Showing Up When Customers Ask AI Instead of Google

A growing share of “who should I call” research now starts in an AI assistant rather than a search box. Someone describes their problem in a sentence — a leak under the sink, a fence that needs replacing before winter — and gets back a short list with reasoning attached.

That's a different competitive surface from ten blue links. Instead of ranking in a list a person scans, you're either included in a recommendation or you're not mentioned at all. There's no page two.

Where these answers come from

The systems differ, but the pattern is consistent: they draw on some combination of live search results, mapping and business data, and whatever the model absorbed during training. For local queries specifically, the assistant usually runs a search behind the scenes and summarises what it finds.

That has a practical consequence worth sitting with: the work that makes you findable in local search is largely the same work that makes you citable by an AI assistant. A complete, consistent, well-reviewed business presence is legible to both. There isn't a separate secret channel.

What makes a business easy to recommend

• Unambiguous identity.A model summarising your business needs to be able to say what you do, where, in one line. If your site says “full-service solutions for residential and commercial needs”, there's nothing to summarise.
• Consistent facts everywhere. If your hours, phone number or service area differ between your website, your Google profile and directory listings, a system reconciling those sources has to pick — and may pick wrong, or skip you.
• Explicit service and area coverage.Name the services and name the towns. “Serving the greater metro area” can't be matched against “plumber in Gilbert”. A list of actual places can.
• Structured data on your site. Schema markup describing your business, services and location gives machines an unambiguous version of the facts instead of asking them to infer from prose.
• Reviews with substance. Volume matters, but so does what they say. Reviews mentioning specific services and specific places give a summarising system concrete material to work with.
• Content that answers real questions. Pages addressing what customers actually ask — what a job costs, how long it takes, what happens on the day — are the kind of source these systems draw from.

What's genuinely different

Two things don't map cleanly onto traditional local SEO.

First, reasoning gets surfaced. An assistant often explains why it suggested someone — open now, strong recent reviews, does the specific thing asked about. Attributes that were previously just filters have become part of the pitch.

Second, the query is longer and more specific. People type differently to an assistant than to a search box: full sentences, context, constraints. Businesses whose online presence only covers broad category terms have less surface area to match against than businesses that have addressed specific situations.

What to do about it now

Not much that's exotic, which is either reassuring or disappointing depending on what you were hoping for. Get the fundamentals genuinely right — complete profile, consistent information, named services and areas, structured data, real review flow — and you are doing the work that matters for both surfaces.

What's worth adding is checking. Ask the assistants your customers use what they'd recommend for your service in your town, and see whether you appear and whether what they say about you is right. It's an unglamorous habit and almost nobody in the trades is doing it yet, which is precisely why it's worth doing.

The Local Trust Audit includes an AI-search visibility check alongside the Maps and review scoring, so you can see where you currently stand across all of it.

← All resources