# Macy's Deploys Conversational AI to Reduce Abandonment > Source: > Published: 2026-06-03 00:20:31.206667+00:00 # Macy's Deploys Conversational AI to Reduce Abandonment Macy's has rolled out "Ask Macy's," a conversational shopping assistant built on Gemini Enterprise to help customers complete purchases. Reporting by Google Cloud says the agent was developed in about four weeks and the catalog covers more than **2.5 million SKUs**. PYMNTS reports that during beta testing revenue per visit was **4.75x** higher for shoppers who used the assistant versus those who did not, and that two months after launch the assistant serves thousands of shoppers daily across Macys.com and the Macy's app. The assistant is multimodal (text and images), asks follow-up questions instead of returning a product grid, and includes a virtual try-on feature, according to PYMNTS and Google Cloud coverage. Chad Westfall, Macy's senior vice president of technology product development and customer experience, is quoted in multiple outlets saying the tool is intended to "remove friction and elevate retail shopping," per PYMNTS and Google Cloud. ### What happened Macy's launched a conversational shopping assistant called "Ask Macy's" built on Gemini Enterprise for customer experience. According to Google Cloud's case writeup, the agent was developed in roughly four weeks and connects to a product catalog of more than **2.5 million SKUs**. PYMNTS reports that during beta testing revenue per visit among customers who used the assistant was **4.75x** higher than among nonusers, and that two months after launch the assistant serves thousands of shoppers daily across Macys.com and the Macy's app. PYMNTS and Google Cloud coverage describe the assistant as multimodal, handling text and images, and offering a virtual try-on capability. ### Technical details Per PYMNTS reporting and Google Cloud's customer case, the assistant frames conversations with follow-up questions rather than returning a simple grid of search results. The experience narrows recommendations by eliciting preferences such as fit, fabric, occasion, or color, and supports image uploads for virtual try-on. Google Cloud materials position Gemini Enterprise for Customer Experience as the underlying platform used in Macy's deployment. ### Editorial analysis - technical context Agentic, multimodal assistants like this routinely combine retrieval of catalog items, conversational state tracking, and reranking or rerendering of product lists based on progressively collected attributes. Companies implementing similar agents often face measurement and attribution challenges: reported per-user revenue uplifts can reflect selection bias because early adopters are frequently higher-intent shoppers. For practitioners, isolating net lift requires randomized experiments or careful intent-matched cohorts rather than comparing raw user-versus-nonuser averages. ### Industry context Public coverage from Google's product blog introduces the Universal Commerce Protocol (UCP), an open standard Google says was co-developed with retailers and platforms and lists Macy's among endorsers. Reporting frames UCP and related Google announcements as infrastructure aimed at enabling agentic commerce across discovery and checkout flows. That ecosystem-level work could reduce integration friction between conversational agents and merchant checkout/payment systems if broadly adopted, according to Google Cloud and Google product materials. ### What to watch For observers and engineers evaluating similar projects, key indicators include: whether uplift persists in intent-neutral A/B tests; how the assistant affects overall cart abandonment and average order value versus per-user revenue; latency and reliability under production load; the cost of serving multimodal inference at scale; and the degree to which emerging standards such as UCP simplify checkout integration. Industry reporting so far does not publish randomized-control results or full methodology for the reported **4.75x** uplift, and outlets note that Ask Macy's users skew toward already high-intent shoppers, per PYMNTS. ### Bottom line Reporting documents an early, large-scale retail deployment of agentic, multimodal conversational commerce built on Gemini Enterprise, with promising per-user revenue signals during beta. Editorial analysis: companies deploying comparable assistants typically face attribution, scalability, and integration trade-offs that practitioners should measure explicitly when validating lift. ## Scoring Rationale This is a notable, practical deployment of multimodal conversational commerce using `Gemini Enterprise` with early uplift signals. It provides a useful case study for practitioners but lacks randomized measurement and full methodology, limiting immediate generalizability. 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