The rapid migration of consumer product research from traditional search engines to artificial intelligence platforms is forcing a fundamental shift in how enterprise brands manage digital presence. According to a July 2026 L.E.K. Consulting survey of 2,650 U.S. consumers, 46% of AI users now initiate purchase research on standalone platforms like ChatGPT, Gemini, Perplexity, or Claude. This represents a significant jump from 25% in 2024, while traditional search usage has simultaneously plummeted from 43% to 24% over the same period. To address this inversion, Azoma is positioning its Agentic Commerce Optimization platform to help brands navigate the complexities of Answer Engine Optimization (AEO) and AI visibility. By tracking how brands are represented and recommended by various AI shopping agents, the company aims to provide the visibility necessary to maintain market share in an increasingly agentic commerce landscape.
The Divergent Logic of AI Shopping Agents
The strategic challenge for enterprise brands lies in the fact that different AI agents rely on vastly different data ecosystems to generate recommendations. Azoma’s Q2 2026 analysis of millions of shopping agent citations reveals significant discrepancies in how these models source their information. For example, Alexa for Shopping draws 73% of its citations from affiliate sites, whereas ChatGPT relies on earned media for 41% of its citations. Gemini shows a different profile, sourcing 41% of its information from retailers and 37% from earned media. Walmart Sparky utilizes earned media for 36% of its citations, while 30% come directly from brand.com sources.
This fragmentation means that a brand's visibility is not uniform across the AI ecosystem. A product might secure a top recommendation on ChatGPT but remain entirely absent from an Alexa for Shopping response for the same query. Azoma is attempting to bridge these gaps by offering prompt-level tracking and citation-source analysis across a diverse range of agents, including Amazon Rufus, Google Gemini, and Walmart Sparky. The company argues that monitoring a single agent provides only a narrow snapshot, whereas comprehensive visibility requires simultaneous monitoring of both open-web chatbots and retailer-embedded shopping assistants to capture the full picture of brand presence.
Scaling AEO Through Multi-Agent Benchmarking
As the AI visibility category matures, the technical requirements for enterprise-grade tools are shifting from simple reporting to actionable optimization workflows. Azoma is positioning its platform as a solution that moves beyond merely describing visibility to actively managing it through end-to-end workflows. This approach targets the core components of Answer Engine Optimization (AEO), which involves refining product data, content, and the third-party sources that AI agents cite during the recommendation process.
For enterprise decision-makers, the ability to perform competitive benchmarking is becoming a critical requirement. Because AI agents can disagree on product rankings even when presented with identical prompts, effective benchmarking requires running comparisons across multiple agents and prompts simultaneously. Azoma provides competitive share-of-voice tracking that breaks down brand performance by prompt, specific agent, and time period. This capability is designed to help brands like L'Oreal, Unilever, Mars, Beiersdorf, and Reckitt understand how their visibility fluctuates across the fragmented landscape of ChatGPT, Gemini, Amazon Rufus, Alexa for Shopping, and Perplexity, allowing them to adjust their data and content strategies to influence how these various AI systems interpret and recommend their products.
Key Takeaways
- AI-driven product research has surged to 46% of users as of July 2026, up from 25% in 2024, while traditional search has dropped to 24%.
- Citation patterns vary wildly by agent, with Alexa for Shopping sourcing 73% of citations from affiliate sites compared to ChatGPT's 41% from earned media.
- Azoma provides cross-platform visibility tracking for major agents including ChatGPT, Gemini, Amazon Rufus, Walmart Sparky, and Perplexity.
TechInsyte's Take
In our view, the data provided by Azoma signals a looming crisis for traditional SEO-centric marketing departments. The inversion of search habits—where AI platforms are now the primary entry point for nearly half of researchers—suggests that the "single source of truth" for brand visibility has been shattered. We see this as a transition from a centralized web index to a fragmented ecosystem of proprietary agent logic. For CIOs and CMOs, the strategic implication is clear: investing in a single optimization strategy is no longer viable. The massive variance in citation sources—ranging from 73% affiliate reliance in Alexa to high earned media usage in ChatGPT—means that enterprise data must be simultaneously optimized for retailer-embedded bots and open-web LLMs. Companies that fail to adopt multi-agent visibility tools risk becoming "invisible" in the very channels where their customers are increasingly making purchasing decisions.
Questions & Answers
How does the shift from traditional search to AI platforms impact brand discovery?
The shift represents a move from keyword-based indexing to agentic recommendation. With 46% of users starting research on AI platforms, brands can no longer rely solely on traditional search visibility; they must ensure their data is structured to be cited by diverse AI agents like ChatGPT and Gemini.
Why is it insufficient to monitor only one AI assistant for brand visibility?
Because different agents use different sourcing logic, a brand may appear highly visible on one platform while being completely absent on another. For instance, Alexa for Shopping relies heavily on affiliate sites (73%), while ChatGPT leans more on earned media (41%), requiring distinct optimization approaches for each.
What technical capabilities should enterprise leaders prioritize in AEO tools?
Decision-makers should look for tools that offer prompt-level tracking, citation-source analysis, and competitive benchmarking across multiple agents simultaneously. Crucially, the tool should provide optimization workflows to fix visibility gaps rather than just reporting on them.
What is the difference between shopping assistants and open-web chatbots in terms of data sourcing?
Retailer-embedded agents like Amazon Rufus and Walmart Sparky often rely on different data sets than open-web chatbots like ChatGPT. An effective AEO strategy must account for these differences to ensure a brand is correctly represented across both commerce-specific and general-purpose AI models.
Source: GlobeNewswire
