Adopt AI the VALUE way.
Assess the value. Prove the case. Scale with confidence.
VALUE-AI is a practical AI adoption framework and assessment platform that helps organisations identify the right AI opportunities, assess readiness and risk, design Proof of Value plans and move from experimentation to trusted implementation.
The VALUE-AI Framework
Explore the framework
The five pillars, the adoption lifecycle, the maturity model, and how it aligns to recognised standards.
Full assessment is a members feature.
Part 1 · Pillars
The five pillars
Every use case is assessed across five pillars and their sixteen dimensions — ordered so value and fit come before build, and controls and readiness come before scale.
Value
Is there a real, owned, measurable business problem worth solving?
AI Fit
Is AI genuinely the right solution — and is it feasible with the data and systems we have?
Lifecycle Controls
Can we run it safely, lawfully, and accountably — before, during and after deployment?
User Readiness
Are the people, workflows and change plan ready for it to actually be used?
Evidence
Can we prove value with a controlled Proof of Value before we scale?
Part 2 · Lifecycle
From AI idea to adoption decision
VALUE-AI gives teams a consistent way to assess, compare, validate and govern AI use cases before they scale.
Discover
Find candidate problems and AI opportunities.
Assess
You are hereScore the five pillars and set the risk tier.
Prioritise
Rank use cases on value vs feasibility.
Design
Define the solution, controls and metrics for the chosen idea.
Validate
Run a controlled Proof of Value.
Operationalise
Move to production with ownership and support.
Scale
Expand with governance and benefits tracking.
Part 3 · Maturity model
Know where you are — and what good looks like next
A quick way for teams to self-identify their AI adoption maturity and set the next target.
Not Started
No clear AI strategy, experiments, governance or ownership.
Exploring
Individual or department-led experiments with limited controls.
Structured
Common assessment method, early governance and prioritised use cases.
Operational
Pilots delivered with owners, metrics, risk controls and a support model.
Scaled
AI embedded across workflows with reusable patterns and governance.
Optimised
Continuous improvement, benefits tracking and an enterprise AI operating model.
Part 4 · Standards alignment
Built on recognised principles
VALUE-AI is designed around recognised AI governance and validation principles — aligned, not certified.
How VALUE-AI aligns to each framework
Aligned, not certified — we align to the ideas below and apply them across the lifecycle and the templates.
| Framework | What we align to | Where it lives |
|---|---|---|
| NIST AI RMF | The Govern–Map–Measure–Manage structure for identifying, measuring and managing AI risk. | Stages: Assess, Design, Validate, Operationalise, Scale Templates: VALUE-AI Scorecard, Responsible AI Gate, Human Oversight Designer, Success Metrics Register, AI Governance Register |
| VALUE-AI Method | LyboAI's plain-language operating disciplines — original wording and structure, adapted from the public-domain NIST AI RMF. | Stages: Assess, Design, Operationalise, Scale Templates: AI Governance Register, Data Readiness Check, 30-60-90 Adoption Roadmap, Executive Decision Report |
| OECD AI Principles | Human-centred, trustworthy AI values: accountability, transparency, fairness and safety. | Stages: Assess, Design Templates: Responsible AI Gate, Human Oversight Designer |
| Lean validation | Build–Measure–Learn validation with explicit decision gates before scaling. | Stages: Discover, Prioritise, Validate Templates: AI Opportunity Canvas, Proof of Value Canvas, PoV Pilot Plan |
Adopts recognised principles from NIST AI RMF, the OECD AI Principles and Lean validation practices. Not endorsed by these bodies; does not guarantee compliance with any law.
Part 5 · Scoring
Evidence, not vague confidence
You answer factual questions and never rate your own readiness — and risk can veto value.
- Answers map to points, so optimism bias is removed.
- A deliberately non-linear scale means partial answers don't earn near-passing credit.
- Risk can veto value. A high score with weak controls is capped — the risk override beats the number.
Score bands
Assess it. Introduce it. Enable it.
AI Proof of Value
Assess a real use case, score readiness and design a Proof of Value plan.
Start assessmentAI Governance & Rollout
Introduce AI in a safe, structured and standards-aligned way.
Explore governanceClaude Setup & Enablement
Roll out Claude or other AI tools with operating model, security, training and adoption plan.
Explore enablementWhere getting AI wrong has consequences
Government
Record-keeping, accountability, transparency and decision support matter.
Health
Protected data, human accountability and workflow safety are critical.
Finance
Risk, auditability and compliance controls must be designed early.
Regulated enterprise
Legal, infrastructure and operational decisions need evidence and oversight.
Start with one idea. Prove it in weeks.
The framework is free to learn. Run the free scan, then unlock the complete assessment across all sixteen dimensions, the AI Advisor and a Proof of Value plan.