The AI readiness checklist

Most failed AI projects didn't fail at the technology — they failed at the pick. A process that isn't ready will stall no matter how good the build is. Run your candidate process through the questions below before you spend anything. Count your yeses as you go; there's a quick way to read the total at the end.

The process itself

• It's repetitive and rules-based. The same kind of task, done often, mostly the same way. High volume plus low variation is the sweet spot.
• You can describe it step by step. If you can write down how it works, it can probably be automated. If it lives only in one person's head as "it depends," that's a mapping job first.
• It's stable. The steps aren't going to be rebuilt next quarter. Automating a process that's about to change is wasted money.
• The pain is real and countable. You can point to the hours it eats or the errors it causes. "It'd be nice" is not a business case.

Your data

• The information it needs already exists digitally. In a system, a spreadsheet, an inbox — somewhere a computer can reach. If the key input is a shoebox of paper, that's step zero.
• It's reasonably consistent. Records are structured enough that the same field means the same thing each time. Perfect isn't required; findable is.
• The tools have a way in. The software involved offers an API or an export, so systems can actually pass data between them rather than a human re-keying it.

Your people

• Someone owns it. There's a person who understands the process and will help shape and sign off the automation. Builds without an internal owner drift.
• The team is on side. The people doing the work today see it as their afternoon back, not their job under threat. That framing decides adoption more than any feature.
• There's an appetite to learn. Even a great system needs people comfortable using it. If AI is brand new to the team, a little training up front pays for itself.

Risk & guardrails

• A mistake is survivable or catchable. Either an error is low-stakes, or a human reviews the output before it counts. You never want AI making irreversible, high-stakes calls unwatched.
• You know where the human stays. You can name the step a person must keep — the judgement, the approval, the final send. Good automation removes the grind and leaves that in place.
• Sensitive data stays controlled. If the process touches confidential or personal information, you're willing to build it somewhere you own rather than paste it into a public tool.

Reading your score

Count the boxes you could honestly tick.

• 11–13: This process is ready. It's the kind we'd happily scope — pick it, build it properly, and measure what it returns.
• 7–10: Close. There's usually one fixable gap — a bit of data tidy-up, a process you need to write down, an owner to name. Sort that and it's ready.
• Under 7: Not yet — and that's useful to know before you spend. Either the process needs stabilising first, or there's a better candidate elsewhere in the business.

The honest truth is that most businesses have several processes in the top band and don't realise it, and one or two they're itching to automate that sit at the bottom. Working out which is which, in priority order, is exactly what an AI audit does — and if you'd rather just talk it through, that's what the form below is for.