FitZone Belgium, a fitness center franchise operating 23 locations across Flanders and Brussels, faced a problem familiar to multi-location businesses: when a prospective member asked an AI assistant for fitness centers near them, the brand consistently underperformed relative to its physical presence and market share. Individual locations appeared sporadically in city-level queries, but the brand itself was rarely recommended as a network. The result was that smaller independent gyms with no other competitive advantage were being recommended ahead of FitZone locations in multiple cities.
The franchise director recognized that managing AI visibility for 23 locations independently was impractical, but that a coordinated network-level approach could generate economies of scale. Each location had its own Google Business Profile and website subdirectory, but none had structured schema beyond basic LocalBusiness data. There was no network-level entity recognition by AI systems. AI models did not understand that 23 separate locations constituted a single brand with network benefits for members. The result was fragmented, inconsistent AI visibility that was less than the sum of its parts.
AISOS was engaged to design and implement a coordinated AI visibility architecture for the FitZone network. The engagement covered network-level entity definition, location-level schema deployment, and a content strategy that balanced brand consistency with location-specific relevance. Our guide on AI visibility for local businesses and our AI visibility overview shaped the framework. For multi-city context, see our pages on Brussels and Antwerp AI visibility.
The Challenge
Multi-location businesses face a structural tension in AI visibility: AI assistants prioritize local relevance for location-based queries, which means each location needs independently optimized AI signals, but brand-level consistency requires a unified entity definition across all locations. Getting both right simultaneously is more complex than optimizing a single-location business or a pure e-commerce brand.
The AI visibility audit at the start of the engagement tested 115 queries across 23 cities, with 5 queries per location covering different use cases: general fitness center search, specific class type search, price comparison query, proximity query, and new member recommendation query. FitZone appeared in 18 of the 115 queries (15.7%). The pattern of appearances was essentially random: some locations appeared in proximity queries but not class queries; others appeared for one specific class type but not others. No location consistently appeared across all 5 query types.
The diagnostic showed three root causes. First, inconsistent business profile data across locations created entity confusion for AI systems. Second, no location had the structured class schedule data or member benefit information that AI systems need to answer service-specific queries. Third, the FitZone brand entity was not recognized as a network by AI systems because there was no structured connection between the parent brand and the individual location entities. Understanding AEO for multi-location businesses was the conceptual foundation for the solution design.
The AISOS Strategy
The implementation was organized in three tiers: network tier (brand entity and architecture), regional tier (city-level cluster pages), and location tier (individual location AI signals). Each tier required different actions and different content assets. The network tier was implemented first because it provided the structural foundation on which the other tiers depended.
Network tier: AISOS created a comprehensive FitZone Belgium brand entity with Organization schema referencing all 23 member locations as individual LocalBusiness entities linked through the parentOrganization property. A network-level Wikidata entry was created. A network llms.txt file was deployed at the root domain describing the brand, its geographic scope, its membership model, and its distinguishing features versus independent gyms. This gave AI systems a structured understanding of FitZone as a network rather than as 23 disconnected businesses.
Regional tier: Five regional cluster pages were created targeting Brussels, Antwerp, Ghent, Leuven, and Liege, each providing AI systems with a structured overview of FitZone locations in that area, including a comparison of facilities, class schedules, and membership options. These pages linked to the relevant city pages on the AISOS site for context and to specific industry pages for fitness sector framing. Location tier: Each of the 23 locations received updated LocalBusiness schema with FitnessCenter type, structured class schedule data, membership pricing ranges, and proximity data referencing the nearest public transport stops. Google Business Profiles were standardized and expanded with Q&A content covering the top 8 questions AI assistants receive about fitness centers in Belgium.
The Results
By day 85, FitZone appeared in 74 of the original 115 audit queries (64%), up from 18 (15.7%). The improvement was consistent across all five query types, with the greatest absolute gain in class-specific queries (from 2 to 18 appearances in 23 location-specific queries). Network-benefit queries, asking AI systems why choosing a FitZone over an independent gym was advantageous, showed the most dramatic improvement: from 0 to 14 appearances in 23 queries. These queries had not been in scope before implementation because there was no infrastructure to answer them.
New membership sign-ups increased by 44% in the 85-day post-implementation period versus the prior equivalent period. Attribution was measured through a "how did you find us" question added to the online sign-up flow. AI assistant was selected by 23% of new online sign-ups, a category that had received zero selections in the prior period baseline survey. The average new member tenure over the first six months showed no material difference between AI-sourced and non-AI-sourced members, suggesting that the quality of AI-driven discovery was comparable to other channels.
The coordinated approach also improved traditional local SEO performance. Fourteen of the 23 locations improved their Google Maps ranking for primary queries within the engagement period, an average improvement of 2.3 positions. The structured data improvements and standardized business profile information contributed to this improvement as a secondary benefit. For multi-location businesses, a coordinated AI visibility program frequently produces measurable traditional SEO improvement as a side effect.
Key Success Factors
The three-tier implementation architecture was the structural insight that made the engagement work. Attempting to optimize 23 locations independently without first establishing the network entity would have produced fragmented, inconsistent results. By building from network to region to location in sequence, each tier benefited from the foundation of the tier above it. AI systems that understood FitZone as a network were better positioned to recommend individual locations as part of that network context.
The network-benefit query type was a strategic opportunity that had not been previously identified. By creating content and schema that enabled AI systems to explain why choosing a network gym over an independent gym was advantageous, FitZone gained access to a query type where it had no competition. No independent gym can answer a network-benefit query. Only franchise networks can, and most franchise networks have not structured their AI signals to capture this opportunity. The class-type and location-specific query improvements were expected. The network-benefit query improvement was the discovery that will have the longest competitive impact.
Standardization of business profile data across all 23 locations had an impact disproportionate to the effort required. Inconsistent business names, address formats, phone number presentation, and opening hours across different platforms created entity fragmentation that confused AI systems trying to match location data across sources. Resolving these inconsistencies reduced AI entity confusion and immediately improved the quality of AI responses about specific locations. For any multi-location business considering an AI visibility engagement, data standardization should be the first action, not an afterthought.
Lessons Learned
The FitZone engagement illustrates a principle that applies to all multi-location businesses: the sum of individual location AI signals is less than the value of a coordinated network AI presence. This is the multi-location equivalent of the domain authority principle in traditional SEO. A brand entity that is recognized and understood by AI systems at the network level generates a quality of recommendation for individual locations that cannot be achieved through location-by-location optimization alone. Franchise networks that invest in network-level AI entity definition before individual location optimization will consistently outperform those that start at the location tier.
The speed of impact varied significantly by city. Brussels and Antwerp, where local AI data sources are denser and more frequently sampled by major LLMs, showed measurable improvement within 30 days. Smaller cities like Namur and Turnhout showed improvement after 60 days. Franchise networks operating across diverse market sizes should plan their measurement timelines accordingly, with initial focus on high-population cities where the signal ecosystem is richest and the AI model sampling rate is highest.
Finally, the engagement demonstrated that AI visibility for franchise networks is a franchisor-level responsibility, not a franchisee-level one. Individual franchisees lack the resources, technical knowledge, and cross-location coordination capacity to implement an effective AI visibility strategy independently. Franchisors that build AI visibility into their brand standards and provide centrally managed implementation through partners like AISOS create a systematic competitive advantage that independent operators and poorly coordinated competitors cannot replicate. Contact AISOS to discuss the franchise network implementation model for your specific situation.