AI-powered, conversational search is no longer hypothetical: it already breaks broad local queries into layered, specific questions—about services, availability, amenities and travel time—and will surface a location only if its listing can answer those precise needs. That difference matters: an incomplete or inconsistent location profile can steer a customer to a competitor even when your brand has capacity.

This shift was the focus of a September webinar featuring Google’s Caroline Dissaux and practitioners from Adecco and Uberall. Speakers warned that the familiar task of keeping hours and a phone number accurate must now scale to meet the expectations of large language models (LLMs) and Google’s newer AI features, which synthesize many signals into a single conversational reply.

What AI search now asks of listings

Conversational search often unpacks a single request into multiple sub-questions: which nearby location does this service this weekend, which has the right amenities, and which one is realistically accessible for the user? For multi-location brands, a single national profile won’t reliably answer those granular queries. As Uberall’s Krystal Taing put it: “What’s different is the scale, the complexity, and importance of getting these right across every location and what it means to LLMs and generative AI.”

Practical priorities for Google Business Profile (GBP) and related surfaces

The panel recommended concrete data points and behaviors that reduce the factual gaps AI will surface. These are accuracy and relevance priorities—not guarantees of AI placement, but necessary signals for AI-driven results.

  • Post timely updates, offers and events. Use GBP posts to share availability, booking windows, event dates and offer details. Match cadence to what each location can sustain—don’t force a social schedule that creates stale or inaccurate posts.
  • Keep social links and visual assets current. Link company social accounts to each profile when available; refresh photos and videos to reflect menu changes, seasonal layouts or new services.
  • Supply structured menus and service lists. Restaurants should provide menu items, ingredients and current prices when feasible. Service businesses should list offerings and relevant constraints (e.g., appointment length, age limits, or required documents).
  • Engage with reviews and messaging. Reviews add customer-driven context and highlight recurring questions about service, availability or amenities. Responding helps surface issues you should resolve at the profile level.

Measurement and scale: where teams still struggle

Dissaux noted that GBP post views and clicks can help evaluate an individual post’s engagement but don’t prove that a post caused AI visibility in Overviews or “AI Mode.” The panel advised comparing post topics to the actual queries and AI answers a location receives, tracked at the location level.

At scale, brands can reuse promotions, but local detail makes content useful to individual customers. The speakers cautioned against publishing prices or specifics that can’t be maintained: inaccurate details can damage trust and drive lost visits. The practical governance rule: designate a single source of truth, assign an owner for each location, and run recurring checks to detect conflicts.

Five operational fixes to implement now

These tactical steps let multi-location teams improve accuracy without wholesale tech investments:

  • One source of truth per location. Audit hours, categories, attributes, services, menus, inventory and booking details; resolve conflicts before syndicating updates.
  • Close the review gap. Assign location owners, set response standards, and use recurring customer feedback to identify missing or misleading facts.
  • Localize content at scale. Reuse approved creative where appropriate, but add neighborhood, image and service details that matter to local searchers.
  • Automate with human judgment. Use systems to flag missing fields and routine inconsistencies; require human review for policy-sensitive claims, brand exceptions and unusual complaints.
  • Measure at the location level. Track a consistent set of customer questions and AI answers for each location and competitors, then compare those signals with engagement and business outcomes over time.

What teams should do next

Start with an audit and a clear owner for each location before expanding automation. The immediate value is operational: accurate, consistent location data reduces the risk that AI will surface competing businesses or incorrect availability. Over time, measure which profile signals correlate with the queries and conversational answers your locations actually receive—then tighten the processes and exceptions humans must review.

What to watch next: how search platforms surface AI-driven answers and which profile fields they rely on most. For multi-location marketers, the opportunity is straightforward: systems can scale many updates, but only human governance can keep those updates reliable.