AI Readiness Assessment: A 20-Point Checklist for Business Leaders

Many AI initiatives stall not because the technology fails, but because the organization was not ready. An AI readiness assessment identifies gaps early so you invest where AI will actually pay off.

Strategy (1–4)

Have you defined 2–3 business outcomes AI should improve?
Is there an executive sponsor with budget authority?
Have you listed candidate use cases and estimated their value?
Do you know which use cases are differentiating vs. commodity?

Data (5–9)

Do you know where key data lives (CRM, ERP, documents, email)?
Is it accessible via APIs or exports?
Is data quality good enough for the target use case?
Are sensitive fields identified and classified?
Is there a single owner for critical data sets?

Technology (10–13)

Are core systems in the cloud or reachable securely?
Do you have identity and access management in place?
Can you monitor and log new applications?
Have you selected approved AI model providers?

People & process (14–17)

Are target processes documented?
Do teams understand how AI will change their work?
Is there capacity to review AI outputs during rollout?
Do you have a plan for training and change management?

Governance & risk (18–20)

Do you have an AI usage policy?
Have you assessed regulatory obligations (e.g. GDPR, EU AI Act, sector rules)?
Is there a process to evaluate accuracy, bias and safety?

How to score

Give each item 0 (no), 1 (partly) or 2 (yes). Low scores in data or governance are the most common blockers, and also the most fixable. The good news: many generative AI use cases can start with existing documents while you improve foundations in parallel.

Next step

Our Data Analytics & AI Consulting team runs fast, structured readiness assessments and delivers a prioritized AI roadmap. Book a free consultation.

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