What an AI readiness audit actually involves

Most failed AI projects don't fail because the model was bad. They fail because the data, the infrastructure, or the team wasn't ready - and nobody checked first. An AI readiness audit is that check: a structured look at whether your business can actually get value from AI before you spend on a build.

A good audit looks at four things.

1. Data

AI is only as good as the data behind it. We check whether the data you'd need is actually accessible, clean enough to use, structured or labelled where it matters, and that you have the permissions to use it. This is where most "AI projects" quietly die - the data isn't ready and nobody said so.

2. Infrastructure

Can your systems support AI in production? We look at integration points and APIs, where a model would run, and the latency and cost profile of actually serving it - not just whether it works in a notebook.

3. Use cases

Which workflows would AI genuinely move the needle on, and which are better left alone? We rank candidate use cases by expected return against effort and risk - and we'll tell you where AI is the wrong tool, which saves you the most money of all.

4. Governance and risk

Access controls, sensitive data, what the AI must never decide on its own, and where a human stays in the loop. If you've already shipped AI-generated code, this also means a security and dependency review of what's live.

What you walk away with

• A ranked list of viable AI use cases, sized by return and effort.
• A readiness scorecard across data, infrastructure and governance.
• The specific gaps to fix first - before you invest.
• A costed, sequenced plan you can act on with us or anyone else.