As generative engine optimization (GEO) gains attention across retail and e-commerce organizations, many brands are approaching it like SEO: a visibility challenge that can be assigned to an agency, measured against competitors, and optimized over time. However, this may prove to be the wrong framework.
GEO’s strategic value isn’t limited to how often a brand appears in AI-generated recommendations, especially compared to competitors. Its value lies in what those recommendations reveal about how consumers think, how they describe their needs, what alternatives they’re considering, and how those behaviors are changing over time.
For retailers navigating an increasingly compressed path to purchase, that intelligence may ultimately be more valuable than the visibility itself.
Consumers are now using AI platforms to ask highly contextual questions about products and categories. It helps them make purchase decisions. Unlike traditional search, these interactions often contain detailed descriptions of preferences and frustrations. The interactions also signify customer goals and tradeoffs. They provide a level of insight into customer intent that marketers have historically spent significant resources trying to uncover through surveys and focus groups. Marketers also study search behavior and sales data.
The emerging question is then how to recognize that the process of monitoring and understanding those interactions is now a strategic capability in its own right. GEO is more than how brands improve their presence within AI-generated responses.
GEO AS A CUSTOMER INTELLIGENCE FUNCTION
Every interaction between a consumer and an AI assistant contains demand signals. Consumers reveal how they define their problems, which attributes matter most in a purchase decision, what alternatives they are considering, and which tradeoffs they are willing to make. When aggregated across thousands of interactions, those conversations create a constantly evolving picture of customer intent.
A beauty brand might discover that consumers are increasingly prioritizing ingredient transparency over efficacy claims. A footwear retailer might identify emerging concerns around comfort, sustainability, or fit before those themes become obvious in sales data. A home goods company might notice customers using entirely new language to describe a category, signaling a shift in how they perceive value.
These are business insights that can influence merchandising, product development, inventory planning, pricing strategy, and positioning. The organizations that learn how to capture and interpret those signals effectively may gain a meaningful advantage over competitors that view GEO solely as an optimization exercise.
WHY OUTSOURCING GEO CREATES RISK
None of this suggests that agencies lack an important role to play. Many organizations will continue to rely on external partners for content strategy, measurement, monitoring, and execution. The challenge arises when GEO is treated exclusively as a service to be performed rather than as a capability to be developed internally.
The most valuable asset created through GEO is the learning generated by understanding why recommendations are being made and how customer behavior is changing around them. When that intelligence sits outside the organization, decision-makers often receive it after it has been filtered, summarized, and packaged into reporting cycles. By contrast, organizations that own the capability internally can connect those insights directly to decisions being made across the business.
The difference is strategic. One approach produces recommendations and reports. The other creates an ongoing feedback loop between customer behavior and business decision-making.
As AI continues to reshape how consumers discover products, the speed at which organizations learn may become as important as the visibility they achieve.
THE COLLAPSING FUNNEL
The rise of AI-driven discovery is also challenging some long-held assumptions about how marketing works. For years, marketers separated brand-building from performance marketing. One created awareness and preference and the other captured demand and generated measurable outcomes. Today, that distinction is increasingly difficult to maintain.
Consumers arrive at a retailer’s website after receiving recommendations, comparisons, and guidance from an AI assistant. The process of discovery and consideration, along with evaluation, are happening within a much tighter window. The traditional funnel becomes compressed.
Many brands are already seeing evidence that performance outcomes are heavily influenced by what happens before a consumer ever clicks an ad. The more informed and confident a customer is when entering the buying process, the easier conversion becomes.
The challenge for marketers is to understand how influence travels through a customer journey. AI is accelerating that convergence, and making it harder to separate into distinct stages.
THE PRACTICAL IMPLICATION
Performance marketing remains essential. Consumers still purchase through retailer websites, marketplaces, stores, and apps. These advertising channels continue to drive the overwhelming majority of retail conversions, with little evidence that this scenario will change in the near term.
What has changed is where many consumers begin forming preferences and narrowing options. Retailers should be evaluating how they appear across major AI platforms. They need to invest in the content infrastructure that supports accurate representation while ensuring that product information, category positioning, and brand narratives are consistent across the sources AI systems rely upon. Just as importantly, they should be building internal processes for understanding what AI-mediated discovery reveals about their customers.
The organizations that approach GEO solely as a visibility challenge may improve their presence in AI-generated recommendations. But the organizations that treat it as a source of customer intelligence may improve far more than that. They may build a deeper understanding of demand, identify market shifts earlier, and make better decisions across the business.
AI is changing discovery, but brands need to own the intelligence created by that shift—or hand it to someone else.
Elizabeth Buchanan is chief commercial officer of Rokt.
