As marketing leaders, we are under constant pressure to adapt our strategies to the ever-evolving digital landscape. The rise of AI assistants has been a particularly disruptive force, and many in our industry have rushed to "optimize for AI" under the assumption that these platforms are simply a new channel for commercial queries. However, a recent analysis by Dan Petrovic, Director of the AI SEO agency Dejan, challenges this assumption and suggests that we may be fundamentally misreading how consumers are using these powerful new tools. Petrovic's research, which analyzed 4.4 billion characters, 613 million words, and 3.9 million conversation turns, reveals that a staggering 65% of AI chats have no commercial intent whatsoever. This finding has profound implications for our content strategies, resource allocation, and our understanding of the customer journey in the age of AI. How Users Actually Engage with AI Assistants The data shows that AI users behave very differently from traditional searchers. While a typical search engine query is a discrete event, an AI chat is often a multi-step task. The median chat is just two turns—a quick question and a quick answer—but this masks a long tail of more complex interactions. Over 80% of chats are under 1,000 words, but a small percentage (4.2%) exceed 2,500 words, representing high-value tasks such as editing, coding, tutoring, and data analysis. The typical user contributes only 16-17% of the conversation, with the AI assistant generating approximately 1.5 times more content than the user inputs. This pattern reveals that users are not simply querying for information; they are engaging in collaborative problem-solving sessions where the AI serves as an active partner rather than a passive search engine. When we examine what users are actually doing in these conversations, the non-commercial nature becomes even more apparent. Petrovic classified 24,259 sessions across 42 intent categories and found that the vast majority of interactions fall into categories such as brainstorming (7.7%), planning (6.5%), conversation and emotional support (6.2%), analysis (5.7%), learning (4.7%), transformation tasks like summaries and translations (4.6%), and creation activities including writing and coding (3.9%). A full 25% of interactions fell into an "other" category that included highly specialized requests, roleplay scenarios, and various experimental uses of the technology.

For multi-location brands, the Google Local Pack represents one of the most critical battlegrounds for customer acquisition. However, the long-held belief that visibility can be achieved through a standardized, checklist-based approach to local SEO is rapidly becoming obsolete. Google's ranking system is not a monolith; it is a dynamic, context-aware engine that rewards businesses based on a principle we call signal-fit. This is the degree to which a business listing aligns with the specific, nuanced expectations of local users within a particular industry. As marketing leaders, it is imperative that we move our organizations beyond generic playbooks and embrace a hyper-localized strategy that treats each location as a unique market. Recent analysis from Yext, covering 8.7 million Google Business Profiles across five major U.S. industries, confirms that brand size and advertising spend are not the primary drivers of Local Pack visibility. Instead, what truly moves the needle is the alignment of a business's digital signals with the specific intent of local consumers. Factors such as review cadence, photo quality, and profile completeness all play a role, but their importance is weighted differently across industries and even geographical regions. This data underscores a fundamental truth: Google plays favorites, but its preferences are rooted in a listing's ability to precisely match local context and a user's immediate needs. Decoding Signal-Fit: Industry-Specific Optimization Strategies The concept of signal-fit is best understood by examining its expression across different industries. A one-size-fits-all approach to Google Business Profile (GBP) optimization will inevitably lead to missed opportunities and wasted resources. For multi-location brands, this means that operational simplicity cannot come at the cost of local relevance. In the hospitality sector, functional information carries significantly more weight than aesthetics. Business hours, detailed descriptions, and complete profiles matter most to travelers, who value reliability over polish. Photo volume beyond a reasonable threshold adds little advantage, as guests care less about another angle of the pool and more about whether there is parking when they arrive at midnight.