How long does an AI readiness assessment take?

The structured, paid version runs two to four weeks. A quick self-serve version can give you a readiness score and your top use cases in a few minutes.

What should an AI readiness assessment produce?

A readiness score, a prioritized map of 8 to 12 use cases ranked by impact and effort, a 90-day roadmap, and a one-page board summary. If it ends in a deck with no plan, it was not an assessment.

Who needs an AI readiness assessment?

Any team with AI pilots that are not reaching production, or a board asking for an AI strategy. It is the lowest-risk first step before committing budget to building.

How to run an AI readiness assessment

A readiness assessment is a two to four week audit that tells you, with a number, whether your team can ship AI and what to build first. Here is what a good one looks at.

The short version

An AI readiness assessment is a short, structured audit, usually two to four weeks, that answers one question: can your organization ship AI to production, and what should you build first? A good one scores you across six dimensions, ends with a prioritized use-case map and a 90-day roadmap, and gives your board a single page they can act on. If it ends in a slide deck with no plan, it was not an assessment.

The six dimensions a real assessment scores

Data readiness. Is your data accessible, clean and mapped, with access controls? Most stalled projects trace back to data that was fine in a demo and messy in reality.
Use-case clarity. Can you name the one use case that would move a business metric, and can you measure it? Vague ambition is the most common failure mode.
Team and skills. Who owns AI, and has anyone shipped it to production before? Curiosity is not the same as delivery experience.
Tooling and stack. Modern, API-driven and integrated, or legacy and siloed? This decides how fast you can wire anything to real systems.
Governance and compliance. Are privacy, data residency, auditability and vendor risk designed in, or bolted on later? In regulated or data-sensitive teams this is the make-or-break dimension.
Budget and urgency. Is there a decision-maker and real budget, or just interest? Intent without an owner rarely ships.

What you should get out of it

A readiness score. A number, not a vibe.
A prioritized map of 8 to 12 use cases, ranked by impact and effort.
A 90-day roadmap with the quick wins identified.
A one-page summary your board can act on.

Do it yourself, or bring someone in

You can self-assess in minutes to get a score and your top use cases. That is enough to know roughly where you stand. A paid sprint goes deeper: it audits your real data and stack, pressure-tests the use cases against your constraints, and hands you a roadmap you can start on Monday. The value is not the score. It is the prioritization and the plan behind it.

FAQ

The lowest-risk way to start is a readiness assessment, because it turns "we should do AI" into a scored, prioritized plan before anyone writes code.