Our Take
Macy's is treating AI as infrastructure for speed and relevance, not a consumer-facing feature layer—the retail lesson most competitors won't learn until they've wasted millions on chatbot pilots.
Why it matters
Legacy retailers are fragmented and losing velocity; the ones compressing the gap between signal and action will move faster on inventory, search ranking, and customer response. This is where competitive advantage actually lives.
Do this week
Retail leaders: audit your current AI pilots and classify each as either a quick-win conversion driver or a slow infrastructure play—kill or fund accordingly before Q4 budgets lock.
Macy's moves AI from isolated pilots to integrated systems
Macy's is embedding artificial intelligence across search ranking, inventory planning, personalization, and software development itself, rather than bolting AI features onto existing workflows. The company's senior director of engineering, Murali Murugan, describes this as an "AI-first" approach: "It's about redesigning how decisions happen so the business moves faster and every experience feels more relevant by default."
The strategy started with narrow, high-impact use cases. Early wins in search recommendations and customer engagement delivered measurable gains in conversion and reduced customer friction. Once internal momentum built around these quick wins, scaling became a business decision rather than a technology debate.
The company is now extending this momentum into conversational commerce through Ask Macy's, an AI-powered shopping assistant designed to mimic a personal stylist. Customers describe what they need conversationally (prom outfit, vacation wardrobe, last-minute event attire) and receive curated recommendations informed by past purchases and preferences. Critically, Macy's positions this as an invisible layer augmenting human judgment, not replacing it.
The real work happens where customers don't see it
Macy's approach inverts the typical retail AI narrative. The flashy elements—virtual try-ons, chatbot shopping—get headlines. The structural advantage comes from compressing what Murugan calls "the gap between the signal and the action": how quickly the business detects a customer behavior, inventory shift, or supply chain bottleneck and responds.
This matters because retail operates on margins measured in percentage points. A legacy retailer still manually tuning search results or managing inventory through batch processes cedes real-time decisioning to pure-play online competitors. Embedding AI into the operational nervous system—search, supply chain, code deployment—means faster iteration cycles and fewer friction points. That compounds into what Murugan describes as "a meaningfully better customer experience."
For a company operating in a hypercompetitive and fragmented retail landscape, speed is structural. The retailer that learns from mistakes quickly, adapts to new technology standards, and executes with timing will outpace one that treats AI as a feature department.
Stop treating AI as a feature layer
Macy's model suggests several hard priorities for retail engineering leaders. First, identify the decisions that block your business velocity: search relevance, inventory allocation, code shipping, customer response time. These are your quick-win candidates, not the consumer-facing chatbot. Second, measure immediately. If search personalization doesn't move conversion, stop. If it does, scale it before building the next thing.
Third, resist the temptation to announce features before infrastructure is solid. Invisible systems that compress decision cycles compound. Announced features that solve cosmetic problems do not. The retail playbook that works is: prove quick wins, scale them hard, extend into adjacent operational systems, then talk about customer-facing conversational tools once the plumbing is reliable.