AI Consulting Toolkit
Free AI assessment templates, AI pilot templates, and governance frameworks — 37 implementation playbooks with 252+ fill-in checklists spanning executive strategy, enterprise architecture, model sourcing, platform design, transformation, workforce adoption, and AI governance.
Which playbook do you need?
Every playbook is free and vendor-neutral, with fill-in checklists rather than summaries. Pick the route that matches the job in front of you.
Enterprise compliance and regulatory alignment
Regulatory alignment is mostly evidence: a written policy, a classified inventory, assessed risks, named owners, monitoring, and an audit trail. These playbooks produce those artefacts rather than describing them.
Long-term scalability past the pilot
Most AI programmes stall between a working pilot and reliable production. Scaling is an operating-model problem before it is a technical one, so these cover both the platform and the organisation around it.
Ethical and lawful data usage
Ethical data handling turns on two questions: are we permitted to use this data this way, and does the result treat people fairly. These playbooks work through provenance and consent, then through measured fairness.
Risk management templates
Risk work needs written criteria applied consistently, not case-by-case judgement. These supply the register, the classification criteria, the autonomy tiers for agents, and the runbook for when something goes wrong.
Starting a first AI project
Start by establishing where you actually stand and which use case is worth funding, before committing to a build. Note that these playbooks assume a professional audience — if you are new to AI itself, the learning resources are the better first stop.
Small teams and limited capacity
A small team cannot run a four-phase programme, and should not try. Take the readiness assessment, prioritise ruthlessly to one use case, charter it properly so the go/no-go is decidable, and pick the sourcing path with the lowest ongoing burden.
Corporate digital transformation
Transformation fails when it funds models instead of workflow change. These start from where value is actually released — roughly a tenth on algorithms, a fifth on technology and data, and the rest on people and process.
AI assessment templates
Readiness assessments, enterprise maturity scorecards, data-estate audits, use-case prioritisation matrices, and capability gap analyses to establish where your organisation actually stands before committing budget.
AI pilot templates
Pilot project charters, success-criteria definitions, vendor evaluation scorecards, build-vs-buy decision frameworks, and AI ethics checklists to run a first deployment that produces a defensible go/no-go decision.
Scaling & operations templates
MLOps maturity roadmaps, change management plans, ROI measurement frameworks, workforce adoption playbooks, and observability and incident-response runbooks for moving from pilot to production.
AI governance templates
AI governance frameworks, EU AI Act compliance checklists, model risk management policies, management-system implementation guides, and agentic-AI autonomy and oversight controls.
Phase 1: Assess
Evaluate your organisation's AI readiness and identify opportunities.
- executivePhase 1: Assess
Stakeholder Alignment Workshop
Templates for getting leadership buy-in.
6 itemsOpen → - executivePhase 1: Assess
Enterprise AI Maturity & Capability Scorecard
Score organisational AI maturity across six capability themes and set a realistic target state.
4 itemsOpen → - managerPhase 1: Assess
AI Readiness Assessment
Score your org's data maturity, talent, and infrastructure.
12 itemsOpen → - managerPhase 1: Assess
Use Case Prioritization Matrix
Rank AI opportunities by impact vs. feasibility.
8 itemsOpen → - managerPhase 1: Assess
Competitor AI Landscape Analysis
Map competitor AI capabilities and identify strategic gaps.
4 itemsOpen → - managerPhase 1: Assess
Data Estate & AI Readiness Blueprint
Inventory, classify, and permission the data estate before AI is switched on across it.
4 itemsOpen → - managerPhase 1: Assess
AI for Enterprise Architecture Playbook
Apply AI to the architecture function itself - roadmaps, asset discovery, solution assurance, and scenario planning.
4 itemsOpen →
Phase 2: Pilot
Run a contained proof-of-concept to validate and learn.
- managerPhase 2: Pilot
Pilot Project Charter Template
Define scope, success metrics, and risk controls.
10 itemsOpen → - managerPhase 2: Pilot
Vendor Evaluation Scorecard
Compare AI vendors across 15 dimensions.
15 itemsOpen → - managerPhase 2: Pilot
Build vs Buy vs Partner Decision Framework
Decide per capability whether to call an API, fine-tune a smaller model, or build in-house - with three-year TCO.
4 itemsOpen → - practitionerPhase 2: Pilot
AI Ethics Checklist
Ensure fairness, transparency, and accountability.
9 itemsOpen → - practitionerPhase 2: Pilot
Prompt Engineering & LLM Integration Playbook
Design, test, and govern LLM prompts for production use cases.
8 itemsOpen → - practitionerPhase 2: Pilot
LLM Fine-Tuning & Model Adaptation Playbook
Decide when to fine-tune and run adaptation experiments that beat prompting.
6 itemsOpen → - practitionerPhase 2: Pilot
AI Platform & Reference Architecture Blueprint
Design the shared platform layer - environments, model gateway, retrieval, security perimeter, and operations.
6 itemsOpen → - practitionerPhase 2: Pilot
AI Infrastructure & Compute Capacity Playbook
Plan the physical and platform layers - facilities, accelerators, networking, scheduling, quota, and tenancy isolation.
4 itemsOpen →
Phase 3: Scale
Expand from pilot to production with governance and change management.
- executivePhase 3: Scale
AI ROI Measurement Framework
Track and report business value of AI investments.
7 itemsOpen → - executivePhase 3: Scale
AI-Led Business Transformation Blueprint
Direct effort where value actually comes from: roughly 10% algorithms, 20% technology and data, 70% people and process.
4 itemsOpen → - managerPhase 3: Scale
Change Management Playbook
Drive adoption with training and communication plans.
12 itemsOpen → - managerPhase 3: Scale
Enterprise GenAI Rollout & Workforce Adoption Playbook
Deploy generative AI assistants across the workforce and convert licences into measured usage and value.
4 itemsOpen → - managerPhase 3: Scale
AI-Powered Customer Experience Playbook
Apply AI across the customer journey with deliberate containment, escalation, and experience measurement.
4 itemsOpen → - managerPhase 3: Scale
AI Talent, Skills & Operating Model Playbook
Sequence AI hires against capabilities to ship, choose an operating model, and build workforce fluency.
4 itemsOpen → - practitionerPhase 3: Scale
MLOps Maturity Roadmap
From manual deployments to automated ML pipelines.
18 itemsOpen → - practitionerPhase 3: Scale
Responsible AI & Bias Testing Playbook
Detect, measure, and mitigate bias before and after deployment.
5 itemsOpen → - practitionerPhase 3: Scale
Data Pipeline & Feature Engineering Playbook
Build reliable, reproducible ML data pipelines from raw data to model-ready features.
6 itemsOpen → - practitionerPhase 3: Scale
LLM Observability & Evaluation in Production
Instrument, trace, and continuously evaluate LLM applications at scale.
5 itemsOpen → - practitionerPhase 3: Scale
RAG System Optimization Playbook
Tune retrieval quality, chunking, and grounding to make RAG applications accurate and reliable at scale.
5 itemsOpen → - practitionerPhase 3: Scale
LLM Inference Cost & Latency Optimization Playbook
Cut serving cost and tail latency for production LLM workloads without sacrificing quality.
6 itemsOpen → - practitionerPhase 3: Scale
AI Incident Response Playbook
Detect, triage, and resolve production AI incidents with a repeatable process.
4 itemsOpen → - practitionerPhase 3: Scale
AI Agent Orchestration & Tool-Use Playbook
Design reliable multi-step agents with tools, guardrails, and fallbacks.
2 itemsOpen →
Phase 4: Govern
Establish ongoing oversight, compliance, and continuous improvement.
- executivePhase 4: Govern
AI Governance Framework
Policies, accountability structures, and review processes.
20 itemsOpen → - executivePhase 4: Govern
C-Suite AI Operating Model & Role Charter
Assign AI accountability across the executive team and run a standing decision agenda with board oversight.
4 itemsOpen → - executivePhase 4: Govern
Agentic AI Governance & Autonomy Framework
Tier AI agents by autonomy and impact, give each an identity and permissions, and scale oversight with autonomy.
4 itemsOpen → - managerPhase 4: Govern
AI Incident Governance & Regulatory Response
Handle bias incidents, compliance breaches, and regulator or customer notification obligations.
8 itemsOpen → - managerPhase 4: Govern
EU AI Act Compliance Checklist
Step-by-step compliance checklist for the EU Artificial Intelligence Act.
4 itemsOpen → - managerPhase 4: Govern
AI Management System Implementation
Stand up a certifiable AI management system using a Plan-Do-Check-Act cycle and the Govern/Map/Measure/Manage functions.
4 itemsOpen → - managerPhase 4: Govern
Data Governance Operating Model & Lifecycle Controls
Establish data ownership roles, knowledge-area coverage, maturity targets, and stage controls across the data and model lifecycles.
4 itemsOpen → - practitionerPhase 4: Govern
Model Risk Management Policy
Monitor, audit, and manage deployed AI systems.
14 itemsOpen →