What Businesses Need to Know About Local Search

Local search has always occupied a strange middle ground. Ask a practitioner and they might talk about rankings, pages and links. Ask a local business owner and they usually care about something simpler: does the phone ring?

The mechanics between those two perspectives has become considerably more complicated.

For the first episode of How About Some Search?, I sat down with Ben Duffy, Claudia Tomina and Ehab Aboud to talk about what local search looks like in 2026. Ben leads local SEO work at Quirky Digital, Claudia founded Reputation Arm and is a Google Business Profile Product Expert, and Ehab works with large multi-location brands at Uberall.

You can watch the full episode on LinkedIn.

A lot of the conversation came back to a single theme: the surface area of local search has expanded. Google Business Profile (GBP) still matters enormously. So does your website. But businesses also have to account for directories, reviews, photos, social content, third-party mentions and increasingly the way AI systems assemble all of that information into an answer.

Local and organic search solve different problems

Ben offered a useful distinction early in the conversation.

Traditional SEO often tries to get a page into the organic results, earn a click, and redirect a visitor to a business’ website. Local search may interrupt that journey entirely. Someone looking for an accountant, restaurant or plumber can see a Google Business Profile, map results, reviews, opening hours, photos, phone number and directions before visiting a website.

The business’ profile, by design, does the heavy lifting.

Claudia pointed to the business category, listed services, customer reviews, and the page linked from the GBP as distinct local ranking inputs. Google evaluates those signals together to understand what the business offers and when it should appear in local results. A customer’s level of urgency also changes how a they interact with search. Ben used plumbers repeatedly during the episode, to the point where it became a running joke, but the example works.

Someone comparing accountants may be willing to submit forms to three firms and wait for quotes. The customer standing beside a leaking toilet has different priorities. They want to know whether you can solve the problem, what it will cost, and how quickly you can get there. Those businesses should not have identical conversion strategies simply because both rely on local search.

AI search makes local visibility a multi-source problem

For years, businesses could treat some directories as an annoyance. Nobody wanted to maintain another profile on a platform they didn’t particularly care about. Claudia’s view is pragmatic: it doesn’t really matter whether the business likes Yelp, Apple, Bing or another directory. What matters is whether search systems use those sources to help understand the business.

You can use Gander to see which third-party sources AI platforms rely on when answering questions about your business and your competitors, then prioritize the directories, publications and review sites that are actually influencing those answers.

A business can now be described using information spread across its own site, Google Business Profile, directories, review platforms, social media and other third-party sources. Claudia described this as a reason to approach local visibility more holistically. Leaving holes in the information becomes more consequential when an AI platform is trying to piece together an answer from multiple places.

It’s also a brand fidelity problem. A business can be highly visible and still be poorly represented. If your website says one thing, directories say another, and recent customer reviews introduce a third, disparate version, the AI platform has to decide which version of your business it believes.

Ehab made the complementary point that businesses already doing local search well have a head start because they have already structured much of the information AI platforms need. The next challenge is prioritization; not every directory or third-party source deserves equal effort, so businesses need to identify which sources actually influence AI answers.

Brand fidelity is a discipline, especially at scale

At Gander, we use brand fidelity to describe how accurately an AI platform understands and represents a company. A local brand fidelity problem can start internally or externally. The business may be publishing inaccurate information itself, or the broader web may be telling an outdated or contradictory story.

Imagine a restaurant that promotes itself as Celiac-friendly, but recent reviews repeatedly mention that the menu only contains a couple of gluten-free options. Or a plumber whose website says 24-hour emergency service while older directories still show standard business hours. A business can update its own information immediately, while the rest of its digital footprint will take time to catch up.

For a single-location business, keeping this information synchronized can be a chore. For a company with 10, 50, or 500 locations, it becomes a data-governance problem.

Ehab framed the challenge for large organizations as establishing a reliable source of truth and then distributing that information consistently. Formatting differences, duplicate locations, and stale data can scale surprisingly quickly. Templates solve part of that problem, but they can also scale mistakes. If the wrong service, attribute, or description is built into a template, one bad piece of information can appear across hundreds of location pages. The challenge is deciding what information should be standardized centrally and what needs to be owned by each individual location.

Start with the sources that are actually showing up in AI answers, correct the ones you control, claim and update important directory profiles, and make sure your website and GBP contain current and factual information. Where contradictions come from reviews, old articles or third-party mentions, you may not be able to edit the source directly. Instead, respond with context where appropriate, generate fresher customer feedback, and give publishers or directories a reason to update stale information. Over time, you are trying to frame a single, factual depiction of your business the easiest version for search engines and AI platforms to corroborate.

Reviews are a veritable goldmine of data

Reviews dominated a good portion of the episode. Claudia made an important observation, describing a review as “original customer language”. Customers describe what they ate, what they bought, whether staff helped them, whether parking was difficult, whether a restaurant accommodated a dietary restriction, whether a contractor arrived on time and hundreds of other details that may never appear in owned sources.

Reviews are a source of non-commodity content that can be mined for subjects that can become FAQs or website content. Ehab offered a great personal example. When looking for halal restaurants, he historically searched the reviews themselves for the word “halal.” Now consider a much more specific prompt:

Find me a nearby halal restaurant that’s dog friendly, serves oat-milk matcha and has a high chair.

Traditionally, this would require several searches and some manual investigation. An AI platform can attempt to resolve those requirements together. The businesses with rich, specific information scattered across reviews and other sources provide more evidence to work with.

This also complicates our obsession with negative reviews.

Claudia gave an example of a restaurant owner upset because a customer complained that the restaurant cooked with beef tallow. To one customer, that was a negative. To another, it may be the precise reason to visit. While not desirable, a one-star review can contain accurate, useful detail about a business, and a thoughtful response can add context of its own.

Think beyond reviews when building local authority

Claudia also argued that businesses should pay more attention to social mentions and local creators. Her example was a creator focused specifically on restaurants serving halal meat. That person may not have tens of thousands of followers, but they have subject expertise, an audience, and a body of content around a very narrow locally-applicable topic.

For a restaurant trying to become associated with halal dining in that area, a mention from that creator carries relevance that the would be difficult for restaurant to duplicate on owned channels. The same idea applies elsewhere. A neighborhood parenting account, local food reviewer, trade association, regional publication or specialized directory can all provide useful third-party context.

Your business category should dictate your strategy

Another useful theme from the panel was how quickly generic local SEO advice breaks down once you look at actual businesses.

Claudia approaches personal injury lawyers differently from restaurants. A law firm may struggle to win highly competitive discovery searches, making their GBP particularly important near the bottom of the funnel. Someone has already heard the firm’s name. Now they’re looking at reviews, expertise, photos, and other evidence to decide whether they reach out.

Restaurants have an entirely different information problem. Menus, hours, atmosphere, dietary options, events, and photos carry much more weight in the decision.

Ben made the same point through conversion design. Accountants may need a short, well-designed lead form. A tradesperson may benefit far more from an immediate call, WhatsApp message or live chat. Ehab pushed the category point even further. EV charging stations can require live availability data and operate under different rules from conventional local businesses.

There isn’t one local-search funnel.

There are numerous small decisions to consider, all which must revolve around providing information that reduces uncertainty.

What customers can see shapes whether they choose you

All three guests kept returning to photos and video. Local search is rooted in the physical world. People want to see the restaurant, hotel room, roof repair, house, storefront, and the people they may eventually meet. The panel offered examples that ranged from before-and-after roofing photos to mortgage brokers photographing customers outside their new homes.

Even businesses without visually exciting products have something to show. An accounting firm’s team may be more useful than another stock photo of a calculator.

The useful photos are the ones that reduce uncertainty.

What does this place look like? Who works there? What happens when I arrive? Is the workmanship good? Does this resemble the experience I’m looking for?

Good local content answers those questions before someone has to ask them.

Local businesses need to prepare for more specific AI-driven searches

I ended the episode by asking whether the map pack would still exist in three years. Ben said no. Claudia said yes, but less visibly. Ehab said it likely doesn’t matter.

Google Maps will still have to decide which businesses to show. AI systems will still have to decide which local businesses to recommend. Someone with water pouring through a ceiling will still need to know which roofer can get there today. A driver running low on charge will care about which nearby EV station is available. Someone looking for a financial adviser will still want evidence that the person they’re about to trust with their money is credible.

The interface may change. The information businesses need to provide largely won’t.

Businesses still rely on accurate data. Accurate hours, services, reviews, photos, location data, useful website content, and credible third-party mentions are all important.

What changes with AI search is how that information gets used.

Ehab’s example is a good illustration of why. While travelling in Scotland, he could ask an AI platform to find a nearby restaurant that was halal, dog friendly, served oat-milk matcha and had a high chair. Previously, that might have meant checking Maps, reading menus, searching reviews and possibly calling a few places. AI can attempt to resolve all of those conditions in a single request.

People no longer need to break a complicated requirements into a series of simple searches. They can describe the full situation in one prompt, which means local businesses need enough specific, current, and consistent information across their website, GBP, reviews, directories, and other relevant sources for AI platforms to determine whether a business actually meets the user’s need.