What Questions Can AI Find You For?

Transcript

00:01Maya: If I type your exact name into an AI system and it knows who you are, congratulations. You are visible to AI. But that does not tell us whether a new customer can truly discover you. There is a huge difference, and you need to understand it. This episode will break that down for you. Here is the mindset shift. Customers do not always begin with your name.

00:24They begin with a situation. They ask complete questions about a problem, a place, a budget, a deadline, or a decision they need to make. That is the discovery environment we need to prepare for. I'm Maya, and this is Accelacore AI Discovery. In Episode 9, we looked at building reliable evidence across more than one source, some you own and some you only get to tend.

00:48Think of the old approach as tending one well-kept garden. Your website may be the garden you manage most closely. It still matters. But AI discovery asks you to think more like a farmer working an entire field of evidence: your site, the credible places that mention you, and the way those sources explain what you actually do. That wider environment gives AI more reliable material. Query Coverage asks the next question: does that evidence connect you to the right customers when they ask for help?

01:21Let us build what's known as a query ladder. It is something we do every day with everyday tasks, just without the fancy name. A ladder of questions, from simple to specific. At the bottom is the branded question. It starts with a name the customer already knows. Next comes the category question. The customer knows the type of help, but not necessarily the provider.

01:46Then the customer adds context. Now the question includes a life event, a location, and an implied need for relevant experience. At the top, the question is specific enough to signal real opportunity. A business can be recognized at Level 1 and still be absent when the customer describes the situation that actually matters. With AI systems, people can often say much more of what they need. The question may contain the customer segment, geography, price range, urgency, preferences, and constraints.

02:20Different AI systems will still answer differently. What stays constant is the job of your evidence: to make a clear connection between your real expertise and the needs you are qualified to solve. In Episode 7, we went into the importance of building and managing evidence, and whether your evidence covers the actual questions people are asking. That is called Query Coverage. The good news: there is no magic phrase to find. The real work is mapping the broad range of customer intent, which simply means what the person is actually trying to get done.

02:55What questions matter to a person's buying decision? Which ones represent your actual strengths? Which ones can AI currently connect to you? And which ones does it connect to competitors instead? Start with the work you want more of. Then identify the customer situations that lead to that work. Then turn those situations into the honest, complete questions a customer might ask. The useful test asks which important questions our evidence can support today, and where the gaps are.

03:26Suppose an attorney wants to grow estate-planning work. Testing only "best estate-planning attorney in Atlanta" misses much of the real intent. A better set of questions could ask: Who can help with business succession and estate planning? Who can help me set up a trust for a blended family? Who can help with estate planning for aging parents? Who helps business owners coordinate wills and buy-sell agreements?

03:52Notice what these questions reveal. Each one points to a different client situation, different expertise, and different proof the practice may need to show. This is the value of the map. It shows the firm whether its evidence reflects the real breadth of the practice, or only its broadest label, and whether the practice could be the right fit for the customer's current situation. The map helps you build the strongest evidence for each relevant customer situation.

04:21As you do that, watch for the traps and pitfalls along the way. This is about working smarter, not harder. There is a trap here. Once you identify a coverage gap, it is tempting to publish a separate page for every possible sentence. Aim for something better. Build substantive evidence around the underlying topics and customer needs, and organize it so people and AI can understand it. Hundreds of thin pages that say very little will not get you there.

04:51Testing important questions also changes how you see competition. The business that appears for a customer need could be someone you never considered a competitor: the professional whose evidence most clearly connects to that particular intent. Good Query Coverage for Discovery is about showing up for the right questions, and no business should expect to show up for all of them. The questions for which AI systems surface you should increasingly align with the problems you are actually qualified and motivated to solve.

05:22The work can feel large when you first see the map. A disciplined process can make it manageable: identify the meaningful questions, assess the evidence, improve the gaps that matter, and test again. Once we begin testing questions, the next challenge is measurement. Results can vary by AI system, by wording, by time, and by how an AI system gathers information. So can AI discovery and visibility really be measured?

05:52Yes, with a repeatable process. A single screenshot will never be enough. In Episode 11, we will look at what that process needs to measure and what it should never pretend to prove. Every customer starts with a question. Make sure your evidence is ready to answer it. See you soon.