This AI readiness checklist has 30 yes-or-no statements across six areas: processes, data, systems and tools, people, security and policy, and goals and budget. Check the ones that are true for your business today, add them up, and you'll know whether you're ready for a first AI project, ready to scale, or better off fixing a few foundations first. It takes about 15 minutes.
Download the free printable PDF to fill in with your team, or work through it below. Prefer something interactive? Our 2-minute AI readiness score gives you an instant result and emails you the breakdown.
How to use this checklist
- Do it with one or two people who do the day-to-day work, not just leadership. They know where the time really goes.
- Be honest. A low score isn't bad news. It tells you what to fix before you spend money on AI tools.
- Score each area out of 5, then add them up for a total out of 30.
- Look at your lowest area first. That's usually your first project, even before any AI is involved.
The checklist
1. Processes
Can you describe how the work actually gets done?
- We can name the 3–5 tasks that take the most repetitive hours each week.
- Those tasks are written down step by step, including the exceptions.
- People who do the same task do it the same way.
- We know roughly how many hours a week each of those tasks takes.
- Each important process has an owner who can say how it should work.
AI automates steps. If nobody can describe the steps, or everyone does them differently, there's nothing stable to automate. Writing down your top three tasks is free, and it's the single most useful thing you can do before any AI project.
2. Data
Is your information in one place, and can you trust it?
- Customer, job and order information lives in business systems, not personal spreadsheets or inboxes.
- Each kind of information has one main source of truth.
- Our data is mostly accurate: few duplicates, gaps or outdated records.
- We can export our data, or reach it through an integration.
- We know which data is sensitive (customer, health, financial) and where it's stored.
AI is only as good as the information it reads. If customer records are duplicated across inboxes and spreadsheets, an automation will copy the mess faster. Cleaning up one system first often pays for itself before any AI is involved.
3. Systems & tools
Can your tools talk to each other?
- Our main tools are cloud-based or offer integrations.
- We have a list of the software we pay for and what each one is used for.
- Logins are company accounts, not someone's personal email.
- Tools that should talk to each other are connected, or could be.
- Someone keeps our website, software and plugins updated.
Most automations move information between tools. Modern cloud tools with integrations make that cheap; older desktop software or logins tied to one person's email make it slow and fragile.
4. People
Is there someone to own it, and will the team use it?
- Leadership supports trying AI on a real problem.
- One person has time to own the first project.
- The people who do the work will help design the automation.
- The team has tried tools like ChatGPT and knows what they can and can't do.
- We're willing to have a person review the results until we trust them.
The biggest predictor of success isn't the technology, it's whether someone owns the project and the people who do the work are involved. Automations designed without them get worked around.
5. Security & policy
Is it safe to connect AI to your business?
- We have simple rules for what staff can and can't paste into AI tools.
- We know which AI tools our staff already use.
- Admin accounts use two-factor authentication.
- API keys and passwords are stored securely, not in documents, emails or code.
- We know our compliance needs (HIPAA, PCI, client contracts) that affect where data can go.
Your team is probably already pasting things into AI tools. A one-page policy on what's okay to share, plus basics like two-factor logins and properly stored API keys, prevents the expensive mistakes. This month's Railway breach is a good example of why keys matter.
6. Goals & budget
Do you know what you want, and how you'll measure it?
- We have one specific problem we want AI to solve first.
- We can measure success: hours saved, response time, errors or revenue.
- We've set aside budget and time for a first project.
- We've agreed what "good enough" looks like before we automate.
- We plan to start with one workflow, not everything at once.
"Use AI" isn't a goal. "Answer new inquiries within five minutes" or "stop re-typing 200 orders a week" is. A clear, measurable target tells you what to build and whether it worked.
How to score it
Count your checks. Each area is out of 5; the total is out of 30.
| Score | Result | What it means |
|---|---|---|
| 24–30 | Ready to scale | Strong foundations. You can automate several workflows and connect your systems, with people approving the important steps. |
| 15–23 | Ready for a first project | Pick one high-volume task with clear rules, automate it with a person in the loop, measure the hours saved, then do the next one. |
| 0–14 | Build the foundations first | Document your top tasks, get your data into one place and set simple AI rules. Most of this costs nothing and makes every later project cheaper. |
Any area with 2 or fewer checks is the first thing to fix, whatever your total. A business with great tools but no documented processes, for example, should write its processes down before buying anything else.
What to do with your results
If you scored 24 or more, you're in better shape than most. Nationally only about one in five businesses use AI at all, according to the Census Bureau (see our look at how Kansas City businesses use AI automation). Pick two or three workflows and connect your systems, with people approving the important steps.
If you scored 15 to 23, you're ready for a first project. Choose one task that's high-volume, rule-based and uses data you trust. Our guide to what a small business should automate first has 12 examples and a simple way to rank them. Automate it, measure the hours saved, then do the next one.
If you scored under 15, start with the foundations. Write down your top three tasks, move customer and job data into one system, put logins under company accounts, and set a simple AI policy. None of that needs a developer, and all of it makes the eventual AI project faster and cheaper.
The gaps that slow most projects down
A few items on this list trip up far more businesses than the rest:
- Undocumented processes. If the steps live in one person's head, the first job is getting them on paper.
- Data spread across inboxes and spreadsheets. Automations need one reliable source to read from.
- No owner. Projects without a named owner stall, no matter how good the tool.
- No AI policy. Staff are already using AI tools; a short policy keeps customer data out of the wrong places.
- No measurable goal. Without a number to move, you can't tell whether the project worked.
Want help turning this into a plan?
A checklist tells you where you stand. An AI readiness assessment goes further: someone maps how work actually flows through your business and hands you a ranked, costed list of what to automate.
That's what our AI Readiness Audit does. It takes two weeks and about four hours of your team's time, the price is fixed and agreed up front, and the fee is credited toward your first build if you start an Automation Sprint within 30 days. If your security area scored low, our care plans cover updates, monitoring and patching so your foundations stay solid.
Book a free call and bring your completed checklist. We'll tell you what we'd tackle first, whether or not you hire us.