Google’s New AI Search Rules Create a Challenge for MULO Brands

Google recently published its first official guide to optimizing websites for generative AI Search features. For single-location businesses, the guidance is fairly straightforward. For multi-location brands (MULOs), the same principles apply, but the complexity of executing them scales dramatically with every location you add.

Here’s what the guide says, and what it actually means if you’re managing 50, 500, or 5,000 locations.

How Google’s AI Search Features Actually Work

Google’s AI features rely on two core techniques. The first is retrieval-augmented generation (RAG), where AI Overviews and AI Mode (powered by the Gemini AI model) query Google’s existing search index to retrieve relevant pages, then synthesize that information into a response with supporting links. The second is query fan-out, where the AI runs multiple related queries simultaneously to build a more comprehensive answer from different angles.

The key takeaway here is that Google’s AI is not working from a separate data source. It’s pulling from the same search index that traditional SEO revolves around, which includes Google Business Profile data for businesses with physical locations or service areas. So, if your web pages rank poorly or your GBP listings aren’t fully optimized, your AI visibility can suffer in exactly the same ways your organic visibility does.

Duplicate Content Is a Bigger AI Search Problem Than You Think

Google’s guide reiterates the importance of reducing duplicate content as part of a strong technical SEO foundation. For MULOs, this is a significant operational challenge that deserves attention.

Most multi-location brands use templated location pages, and many of those templates produce near-identical content across hundreds of URLs. Swapping out the city name and address does not make a page unique. Google’s AI is specifically designed to surface content with a unique point of view and non-commodity information. In other words, content that provides real insight rather than repeating generic information.

That means each of a MULO brand’s location pages needs specific optimization. Localized content should reflect genuine differences, including regional service variations, location-specific staff or history, locally relevant FAQs, and neighborhood context.

On the technical side, the guide also emphasizes crawlability, semantic HTML, and good page experience as prerequisites for Google’s AI to access and use your content at all. At MULO scale, these issues tend to get buried. Indexing gaps, slow-loading location pages, and JavaScript rendering problems across a sprawling site architecture can quietly exclude large portions of your footprint from AI-generated results entirely.

GBP Optimization Is Non-Negotiable

The guide is explicit that Google’s AI, Gemini, relies heavily on Google Business Profile data when generating local recommendations. Wrong hours, missing categories, an incorrect primary category, or an incomplete services list can all reduce a location’s visibility in AI-generated responses.

For a single-location business, keeping a GBP updated, accurate, and optimized is a relatively straightforward task that can easily be done manually. For an enterprise-scale MULO with hundreds or thousands of locations, it’s one of the most operationally demanding challenges in local SEO.

Business information gets outdated. Listings get edited by third parties or flagged incorrectly. Seasonal hours need updating. New services need to be added. Primary categories need to be audited periodically as Google’s category taxonomy changes.

Doing this manually at scale is not realistic. This is where an AI agent purpose-built for local SEO, such as Local Falcon’s Falcon Agent, truly shines. The agent can manage GBP listings across your entire footprint, automate routine optimization tasks, create and schedule posts, reply to reviews, and surface actionable insights across locations, all without requiring a dedicated team and countless hours of manual local SEO work.

Creating Useful Content Across a Large Footprint

Beyond GBP, the guide puts significant weight on content quality. Specifically, Google emphasizes that AI search features are designed to surface content that is helpful, reliable, and people-first, with a genuine point of view rather than generic information anyone could find anywhere.

For MULOs, this raises the question of how to produce that kind of content at scale without it becoming homogeneous. The answer is not to completely get rid of templates and write every location page from scratch; that’s simply not scalable.

Instead, brands should build templates that pull in genuinely unique data points per location and establish a clear process for adding real local context where it exists. Think franchisee spotlights, local service nuances, and community-specific details. Google’s AI needs something to differentiate one location from another. Give it that, and it will.

Tracking AI Visibility Across Your Footprint

None of this optimization work means much without measurement. MULOs need visibility into how their locations are appearing, or not appearing, across Google AI Overviews, AI Mode, and Gemini.

Tracking Share of AI Voice (SAIV) at scale helps brand and agency teams understand which locations are underperforming in AI results, what sources Google’s AI is pulling from, and where gaps in GBP data or content quality are costing you visibility.

Treating SAIV as a core performance metric alongside traditional local search rankings (local pack performance) is crucial for ensuring MULO brands earn and retain visibility across Google’s generative AI search features as they continue to grow in prominence.

Google’s recent guide reinforces something MULOs should already know: local search success comes down to accurate data and genuinely useful content, across owned web pages and GBP listings. At enterprise scale, achieving this requires the right tools and automations.