Work through the five sections honestly — yes or no, no partial credit. You'll have a clear picture in ten minutes. If you want the scored, interactive version of this thinking applied to your systems generally, our free Systems Sprawl Diagnostic is the companion tool.
1. Data — can AI tools reach what your business knows?
- Customer information lives in one or two systems — not scattered across texts, inboxes, and someone's memory.
- Your core operational records (jobs, quotes, invoices) are digital and reasonably current.
- You could export your key business data (customers, jobs, financials) without asking a vendor for help.
- Reports from different systems mostly agree — you're not reconciling conflicting numbers by hand.
If you answered no more than once here, stop: fix the information plumbing before buying any AI. AI amplifies your data — including its chaos. This is the most common finding when we do AI readiness consulting: the "AI problem" is a data-organization problem wearing a costume.
2. Process — is your work repeatable enough to automate?
- Your highest-volume tasks follow a pattern — quoting, scheduling, follow-ups, the same customer questions.
- At least one weekly task is pure rules ("when X happens, we always do Y").
- Somebody could write down how a core process works — it isn't only in one person's head.
- You know roughly how many hours per week your team spends on your most repetitive task.
No's here mean the work is too improvised to automate yet. Map and standardize one workflow first — that's cheaper than any software and makes everything after it work better.
3. People — will your team actually use it?
- Your team adopted the last tool you rolled out — it didn't quietly die after a month.
- Someone owns making new tools stick — training, checking usage, adjusting.
- Your key people have bandwidth to change one habit — they're not too buried to learn anything new.
- Leadership will actually use the output — reports and automations nobody reads don't save time.
No's here are the silent killer. Abandoned software is rarely the software's fault — see the technology mistakes growing businesses repeat.
4. Security — do you have basic rules?
- You have a rule about what can't go into AI tools (customer payment details, personal data, anything contractually confidential).
- Business accounts, not personal ones — AI tools your team uses are on company accounts you control.
- You know whether your tools train on your data — and have turned that off where it matters.
- Someone would notice if an employee pasted something sensitive somewhere they shouldn't.
No's here don't block a pilot — they're the guardrails to set up in the same week you start one. This takes an afternoon, not a compliance department.
5. First use case — do you know your opening move?
- You can name ONE specific task you'd automate first — not "use AI," but "stop hand-writing the same five quotes a week."
- That task has a number attached — hours saved, response time, jobs won — that would prove it worked.
- You could run a two-week test without betting anything important on it.
- You've resisted buying a platform before picking the task. Tools chosen before use cases become shelfware — and their real costs run well past the advertised price.
This section is the whole game. A business that answers yes here with a few no's elsewhere will beat a business with perfect data and no opening move.
How to read your results
Don't count total no's — look at where they cluster. Cluster in Data: you have a systems problem before you have an AI opportunity; a Business Systems Assessment finds it faster than trial and error. Cluster in Process: standardize one workflow first. Cluster in People: fix adoption before adding tools — the next one will die like the last one. Security only: set the rules this week and proceed. Mostly yes: pick the section-5 task, run the two-week test, measure, and expand from what works — our AI readiness assessment guide covers what a fuller review looks like when you're ready to go deeper.
That sequencing — readiness before spending, diagnosis before tools — is the entire premise of our AI readiness consulting: figure out where AI genuinely pays off in your operation, and just as importantly, where it doesn't yet.