Bridging the Gap: How Local Brands Can Measure, Track, and Win in the Age of AI Search

As artificial intelligence fundamentally reshapes how consumers discover local businesses, marketers are grappling with a complex new paradigm: visibility without traditional website traffic.

During a high-profile panel at SEJ Live, Sean McCrohan, Vice President of Technology at CallRail, and Steve Wiideman, an industry-recognized search engine optimization contributor and advisor for multi-location brands, pulled back the curtain on AI-driven local search. The experts addressed the growing disconnect between what generative AI assistants are achieving for local enterprises and what currently appears on standard analytics dashboards.

While AI adoption is accelerating rapidly, tracking these interactions remains notoriously tricky. However, insights shared during the event provide a vital roadmap for brands striving to decode AI attribution, optimize multi-location visibility, and capture high-intent leads in an automated ecosystem.

Main Facts: The Current State of AI Citations and Local Search

The core revelation from the SEJ Live session is that AI citation clicks currently account for a modest—yet rapidly growing—share of inbound call volume for businesses. CallRail data indicates that AI-driven citation clicks represent approximately 1% to 2% of total customer calls, a figure that has roughly doubled since January.

Despite this seemingly small percentage, industry experts argue that viewing AI search merely through the lens of direct web traffic is a mistake. Instead, AI search functions as a powerful top-of-funnel discovery channel.

Key takeaways regarding the current landscape include:

• The Dual Trackable Signals: Marketers can currently measure AI impact through two primary methods: referral clicks originating directly from citations within AI responses, and self-reported attribution where customers explicitly state an AI assistant (such as ChatGPT, Gemini, or Perplexity) recommended the business.
• The 24/7 Shift: Traditional web traffic often slows down outside standard operating hours, but AI search traffic skews heavily toward off-hours, with nearly two-thirds of interactions occurring outside the 9-to-5 window.
• The Volatility of AI Answers: Unlike stable traditional search engine result pages (SERPs), generative AI engines display high "prompt drift." Repeating the exact same local search query often yields drastically shifting citation sources.

Chronology: From Early Skepticism to Emerging Tracking Methodologies

To understand where AI search optimization stands today, it is helpful to trace its evolution over the past year:

• Early 2026: AI citation tracking was virtually nonexistent, treated as an unpredictable novelty by local marketers accustomed to traditional Google Maps and local pack rankings. CallRail noted baseline figures near zero for distinct AI-driven phone call attribution.
• January 2026: CallRail began noticing a small but distinct uptick in customer interactions traced back to generative AI tools. At this stage, growth metrics suggested a sharp trajectory, though absolute volumes remained minimal.
• Mid-2026: Platforms like Google Analytics 4 (GA4) began seeing more recognizable traffic patterns from AI engines such as ChatGPT, Claude, Gemini, Perplexity, and Grok. Multi-location brands managed by Wiideman began hovering around the 1% threshold for verified AI-influenced discoveries.
• August 26, 2026 (SEJ Live Session): McCrohan and Wiideman formally presented updated metrics, revealing that AI citation volume had doubled since the start of the year. The conversation shifted from whether AI drives business to how brands can technically adapt their tracking infrastructure, call routing, and data governance to capture these opportunities.

Supporting Data and Technical Realities

The metrics surrounding AI search reveal a landscape characterized by low absolute numbers, high volatility, and unique technical hurdles.

The Data Breakdown

• 1% to 2%: The current share of CallRail customer calls attributed directly to AI citation clicks, representing a 100%+ increase since January.
• Two-Thirds (66%): The portion of AI search traffic that arrives outside regular business hours, peaking well past midnight.
• 7% vs. 90% Consistency: According to analysis by Steady Demand, repeating the exact same local search in Google Gemini results in the same top-cited business only 7% of the time. By comparison, traditional Google Local Packs display roughly 90% consistency for identical queries.
• The Review Threshold: Industry benchmarks suggest that maintaining an average review score between 4.5 and 4.7 stars across platforms (Google, Yelp, TripAdvisor) is crucial for retaining AI recommendations. Falling below this range drastically reduces the likelihood of an AI assistant suggesting a business.

The Technical Hurdle: In-Browser vs. Server-Side Tracking

One of the most critical technical insights shared by McCrohan involved the mechanics of AI crawlers. Traditional dynamic phone number insertion (PNI) scripts that execute inside a web browser fail when encountered by AI data-retrieval agents.

"The crawlers that AI agents are using to retrieve this data do not execute scripts on your page when they go to look at your website. They just don’t, they simply don’t," McCrohan explained.

As a result, marketers relying on in-browser number swapping will miss attribution data from AI bots. McCrohan emphasized that server-side number swapping is essential for businesses wanting to assign distinct phone numbers specifically for AI crawlers—a technical implementation that will become mandatory as privacy regulations tighten and AI discovery matures.

Official Responses and Expert Insights

Throughout the session, both technologists and marketing strategists emphasized that while the tools are changing, the fundamental goals of local marketing remain anchored in trust, consistency, and responsiveness.

Sean McCrohan urged businesses to prepare for a 24-hour economy. Because AI assistants frequently present a curated list of top recommendations (often the top three businesses), a missed call or an unanswered inquiry immediately diverts a high-intent lead to a competitor.

"You don’t have to use mine," McCrohan stated, referring to AI voice agents. "Get something, because more and more of this business is a 24-hour business."

Steve Wiideman highlighted the importance of looking beyond traditional single-platform optimization. Because AI models aggregate data from multiple web ecosystems—including Yelp (which feeds Apple Maps and Bing) and Reddit—brands must diversify their footprint. Furthermore, Wiideman pointed out that a website remains immensely valuable even if human visitors never land on it directly, simply because it serves as the foundational data source feeding the AI assistant’s answer.

On the topic of understanding customer intent, McCrohan noted that call transcripts and site chat logs are goldmines of untapped data. Customers often describe their problems using colloquial language that differs significantly from corporate website copy. Aligning digital content with the actual phrasing found in recorded calls and chat logs dramatically improves a brand’s chances of being cited by an LLM.

Implications for Multi-Location Brands and Local Marketers

The shift toward AI-dominated search carries profound strategic implications for businesses managing anywhere from four to four thousand locations.

1. Centralized Governance Overrides Local Tweaks

When scaling local SEO for dozens or thousands of locations, decentralized management opens the door to rogue listings, inconsistent NAP (Name, Address, Phone) data, and unauthorized phone numbers. Wiideman stressed that centralized corporate governance—managing schema markup, data feeds, and tracked phone numbers from headquarters—is now more important than traditional ranking signals. Data consistency across Maps, directories, and structured data is the bedrock of AI trust.

2. Moving From Keywords to "Semantic Triples"

To succeed in prompt-based discovery, marketers must transition from rigid keyword targeting to semantic triples—specific factual claims that a business wants to be recognized for. Creating a prompt library of 100 to 125 unique inquiries allows marketing teams to monitor "prompt drift" and track overarching trends rather than obsessing over daily ranking fluctuations on volatile AI platforms.

3. Embracing the New Normal

Despite the technical complexities, both speakers offered a reassuring concluding message. The rise of generative AI search introduces exciting capabilities, but it does not completely erase established marketing principles.

As McCrohan aptly summarized:

"It is an exciting new world, but it is not a 100% new world. There is carryover. It will be OK."

For local brands willing to adapt their tracking infrastructure, clean up their review profiles, and embrace 24-hour lead management, the growing wave of AI citations represents an unprecedented frontier for sustainable growth.

(To dive deeper into these strategies, marketers can watch the full, on-demand SEJ Live session for free via Search Engine Journal.)