What an AI readiness assessment costs, and what you should get for it

Market ranges run $8,000 to $25,000, and the spread is scope rather than company size. What drives the number, what you should get for it, how it differs from a maturity assessment, and when you can skip it.

Why this has a price at all

An AI readiness assessment exists because of a gap between what teams expect to spend on AI and where the money actually goes. Only about 7 percent of organisations report data that is ready for AI. Gartner expects 60 percent of AI projects unsupported by AI ready data to be abandoned through 2026, and 42 percent of companies abandoned most of their AI initiatives in 2025, up from 17 percent the year before.

Almost none of that is a model problem. It is data that was incomplete, undefined, or ungoverned, discovered six months into a build rather than four weeks before one.

What the market charges

Published ranges for an AI readiness assessment from an IT services provider run roughly $8,000 to $25,000. The spread is driven by how many domains and source systems come into scope, not by company size.

Two things should make you cautious at either end. Under about $8,000 you are usually buying a workshop and a template, which tells you what good looks like in general rather than what is true about your environment. Above $25,000 the engagement is often a strategy programme with an assessment attached, which may be what you want, but it is not the same product.

We price by deliverable rather than an hours bucket, so the scope and the number are agreed before the work starts.

What you should get for it

A credible assessment inspects systems rather than interviewing people about them, and it returns something a team can act on in the first week. Ours scores five dimensions:

Data quality and coverage. Whether the data your use case needs exists, is complete, and can be trusted.

Governance and access. Ownership, definitions, lineage, and the access controls AI inputs require.

Infrastructure and pipelines. Whether your pipelines can feed a model reliably and on time.

Process and people. Who owns the use case, and whether the workflow can absorb AI in the loop.

Score and roadmap. A maturity score and a prioritised path to close the gaps that block the thing you actually want to build.

If a proposal does not tell you what lands on the table at the end, it is a discovery call with an invoice attached.

How long it takes and who it needs

Four weeks end to end for a single domain. The working sessions need the people who know where the data actually comes from, which usually means a data engineer, whoever owns the source systems, and one business owner who can settle what a contested metric means. The system inspection is on us, so the demand on your team is measured in hours across the month, not days.

How it differs from an AI maturity assessment

A maturity assessment benchmarks you against a general model and tells you which stage you occupy. That is useful for a board conversation and rarely actionable on Monday. A readiness assessment is scoped to what you intend to build and answers whether that specific thing can work on the data you have now.

Maturity tells you where you rank. Readiness tells you what is blocking you and in what order to fix it.

When you can skip it

If you have already shipped AI to production against governed data and you are extending into an adjacent use case on the same sources, the assessment will mostly confirm what you know. Spend the money on the build.

The case for running one is strongest when the use case is new, the data crosses systems that were never designed to agree, or a previous pilot stalled and nobody can say precisely why. That last one is the most common reason we get called.

Frequently asked questions

What does an AI readiness assessment cost?

Published market ranges run roughly $8,000 to $25,000, driven by how many domains and source systems are in scope rather than by company size. Below that range you are usually buying a workshop and a template; above it the engagement is often a strategy programme with an assessment attached.

How long does an AI readiness assessment take?

Four weeks end to end for a single domain. The demand on your team is hours across the month rather than days, because the system inspection is done by the assessing team.

What is the difference between AI readiness and AI maturity?

A maturity assessment benchmarks you against a general model and tells you which stage you occupy. A readiness assessment is scoped to a specific use case and answers whether it can work on the data you have now. Maturity tells you where you rank, readiness tells you what is blocking you and in what order to fix it.

Do we need one if we already have AI in production?

Often not. If you are extending into an adjacent use case on data you already govern, spend the money on the build. The case is strongest when the use case is new, the data crosses systems that were never designed to agree, or a previous pilot stalled and nobody can say why.