How Local Businesses Can Build Visibility in AI Search

I recently had the pleasure of hosting the opening session of SEJ Live with Kevin Chen and Jonathan Berthold from Moz, the sponsors of the session. Kevin, the VP of Business Development, and Jonathan, the VP of Revenue, shared valuable insights during the session.

We made the most of our hour by focusing on questions attendees submitted beforehand. Here's a summary of our engaging discussion.

AI Visibility Means Being The Recommendation

Jonathan started with a definition that described AI visibility for a local business as being the company an assistant recommends when someone describes what they want. He told a story about a talented tattoo artist friend and sees people. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

The operational question is whether the public business data is complete enough to support the query. Hours, categories, services, reviews, photos, and page content need to reinforce each other so Google can understand the business in a specific situation, not only as a generic listing.

Google Closed The 'Mention My Name' Loophole In April

For years, the common advice was to ask customers to share the service they received and the name of the person who helped them. Jonathan pointed out that Google changed its Maps content policy in April, and now it's no longer allowed. Local visibility depends on whether the details across pages, profiles, categories, reviews, photos, and service descriptions reinforce the same answer for a specific location based query. A useful companion note is AI Assistants Are Choosing Local Businesses, because it looks at a nearby part of the same system.

Answer The Negative Review For The Next Reader

Jonathan said to respond to everything, positive and negative, and Julie gave a really helpful explanation in the chat, making things clearer. When replying to a negative review, remember that it's for everyone who reads the exchange. Local visibility depends on whether the details across pages, profiles, categories, reviews, photos, and service descriptions reinforce the same answer for a specific location based query. This connects with So Build What It Can Read when the same signal needs a clearer operating decision. The same pattern also shows up in Local Signals AI Now Reads, where the practical question is how the signal becomes visible.

The Central Office Was The Bottleneck

I first got involved with franchises in local search when the map pack was just beginning to emerge. Since it took some time for the head office to develop a clear plan, location managers got impatient and launched their own MySpace pages. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

Digital Can't Fix A Bad Burger

During summer vacation, I saw a lively crowd outside a popular burger restaurant that had gone viral on TikTok, while the burger place across the street sat empty. When the food arrived, the patty was sliding off the bun, and the bacon. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

Old Reddit Threads Are Still Quoting Your Prices

A school chain I collaborated with kept hearing from prospective students who had been misled by Google's AI. Google's AI had told them tuition was several thousand dollars lower than it was, and the source was a Reddit post from years. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

Ranking First On Google Doesn't Guarantee An AI Mention

My connection dropped near the end, so Heather Campbell kindly stepped in to manage the Q&A. The main question was what to investigate first when a business ranks number one on Google but doesn't appear in AI search results. Jonathan would. The strategic issue is whether automated visitors can understand, trust, and complete the same journey a human visitor can. Agent readiness is partly technical, but it is also about clear tasks, accessible flows, and reliable evidence.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

AI Visibility Means Being The Recommendation in practice

Introduction I recently had the pleasure of hosting the opening session of SEJ Live with Kevin Chen and Jonathan Berthold from Moz, the sponsors of the session. Kevin, the VP of Business Development, and Jonathan, the VP of Revenue, shared. For search teams, the important part is not the headline movement by itself. It is whether the shift changes which communities, forums, video surfaces, or publisher pages now satisfy the query better than the old ranking pattern.

What the visibility signal actually changes

What the visibility signal actually changes: how Local Businesses Can Build Visibility in AI Search: the Strategic Visibility Angle should be treated as a visibility signal, not a standalone headline. Introduction I recently had the pleasure of hosting the opening session of SEJ Live with Kevin Chen and Jonathan Berthold from Moz, the sponsors of the session. Kevin, the VP of Business Development, and Jonathan, the VP of Revenue, shared valuable insights.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.

Where the evidence needs to be tested

Where the evidence needs to be tested: a single study or ranking observation should not become a strategy by itself. It should become a diagnostic prompt: which source is being trusted, which query pattern is affected, and which part of the site would make that trust easier to earn?

Where the evidence needs to be tested: that keeps the response grounded. The goal is to improve the evidence chain around the topic rather than publish another summary that repeats what every other page already says.

Where the evidence needs to be tested: the important distinction is between a useful signal and a fashionable talking point. A useful signal changes the brief, the page structure, the linking plan, or the measurement view.

How to avoid overreacting to one data point

How to avoid overreacting to one data point: for content teams, the strongest move is to map the claim to existing assets before creating anything new. The right page may already exist, but it may need clearer headings, stronger internal links, fresher proof, or a better explanation of why the brand belongs in the answer.

How to avoid overreacting to one data point: this is also where title rewriting matters. A title should not copy the source headline; it should frame the practical implication so readers immediately know why the topic deserves attention.

How to avoid overreacting to one data point: the same standard should apply to every section. Each heading needs to earn its place by moving the reader through the evidence, not by repeating the outline in a more polished voice.