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Retail is entering a new era where the interface between shoppers and merchants will increasingly be powered by AI.
Agentic e-commerce embodies this shift. Consider instead of humans searching, filtering, and comparing products, intelligent agents will do the work on their behalf. This capability runs the gamut from discovery to negotiation to checkout. For mid-market and enterprise retailers, this transformation represents both risk and opportunity. Those who prepare their product content enrichment, PDP quality, and structured data for machine readability will become the preferred suppliers in a world where algorithms, not browsers, decide what gets seen and bought. For more detail, check out Sameer Dhingra’s Substack essay on the topic of agentic e-commerce.
Agentic e-commerce refers to a new model of online retail where autonomous, AI-driven agents act on behalf of retailers, brands, and even consumers to streamline, personalize, and optimize the entire commerce lifecycle. Rather than relying on static systems or manual workflows, agentic e-commerce enables self-directed digital agents to handle tasks like product content enrichment, merchandising, pricing, inventory allocation, and even customer engagement in real time.
Traditional e-commerce requires significant manual oversight. Merchandising teams adjust product data, marketers optimize campaigns, and operations staff manage SKU onboarding and syndication.
With agentic e-commerce agents act proactively, ingesting data, identifying gaps, enriching product content, testing changes, and optimizing outcomes without waiting for human initiation. Humans set rules, but the system executes adaptively.
Enterprise retailers and marketplaces manage massive SKU counts, fragmented channels, and increasingly scrutinizing consumer expectations. Agentic e-commerce promises:
Agentic commerce takes the concept of unified commerce, where shoppers have consistent experiences across channels, one step further. Instead of simply streamlining the customer journey, it hands that journey over to AI agents that understand goals, interpret preferences, and transact autonomously. The result is a compressed buying funnel where visibility depends less on storefronts and more on whether agents can easily parse and trust your data. This shift is already underway as major platforms experiment with AI shopping agents that fill carts directly inside conversational apps.
Key points
AI agents don’t browse the internet like humans. They scan and evaluate inputs according to a specific structure. That means your PDPs must be complete, accurate, and machine-readable to even be considered. Today, most e-commerce sites still fall short. Nearly half of large US and EU retailers have mediocre PDP UX, global cart abandonment remains near 70%, and poor image quality continues to drive costly returns. By contrast, retailers that invest in enriched content, consistent schema, and clear images not only lift conversion but also reduce returns. For retailers, that's a direct bottom-line win.
Key points
Shameless plug: Retailers can use Trustana’s enrichment layer to standardize attributes, enrich content, and generate machine-readable data across PDPs, feeds, and agent surfaces.
For AI agents, accuracy and transparency are non-negotiable. They evaluate supply availability, shipping timelines, and policies automatically. If your data is incomplete or misaligned, your offers simply won’t surface. That makes supplier catalog accuracy, price consistency, and return policies not just operational concerns but visibility levers. Studies show consumers heavily factor returns friction into buying decisions, and “not as expected” remains one of the top reasons for returns. These are gaps that can be closed with better enrichment. At the same time, personalization remains a high-ROI lever, with McKinsey reporting 10–15% revenue lift when executed effectively.
Key points
Shameless plug: Trustana can harmonize supplier data, align pricing/policy metadata, and expose consistent, agent-ready feeds.
Agentic e-commerce can’t be managed ad hoc, it requires discipline and measurable KPIs. Eligibility for rich results, structured data accuracy, checkout performance, and return rates are the levers that drive profitability. Baymard research shows that improving checkout UX alone can increase conversion by 35%. Pair this with structured data monitoring in Search Console, realistic return benchmarks, and KPIs like agent pick-rate and PDP completeness, and retailers can quantify the ROI of their investments.
Key points
Shameless plug: Trustana’s platform helps unify enrichment workflows, schema coverage, and feed accuracy for agent readiness.
Trustana is a foundational enabler of agentic e-commerce, turning enrichment into an autonomous, intelligent process rather than a manual slog. With Trustana, retailers no longer just “store product data”; they deploy agents to enrich, optimize, and govern content at scale, ensuring their digital shelf is always optimized for discovery and conversion.
Agentic e-commerce rewards retailers who prepare for a future where machines are the first customer. By investing in enriched, structured product data, aligning return policies, and measuring ROI rigorously, retailers can gain visibility and trust with AI agents. With an enrichment layer like Trustana, businesses can operationalize these practices at scale and position themselves well ahead of the curve.
Effective personalization yields 10–15% revenue lift; targeted promotions improve margins an additional 1–3%. McKinsey
Fix checkout UX to see conversions improve up to 35%. Add product structured data to boost visibility. Baymard, Google
US retail returns hit 14.5% of sales in 2023; online it's about ~17.6%. NRF, Narvar
Close expectation gaps with accurate specs and high-quality images; “not as expected” drives 42% of returns. NielsenIQ, Oberlo
Machine-readable product data, that is product structured data plus feed accuracy, unlock rich results. Google
Pilots are already active that let users shop inside conversational apps, reducing site visits and shortening the buying cycle. Financial Times
