AI Increasingly Shortlists Suppliers Before Buyers Make Contact

SAP Customer Experience laid out research findings that describe a buying journey split between AI tools and the businesses selling through them. AI increasingly handles discovery and shortlisting, while the selling company keeps purchase, fulfillment, service, and returns.

Jessica Keehn, the chief marketing officer at SAP Customer Experience, calls the person who moves between both sides the hybrid AI buyer. In her account, that buyer expects a business to be ready on either side, and most are not.

Product content tools cover discovery in SAP’s framing, and connected enterprise systems sit behind every stage.

The Buying Journey Splits Between AI and the Brand

Companies designed the journey around discovery and evaluation on their own site, store, or sales team, SAP says, while buyers now follow one where AI increasingly handles both before any visit or call.

Keehn writes that SAP’s AI in Customer Experience Report 2027 surveyed 4,800 people in the U.S. and Germany, spanning consumers and business buyers as well as CX practitioners and decision-makers. Nearly all business buyers reported turning to AI when buying or choosing vendors, and she adds that AI figures in at least some buying journeys for half of U.S. consumers.

Closing a business purchase still means working through price negotiations, contract language, approvals, and implementation planning. In SAP’s account, AI leads early, and later stages lean on business context, process, and relationships.

SAP Ties AI Discovery to Product Content Quality

In agentic search, an AI agent researches products on a buyer’s behalf, so the content a business publishes becomes the material the agent works from. As Keehn recounts it, Balaji Balasubramanian, who leads SAP Customer Experience as president and chief product officer, holds that an offering unknown to the AI engine never surfaces.

Keehn argues that a catalog audit has to run continuously, because AI models keep changing what they recommend and every attribute, image, specification, and availability signal affects whether a product reaches a shortlist.

The Merchandising Assistant, listed under SAP’s Autonomous CX offerings, is aimed at commerce merchandisers. It scores product content across a catalog, optimizes descriptions and attributes in bulk, and studies shopper search behavior for demand the catalog does not yet meet. The Catalog Optimization Agent checks catalog entries against quality rules and flagging mismatches between languages and sales channels.

Connected Systems Anchor SAP’s Autonomous CX Strategy

Among U.S. CX decision-makers surveyed, 93% feel prepared for AI-enabled customers and 6% say all relevant systems are connected end to end, SAP reports. Keehn attributes the gap to the company systems behind customer-facing work, which are too loosely linked for AI.

Her example is a retailer holding perishable stock. If AI can see expiry dates and stock levels in ERP alongside demand patterns and promotion history, it can spot a spoilage risk days early. It can then build a targeted promotion with the lowest workable discount.

SAP describes tying customer experience to financial, supply chain, and wider enterprise data. Balasubramanian, who first outlined the approach at SAP Sapphire earlier in 2026, has previously said that an AI agent seeing only CRM data is “practically a guess.”

What This Means for SAPinsiders

Product content becomes part of supplier selection. SAP places discovery and shortlisting inside AI tools, where it says product attributes, images, and availability data decide what surfaces. Content quality therefore weighs on buying decisions that begin before any sales conversation.

Customer experience results reach into operations. SAP’s retail example has AI drawing on inventory and expiry data in ERP, and Keehn argues that AI improves when it learns from complete data instead of fragments. Customer-facing outcomes therefore extend to the teams running those systems.

Brand differentiation moves toward reliability. Keehn describes brand as becoming “about proof, reliability, service quality, and outcome certainty,” the factors AI weighs when deciding which businesses to recommend. Checkout, fulfillment, and returns show customers whether a brand matches what AI suggested.