Beyond Search: AI Visibility the New Growth Lever for MULO Brands
For years, agencies have measured local search success with a simple formula: check Google Maps and Search rankings, optimize, repeat. However, the rise of AI-driven search experiences, including Google’s AI Overviews and AI Mode, Gemini, and of course standalone tools like ChatGPT, has fractured what “local visibility” means, with AI visibility creating both a challenge and an opportunity for digital marketing agencies working with multi-location brands.
Going forward, the agencies that can measure and interpret AI visibility and turn insights into action will be the ones driving the most growth for their multi-location clients.
Why AI Visibility Matters
AI search tools are not replacing traditional search altogether, but they are changing the way consumers discover local businesses.
Google’s AI Overviews, for instance, increasingly surface AI-generated summaries above map packs and organic results. Pew Research Center recently found that most people encountering these summaries don’t even click through or scroll further down the page, with over a quarter ending their search on the spot.
For multi-location clients, this means that visibility in AI answers can be just as important as a map ranking. A business could rank number one in a local pack but still be absent from the AI-generated summary that a growing percentage of users see first. Agencies need to adapt their local SEO frameworks accordingly.
Moving from Ranking Snapshots to Hyperlocal Visibility Intelligence
To adapt to AI-driven search trends, agencies must move away from treating rankings as a single snapshot of local visibility. A client might dominate traditional search in an area while being completely invisible in AI search, but they’d never know if they were only looking at traditional ranking reports.
Geo-grid visibility intelligence offers a more complete view, mapping how a location shows up across different neighborhoods and service areas in both traditional and AI search. For agencies, this insight becomes fuel for strategy.
Coverage gaps aren’t just abstract data, but rather point to missed opportunities where competitors are capturing attention. This helps agencies implement hyperlocal campaigns, turning abstract rank data into actionable marketing direction.
Expanding the Definition of Presence
Another key evolution is recognizing that AI search often pulls data in from beyond traditional local search ranking factors. Reviews, directory entries, social media posts, niche blog mentions, and even Reddit threads can find their way into AI summaries.
That means agencies must take a more holistic approach to managing their clients’ digital presence. It’s not enough to polish a Google Business Profile; agencies should be auditing and improving the broader footprint of brand mentions, ensuring accuracy, consistency, and authority wherever a location is referenced.
This also opens new creative avenues. Agencies can help clients produce localized FAQ content, publish structured answers to common questions, and even seed user-generated content that’s more likely to be surfaced by AI tools. When done strategically, these assets don’t just support SEO, they directly feed the data pipelines that AI draws from.
Visibility Share as the New KPI
If traditional SEO asked “Are we ranking number one?,” the AI era demands a more nuanced metric: share of visibility. In other words, “How visible are we compared to competitors across the entire discovery ecosystem?”
Agencies should introduce this share of visibility concept to clients, reframing reporting from a binary win/lose mentality toward a wider view of the overall competitive landscape. For example, rather than simply showing that a brand ranks in the top three for certain queries, an agency can demonstrate how often that brand appears across both traditional and AI search results in all their target markets.
This shift does two things: it provides a more realistic picture of local discovery in 2025, and it gives agencies a powerful narrative for justifying strategic investments. If a competitor is consistently cited in AI overviews while your client is not, that gap becomes both urgent and measurable.
Turning Insights into Action
Data alone doesn’t drive business outcomes; agencies need to translate AI visibility insights into concrete action plans. That could mean doubling down on review campaigns where competitor sentiment is stronger, creating city-specific landing pages where a coverage gap appears, or fixing inconsistent directory information that is preventing AI systems from trusting a location.
Equally important is building content tailored to conversational discovery. AI tools thrive on clear, structured answers. Agencies can guide clients to create localized Q&A/FAQ content that addresses customer intent directly, from “Does this store offer curbside pickup?” to “Is there parking near this restaurant on Sundays?” These kinds of assets not only help traditional SEO but also increase the likelihood of being cited in AI summaries.
Preparing Clients for the Future of Local Discovery
The old days of measuring local search success with a single Google-centric ranking are over. Multi-location visibility is now distributed across map packs, organic SERPs, AI-generated summaries, and conversational AI search tools. Agencies that embrace this reality will not only provide more accurate reporting but also carve out a competitive advantage for their clients.
Finally, agencies should be careful not to divorce visibility from business outcomes. Clients ultimately care about calls, visits, and revenue. Reporting on AI visibility metrics should always be tied back to tangible impact, whether it’s increased discovery in weak coverage zones or higher engagement driven by improved AI answer presence.
In short, agencies that make AI visibility a core part of their offerings won’t just future-proof their clients’ local presence; they’ll future-proof their own business as well.
