AI Readiness Assessment

What does the AI readiness assessment measure?

It rates five capabilities separately: business direction, data foundation, delivery, governance, and adoption and value. Each one gets a level from Ad hoc to Scaled, based on what you can show evidence for today rather than what is planned.

• Business direction. How AI ideas are chosen, funded and stopped, and whether outcomes have a baseline and an owner.
• Data foundation. Whether the team can get the data it needs, whether definitions and grain are written down, and what happens when data is wrong.
• Delivery. How AI outputs are tested before release, who supports them after launch, and how pilots reach production.
• Governance. How risk is reviewed, who decides when AI output conflicts with a person’s judgment, and what AI tools can access.
• Adoption and value. Whether people use the changed workflow, how value is proven, and what happened to the work AI was meant to replace.

How is the AI readiness profile scored?

Each capability takes the middle of its three answers, capped one level above the weakest answer. The five capability ratings are never averaged into a single score, because a strong area cannot make up for the gap that will actually stop a project.

The lowest capability becomes the limiting gap. When capabilities tie, business direction comes first, then data, delivery, adoption and value, and governance last. If the work influences customer, financial, safety or regulated decisions, governance moves up to second, ahead of data and delivery.

What is the difference between AI readiness and AI maturity?

Readiness asks whether one workflow or team can take the next AI step now. Maturity describes how reliably the whole organization chooses, delivers, controls and proves AI work over time. A company can be mature overall and still not ready for a specific use case.

Use this assessment for a single decision in front of you. For the full model, the four levels and what to do at each one, read my AI maturity assessment guide.

What should I do with my AI readiness result?

Fund the limiting gap before you fund more of what already works. Then ask business, data, technology and risk leaders to take the assessment separately and compare. Where answers differ, people are working from different evidence.

Once the gap is clear, put a number on the fix. The AI ROI Calculator builds the payback month and conservative case Finance will ask for. If data is the gap, my guide to data readiness for AI covers what to check first.