Multi-location brands are currently reviewing their Google Search Console click traffic, comparing 2026 to 2025, and trying to convince themselves and key stakeholders that AI Overviews are responsible for a year-over-year drop in non-branded clicks.
Today, visibility is distributed across a multitude of destinations, including features in Google Maps such as “Ask Maps,” AI Overviews, AI Mode, ChatGPT, Gemini, Perplexity, Apple Maps, and social search.
The challenge for multi-location brands is that while more locations create more opportunities, they also create more complexity. This is why enterprise and franchise brands require a completely different approach than single-location businesses.
Building on fundamentals, we’re going to explore how we leverage AI to improve our data, landing pages, citations, and reputation. We’re going to discover how to replicate our website content strategy across the web, natively within each discoverability opportunity beyond Google alone.
The Modern Local Discover Ecosystem
With agentic technology emerging, there may even be a point in time where users rarely visit our website at all, as the platforms will provide the appropriate integrations for users to transact directly within them.
The new Local Search Supply Chain includes traditional elements, such as our brand website, business listings, data aggregators, and industry directories, as well as review platforms and user-generated content.
The role of knowledge graphs and entity understanding is increasingly important. Which means, if you’re leaning on an industry data management platform that’s staying ahead, such as Yext, Rio SEO, Birdeye, SOCi, or Locl, you’re already one step ahead.
From what we can tell, AI systems need the following to recommend a business:
- Trusted business information: N.A.P. beyond the old roster of directories.
- Location-specific relevance: Supported by user-generated content.
- Strong reputation signals: Beyond Google Maps and Yelp.
- Third-party validation: Neglected industry directories we should have paid closer attention to.
- And clear entity relationships: Think “Semantic Triples” (QDOBA → offers → burritos, for example).
From Rankings To Recommendations
As we wrap our heads around this “evolution of search visibility,” a common perception is that traditional SEO focused on rankings, where modern discovery focuses on recommendations.
At a very broad level, the experience differences can be broken down into the following stages:
| Stage | Traditional Local Search | AI-Powered Discovery |
| Input | “Tacos near me” | “Find a family-friendly taco place nearby” |
| Evaluation | Search engine ranks results | AI aggregates information from multiple sources to compare options after evaluating confidence and trustworthiness. Authority alone isn’t enough. |
| Evidence | Rankings, proximity, relevance | Reviews, reputation, listings, content, third-party validation. |
| Output | List of businesses | Recommendation with… |
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