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📊 AI Adoption Readiness Assessment

Rate ten statements about your organisation to score its readiness for AI across strategy, data, platform, people and governance, and get specific next steps for the weakest areas. It takes about three minutes.

Strategy

AI initiatives are tied to specific business outcomes, with owners and success measures.

Leadership has agreed where AI should and shouldn’t be used, and funds it beyond one-off pilots.

Data

The data AI needs is documented, reachable through APIs, and owned by named teams.

Data quality is measured, and sensitive data is classified so it can be used safely.

Platform & engineering

Teams can ship an AI feature to production with existing CI/CD, monitoring and cost controls.

Models and AI services are reached through a shared platform, not one-off integrations in every team.

People & skills

Engineers and business staff are trained to use AI tools well, and good use is encouraged.

Named people own AI architecture, evaluation and adoption, beyond a few enthusiasts.

Governance

A written AI policy covers data, security, intellectual property and acceptable use.

AI outputs that affect customers or decisions are reviewed, logged and explainable.

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The five areas

Strategy asks whether AI is aimed at outcomes the business cares about. Data checks that the information AI needs is reachable, owned and safe to use. Platform & engineering covers shipping, monitoring and paying for AI features like any other software. People & skills looks at training and ownership. Governance covers the policies and oversight that let AI scale without new risk.

Organisations rarely fail on all five. Most stall on one or two, typically data and governance, while the pilots themselves look promising. Fixing the weakest area first usually unlocks more than polishing the strongest.

Frequently asked questions

What does AI readiness mean?

AI readiness is how prepared an organisation is to use AI for real business outcomes: a clear strategy, usable and governed data, a platform to ship and monitor AI features, people with the right skills, and policies that keep AI use safe.

How is the readiness score calculated?

Each of the ten statements scores 0 (not at all) to 3 (fully). Scores are averaged within the five areas and overall, shown as a percentage. Under 40% is Exploring, 40–59% Piloting, 60–79% Scaling and 80% or more Leading.

Why do most AI initiatives stall after the pilot?

Usually because the pilot had no business owner or success measure, the data it needs isn’t reliably available, or there is no platform and governance to run it in production. Those gaps show up as low scores in Strategy, Data, Platform or Governance.

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