Event Overview
In a recent interview, Vivek Pandya, lead analyst at Adobe Digital Insights, detailed how data-driven analytics and the rise of generative AI (GenAI) are fundamentally transforming the e-commerce landscape. This conversation, part of the lead-up to Adobe’s widely followed Holiday Shopping Forecast, highlighted both Adobe Analytics’ role in providing market-wide, real-time benchmarking for retailers and the explosive impact of GenAI tools such as ChatGPT on consumer behavior and digital shopping journeys.
Pandya emphasized two core shifts. First, Adobe’s aggregated analytics now track not just individual business performance, but also competitive positioning across the whole retail sector. Second, GenAI-driven discovery—personalized recommendations, price comparison, and shopping research—has experienced massive traffic growth, with over 700% increases in some channels over the past year. These dual forces are converging, reshaping retailer strategy and the technical infrastructure of online commerce.
Significance for E-Commerce and Content Infrastructure
Pressure on Product Feeds and Catalog Quality
As traffic from GenAI-powered platforms surges—Adobe data reports a 4,700% year-over-year rise in AI-driven site visits as of July 2025—a clear consequence is heightened demand for high-quality, machine-readable product feeds. GenAI tools recommend products on the basis of structured product data, pricing, and attribution; incomplete or poorly formatted feeds diminish retailer visibility and conversion rates. The technical ability to rapidly update and enrich feeds across hundreds of thousands of SKUs is now a competitive necessity, not an advantage. AI engines, unlike traditional search, strictly enforce data consistency, so poor catalog taxonomy or outdated listings will be increasingly penalized by AI-powered discovery channels.
Standards in Cataloging and Schema Adoption
The rapid evolution of generative search and recommendation tools is driving e-commerce platforms to prioritize universal cataloging standards. Platforms are converging on standardized schemas (such as schema.org and GS1) to ensure compatibility with GenAI agents and voice commerce technologies. Industry-wide benchmarking—enabled by opt-in, anonymized datasets aggregated by systems like Adobe Analytics—makes category-level performance transparent, accelerating adoption of best practices in data structuring. Retailers lagging in catalog completeness or attribute richness risk reduced AI visibility, especially as “zero-click” experiences become more prevalent on GenAI-enabled touchpoints.
Data Completeness and Speed to Market
With event-driven retail moments (e.g., Black Friday, Singles’ Day, major sports finals) delivering short but intense demand spikes, the ability to onboard, update, and retire product listings in real time has become critical. Generative AI tools consume real-time inventory and pricing data to generate recommendations; stale feeds can result in missed sales opportunities or customer dissatisfaction. Retailers are investing in automation and no-code solutions to streamline feed management, inventory sync, and variant mapping, meeting the lowered latency expectations of both GenAI platforms and end consumers.
Expanding Role of No-Code and AI in Content Operations
Automation technologies, including no-code platforms and AI-powered content generation, underpin the ability to scale and personalize product content. As GenAI platforms influence a growing share of product discovery and conversion—Adobe noted over 90% of surveyed consumers trust AI-generated suggestions—retailers require dynamic content pipelines. No-code solutions allow merchandising teams and category managers to launch and optimize product cards, descriptions, and promotional content without engineering dependencies. Automated enrichment, powered by AI, ensures that key product attributes and customer reviews are up to date and structured precisely for AI consumption.
Analysis of Current Market Dynamics
Consumer Behavior and AI-Driven Personalization
Recent data underscores the expanding role of GenAI in the shopping journey. For the 2024–2025 holiday season, Adobe reported that 38% of U.S. consumers used AI tools to plan purchases, and GenAI-driven sessions now constitute a substantial share of pre-purchase research. The demographic reach of this adoption is broad: while Gen Z leads, Millennials and older generations are increasingly leveraging GenAI for discovery and price comparison. The market is witnessing not only early adoption, but cross-generational normalization of AI-assisted shopping. Traditional advertising and influencer marketing now intersect with AI-driven discovery, shifting the emphasis from mass targeting to real-time, preference-aware personalization.
Fragmentation and Acceleration of Shopping Timelines
The conventional November–December holiday shopping “script” is fading. Adobe and eMarketer data confirm shoppers now begin as early as September, with discovery and research occurring predominantly on mobile, then converging with AI-facilitated platforms as the season peaks. Retailers and brands must synchronize their inventory, pricing, and content calendars with these fragmented, variable cycles. Real-time analytics become essential—retailers that spot and capitalize on early demand signals, or prepare for non-traditional shopping spikes tied to social or sporting events, can optimize conversion and margin far more effectively.
Technology-Driven Shifts in Retail Infrastructure
Mobile commerce continues to outpace desktop; Adobe data found that, as of 2025, over 90% of net new holiday e-commerce growth comes via mobile channels. AI-driven product discovery, initially a desktop phenomenon, is rapidly shifting to mobile; LLM-fueled traffic from mobile devices increased from 18% to 26% of total AI-driven sessions within six months, and is forecasted to surpass one-third by the 2025 holiday season. The integration of AI and mobile not only opens up personalization and discovery for a wider demographic, but also demands that retailers optimize their mobile product feeds, imagery, and checkout flows for AI consumption and recommendation in mobile-first contexts.
Implications for Retail Strategy
Retailers navigating this new landscape face a set of clear imperatives:
- Invest in robust feed management, leveraging automation to maintain real-time accuracy across all product attributes and inventory signals.
- Adopt and enforce universal cataloging standards to ensure consistent, high-fidelity data transfer between internal systems and GenAI-powered discovery surfaces.
- Prioritize mobile optimization—not just for user interface, but for AI readiness, with structured content and frictionless mobile checkout.
- Enable agile, no-code content operations allowing for rapid product onboarding, updates, and dynamic campaign management without developer lag.
- Monitor market analytics closely to distinguish between ephemeral fads and sustained behavioral shifts, using tools like Adobe Digital Insights to adapt not only to the pace of change, but to its direction.
Outlook
The coming months, punctuated by the 2025 Holiday Shopping Forecast, are set to validate the thesis that data-driven analytics and GenAI will continue to redefine competitive advantage in retail. Those who lead in data completeness, catalog standardization, and real-time content agility will capture disproportionate share, as shopping journeys increasingly pass through AI-powered and mobile-dominated environments. Retail infrastructure is evolving from static catalogs and legacy feed systems to intelligent, dynamic, and highly automated pipelines attuned to both consumer demand and the relentless pace of technological innovation.
Sources: eMarketer; MetricsCart; Adobe Digital Insights
The trends highlighted in this analysis underscore the critical importance of well-structured and easily accessible product data for e-commerce success. As GenAI tools become integral to the shopping journey, the need for clean, standardized product information becomes paramount. NotPIM enables retailers to meet these challenges head-on by automating the conversion, enrichment, and standardization of product feeds, accelerating the ability to adapt to the dynamic demands of AI-driven product discovery and mobile-first shopping experiences. This proactive approach ensures that businesses can capitalize on the opportunities presented by GenAI and maintain a competitive edge in the rapidly evolving retail landscape.