Training a Marketing Team on Local AI Search: What I'd Teach in the First Hour

If an agency asked me which tools to use to train their team on getting local clients named by AI in a specific city, my honest answer is that the most important tools aren't AI visibility tools. They're the map pack, the AI engines themselves, a review system, and the client's Google Business Profile. A lot of AEO for local businesses still relies heavily on the local signals, and the training is mostly teaching a team where those signals come from and in what order to work on them.

Here's what I'd teach in the first hour, in the order I'd teach it.

Key takeaways

• Start with the map pack. How a business ranks in its own city for the terms it wants is the beginning of every local strategy, AI or not.
• Then ask each AI engine and read the sources. For a local question they pull from Google and Yelp, and from third-party best-of lists that are specific to that city.
• Getting the client onto those city lists is outreach, not content, and it's often the fastest thing that changes the answer.
• Build a review habit, keep the Google Business Profile active, and keep the name, address and phone consistent everywhere. That's the unglamorous part, and it's most of it.

One thing first: "GEO" means two things

When I asked Perplexity this exact question, it read "geo" as Generative Engine Optimization, which is what a lot of the industry calls this whole field, and it answered with a list of AI tracking tools. That's a fair reading. But if your team works with local clients, the other meaning is the one that matters: getting a business named when someone asks for the best roofer, dentist or lawyer in their city. That's what this post is about.

1. Look at the map pack

The first thing I would do is look at the map pack. That is always the beginning of the strategy. You have to see how the business currently ranks inside its city, in its area, for the terms it wants.

It comes first because it's the baseline, and because Google's own AI answers lean on the same profile the map pack runs on. In BrightLocal's August 2026 study of 1,355 business locations, the Google Business Profile was the most cited source in both AI Overviews and AI Mode.

But don't assume the map pack carries over to every engine. Local Falcon looked at more than 350,000 locations of multi-location brands: 35.9% showed up in Google's local 3-pack, and ChatGPT recommended 1.2%. Winning the map pack is the start, not the finish.

2. Ask each AI engine, and read the sources

Once you have an idea of where the business stands, ask each AI engine the question a customer would ask: "best orthodontist in [city]", "who should I call for a roof leak in [city]". Then look at the sources each engine pulled from.

There are tons of aggregators. A lot of the time it'll pull from Google or Yelp. ChatGPT especially: in that same BrightLocal study, Yelp showed up as a source in 80% of ChatGPT's local answers. But sometimes it pulls from third-party roundup sites that are specific to that city: the "best dentists in Tulsa" article, the local magazine's annual list, a neighborhood blog. Find all of the ones a person in that city might care about, and write them down.

That list is the most useful thing the team will produce all day.

3. Get the client onto those lists

When you find a list, the goal is simple: how can we get this business into this roundup? Or, how can we get this person to make a new roundup or a new best-of list for a certain month?

That's outreach. Nobody's software does it for you. It's an email to the editor of a local site, a pitch to the person who writes the monthly list, and sometimes paying for a listing if that's how the site works. It's also one of the few things that can change the answer without touching the client's website, because the engine was already reading that list. You're just getting your client's name onto it.

4. Check the review habit

Next, look at how actively customers are leaving reviews. Does this business have a review habit? Recent reviews matter. The practitioners Whitespark surveys for its local ranking factors report count review recency among the signals for the map pack, and a business with a steady stream of reviews reads as more current than one whose last review is from years ago.

If they don't have a habit, set one up. I'd use a system like HighLevel to build an automation that sends a text sequence when a job is marked done, so asking for the review isn't something anyone has to remember. Then I'd give them tap-to-review tags they can keep where the work happens: in the truck for a roofer, on the front desk for a lawyer, a dentist, an orthodontist, any public-facing service business. The point is to make leaving a review take ten seconds.

5. Keep the Google Business Profile active

Then I'd look at how often they're actually posting on their Google Business Profile. Photos of real jobs. Things that are relevant to the community or to people who might be looking into their services. A profile that was set up once and never touched looks like a business that was set up once and never touched.

6. Profile optimization and NAP consistency

And lastly, the overall Google Business Profile optimization, and NAP consistency: the business name, address and phone number written exactly the same way everywhere they appear. On the website, the profile, Yelp, the directories, the lists. An engine that finds three versions of a business can't be sure they're the same one.

What the team probably already knows

I'm sure a good local agency is aware of 60 to 70% of that already. Map pack, reviews, profile, NAP: that's local SEO. What changes with AI is steps 2 and 3. The team has to learn to ask the engines, read the sources, and treat the city's best-of lists as the target, because that's where a lot of the answer is coming from.

So which tools actually matter?

For training a team on local AI answers:

• The AI engines themselves. ChatGPT, Perplexity, Claude and Gemini, asked the question a customer would ask, with the sources read every time. It's free and it's the most important habit to build.
• A map pack rank check for the terms and the city.
• A review automation system, like HighLevel, plus tap-to-review tags.
• The Google Business Profile itself.
• An AI answer tracker once the team has more clients than it can check by hand. The AI Visibility Tracker asks each client's buyer questions across all four engines every week and shows the sources behind each answer, which is step 2 on a schedule. Step 3 is still yours. To be straight about it: it asks each question as written. It doesn't ask the same question from ten different cities, so put the city in the question.

For the version of this that's about why an engine picks one business over another, this is how I diagnose it.

Frequently asked questions

Is local AI search different from local SEO?

It's built on it, but it isn't the same. Google's own AI answers lean on the Google Business Profile. ChatGPT leans on Yelp and other directories. And most businesses that make Google's map pack don't get named by ChatGPT. The new part is that a single answer names two or three businesses and cites what it read, so every source it reads matters more than it used to.

What should a team do first with a new local client?

Check the map pack for the client's terms in its city, then ask each AI engine the customer's question and write down every source it used. Those two steps tell you almost everything about where the client stands.

How do you get a client onto a city's best-of list?

Find who writes it, and reach out. Sometimes it's a paid listing, sometimes it's a pitch to the editor, and sometimes the right move is asking them to write a new list for a month or a category the client fits.

Do reviews really affect what AI says?

Yes. Reviews sit on the exact sources the engines read most for local questions: the Google Business Profile and Yelp. A steady stream also shows the business is still active, which a big pile of old reviews can't.

Sources: BrightLocal, where AI search gets local business information (August 2026), Local Falcon research, Whitespark Local Search Ranking Factors.

If your team is working through this and hits a city where the sources make no sense, shoot me a message on LinkedIn with the question and what came back. I'll tell you what I see.