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CIO and CDO self-assessment

AI readiness balance sheet

Score each capability from 1 to 5. Assets minus liabilities reveals your AI readiness level.

Read the article: The AI Readiness Framework: Five Foundations Organisations Need Before AI Can Scale

How to score

  1. 1Strongly disagree. Critical gap: not in place or severely lacking
  2. 2Disagree. Developing: early stages, significant improvement needed
  3. 3Neutral. Adequate: functional but room for meaningful improvement
  4. 4Agree. Strong: consistently performing, minor gaps only
  5. 5Strongly agree. Exemplary: best practice, a genuine organisational strength

Data foundations

AssetTrusted, consistent data

Our critical data is clean, well-structured, consistently defined and accessible across systems.

LiabilityFragmented or duplicate data

Data is spread across siloed systems with inconsistent definitions, duplicates or poor quality controls.

Technology infrastructure

AssetModern, integrated systems

Our platforms are cloud-ready, interoperable and capable of supporting AI workloads at scale.

LiabilityLegacy infrastructure and technical debt

Outdated platforms and unresolved technical debt limit system integration and AI processing capability.

Governance and compliance

AssetClear AI governance framework

Accountability for AI decisions, data privacy, regulatory compliance and model auditability are formally defined.

LiabilityGovernance gaps and compliance exposure

AI outputs cannot be reliably audited or explained, creating reputational and regulatory risk, especially in regulated sectors.

Data architecture and quality

AssetReliable data pipelines and lineage

Data flows are documented, pipelines are reliable and data lineage is visible and trustworthy across the organisation.

LiabilityUngoverned or unreliable pipelines

Data quality is inconsistent, lineage is unclear and AI models risk reproducing existing errors at scale.

Workforce capability

AssetAI literacy and workforce readiness

Teams have the skills, confidence and change readiness to effectively adopt and act on AI-driven insights.

LiabilityCapability gaps and adoption resistance

Workforce lacks AI literacy or trust in AI outputs, causing tools to remain underutilised after deployment.

Operating model and ownership

AssetDefined AI ownership and accountability

AI initiatives have clear executive sponsorship, defined ownership, measurable outcomes and a path from pilot to enterprise scale.

LiabilityUnclear ownership and stalled pilots

AI initiatives lack accountability, making it difficult to move beyond experimentation to operational capability.

Strategic alignment

AssetAI aligned to business outcomes

AI use cases are tied to defined business goals with clear ROI expectations and board-level sponsorship.

LiabilityMisaligned or undefined use cases

AI initiatives are driven by enthusiasm rather than defined business need, making impact measurement difficult.

What your score means

  • +21 to +35

    AI-ready

    Exemplary organisational readiness. You have the data, governance, infrastructure and operating model to scale AI confidently across the enterprise.

  • +8 to +20

    Ready to progress

    Strong foundations in place. Targeted improvements in specific areas will strengthen your ability to deploy AI safely and at scale with measurable business outcomes.

  • -7 to +7

    Developing

    Some readiness building blocks are in place, but gaps remain across data quality, integration or governance. A structured readiness assessment is recommended before scaling.

  • -35 to -8

    High risk

    Significant data and governance gaps. AI deployment carries substantial operational and compliance risk. Foundational remediation required before any AI initiative proceeds.

Talk it through with us

A short conversation with one of our partners can turn your score into a clear set of next steps.

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