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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.

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.

A two-part executive briefing deck framing the Day 1 strategic stance — building a minimal general IR skeleton with AI-specific layers — and presenting the Day 30 completed capability with a sequenced 60-90 day roadmap. Designed for the shared-responsibility team and executive sponsor to align on commitment and review deliverables.

A fill-in-the-blanks containment runbook scaffold for the highest-risk AI incident scenario: a production LLM producing harmful, biased, or factually inaccurate outputs at scale. Built for the 2-3 person shared-responsibility team to populate during Week 4 (Day 22-28) of the 30-Day AI Incident Response Build.

A fill-in-the-blanks worksheet providing the four-tier severity classification framework and escalation matrix the team builds during Week 2 (Day 8-14) of the 30-day AI IR build. Designed for a 2-3 person shared-responsibility team with no prior IR function, plus the executive sponsor who needs to validate the escalation timeframes.

Executive briefing for technical leaders deciding whether to build or buy AI products and components. Focuses on concrete criteria, architectural implications, and decision patterns for modern ML/LLM systems.

Executive-ready template to present a risk-aware, token-savvy AI budget envelope, GenAI cost drivers, AI portfolio, and AI FinOps rhythms. Designed for CFOs, CTOs, and named AI leaders to integrate the AI Budget Governance Playbook into budgeting cycles and board updates.

Structured intake to register each AI initiative into the AI budget envelope, link it to business outcomes and draft budget lines, and capture an initial GenAI risk and cost profile. Completed by the business owner together with the AI leader and finance partner.

Briefing deck template to present a scored, tiered AI use case backlog for governance review and resource allocation. Designed for practitioners running the intake process and turning requests into a living portfolio and strategic asset.

Defines the weighted scoring model, Five Dimensions, and governance tier rules used in the AI Use Case Intake Process. For practitioners converting raw AI requests into a scored, prioritized, governance-ready backlog.

Operational checklist for quickly screening incoming AI requests before any scoring. Designed for the practitioner managing the intake channel who must decide whether a use case moves into the weighted scoring model or is parked/eliminated.

Submit a new AI use case for review by the AI Governance Office. Business teams should complete this form to have their proposed use case triaged for feasibility, risk, and prioritization.