AI Readiness Assessment — Score Your Business in 3 Minutes
Twelve questions across data, process, technology, people, leadership and governance. Get a score, a maturity level and where to start. No email required.
Twelve statements, four answers each. Answer for your organisation as it is today, not as planned. Takes about three minutes; the full result is shown here, no email required.
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This free AI readiness assessment scores how prepared your organisation is to get real results from AI, across the six things that decide it: data, process, technology, people, leadership and governance. Twelve statements, four answers each, three minutes. You get a score out of 100, a maturity level, a breakdown by pillar and concrete first steps for the two weakest areas, shown in full on the page with no email required.
Most AI projects that fail do not fail on the model. They fail because the data was not accessible, the process was never written down, nobody owned the outcome, or there was no rule about what could be sent to an external service. Those are readiness problems, they are checkable in advance, and they are what this assessment checks. A high score does not mean AI will transform your business next quarter; it means a first project has a fair chance of producing a number you can show the board.
How to use
- Answer each of the twelve statements for your organisation as it is today. Be honest; the result is only for you.
- Press See my result. The score, level and pillar breakdown appear below the questions.
- Read the recommendations for your two weakest pillars. They are the first things to fix, and they are usually cheaper than the AI project itself.
- Download the result if you want to share it internally, then decide whether a scoped first project makes sense.
Frequently asked questions
What does “AI ready” actually mean?
That the conditions for a project to succeed are in place: accessible and reasonably clean data, a documented process to automate, systems that can be integrated, people who can judge AI output, leadership that has named an outcome and an owner, and rules about data and review. Readiness is about the organisation, not about which model you pick; models are the easy part now.
Is a low score bad?
It is normal. Most small and mid-sized businesses score in the Exploring or Emerging range, because nobody built their data and processes with AI in mind. A low score is useful information: it says which foundations to lay first, and it usually points at a small pilot on data you already control rather than a big platform decision.
Do I need to give my email to see the result?
No. The full result is shown on the page and can be downloaded. Gating a self-assessment behind an email form produces a list of people who wanted a score, not people who want help; if you want to talk, the button is there.
How is the score calculated?
Each answer scores 0 to 3; the total is scaled to 100. Pillar scores use the same scale over that pillar's two questions. Levels: under 40 Exploring, 40 to 64 Emerging, 65 to 84 Ready, 85 and above Leading. It is a structured self-assessment, so it reflects what you know; a real audit most often finds the data is messier than assumed.
What should a first AI project look like?
Small, measurable and boring: one repetitive task with a clear input and output, a baseline measured for a week, an owner, a 6-to-8-week timebox, and a human reviewing outputs before they reach customers. Document processing, drafting replies from a knowledge base, and internal Q&A over company documents are the usual candidates. The tools on this site's AI ROI calculator help size the value.
The six pillars, and why each one matters
• Data: AI is only as good as what it can read. Scattered spreadsheets and inboxes are the most common blocker.
• Process: you cannot automate what you cannot describe. A written procedure with examples is the specification.
• Technology: APIs, access control and somewhere to run things. Without them every pilot is a workaround.
• People: someone has to judge when the output is wrong, and staff need time and permission to learn.
• Leadership: a named outcome, an owner and a budget. “Use AI” is not an outcome.
• Governance: what data may leave the building, who reviews what, and how a decision can be explained afterwards.
After the assessment
Fix the two weakest pillars in parallel with a small pilot, not before it; the pilot is what makes the fixes concrete. Train the team on the tools they already have before buying new ones. And measure: hours saved, response time, error rate, whatever you named as the outcome. The organisations that get value from AI are the ones that treated the first project as a way to build the muscle, not as the finish line.
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