AI readiness scorecard: is your company ready to build AI?

AI readiness is how prepared a company is to take an AI system from idea to production: a clear use case, usable data, a cloud and security setup that can host it, people who can build and run it, rules for using it safely, and a way to ship and measure it. This scorecard checks all six in twelve questions. Your answers stay in your browser; nothing is sent to us.

How the score works

Each answer scores 0 to 3 points, and the twelve answers add up to a maximum of 36, shown as a score out of 100. The six areas carry equal weight because a gap in any one of them can stop a system from reaching production: a great model is no use without data, and a working pilot stalls without someone to run it. Your next steps come from your two lowest-scoring areas.

The four levels

• Exploring (0 to 39): no committed use case yet. Start with one problem, one owner and one metric.
• Emerging (40 to 64): some foundations in place. A pilot can work once the weakest area is fixed.
• Ready (65 to 84): you can take a scoped pilot to production now.
• Scaling (85 to 100): ready for several systems, shared tooling and a repeatable way to ship them.

What to do with your result

Most companies score highest on interest and lowest on data and operations. That is normal. The fix is rarely a platform purchase; it is usually a focused team that picks one workflow, gets the data for it into shape, and ships something real users touch. To estimate what that team costs, use the AI development cost calculator or read how much an AI pod costs. For the terms used here, see the AI readiness entry in our glossary.

Ryz Labs builds AI systems with AI pod teams of senior engineers who work on your team, in your cloud and repositories, on US business hours. Only the top 1% of the engineers we interview make it, and teams usually start in under two weeks once you say go.

FAQ

What is an AI readiness assessment?

An AI readiness assessment checks whether a company has what it needs to put AI into production: a clear use case, accessible data, a cloud and security setup that can host it, people to build and run it, governance, and a way to ship and measure results.

How long does the scorecard take?

About three minutes. There are twelve multiple-choice questions, and your score updates as you answer.

Is my data stored?

No. The scorecard runs entirely in your browser, and your answers are not sent to Ryz Labs or saved.

What is a good AI readiness score?

65 or higher means you can take a scoped pilot to production now. Below that, fix your two weakest areas first; the scorecard lists them with next steps.

What should we do if we score low on people?

Decide who builds and who runs the system. Many companies add senior engineers or an AI pod to their own team for the build and name an internal owner for the long run, so the knowledge stays in-house.

Questions we didn't answer? Email info@ryzlabs.com.