The definition.
An AI readiness assessment is a short piece of advisory work that looks at the jobs your business repeats, the records you already keep and the systems they sit in, then says plainly whether AI would pay yet — and if so, on which job first.
It is not a maturity score out of ten, and it is not a technology audit. The question it answers is narrower and more useful: is there a specific job here where a system would earn its keep in weeks rather than years, and what is standing in the way of it?
It is the most common way into technical advisory, because “should we be doing something with AI” is the question most businesses are actually holding.
The five things we check.
You can run these yourself before you speak to anyone. If you can answer all five, you are ready enough to start.
Is there a job that repeats?
AI pays on work that happens weekly, not on one-offs. Look for the task somebody does every week in roughly the same way — quoting, chasing, triaging, summarising, re-keying between two systems.
Is the work written down somewhere?
Emails, documents, tickets, records in a system — anything a machine can read. It does not need to be tidy. If the knowledge only exists in one person’s head, that is the first thing to fix, and it is worth fixing whether or not AI follows.
Can the systems be reached?
A useful system has to read from and write to the tools you already use. Most modern tools allow this. A tool that has no export and no way for other software to talk to it is a real blocker, and much better to discover now than halfway through a build.
Who approves the output?
Name the person who signs off the send, the payment or the change. Readiness is as much about the approval step as the technology — and a named approver is what makes it safe to start on real work rather than a pilot nobody trusts.
What does a good result look like?
Hours back on a named task, a backlog cleared, a response time met. If nobody can say what success is, the project cannot be judged — and it should not start.
“Not ready” is rarely a no.
Almost nobody fails all five checks. What usually happens is that one is missing, and it is specific: a process nobody has written down, a tool nothing else can reach, or no agreed definition of a good result. Each of those is a small piece of work, and each is worth doing on its own merits.
The assessment may point to business intelligence, conventional software or a process change where that intervention offers the stronger return. That is a valid result: readiness work establishes the right investment before delivery begins.
And when the answer is yes, it usually points at one of three shapes: reading and writing language is LLM integration, predicting from your own history is machine learning, and a job that runs end to end across your tools is agentic AI.
What you get at the end.
A written answer to the question you asked, in plain English: whether AI would pay yet, on which job first, what is in the way, and what it would take to clear it. Where the answer is no, it says why and what would change it.
It is written to be handed to your own team or another firm. A recommendation you cannot take elsewhere is not really advice, and we would rather be useful than locked in.
If you do want us to build it, that runs as Build · Deploy · Elevate: tune a system to the real work, install it, then keep it running and add to it as you grow.
Frequently asked questions
What is an AI readiness assessment?
A short piece of advisory work that looks at the jobs your business repeats, the records you already keep and the systems they sit in, then says plainly whether AI would pay yet — and if so, on which job first.
Do we need clean data before we start?
Not perfect data. You need the work written down somewhere a system can read — emails, documents, tickets, records. Tidying is usually part of the first project rather than a prerequisite for asking the question.
What does it mean if we are not ready?
Almost never that you should give up. Usually it means one specific thing is missing — a process nobody has written down, a tool nothing else can reach, or no agreed definition of a good result. Those are fixable, and the assessment says which one is in the way.
Can the assessment recommend a non-AI solution?
Yes. The assessment may recommend business intelligence, conventional software or a process change where those options offer a stronger return or lower operating risk. Its purpose is to establish the right investment case before delivery begins.
How long does it take?
It is deliberately short — a conversation about the work, a look at the systems involved, then a written answer. It is sized to be much smaller than the project it is deciding on.
Where would our data go?
We host every system we build on infrastructure we manage, behind a secure login, and it is never listed or indexed. For sensitive or regulated work an open model runs on our own infrastructure, so no request text is sent to a third-party model provider.