mapped AI portfolio and GenAI cost drivers. CFOs, CTOs, and named AI leaders should use this to turn Steps 4 and 7 of the AI Budget Governance Playbook into concrete decision rules tied to budget lines, business outcomes, and governance rhythms.

A spreadsheet-oriented decision tool that helps CFOs, CTOs, and AI leaders translate GenAI cost drivers, LLM token usage, and shadow AI costs into structured budget scenario bands. It guides you to define use cases, configure key model and usage parameters, layer in non-token cost drivers, and design AI spend guardrails that fit within your AI budget envelope.

Build a ranked, plotted portfolio of your candidate AI use cases. Score each on five weighted criteria, then see them ranked and mapped onto an Impact × Feasibility matrix with a priority tier for each.

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.

Operationalises Stage 10 of the deployment cycle by providing ready-to-deploy Prometheus scrape configurations, Grafana dashboard layouts, and alerting rules branched by Model Paradigm. Designed for the Cycle Owner to implement immediately after completing Stage 9 (CD release).

A blocking sign-off artifact for the two Governance Gates in the AI deployment cycle. The Cycle Owner prepares evaluation evidence and pre-deployment checklists; the security stakeholder reviews, records a decision, and signs before any progression. Use this template at Stage 6 (Gate 1) and Stage 8 (Gate 2).

Operationalises Steps 2–4 of the deployment cycle as a sequential build checklist. Covers Git setup, Docker by Model Paradigm, CI construction, the Provenance Fork, and MLflow Model Registry with paradigm-specific artifact handling. The Cycle Owner works through each stage in order.

A structured template for organisations to draft, ratify, and operationalise an enterprise AI policy. Designed for cross-functional use by policy makers, legal counsel, AI engineering teams, and strategy leadership. Provides clause-level guidance, decision criteria, and governance checkpoints.

Operationalises Steps 6 and 7 of the AI Vendor Governance Architecture Playbook by defining the exact data fields, monitoring cadence, alert triggers, and visual layout for the vendor dashboard. Designed for a governance lead with stop-procurement authority who needs a buildable specification for the living inventory.

A practitioner reference mapping due diligence depth and contract clauses across the four vendor tiers. Designed for the AI governance lead operationalising Steps 4 and 5 of the vendor governance architecture playbook — calibrating proportionate scrutiny and codifying non-negotiable versus tier-dependent terms before procurement sprawl sets in.

Mandatory intake instrument submitted by procurement officers for every incoming AI vendor, triggering four-tier classification and intake gate checkpoints with stop-procurement authority. The governance lead reviews each submission and completes the gate status fields before any procurement milestone is reached.

A board-endorsable strategy briefing framing the greenfield AI vendor position as a one-time architectural opportunity and committing the organisation to enablement-first access to best-in-class AI with sovereignty-preserving controls. Designed for delivery by the AI governance lead to their CFO or COO sponsor and ultimately to the board for endorsement.

A structured framework for evaluating the financial, operational, risk, and strategic value of AI use cases. Designed for business and governance teams to prioritize investments using quantifiable criteria and standardized equations.

An executive briefing for business and governance leaders on establishing a value realization framework and ROI calculator for AI investments. The deck compares three measurement approaches, recommends a composite model, and provides a practical implementation roadmap.

A practical reference for HR and Technology Development teams to identify, evaluate, and prioritise generative AI applications across the employee lifecycle. It covers efficiency drivers, concrete use cases, and a structured framework for responsible implementation.