An AI readiness assessment answers one question: if you spent money on AI tomorrow, would it turn into better work, or into licenses nobody uses? The fastest honest version is seven questions about your people, your process and who owns the results. You can answer all seven yourself, and the answers tell you which of four stages your organization is in and what to do next.
Most readiness checklists you'll find online are about infrastructure: data warehouses, cloud platforms, model governance. Those matter for some companies. For a business of 20 or 80 people in Cedar Rapids or Iowa City, they're rarely what's in the way. What's in the way is usually human: who knows how to use these tools, whether anyone can see what's working, and whether getting better at your job is safe.
What does an AI readiness assessment actually measure?
An AI readiness assessment measures whether your organization can turn AI use into improved work that lasts. For a small or mid-sized business, that comes down to a handful of things: how skilled your people are with AI today, whether their use is visible or hidden, how quickly a good idea gets a yes or no, whether there's a safe and approved way to use company data, who benefits when someone saves time, whether key processes depend on a single person, and whether anything has actually changed, not just sped up. Technology readiness (your systems, your data quality) is part of it, but it's rarely the limiting factor at this size. The output of a good assessment isn't a score to frame. It's a stage, and a specific next move that fits that stage, so your next dollar goes to the thing that's actually holding you back.
The seven questions
Answer each one about your organization as it is today, not as the strategy deck describes it.
1. Where do your people sit on AI proficiency?
We use a simple three-level scale. A Beginner uses AI rarely, or with uneven results. An AI Champion gets repeatable results that other people use. A Systems Builder redesigns the work itself, and knows when not to use AI. (We'll cover the full scale, and how to place people on it, in a later post.)
You don't need precise numbers. You need a rough census: mostly Beginners with a few Champions is the most common answer, and it's a fine place to start. What matters is that you know.
2. Is AI use visible, or alone and invisible?
If people use AI quietly and on their own, you can't learn from what's working, and you can't see the risks. This is more common than most leaders think. A 2025 global study of more than 48,000 people by KPMG and the University of Melbourne found that 57% of employees surveyed said they conceal their use of AI and present AI-generated work as their own. We wrote about the reasons in why employees hide their AI use.
3. How long does it take an idea to reach a decision?
Pick the last time someone asked to try a new tool or automate a step. How long until they got a yes, a no or a "not yet, and here's why"? If the honest answer is weeks or months, people stop asking and start working around you. A six-week approval process all but guarantees shadow IT.
4. Is there a clear, safe path for data and tools?
Can an employee tell you, without looking it up, which AI tools are approved and what data can go into them? Is there an approved tool at all, on a business account rather than a personal one? If the safe path is harder than the unsafe one, people take the unsafe one. A written usage policy is part of this, and we covered where it fits in AI policy vs. AI strategy.
5. Who keeps the time saved?
When someone finds a way to do a two-hour task in twenty minutes, what happens next? If the only reward for working smarter is more work, nobody changes, or they change and hide it. This question predicts adoption better than any technical one on the list.
6. Does any process depend on one person?
Every business has the report only one person knows how to run, or the approval that lives in one inbox. Those are your best opportunities and your biggest risks. If your processes depend on heroics, AI will mostly help the heroes go faster, and the organization still won't own the result.
7. Can you name a process that changed, not just got faster?
Faster is good. Changed is different: a step that no longer exists, a handoff that disappeared, a decision made with information you didn't have before. If you can't name one, that's normal, and it tells you where you are.
The four stages, and what to do at each one
Match each answer to the stage it sounds like. Your stage is where most of your answers land. If they split, choose the lower one: starting too low costs you a few weeks, and starting too high can cost you the whole effort.
| Stage | What it looks like | The next move |
|---|---|---|
| Scattered | Some people use AI, alone and invisibly. No approved tool, or one nobody uses. A few people hold everything together. | Make use visible and safe: one approved tool on a business account, plain rules for data, and an open invitation to share what's already working. |
| Supported | Approved tools and basic guidance exist. Some training has happened. A few Champions get good results, but their wins stay personal. | Find your Champions and turn one personal win into something a teammate uses without them. Train in small groups by level, not all at once. |
| Shared | Wins travel between people and teams. Ideas reach a decision in days. People can see who is doing what with AI. | Pick one team process and redesign it with the people who do it. Decide, in writing, how the time saved will be shared. |
| Systematic | Redesigned processes don't depend on any one person. Ownership, standards and a front door for new ideas are routine. | Keep the pipeline full: regular review of new requests, retire what isn't used, and measure changes in the work, not logins. |
Most small and mid-sized businesses we talk to across Iowa land in Scattered or Supported. That isn't a criticism. It means the next step is about people and visibility, not a bigger software purchase.
Score it yourself, or have us run it with you
You can run this on your own in an afternoon. Write down your answers, ask two or three people at different levels of the organization the same questions, and compare. The gaps between your answers and theirs are often the most useful part.
If you'd rather have an outside view, we run this assessment with leadership teams as part of our Advise, Educate & Lead work: interviews across roles, a stage for the organization and each team, and a short list of next moves sized to your budget. For individuals who want to reflect on their own habits first, our free, interactive AI Enablement exercise ends with a short profile of how you approach AI at work.
Either way, the point is the same. Know where you are before you spend, so you buy the thing that moves you forward. If you want help with the assessment or the plan that follows it, that's what our AI strategy and fractional leadership service is for.