Have you ever had an idea for an assessment, calculator, decision tool, or app and thought, “I wouldn’t know how to build that”?
You may know exactly what the tool should help someone do. The technical part is where the idea stops.
That’s where I was when I started working on the Visibility Momentum Map.
I’ve spent 30 years working online, but I don’t code or build software. What I did have was an idea based on something I know well: helping solo business owners figure out where their visibility needs attention.
Could I take my expertise from years of working with solo business owners and turn it into a simple AI tool people could use?
That question got me started.1
If you have a process, set of questions, or decision you use repeatedly in your work, it may be a good candidate for a simple AI tool. Start with one problem, one action, and one outcome before you think about features.
What I can share now is the thinking behind the build. That may be useful if you’ve wondered whether something from your own experience could become a tool people can use.
What expertise could you turn into an AI tool?
You probably have ways of working that feel ordinary to you after using them for years.
Maybe you ask every new client the same 5 questions. Maybe you can hear someone describe a problem and quickly spot where they’re stuck. Maybe you have a process for comparing choices or deciding what should happen next.
That kind of knowledge can be a starting point.
The Visibility Momentum Map came from patterns I’ve seen repeatedly with solo business owners.
Someone can do plenty of marketing and still struggle to get inquiries. Their offer may be unclear. Their content may answer questions their audience isn’t asking. Their expertise may be hard to see. They may give people no clear next step. Or they may lose contact after someone shows interest.
Those patterns became 5 areas in the assessment:
Value Clarity
Audience Questions
Expertise Evidence
Next Step
Ongoing Connection
The framework existed before the app. That gave AI something specific to work with.
Look at the work you already do. What problem do you know well? What decision do you help people make? What part of your process could someone use without having you sitting beside them?
It may be the beginning of your tool.
How small should your first AI tool be?
Once you start building with AI, ideas can multiply fast.
A simple assessment can suddenly have accounts, dashboards, saved results, integrations, personalization, extra questions, more data, and all kinds of features you hadn’t considered 10 minutes earlier.
Some of those ideas may make sense later.
The rule that has kept the Visibility Momentum Map manageable is:
One problem. One action. One outcome.
For the Map, the problem is lack of clarity about where visibility may be losing momentum. The action is taking the assessment. The outcome is one priority area and one practical next step.
That’s enough for a first version.
Your idea can be small too.
Maybe someone answers a few questions and receives a recommendation. Maybe they enter information and get a calculation. Maybe the tool helps them decide between 2 options or identify which issue deserves attention first.
A useful question here is:
What does someone need to walk away with for this tool to have done its job?
That answer can save you from building a lot of things before you know whether people want the core idea.
Where does your judgment matter when building with AI?
AI can give you options very quickly. Choosing among them is a different job.
One of my beta testers helped me see this when she questioned whether the Map applied to her. The assessment used the word “client,” and that word made the tool feel narrower than I intended.
In my work, the person you want to reach could be a client, customer, buyer, reader, member, podcast listener, video viewer, or someone taking another step with you.
The assessment language didn’t communicate that. Instead, it revealed my bias for the word “client.”
There were several possible ways to respond. I could add N/A choices, create different paths for different business models, or add more personalization.
I went back to what the Map was meant to measure.
The 5 areas still applied. The scoring still made sense. The wording was creating confusion.
So “Buyer Questions” became “Audience Questions.” “Invitation” became “Next Step.” “Follow-Up” became “Ongoing Connection.” I made the welcome page clearer about who the assessment is for and left the scoring alone.
That’s one place your own experience becomes very useful.
AI can suggest a feature, recommendation, workflow, or fix. You know the people you serve. You know what language they use. You know whether the advice fits how they work. You know whether a recommendation sounds like something you would give them yourself.
Your judgment gives AI criteria.
How should you use feedback on an AI tool?
If you put a tool in front of real people, they’ll show you things you missed. And it can be humbling!
They may tell you a button moved too fast, a question was confusing, a result felt wrong, or they expected something different.
Their comments give you clues. Your job is deciding what those clues mean.
The “client” issue taught me that. The beta testers were pointing to a real problem, and changing the language fixed it without changing the underlying method.
Another tester noticed something completely different. After clicking an answer, the next question appeared so fast that she wasn’t sure her answer had registered.
AI and I tried a few possible fixes. One version briefly changed the background color between questions.
When I tested it, my reaction was immediate: it looked like an error.
So we tried a simpler response. The selected answer now stays visible for a moment before the next question appears.
Then came a tiny decision that I never expected to make when I started this project.
How long should that moment last?
450 milliseconds felt too fast.
750 milliseconds felt better.
That choice didn’t require coding knowledge. It required using the tool and paying attention to the experience.
You can do the same with anything you create.
Where do you hesitate? Is the question clear the first time you read it? Do you know whether your click worked? Does the result make sense without extra explanation? Would someone know what to do next?
Those observations are part of testing too.
How do you decide what to fix first?
Building something new gives you an endless supply of things you could improve.
Deciding what deserves attention now can save a lot of time.
At one point, the live Map took about 15 seconds to load. That was long enough to make me wonder whether I had a hosting problem.
Then I tested it again.
The second load took about 1 second.
Now I had more information. The longer wait appeared to happen on the first load after the app had been inactive.
I changed the loading message so the person using the Map could see something was happening, then kept testing before taking on a larger technical project.
You’ve probably made similar decisions in your business.
How often is this happening? How much does it affect the customer? Do you have enough information yet? What size of response fits the problem you can see?
AI may offer 10 possible fixes. You still get to decide whether any of them deserve your time today.
What belongs on the “later” list?
One of the easiest ways for a small AI project to grow is by saying yes to every good idea.
The Visibility Momentum Map has plenty of possibilities for future versions.
Saved results could be useful. So could more personalization and integrations. I’d also like to test some visual ideas.
For now, Version 1 has a much shorter job description.
Can people complete the assessment?
Do the questions make sense?
Does the result feel accurate?
Is the recommended action useful?
Do the emails arrive?
Those answers will tell me whether the core idea deserves further development.
You can use the same thinking with your project.
When a new feature comes up, ask whether it improves the main result your first version is meant to deliver.
If the core experience works without it, “later” may be the right answer.
AI is very good at giving you more things to build. You still choose when the first version has enough.
Should you build your AI tool yourself or get help?
Your answer may be different from mine.
I could have hired someone to build the Visibility Momentum Map. I may bring in someone with deeper technical skills as the project develops.
For this first version, curiosity won.
I wanted firsthand experience with the process. I wanted to see how far I could get, where I would run into problems, and what I would learn by trying to solve them.
That learning has changed how I would approach outsourcing too.
After working through the first version, I know much more about what I would ask someone to build. I know which parts matter to me. I know what needs explanation and where I’d want technical guidance.
You might decide to build your own first experiment. You might work with someone technical from the start. You might work out the problem, audience, outcome, and first version so you can give a developer clearer direction.
The useful choice is the one that fits your interest, skills, time, and project.
What could you build from your expertise?
You may have been doing something for so long that you barely notice the judgment involved anymore.
You hear a client describe a situation and know which question to ask next. You look at a plan and see the weak point. You compare 2 choices and know what information matters. You have a process you’ve refined through dozens or hundreds of conversations.
That knowledge may be useful in another form.
It could become an assessment, decision tool, calculator, planning assistant, Skill, small app, or something you haven’t named yet.
You can start with one small question:
What do you know how to help someone do?
Then see where your curiosity takes you.
And now I’m curious about you.
What’s something you’ve thought about creating from your own experience or expertise?
Tell me in the comments.
If you already have an idea and you’re ready to work out what it could become, we can do that together in a Clarity & Action session. We can define who it’s for, what it needs to do, what belongs in the first version, and create a practical plan to get started.
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The Map is still in beta, so I’ll answer the question I expect you may be already asking: “Can I try it?” Soon, I hope. I’m still testing, fixing, and learning.
I LOVE this Denise! You make this so approachable!
I have a hypothesis about how we should be turning our expertise into tools that *give us this kind of data back* - not just PDFs or CustomGPTs where it's a black box and we don't know where people got stuck or what part of the methodology can be improved. We need data!!!!
Can't wait to see the Visibility Momentum Map when you're ready.
I love the framing of this post. Thanks Denise🩷🦩