The Commercial Intent Myth: Rethinking Your AI Optimization Strategy

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.

This tells us that users are not simply using AI assistants as a new way to search for products and services. Instead, they are using them as tools for creation, cognition, and conversation. The most common use cases are not commercial in nature, but rather represent a fundamental shift in how people interact with technology to accomplish their daily tasks.

The Commercial Intent Breakdown: A Nuanced Picture

While the majority of AI chats are non-commercial, the 35.4% that do show commercial intent provide valuable insights for marketers. However, even within this commercial segment, the distribution of intent is not what many would expect.

Awareness (10%) and Consideration (8.5%) together make up 18.5% of all commercial chats, representing the strongest territory for product content. This is where users are exploring problems, comparing options, and forming their initial opinions about brands and products. These early-funnel interactions are critical for building brand recognition and establishing credibility with potential customers.

Interestingly, post-purchase needs (5.1%) outrank all other categories except for awareness and consideration. This suggests that users are turning to AI for help with using or troubleshooting products they have already purchased. This represents a significant opportunity for brands to build loyalty and reduce support costs by ensuring their post-purchase content is well-represented in AI responses.

The lower-funnel categories tell an equally interesting story. Transactional support (4.8%), Discovery (4.1%), and Decision support (2.8%) collectively represent only 11.7% of all chats. While these interactions are important, they are far less common than many marketers might assume. Users are not flooding AI assistants with "buy now" queries; instead, they are using these platforms for exploration and education.

This data paints a nuanced picture of the role that AI assistants play in the customer journey. They are not simply a new channel for driving transactions, but rather a powerful tool for building awareness, educating consumers, and providing post-purchase support.

Strategic Implications for Marketing Leaders

The findings from this research have several important implications for how we should approach AI optimization:

Rethink Your Content Strategy: A content strategy that is solely focused on driving conversions is likely to be ineffective on AI platforms. Instead, we need to create a balanced portfolio of content that addresses the full spectrum of user intent, from non-commercial brainstorming and learning to early-funnel awareness and consideration. This means investing in high-quality, authoritative content that helps users solve problems, answer questions, and achieve their goals, even if those goals are not directly related to a purchase. The brands that win in the AI era will be those that provide value across the entire spectrum of user needs.

Focus on Building Brand Trust: In an environment where users are exploring problems and comparing options, brand trust is more important than ever. By providing valuable, non-commercial content, we can position our brands as trusted advisors and build relationships with consumers long before they are ready to make a purchase. This is a long-term play, but it is essential for success in the age of AI. The 18.5% of commercial chats that fall into awareness and consideration represent a critical opportunity to influence user perceptions and build brand preference.

Don't Neglect Post-Purchase Support: The fact that post-purchase needs are a significant driver of commercial chats highlights the importance of providing excellent customer support. By creating content that helps users get the most out of their purchases, we can increase customer satisfaction, build loyalty, and drive repeat business. This is an area where many brands are currently underinvested, and it represents a significant opportunity for differentiation.

Adapt Your Measurement and Attribution Models: Traditional last-click attribution models are not well-suited for measuring the impact of AI optimization efforts. We need to develop new models that can capture the value of non-commercial interactions and the role that they play in building brand awareness and driving long-term growth. This will require a shift in mindset from short-term conversion metrics to longer-term brand health indicators.

Conclusion: A New Era of AI Optimization

The research from Dan Petrovic and his team at Dejan provides a much-needed reality check for our industry. It is clear that we cannot simply apply the old rules of SEO to the new world of AI. Instead, we need to develop a more nuanced and strategic approach to AI optimization, one that is grounded in a deep understanding of how users are actually engaging with these platforms.

This means moving beyond a narrow focus on commercial intent and embracing a broader view of the customer journey. It means investing in high-quality, non-commercial content that builds brand trust and positions our organizations as trusted advisors. And it means developing new measurement and attribution models that can capture the full value of our AI optimization efforts.

The age of AI is not about finding new ways to sell; it is about finding new ways to serve. The brands that understand this and adapt their strategies accordingly will be the ones that thrive in the years to come.

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