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AI readiness assessment

Find out what is actually holding your AI back.

Twelve questions in around four minutes. You will get your readiness tier across six dimensions, the gaps ranked, and a sequenced ninety-day plan for the ones that gate everything else.

  • 12 questions
  • ~4 minutes
  • Your tier is free — email only for the full report

What you get

Foundational
Developing
Scaling
Leading
Measured across six weighted dimensions
  • Strategy & leadershipPrerequisite
  • Data readinessPrerequisite
  • Technology & infrastructure
  • Talent & adoption
  • Governance, risk & responsible AI
  • Use-case & value
No rigged results. If you are further along than most, we will say so — and if a prerequisite is missing, we will not let a good average hide it.

The four readiness tiers

Every answer maps to one of four tiers. A tier describes how ready the organisation is to put AI into production and keep it there — not how much AI it is currently talking about.

  1. 1

    Foundational

    Interest without foundations

    Here is what Foundational looks like: AI is being discussed rather than prepared for. The prerequisites an AI rollout depends on — owned data, a named owner, a way to put something into production — are not yet in place, so a project started now would spend most of its budget building what should already exist.

  2. 2

    Developing

    Pockets of progress

    Here is what Developing looks like: there is interest, some capability and probably a pilot or two, but the foundations are uneven. Work gets done where an individual pushes it, and stops where the data, the platform or the rules run out. Nothing is failing loudly, which is why the gaps persist.

  3. 3

    Scaling

    Repeatable and governed

    Here is what Scaling looks like: the organisation can take a use case from idea to production and govern it. Data is documented, ownership is clear, and deployment is repeatable. What remains is breadth — extending the same discipline across more of the business and measuring the value it returns.

  4. 4

    Leading

    AI in how you compete

    Here is what Leading looks like: the foundations hold. Data is treated as a product, deployment is routine and monitored, governance is current, and AI is attached to how the organisation plans to compete rather than to a list of experiments.

What the assessment measures

Six dimensions, weighted. Strategy and data readiness are prerequisites: weak foundations cap the tier rather than being offset by a strong score elsewhere, because a rollout started without them spends its budget building what should already exist.

  • Strategy & leadership
  • Data readiness
  • Technology & infrastructure
  • Talent & adoption
  • Governance, risk & responsible AI
  • Use-case & value

Where the result leads

Your result ranks the gaps by what they cost and sequences the first ninety days. If you would rather skip the questions, these are the same conversations.

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