AI strategy, governance, data platforms and readiness — frameworks and playbooks for the AI-enabled enterprise.
A standard intake form for proposed AI use cases so each is captured consistently and triaged on the same basis. It records the problem, expected value, data needed, feasibility, and risk tier, feeding a value×feasibility prioritisation.
A readiness assessment scores an organisation's preparedness for AI across strategy, data, technology, talent, and governance, so investment targets the gaps. It produces a maturity baseline and a prioritised readiness gap list.
A practical checklist that operationalises responsible-AI governance for a specific AI system or release, mapped to the NIST AI RMF functions and EU AI Act obligations. It verifies accountability, risk classification, bias/safety testing, transparency, and monitoring before go-live.
A model risk review evaluates an AI/ML model's risk before and during use — performance, bias, robustness, explainability, and drift — proportionate to its risk tier. Drawing on model-risk-management discipline and the NIST AI RMF Measure function, it documents findings and required controls.
An AI vendor evaluation scores prospective AI vendors/models on capability, data and security posture, responsible-AI practices, commercials, and lock-in, so selection is evidence-based.
An AI business case quantifies the costs, benefits, and risks of an AI initiative so leaders can fund with confidence — combining standard appraisal (NPV/ROI/payback) with AI-specific cost and risk factors (data, model, run-cost, governance).
An AI pilot charter scopes a time-boxed AI experiment with a clear hypothesis, success criteria, data, guardrails, and a go/no-go-to-scale decision — so pilots produce decisions, not perpetual experiments.
A data product canvas specifies a data product on one page — its consumers, the decisions it serves, the data and sources, quality/SLAs, ownership, and interfaces — applying data-mesh 'data as a product' thinking.
A framework for the AI operating model — how an organisation organises, governs, and runs AI across a CoE, federated teams, platform, and governance. It defines the components and how they interlock so AI scales safely.
A framework to select and prioritise AI use cases on business value and feasibility, sequencing quick wins and strategic bets.
A framework to classify AI systems into risk tiers and attach proportionate controls, aligned to the EU AI Act risk categories and the NIST AI RMF.
A framework for the end-to-end AI/ML model lifecycle — from problem framing and data through build, validation, deployment, monitoring, and retirement — with the governance gates at each stage.
A framework for treating data as a product across the organisation — domain ownership, product thinking, a self-serve platform, and federated governance, per data-mesh principles.
A framework to stand up an AI governance council (or board) — its mandate, membership, decision rights, and cadence — to oversee AI risk, approve high-risk use cases, and steward responsible AI.
A framework for the digital operating model — product/experience pods, platform and shared services, agile ways of working, and outcome-based governance — so digital delivery is fast and accountable.
A cyber resilience framework goes beyond prevention to ensure the organisation can anticipate, withstand, recover from, and adapt to cyber incidents — keeping critical functions running. It builds on the NIST CSF (incl.
A short, structured sprint to assess an organisation's readiness for AI across strategy, data, talent, technology, and governance, ending in a prioritised action list.
An end-to-end AI governance program: policy, roles, risk management, model lifecycle controls, and monitoring, aligned to recognised standards.
A tightly scoped 10-day sprint that takes a team from AI ambiguity to a prioritised, costed set of use cases and a readiness verdict.
An assessment that benchmarks current AI governance against recognised standards and surfaces the priority gaps to close.
A deeper, multi-week AI readiness program covering strategy, data, platform, talent, governance, and a piloted use case, ending in a scaling decision.
A data-to-AI pathway playbook sequences the foundations an organisation must lay — data, platform, governance, talent — before scaling AI, so AI is built on solid ground rather than hype. It maps the maturity pathway from data foundations through analytics to AI/agentic capability, with gates at each stage.
A playbook to activate senior leaders as sponsors and role models for AI adoption — building conviction, literacy, and visible commitment.
A RAID log playbook — Risks, Assumptions, Issues, Dependencies — to keep a programme's uncertainties visible, owned, and managed.
A playbook to take a model from validated to production safely — packaging, testing, deployment, monitoring, and rollback — under MLOps discipline.
A practical AI governance playbook for day-to-day oversight — intake, risk triage, review gates, and monitoring — so AI is used responsibly without blocking value.
An enterprise AI strategy aligns AI investment to business value, sets the operating model and governance, and sequences a portfolio of use cases. This template defines the AI ambition and value pools, prioritises use cases on value × feasibility, sets the responsible-AI governance baseline (NIST AI RMF / EU AI Act), and builds the data, talent, and platform foundations.
A generative-AI roadmap sequences an organisation's GenAI adoption from discovery to scaled production, prioritised by business value and feasibility. This template runs a discovery and use-case prioritisation, designs the patterns (RAG, fine-tuning, agents) and guardrails, and stages a 12–18 month plan from quick-win prototypes to production.
An agentic-AI operating model defines how an organisation builds, governs, and scales AI agents that plan, reason, and act across multi-step workflows with human oversight. This template sets the agent operating model — a Center of Excellence, orchestration standards, graduated autonomy (assisted → supervised → autonomous), agent inventory, and guardrails — so agents are deployed safely at scale.
An AI governance framework manages AI risk and ensures responsible, compliant AI across its lifecycle. This template applies the NIST AI RMF functions (Govern, Map, Measure, Manage), aligns to the EU AI Act risk tiers and ISO/IEC 42001, and sets accountability, model risk classification, bias/safety testing, and monitoring.
A data strategy turns data into a managed asset that drives decisions and AI. This template sets the data vision and value use cases, defines the operating model and governance, and plans the architecture and capabilities — balancing defensive (control, compliance) and offensive (analytics, AI) objectives.
A data platform architecture provides the foundation to ingest, store, process, and serve data for analytics and AI. This template applies the lakehouse pattern with medallion (bronze/silver/gold) layering to progressively improve data quality, and covers ingestion, storage, processing, governance, and serving.
A data governance framework defines the roles, policies, standards, and processes that manage data as a trusted enterprise asset. This template applies the DAMA-DMBOK knowledge areas with governance at the centre — ownership and stewardship, data quality, metadata, security/privacy, and a governance operating model phased to priority areas.
An analytics and BI strategy turns data into decisions through the right metrics, self-service capability, and analytics maturity progression. This template defines decision use cases and KPIs, designs the BI/semantic layer and self-service model with governance, and stages maturity from descriptive to predictive/prescriptive.
MLOps operationalises machine learning — taking models reliably to production and keeping them performing. This template structures the ML lifecycle across experimental, production, and monitoring phases, applying DevOps principles (CI/CD, automation, version control) plus model monitoring, retraining, and governance.
AI use-case discovery finds and prioritises where AI creates value, so investment goes to the highest value, feasible opportunities. This template runs structured discovery across the business, scores candidates on business value × feasibility, and sequences a portfolio of quick wins and strategic bets with value cases.
