### What Happened: The Emergence of Agentic CommerceIn a recent interview with InternetRetailing, Liva Ralaivola, VP of research at Criteo, outlined the rapidly approaching future of agentic commerce—a new paradigm in online retail where AI agents act autonomously on behalf of consumers. Unlike previous waves of AI, which focused on assistance and recommendations, agentic commerce introduces self-directed systems that proactively perform product discovery, comparison, negotiation, purchasing, and post-sale management, all transparently connected to user intent and constraints.Ralaivola emphasized that these AI agents will soon become the core interface for shopping, supplanting traditional websites as primary consumer touchpoints. This vision transcends mere automation: agentic commerce systems are designed to interpret nuanced preferences, orchestrate complex transactions, and learn from every user interaction. Criteo has responded by developing foundational AI models to support this transformation, while simultaneously focusing on transparency and privacy amid the increased flow and value of consumer data.### The Significance for E-Commerce and Content InfrastructureThe rise of agentic commerce marks a decisive shift in digital retail, carrying extensive implications for every element of e-commerce infrastructure and content operations.#### Impact on Product Data FeedsAI agents require detailed, accurate, and structured product information to act effectively on behalf of users. Unlike static catalog browsing, agentic commerce depends on machine-readable, up-to-date feeds that support real-time comparison and selection. Retailers must invest in robust, standardized feeds with expanded attribute coverage: color, fit, ratings, sustainability, and more. The transition forces an industry-wide upgrade of data pipelines and validation protocols, minimizing latency and error tolerance. Failure to provide high-quality feeds risks invisibility in the AI-facilitated buying process, as agents will automatically filter out incomplete or inconsistent listings.#### Evolution of Catalog StandardsAgentic commerce demands interoperable content standards, enabling frictionless data exchange across platforms, marketplaces, and AI ecosystems. Legacy catalog structures, often fragmented and brand-specific, lack the semantic depth required by AI agents. The move toward universal schemas—both for product and offer metadata—will likely accelerate, driven by the need for automatic interpretation and decision-making accuracy. Emerging standards such as GS1 and open data models will underpin integrations, ensuring descriptions, specifications, prices, and availability are machine-consumable and always updated.#### Quality and Completeness of Product PagesAgentic AI models automatically evaluate listings for the quality and completeness of information. Missing attributes, ambiguities, or poorly structured images and text significantly decrease the likelihood of an item being presented by an AI agent. In practical terms, this raises the competitive stakes for merchants: investing in enriched content (from detailed dimensions and media assets to third-party certifications) directly affects visibility and conversion within agentic-driven customer journeys. Automation and content audit tools powered by AI will increasingly become strategic assets to monitor and optimize catalog quality at scale.#### Speed of Assortment OnboardingThe autonomy of AI agents compounds expectations for near-instantaneous assortment updates—both for new products and inventory changes. Retailers and brands must streamline onboarding workflows, automating data collection, normalization, and publishing. No-code and AI-powered platforms that enable business teams to quickly adapt or extend assortments without IT bottlenecks become essential. This agility is no longer a differentiator, but a baseline requirement in a commerce landscape where agents will gravitate to the most complete and current assortment pools.#### Proliferation of No-code and AI AutomationThe architecture of agentic commerce creates new use cases for no-code platforms and AI-driven automation:- Content managers can orchestrate personalized recommendation rules, A/B tests, and campaign launches without intervention from developers.- Product content can be enriched or translated using generative AI, matched against catalog standards, and validated for agentic compatibility in real time.- Pricing optimization, stock allocation, and promotional decisions can be executed autonomously within set business guardrails.This democratization of complex commercial logic shortens go-to-market cycles and empowers non-technical teams to iterate rapidly.### Data Privacy, Transparency, and TrustAs agentic commerce amplifies the scale and depth of data processing, concerns about privacy, bias, and explainability intensify. Shoppers will increasingly interact not with brands directly, but through their AI agents—a trend that obscures the boundary between personalized service and algorithmic manipulation. Regulations such as GDPR provide legal frameworks, but Ralaivola points out that true user trust hinges on transparent value exchange: consumers must understand what data is used, why, and what benefit they receive.Retailers and solution providers are developing explainability tools, enabling agents to communicate the rationale behind recommendations or product selections. Building clarity into these opaque AI-mediated experiences is critical, not only for compliance but also for brand reputation in a market where consumer trust is a moving target.### Strategic Imperatives for RetailersThe timeline highlighted by Ralaivola and echoed in recent research suggests that agentic commerce will achieve mainstream adoption by 2026. Major players are already experimenting with embedded AI shopping agents and foundational commerce models, signaling an inflection point reminiscent of the early days of mobile or marketplace retail. Retailers that delay adapting to agentic commerce risk marginalization, as AI agents become the arbiters of product exposure and transaction flow.Adapting to this future means:- Replatforming product data management for automation and interoperability.- Upgrading content generation and enrichment for AI agent visibility.- Investing in <a href="/blog/product_feed/">product feed</a> completeness, with ongoing automated audits.- Accelerating assortment onboarding through no-code and AI workflow solutions.- Embedding transparency and explainability into every recommendation and transaction.### Broader Industry PerspectiveThe significance of agentic commerce is widely recognized beyond individual companies. According to McKinsey, advanced AI agents will soon anticipate, personalize, and automate every stage of the shopping journey, turning product search and discovery into frictionless background operations. Gartner forecasts that by 2028, a third of enterprises will have adopted agentic AI, fundamentally altering customer engagement and logistics. Early pilot programs by global payment networks and retail conglomerates serve as public validations of this trajectory.Despite these advancements, critical challenges remain: ensuring robust fraud prevention, reconciling competing algorithmic interests, and maintaining ethical standards in data use. The next wave of innovation will likely focus on governance, securing the AI-driven commerce pipeline, and defining clear accountability as billions of autonomous agents operate across interconnected digital storefronts.Agentic commerce is set to redefine e-commerce infrastructure, content processes, and the very nature of consumer-brand relationships. Its adoption is not merely a technological upgrade—it is a deep, systemic transformation that will determine future winners and losers in digital retail.As agentic commerce reshapes the e-commerce landscape, the need for robust product data management becomes paramount. The ability to provide comprehensive, structured data feeds will be crucial for retailers to stay visible to AI-driven shopping agents. NotPIM is designed to help businesses address precisely these challenges, offering automated solutions for catalog enrichment, data standardization, and seamless integration with various e-commerce platforms. We believe that by investing in high-quality product data and leveraging automation, retailers can position themselves for success in this evolving, AI-powered future.