As generative AI becomes a key part of how consumers discover and evaluate products, a new question emerges for marketers: how can they ensure their brands show up in AI-driven recommendations? This ARF and MSI experiment, the second phase of the seventh study in the Psychology of Gen AI series, reveals that even small changes in prompt wording can significantly influence which brands appear—helping non-market dominant brands carve out visibility by aligning with specific product attributes rather than competing broadly for “best” status.
Member Only Access
Retail media networks and commerce media are redefining the data and measurement landscape, creating new opportunities—and complexity—for marketers and researchers alike. At Commerce & Shopper Intelligence 2026, brands, retailers, and researchers revealed how they’re adapting methodologies and frameworks to better understand increasingly fluid, data-rich shopping journeys.
Member Only Access
Bharad Ramesh – CEO & Founder, Aqxle
Bharad Ramesh (Aqxle) discussed how Lenovo and Unilever automated manual legacy workflows for research and insights with AI agents. Demonstrations of Unilever’s AI agent, Janus, and Mercurion, Lenovo’s AI agent, provided insights into their AI search processes and the outcomes received by the marketers. The objective of AI agents is to provide faster, more reliable but and more comprehensive insights by unifying fragmented data and enabling immediate, actionable interpretation. However, there is no one gold AI agent standard to help marketers make decisions. Aqxle’s solution was to develop a neutral aggregation layer. This solution involves aggregating data from leading answer engine optimization (AEO) data providers, normalizing queries and answer types into a common schema, weighting, calibrating and quality checking the inputs and developing a single trusted visibility score. This approach allows more than one AI agent to be queried simultaneously to provide aggregated data. As a result, marketers can consolidate all sources of information for insights.
Key takeaways:
- AI-driven agents can automate and accelerate marketing analytics by eliminating manual workflows.
- Interactive, query-based insight systems are replacing static reporting formats.
- Cross-source data integration is essential as marketing datasets expand in volume and continue to fragment.
- Aggregating multiple data sources can expose discrepancies that would otherwise drive misinformed decisions.
- Multiple AI agents should be used by marketers.
Watch the Presentation
Download Presentation
Member Only Access
Bharad Ramesh – CEO & Founder, Aqxle
Bharad Ramesh (Aqxle) discussed how Lenovo and Unilever automated manual legacy workflows for research and insights with AI agents. Demonstrations of Unilever’s AI agent, Janus, and Mercurion, Lenovo’s AI agent, provided insights into their AI search processes and the outcomes received by the marketers. The objective of AI agents is to provide faster, more reliable but and more comprehensive insights by unifying fragmented data and enabling immediate, actionable interpretation. However, there is no one gold AI agent standard to help marketers make decisions. Aqxle’s solution was to develop a neutral aggregation layer. This solution involves aggregating data from leading answer engine optimization (AEO) data providers, normalizing queries and answer types into a common schema, weighting, calibrating and quality checking the inputs and developing a single trusted visibility score. This approach allows more than one AI agent to be queried simultaneously to provide aggregated data. As a result, marketers can consolidate all sources of information for insights.
Watch the Presentation
Download Presentation
Member Only Access
