AI Readiness Checklist

Use this before buying tools

Most AI projects go sideways because the tool decision happens before the work is understood. This checklist is a simple way to map where AI could genuinely help, where it could create risk, and where the business is really dealing with a process problem.

1. Map the work

• What job is the team trying to get done?
• Where does work enter the system?
• Who touches it before it is complete?
• Where are the delays, rework loops or judgement calls?
• What evidence do you have: tickets, calls, docs, emails, spreadsheets, CRM notes?

2. Separate judgement from admin

Look for the boring repeatable work first. That is usually where AI and automation can help without making a mess.

• Can the task be described clearly?
• Is there a known good output?
• Does the person doing it need commercial judgement, taste or empathy?
• Would a draft, summary or first pass save time without replacing the decision?

3. Check the risk

• What happens if the output is wrong?
• Who approves it before it reaches a customer?
• Does it touch private, financial, medical or regulated information?
• Is the source data good enough to trust?
• Can the team spot hallucinations or bad assumptions?

4. Choose the first useful use case

A good first AI project is small enough to ship, visible enough to matter, and safe enough to learn from.

Prioritise work that is:

• repetitive;
• high-volume;
• annoying but important;
• easy to review;
• connected to revenue, service quality or team capacity.

5. Decide what to build next

For each candidate use case, write one sentence:

We will use AI to help [person/team] do [specific task] faster or better, while [human owner] remains accountable for [judgement/risk].

If you cannot complete that sentence cleanly, the project probably needs more mapping before more tools.