Google’s AI Search Roadmap and 5 Local Marketing Fixes

AI-driven search is evolving from simple keyword matching to understanding complex, conversational intent. Modern users no longer just seek a business name; they demand specific details such as service availability, appointment slots, amenities, and precise location fit. For many local enterprises, providing only basic contact information and hours of operation is insufficient to satisfy these nuanced queries. The gap between what customers expect and what standard listings provide highlights the urgent need for richer, more structured data.

During the September 24 webinar titled Google On What’s Next In AI Search + 5 Local Marketing Strategy Fixes, industry experts explored how local businesses can adapt to this shift. Google’s Caroline Dissaux, Adecco’s Bonnie White, and Uberall’s Krystal Taing discussed the critical role of accurate, multi-location data in achieving visibility within AI search results. A central theme emerged: maintaining local information is no longer a static task but a dynamic requirement that must hold up across every single branch of a business.

The Imperative for Granular Location Data

As search algorithms become more sophisticated, they break down broad inquiries into related sub-questions. A customer asking if a nearby branch offers a specific service this weekend expects an immediate, detailed answer. According to Taing, businesses must ensure that categories, attributes, services, menus, inventory levels, and availability are not only complete but also consistent across their website, Google Business Profile, and other digital surfaces. Consistency here does not require identical text everywhere; it means eliminating conflicting facts that confuse both users and search systems.

Taing emphasized the scale of this challenge: “Most of us really have been talking about these things for years. 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.” (31:19)

This distinction is vital for chains. A strong brand-level presence does not guarantee that individual branches can fulfill specific requests. Consequently, a location-by-location data audit is far more effective than assuming a national profile represents every store. Brands must treat each location as a unique entity with its own set of needs and constraints.

Optimizing Google Business Profile Content

Dissaux and White outlined several strategies to keep Business Profile information fresh and relevant. These actions focus on content quality and accuracy rather than promising that adding any single field will automatically trigger an AI answer.

First, businesses should publish regular updates, offers, and events via Google Posts. While timely details like event dates are valuable, the posting cadence should be realistic for the business rather than mimicking social media schedules. Second, linking existing social profiles to the Business Profile helps refresh images and videos when products, spaces, or seasonal offerings change. If a location lacks a dedicated social account, a brand-wide profile can still serve this purpose.

Third, structured data enhances visibility. Food businesses should list menu items, ingredients, and current prices directly, rather than relying on photos of uploaded menus. Service businesses should describe their offerings in detail within their service lists, avoiding the need to create food-style menus where they do not apply. Finally, responding to reviews provides customer-specific context that the business’s own profile cannot supply. It also offers teams insight into which experiences need clarification. Ensuring that messaging channels are easy to find further supports this engagement.

Measuring Impact and Managing Scale

In the Q&A segment, Taing clarified that while Google Business Profile post views and clicks help assess individual post performance, these metrics do not prove that a post caused visibility in AI Overviews or AI Mode. She suggested comparing post topics with the actual queries and AI answers a business encounters. Dissaux added that there is currently no clear-cut metric tying posts directly to AI visibility.

For brands managing multiple locations, the speakers advised reusing promotions but tailoring them with local details. Adding prices is recommended only when they can be kept accurate; variable pricing may be better described clearly without risking unreliable figures. The full webinar underscores that the goal for large teams is not to add every field at once, but to assign ownership, establish a source of truth, and implement gap detection mechanisms.

Five Actionable Fixes for Local SEO

Automation can handle volume, but human oversight remains essential for strategy and exceptions. Taing noted: “It doesn’t mean that a human needs to manually review every single thing. You know, you can have models, you can have these elements, but it does mean that humans should be the ones establishing the strategy, the standards, the guardrails.” (38:29)

To implement this division of labor, businesses should follow these five steps:

1. Establish one source of truth for each location. Audit hours, categories, attributes, services, menus, inventory, and booking details. Resolve conflicts before syndicating updates to ensure accuracy.