By John Wheeler | Wheeler Intelligent Systems
A customer needs a service you provide. Instead of typing a few words into a search engine and opening ten links, she asks an AI assistant: “Who can help me with this near me, and how do I choose?”
The assistant replies in seconds. It names a few businesses, explains what each one does, and suggests the next step. Your company might appear. It might be left out. Worse, the assistant might mention you but describe your work incorrectly.
That is a new kind of first impression. Many managers have spent years improving their websites and search rankings. Those efforts still matter. But it is time to ask another question: Can an AI assistant find enough sound information to recommend your business for the right reason?
I have been testing this question for Wheeler Intelligent Systems and other businesses. The work has shown me how easy it is to mistake a good-looking website for a clear answer to a buyer’s question. A site may explain the company’s history and still leave out what it sells, whom it serves, where it works, and how a buyer can start.
What “found by AI” means
AI discoverability is the ability of an AI system to identify a business, understand its offers, and give a useful answer when someone asks a related question. It has several parts.
First, the assistant must identify the right business. A common name, an old address, or several versions of a company name can make this harder. Second, it needs to understand what the business does. Third, it needs enough detail to connect a buyer’s problem to a specific offer. Finally, the answer should give the buyer a sensible way to reach the business.
This does not mean an AI system will always recommend a company with a well-written website. AI services differ. Their answers can change, and no business controls the final response. A useful test tells you what the systems can answer today and where your public information may be thin. It does not promise a fixed rank or a stream of leads.
Think of a local interior designer. A website that says “beautiful spaces for inspired living” may look appealing. But a buyer may ask: “Who can redesign a small medical office in Tacoma while keeping it open?” To answer well, an assistant would need to know whether the designer handles commercial work, serves Tacoma, has experience with occupied offices, and offers the services the buyer needs. A nice slogan cannot supply those facts.
Start with the questions customers ask
Most companies describe themselves from the inside out. They list a mission, a story, and perhaps a long menu of skills. Buyers begin elsewhere. They have a problem, a budget, a deadline, a location, or a risk they need to manage.
A better way to test your public information is to write down actual buyer questions. Include simple questions such as “What does this company do?” Then move to questions that require a choice: “Does this company serve small manufacturers?” “Can it help us prepare for an ERP change?” “What is included in its first assessment?” “How do I contact it?”
Ask questions in two ways. A branded question names your business: “What services does Wheeler Intelligent Systems offer?” An unbranded question starts with a need: “Who can help a small business find out whether AI can answer customer questions about it?” The first tests whether AI understands your identity. The second tests whether your company comes to mind when the buyer has not heard of you.
These are different tests. You may score well when an assistant is given your name but rarely appear when it must find a provider on its own. Record both results so you can see the gap clearly.
Run a small, repeatable test
You do not need a huge project to get started. Choose 15 to 20 questions across a few areas: who you are, what you offer, who you serve, proof of your work, and how to engage you. Save the exact wording. Then ask the same questions in the AI systems your customers are likely to use.
For each answer, record four things:
- Was the business identified correctly? Check its name, location, and website.
- Was the answer useful? Could a buyer tell whether the company fits the need?
- Was it supported? Did the answer point to a source that actually says what the assistant claims?
- What was missing or wrong? Note vague claims, old facts, invented services, and unanswered questions.
Keep copies of the answers and the date of the test. An answer is evidence of what happened in that run, not proof that every customer will see the same thing. Repeating the same questions later helps you see whether your changes made a difference, while allowing for normal changes in AI responses.
Do not give full credit to an answer that merely sounds confident. If an assistant says your company serves a market you have never served, that answer could hurt trust even though the company was mentioned. Accuracy matters more than the thrill of seeing your name.
Improve the information buyers can use
Once you see the gaps, begin with facts you control. Make sure your website states your company name clearly and explains your offers in everyday language. Give each important offer enough space to answer basic questions: who it is for, what problem it solves, what the customer receives, and how work begins.
Show where you work and how to contact you. If you serve a narrow market, say so. If you have limits, explain them. Specific limits can help a buyer decide whether to call. They also reduce the chance that an assistant will stretch a broad claim into an offer you cannot deliver.
Proof helps too. A case example can explain the starting problem, the work performed, and the result. Use examples you have permission to share, or label a sample as a sample. Avoid making up customer results to fill a page. A reader should be able to separate demonstrated experience from a service you are still developing.
Then check other public places where your business appears. Old profiles, mismatched descriptions, and broken links can confuse a buyer and any system reading them. Aim for a consistent name and a plain account of what the company does. Do not assume that adding technical markup alone will solve missing content. A machine-readable label cannot make an unclear offer clear.
What our own test taught me
When we began asking AI about Wheeler Intelligent Systems, I was pleased that we had a working website. But the test raised a harder issue: could a visitor actually find a clear list of products or services there? I could not rely on the site’s existence as proof that our offers were understandable.
That is a useful lesson for any manager. The test is not a contest to earn a flattering score. It is a way to spot the work a real buyer would have to do to understand your business. If the answer is buried in a conversation with the founder, it may also be missing from the public information an assistant can use.
Our next step is to make the offer clearer, then repeat the test with the same questions. We will look at whether the answers become more complete and accurate. We will also keep checking the unbranded questions, because being understood by name is only part of being discovered.
The manager’s first move
Pick five questions a buyer might ask before calling you. Ask two AI assistants those exact questions. Read the answers as if you knew nothing about your company. Would you understand the service? Would you trust the claims? Would you know what to do next?
Write down the first three gaps you find. Fix the public facts you can support. Then run the test again. That small cycle is more useful than guessing what an AI system wants to hear.
Customers are learning new ways to find help. Businesses can respond by making their knowledge easier to find, easier to check, and easier to act on. The goal is simple: when someone asks a fair question about your business, there should be a fair, useful answer available.
John Wheeler is the founder of Wheeler Intelligent Systems. He writes about practical ways businesses can use and prepare for AI.