For Retailers, Agentic Commerce Is Here
The clear message for retailers attending National Retail Federation’s 2026 Big Show in New York last week was that they need to urgently address the challenge brought about by the rapid adoption of generative AI tools by consumers and update their back-office and data systems if they are to thrive in the agentic commerce era.
Agentic AI was everywhere at NRF, emblazoned across the booths of technology exhibitors and the focus of many of the daily conference sessions. The message was simple: retailers face a major upheaval as consumers switch from traditional browser-based search to AI-enabled product discovery.
Consumers are rapidly adopting AI agents to help them find, compare, and, increasingly, buy products—this while many brands are still optimizing for search engines and are quietly disappearing from the models driving the next generation of product discovery.
“Agentic commerce—shopping powered by AI agents acting on our behalf—represents a seismic shift in the marketplace,” McKinsey, the strategic management consultancy, noted in a report published late last year. “It moves us toward a world in which AI anticipates consumer needs, navigates shopping options, negotiates deals, and executes transactions, all in alignment with human intent yet acting independently via multistep chains of actions enabled by reasoning models.”
This, as speakers and panelists at the NRF conference acknowledged, isn’t just an evolution of e-commerce; it’s a rethinking of shopping itself, in which the boundaries between platforms, services, and experiences give way to an integrated, intent-driven flow through highly personalized consumer journeys that deliver a fast, frictionless outcome.
As the McKinsey report noted, the stakes are high. By 2030, the U.S. B2C retail market alone could see up to US$1 trillion in orchestrated revenue from agentic commerce, with global projections reaching as high as $3 trillion to $5 trillion.
This means all the participants in the retail chain, from brands and retailers to logistics and payment service providers, will need to adapt to the new paradigm and successfully navigate the challenges of trust, risk, and innovation.
To help retailers address the immediate challenges posed by the shift to agentic commerce, SAP argues that three steps are necessary: first, restructuring web-page product data to be machine-readable; second, adding semantic summaries for LLM reasoning; and third, tagging products by the problems they solve, not just their attributes.
SAP announced a series of AI-enhanced retail innovations at NRF 2026, including a new storefront model context protocol (MCP) server that enables retailers to make their digital storefronts intelligible to AI and the new AI-native Retail Intelligence solution in SAP Business Data Cloud that leverages data from across SAP software and third-party systems to help provide accurate demand planning, improved forecast accuracy, and lower inventory costs to drive more seamless omnichannel engagements.
SAP Customer Experience has also unveiled a Digital Service Agent recently that can be combined with the Shopping Agent, creating one conversational AI that can handle the entire journey from product discovery and transaction to post-sales support.
These moves reflect a recognition that that LLMs have become a legitimate shopping channel, and that product discovery is moving from search engines to AI recommendations.
This shift challenges years of SEO and brand building. To stay relevant, SAP believes retailers must take an AI-first approach and have strong, connected data that helps agents understand products, predict demand, and respond quickly. Without this strong data foundation, brands will be at risk because if customers get poor recommendations and errors in pricing, trust can disappear fast.
Although some early agentic AI adopters in the retail sector are already seeing the benefits of agentic commerce, many global retailers are still ill-prepared for the holistic transformation they need to succeed in this new retail environment.
As McKinsey noted in a separate report published to coincide with NRF, “while most retail merchandising teams have invested in automation tools and experimented with AI, 71% of merchants say that AI merchandising tools have had limited to no effect on their business so far.”
“The challenge,” McKinsey said, “often lies less in the technology than in how it’s integrated and used. Systems remain fragmented, data is too messy to use to deliver useful recommendations, and adoption is uneven: 61% of respondents say that their organization isn’t at all or is only slightly prepared to scale AI across merchandising.”
Onstage at NRF, Andre Bechtold, president for SAP Industries & Experience, also emphasized that retailers should prepare now for agentic commerce and noted that simply “bolting on” AI tools to existing systems is not enough.
“Retailers are operating in an environment defined by volatility—tariffs, margin pressure, supply chain disruption, and customers that expect real-time, hyper-personalized experiences everywhere,” Bechtold said during a discussion with Gymshark, the workout apparel retailer. “At the same time, boards and investors are asking a tougher question than ever before: what outcomes are we actually getting?”
“The challenge,” he said, “isn’t a lack of innovation. In fact, most retailers have plenty of tools, pilots, and point solutions. The real issue is that disconnected technology doesn’t translate into resilient growth. That’s why the conversation is shifting. It’s no longer about isolated AI use cases or shiny new features. It’s about whether AI and data are embedded across the business—connecting supply chains, finance, merchandising, and customer engagement—in ways leaders can trust.”
Echoing the same point, Thomas Saueressig, member of the Executive Board of SAP SE, Customer Services & Delivery, commenting in a Handelsblatt article this week about a PwC survey of global CEOs that found that companies rarely achieve lower costs or higher sales through the use of AI, emphasized that AI only contributes value when consistently embedded in business processes. “As long as AI runs alongside the core business as an isolated project, the effects remain limited,” he said.
