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Model 30 of 74 · Technology & Data

AI Readiness Maturity Assessment

Assess your organisation's readiness to adopt, deploy, and govern AI responsibly.

Pillars & areas

Strategise

Set the direction

  1. 01AI Strategy & Vision
  2. 02Data Foundations for AI

Enable

Build the capability

  1. 03Talent & Skills
  2. 04Technology & Infrastructure

Scale

Deploy responsibly

  1. 05Ethics, Governance & Trust
  2. 06Adoption, Scaling & Value

Levels

  1. 1ExploringFragmented & experimental
  2. 2OperationalisingPlanned & resourced
  3. 3Scaling ResponsiblyCore capability at scale

01 AI Strategy & Vision

Does the organisation have a clear, funded plan for how AI will create value?

0/4 answered

SV-1How does your organisation articulate its goals for AI?

Think about whether there is a written AI strategy tied to business outcomes, or just general enthusiasm.

SV-2How does leadership prioritise and fund AI initiatives?

Consider whether AI has a dedicated budget and executive sponsor, or competes for leftover project funds.

SV-3How does your organisation identify and select AI use cases?

Think about whether use cases are chosen based on hype or based on where AI can genuinely solve a problem.

SV-4How well does your organisation understand its current AI maturity relative to its ambitions?

Consider whether anyone has honestly assessed the gap between where you are today and where your strategy assumes you will be.

02 Data Foundations for AI

Does the organisation have the data quality, access, and governance needed to build trustworthy AI?

0/4 answered

DF-1How accessible is the data needed for AI initiatives?

Think about whether AI teams can find and access relevant data without weeks of negotiation and manual extraction.

DF-2How does your organisation ensure data quality for AI systems?

Consider whether anyone measures or monitors the quality of data feeding your AI models, or whether it is assumed to be good enough.

DF-3How does your organisation handle bias in training data?

Think about whether anyone checks whether the data used to train models fairly represents the populations or scenarios the model will encounter.

DF-4How does your organisation govern data used in AI systems?

Consider whether there are clear rules about what data can be used for AI, who owns it, and how long it is retained.

03 Talent & Skills

Does the organisation have the people, skills, and culture needed to build, deploy, and use AI effectively?

0/4 answered

TS-1How does your organisation build AI and machine learning skills?

Think about whether you rely entirely on hiring external specialists, or whether you also develop skills in your existing workforce.

TS-2How well do non-technical staff understand AI and its implications?

Consider whether business leaders, managers, and frontline staff understand what AI can and cannot do, or whether they treat it as magic or a threat.

TS-3How effectively do AI specialists and business teams collaborate?

Think about whether data scientists work in isolation or are embedded within business teams who understand the problem domain.

TS-4How does your organisation retain AI talent?

Consider whether your AI specialists stay and grow, or leave after 12-18 months for better opportunities.

04 Technology & Infrastructure

Does the organisation have the platforms, tools, and engineering practices to build, deploy, and operate AI reliably?

0/4 answered

TI-1How does your organisation decide whether to buy, adapt or build AI capabilities?

Think about whether there is a deliberate choice between using AI features in existing software, buying AI services, adapting existing models, or building your own — or whether each team does whatever it finds.

TI-2How does your organisation manage staff use of generative AI tools?

Consider tools such as AI chat assistants, writing and coding assistants, and AI features built into everyday software — and whether people know what they can use them for and what data they can share.

TI-3How does your organisation monitor AI models after deployment?

Think about whether anyone is watching model performance in production, or whether models run unattended until something breaks.

TI-4How does your organisation manage the security of AI systems?

Consider whether AI systems are subject to the same security standards as other production systems, plus AI-specific threats like adversarial inputs or data poisoning.

05 Ethics, Governance & Trust

Does the organisation ensure its AI systems are fair, transparent, accountable, and aligned with its values?

0/4 answered

EG-1Does your organisation have a published set of AI ethics principles?

Think about whether there is a documented commitment to responsible AI, or whether ethics is left to individual judgement.

EG-2How does your organisation govern high-risk AI decisions?

Consider whether AI systems that affect people's livelihoods, safety, or rights receive additional scrutiny before and after deployment.

EG-3How explainable are your organisation's AI systems to the people they affect?

Think about whether someone affected by an AI decision can understand why that decision was made and challenge it if needed.

EG-4How does your organisation track accountability for AI outcomes?

Consider whether it is clear who is responsible when an AI system produces a harmful or incorrect result.

06 Adoption, Scaling & Value

Is AI delivering real, measurable value across the organisation — not just in isolated experiments?

0/4 answered

AS-1How many AI use cases has your organisation moved from experiment to production?

Think about whether your AI efforts are producing working systems that people rely on, or a graveyard of proofs-of-concept.

AS-2How does your organisation measure the business value of AI?

Consider whether you can quantify what AI has delivered in terms of revenue, cost savings, time savings, or customer outcomes.

AS-3How widely is AI adopted by end users across the organisation?

Think about whether the people who are supposed to use AI tools actually use them, or whether they quietly ignore them and revert to manual processes.

AS-4How does your organisation scale successful AI solutions across teams or geographies?

Consider whether a model that works well in one team or region can be reused elsewhere, or whether each team starts from scratch.

Self-assessment report

AI Readiness

Answer the questions above and your report builds here: an overall score, a score and target for each area, and the three changes that matter most.

About this model

About the AI Readiness maturity model

AI is not a technology project — it is an organisational capability. The organisations that succeed with AI are not necessarily those with the most advanced models; they are the ones that connect AI to real business problems, build the data foundations to support it, develop the skills to use it, and govern it responsibly. This model assesses readiness across all of these dimensions, because a weakness in any one of them will stall even the most technically impressive AI effort.

This assessment deliberately includes ethical AI as a core component, not an afterthought. Organisations that treat ethics, fairness, and transparency as separate from "real" AI work will find themselves facing regulatory action, reputational damage, and loss of trust from the people their systems affect. Responsible AI is not a constraint on innovation — it is a prerequisite for sustainable adoption.

24 questions across 6 key areas, assessing your organisation's readiness to adopt, deploy, and govern AI responsibly. Takes approximately 10-15 minutes to complete.

Scoring is based on 3 maturity levels per question. Your overall score reflects the average maturity across all areas, giving you a clear picture of where your AI readiness is strong and where the gaps will slow you down.

Who this assessment is for

  • CIOs, CTOs, and CDOs shaping AI strategy and investment
  • Data science and ML engineering leaders building AI capabilities
  • Business leaders evaluating AI opportunities in their domain
  • Risk, compliance, and ethics teams responsible for AI governance
  • HR and L&D leaders planning AI skills development
  • Anyone responsible for deciding whether and how their organisation should adopt AI

The three maturity levels

  • Level 1 — Exploring: AI interest exists, but efforts are fragmented and experimental. Individual enthusiasts run proofs-of-concept with no strategic direction, limited data readiness, and no governance. There is no consistent approach to deciding which problems AI should solve, and most experiments never reach production. Skills are concentrated in a few individuals, and the rest of the organisation views AI with a mixture of excitement and anxiety.
  • Level 2 — Operationalising: AI initiatives are planned, resourced, and governed. A written strategy links AI to business outcomes, dedicated roles and budgets exist, and data quality is actively managed. Governance frameworks classify risk and enforce review gates for high-impact systems. Models reach production through defined processes, and the organisation is building the muscle to scale. Ethics principles are published and applied during development.
  • Level 3 — Scaling Responsibly: AI is a core organisational capability deployed at scale with strong governance, continuous learning, and measurable value. The organisation runs a portfolio of AI investments, tracks cumulative ROI, and retires underperforming models. Data platforms support self-service access with automated quality monitoring. AI literacy extends beyond technical teams. Ethics and accountability are embedded into the system lifecycle, not bolted on at the end. The organisation does not just use AI — it improves how it uses AI.

The 6 areas assessed

  1. AI Strategy & Vision — Does the organisation have a clear, funded plan for how AI will create value? Assesses whether AI ambitions are grounded in business reality, backed by executive sponsorship, and connected to a prioritised roadmap.
  1. Data Foundations for AI — Does the organisation have the data quality, access, and governance needed to build trustworthy AI? Covers data accessibility, quality management, bias detection, and governance of data used in AI systems.
  1. Talent & Skills — Does the organisation have the people, skills, and culture needed to build, deploy, and use AI effectively? Examines specialist hiring and retention, AI literacy across the workforce, and collaboration between technical and business teams.
  1. Technology & Infrastructure — Does the organisation have the platforms, tools, and engineering practices to build, deploy, and operate AI reliably? Covers compute and platform capabilities, MLOps practices, model monitoring, and AI-specific security.
  1. Ethics, Governance & Trust — Does the organisation ensure its AI systems are fair, transparent, accountable, and aligned with its values? Assesses ethics principles, risk-tiered governance, explainability, and accountability structures.
  1. Adoption, Scaling & Value — Is AI delivering real, measurable value across the organisation — not just in isolated experiments? Covers the pipeline from experiment to production, business value measurement, end-user adoption, and the ability to scale solutions across teams and geographies.

How this model relates to other frameworks

  • OECD AI Principles (international guidelines for trustworthy AI)
  • ISO/IEC 42001 (AI management system standard)
  • EU AI Act (risk-based regulatory framework for AI systems)
  • Singapore Model AI Governance Framework
  • Google PAIR (People + AI Research) guidelines
  • Microsoft Responsible AI Standard

Frequently asked questions

What is the AI Readiness maturity model?

Assess your organisation's readiness to adopt, deploy, and govern AI responsibly. It is an original, simplified model with three maturity levels (Exploring, Operationalising and Scaling Responsibly), assessed through 24 questions across 6 areas: AI Strategy & Vision, Data Foundations for AI, Talent & Skills, Technology & Infrastructure, Ethics, Governance & Trust and Adoption, Scaling & Value.

How long does the AI Readiness assessment take?

Most people finish in 10–14 minutes. Each of the 24 questions has three concrete answer options. Your progress is saved in the page URL, so you can bookmark it and come back later.

How is the score calculated?

Each answer scores 1 (Exploring), 2 (Operationalising) or 3 (Scaling Responsibly). An area's score is the average of its answered questions, and the overall score is the average of the area scores, so every area counts equally. Scores run from 1.0 to 3.0.

Is my data stored or shared?

No. Your answers are never sent to a server. They are stored only in the page URL, which you control. Copy the link to share results with colleagues, or export a PDF or CSV report.

How should I use the results?

Start with "Where to focus" in your report. It lists the questions with the biggest gap to the target you set for each area, and shows what the next level looks like in practice. For a fuller picture, ask several people from different teams to complete the assessment and compare their links. Differences in perception are often as revealing as the scores themselves.