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Is your business ready for AI?

This free quiz scores your business from 0 to 25 across five areas (data, tools, processes, people, and security) and tells you what to fix first.

Every answer below is prefilled with an example so you see a real score with zero typing. Change any answer to match your business; your score updates as you go, and nothing you enter leaves your browser.

Data and documents

Your core customer information lives in one system, not scattered across spreadsheets, email, and sticky notes. Why it matters: AI tools are only as useful as the data you can actually point them at.

Yes Partly No

Your files and folders have a consistent naming and organization pattern. Why it matters: AI that reads your documents needs to find the right ones, the same way a new employee would.

Yes Partly No

You could hand someone your last twelve months of a core process's records without spending a day cleaning them up first. Why it matters: messy source data produces confidently wrong AI output, not obviously wrong output.

Yes Partly No

Your most-used templates (estimates, contracts, intake forms) are digital and editable, not scanned images or paper. Why it matters: AI can draft from and fill in a digital template. It cannot usefully touch a paper form.

Yes Partly No

You know roughly how much of your customer or business data is sensitive (financial, medical, legal) versus routine. Why it matters: this is the line that determines what is safe to paste into a general AI tool and what is not.

Yes Partly No

Tools and accounts

Your business has a paid or verified account with at least one major AI tool (ChatGPT, Claude, Gemini, or Copilot). Why it matters: free, unverified personal accounts mean no admin control and no visibility into what is being used for what.

Yes Partly No

You know whether your CRM, accounting software, or scheduling tool already has AI features built in. Why it matters: the fastest wins usually come from turning on what you already pay for, not buying something new.

Yes Partly No

Someone in the business could explain, in one sentence, the difference between ChatGPT and a workflow tool like n8n or Zapier. Why it matters: chat tools and automation tools solve different problems, and conflating them leads to buying the wrong thing.

Yes Partly No

You have a way to see which AI tools your team is actually using, even informally. Why it matters: unmanaged use of free AI tools on business data is the most common security gap in small businesses right now.

Yes Partly No

Your accounting, CRM, and email systems can be connected to each other in some way (native integration, Zapier, n8n, or an API). Why it matters: automations that save real time usually connect two systems, not just one.

Yes Partly No

Processes

Your top three recurring tasks (the ones that happen weekly, without fail) are written down somewhere, not just known by one person. Why it matters: you cannot automate or train on a process that only exists in someone’s head.

Yes Partly No

You could describe, step by step, what happens between a new lead coming in and that lead getting a first response. Why it matters: this is usually the highest-value process to improve, and you cannot improve what you cannot describe.

Yes Partly No

Someone has actually timed how long your team spends on at least one repeated task. Why it matters: without a real baseline, you cannot tell whether an automation helped.

Yes Partly No

Your business has a consistent way customers get a response, follow-up, or reminder, rather than it depending on who happens to be free. Why it matters: consistency is what makes a process automatable in the first place.

Yes Partly No

You have identified at least one task that is repetitive, rule-based, and does not require a judgment call unique to each situation. Why it matters: that is the shape of task AI handles well. Judgment-heavy, one-off work does not fit the same way.

Yes Partly No

People and training

At least one person on your team has spent real time (a few hours, not a five-minute demo) using an AI tool on actual work. Why it matters: hands-on time is what turns a vague idea about AI into a specific, useful opinion about it.

Yes Partly No

Your team has had some structured introduction to these tools, rather than being left to figure it out alone. Why it matters: unstructured, solo experimentation is the most common reason adoption stalls.

Yes Partly No

There is someone in the business, even informally, who people ask when they have an AI question. Why it matters: without a point person, good habits do not spread past whoever discovered them first.

Yes Partly No

Your team knows how to tell when AI output is wrong, not just how to generate it. Why it matters: AI that is used without review is where the real damage happens, not AI itself.

Yes Partly No

Leadership has said out loud, at least once, that using AI on appropriate tasks is expected, not just tolerated. Why it matters: without that signal, cautious employees default to not using the tools at all, even when it would help.

Yes Partly No

Security and policy

You have a written answer, even a short one, to "what should never be pasted into an AI tool" at your business. Why it matters: this is the single most important guardrail, and it takes twenty minutes to write.

Yes Partly No

Your team knows the difference between your business's paid AI accounts and someone's personal free account. Why it matters: data typed into a personal account is outside your control and often outside your visibility entirely.

Yes Partly No

You know which of your existing tools keep your data private to your account versus which may use it for training their models. Why it matters: this varies by tool and by plan, and it is a one-time check worth doing before sensitive data goes anywhere near AI.

Yes Partly No

Someone owns the decision of which new AI tools get approved for use, rather than it being first-come-first-served. Why it matters: without an owner, tool sprawl happens quietly and nobody notices until something goes wrong.

Yes Partly No

If an AI tool produced a wrong or embarrassing result today, you know who would find out and what they would do about it. Why it matters: systems fail quietly more often than loudly, and knowing the response plan in advance is cheaper than improvising one.

Yes Partly No

Frequently asked questions

What is an AI readiness checklist?

A set of yes, partly, or no questions about your data, tools, processes, people, and security that scores whether AI would be easy or hard to adopt right now, and which gaps to close first.

How is my score calculated?

Each answer is worth 1 point for yes, half a point for partly, and 0 for no. Each of the five areas is scored out of 5, and the total out of 25 maps to the same bands as the checklist post: 20 or more is mostly yes, 12 to 19 is mixed, and fewer than 12 is mostly no.

What usually blocks small businesses from being AI-ready?

Two things most often: nobody has actually spent real time using the tools yet, and the processes AI would help with were never written down anywhere, so there is nothing consistent to automate. Both are fixable in days, not months.

What should I do after I get my score?

Look at which of the five areas scored lowest first. That is the highest-value place to start, not the total number itself. A mostly-yes score means you are ready for a deeper assessment. A mixed or mostly-no score means fixing the weakest area, often people and training or process, before spending on anything else.