Which customer questions should you track in AI search?
Choose AI search questions around real buying needs, the requirements that decide fit and the work your business can deliver.
Track questions that connect a customer's buying decision to something your business can provide. Start with what people ask before they buy and keep the requirements that would change which product or provider they choose. Ask those questions again over time to see which businesses or products AI answers recommend and how they describe them.
A broad question such as “Who are the best accountants in this city?” can belong on that list. It gives you a general view of which firms an assistant recommends. Add questions about particular customer needs to see whether it recommends firms with the relevant experience.
Keep the detail that decides who fits
Imagine an accounting firm that specializes in restaurants. A restaurant owner opening a second location needs help handling payroll and tips across both sites. The firm can do that work, but a general question about accountants does not ask the assistant to consider it.
“Which accountants in this city work with restaurants and handle payroll across several locations?” asks the assistant to consider restaurant experience and payroll across locations. You can examine whether it identifies the firm as a suitable option and whether its explanation reflects the firm's actual experience. Being named in a general list would not show whether the assistant recognizes those capabilities.
The same principle applies to products. Customer support software might appear in a general recommendation but be omitted when the buyer asks for software that works with their existing customer relationship management (CRM) system. If compatibility with that system is essential to the purchase, include it in the question. If the software does not work with the buyer’s CRM, being left out may reflect a missing capability rather than a problem with how the product is described online.
Keep only the conditions that matter to the decision. Adding every feature your business offers can produce a question designed to favor you rather than one that helps you understand a buyer's choice.
Start with what customers need to know
Sales conversations, quote requests and proposal emails give you a starting point. Look for questions that helped someone establish whether you could do the work, meet a requirement or solve a problem. Keep the customer's wording alongside the situation that prompted it.
Whether someone is choosing a service or already using it matters too. “Can you handle payroll for two restaurants?” helps a buyer choose an accountant. “How do I upload this month's payroll file?” helps an existing client use the service. Both deserve a good answer, but a list intended to examine new-business opportunities should not treat them as the same need.
Group different ways of asking about the same requirement. “Which support tools work with our CRM?” and “Does this support software work with our CRM?” share a compatibility concern, even though one asks for options and the other evaluates a product. Whether existing customer records can be transferred to the new software is a separate concern and needs its own answer.
Choose the questions worth returning to
When several concerns compete for attention, favor those that could change a buyer's choice and concern work your business wants and can deliver. Repeated inquiries help establish relevance. A less frequent question can also deserve a place if it decides whether you qualify for valuable work.
For the restaurant specialist, payroll across locations could deserve a place because it concerns a service the firm wants to grow. If the firm does not handle international tax, a question about choosing an international tax adviser would not help it assess whether its current strengths are understood.
Flaregraph's published work with an established law firm included questions about finding a construction accident lawyer and finding representation for a claim beyond workers' compensation. These paraphrased topics concern different client situations within the firm's practice. They illustrate how the firm's services can guide question selection without establishing how often customers ask those questions.
Search data can add context to this selection. Google Keyword Planner estimates search activity for keywords; it does not count questions put to AI assistants. Likewise, Bing's AI Performance report groups references to your website in AI answers under short phrases rather than showing the full questions people asked. Use this data to identify possible customer questions while keeping them distinct from questions you have actually heard from customers.
If you are entering a new market, some questions will begin as informed guesses. Keep those marked as assumptions until customer conversations or other evidence support them. Writing a plausible question does not establish demand for it.
Check discovery and evaluation separately
A buyer who has not heard of the accounting firm might ask for restaurant accountants in the city. Leaving the firm's name out lets you see whether the assistant introduces it in that answer.
Another buyer might receive the firm's name from a colleague and ask, “Does [firm] handle payroll for restaurants with several locations?” Here the buyer already has an option to evaluate. Including the firm's name lets you examine whether the answer describes its capabilities accurately.
Both checks are useful, but they answer different questions. An accurate description of a named firm does not establish that it appears when the assistant is asked to suggest firms. Neither check tells you how many buyers have seen that answer.
Some questions seek an explanation rather than a provider. A restaurant owner asking what changing accountants during the tax year involves may need practical guidance without a list of firms. Decide what the answer should help that person understand before treating the absence of your name as a problem.
Keep the comparison useful over time
AI answers can change between checks. A 2026 preprint studying repeated questions found changes in brand mentions and cited sources when the same questions were asked repeatedly. That is a reason to examine more than one answer before treating an appearance or omission as a pattern.
Keep the question wording, relevant location and requirements consistent when comparing answers over time. Record which AI assistant you used and the date of each check. If you add questions or change a requirement, note the change so it is not mistaken for a change in how the business is represented.
A useful question list makes each result worth examining for a specific reason. If the restaurant specialist repeatedly goes unmentioned in answers about payroll across locations, you have a commercially relevant finding to investigate. You have not yet established why the omission occurs or which change would address it.
Flaregraph's AI visibility report examines how your business appears in answers to relevant customer questions and identifies what to improve first. Where information is missing or inaccurate, we investigate whether clearer explanations, technical improvements or corrections to information in industry sources would help.