{"id":99881,"date":"2026-06-25T18:29:22","date_gmt":"2026-06-25T18:29:22","guid":{"rendered":"https:\/\/youzum.net\/repositioning-retail-for-the-ai-era\/"},"modified":"2026-06-25T18:29:22","modified_gmt":"2026-06-25T18:29:22","slug":"repositioning-retail-for-the-ai-era","status":"publish","type":"post","link":"https:\/\/youzum.net\/it\/repositioning-retail-for-the-ai-era\/","title":{"rendered":"Repositioning retail for the AI era"},"content":{"rendered":"

Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and how retailers respond to customer behavior in real time. As legacy retailers navigate a fragmented and hyper-competitive landscape, AI is becoming an operating philosophy.<\/p>\n

<\/div>\n

At Macy\u2019s, that philosophy is more often defined by what senior director of engineering Murali Murugan describes as an \u201cAI-first\u201d approach. \u201cAI first isn\u2019t about adding intelligence on top,\u201d Murugan says. \u201cIt\u2019s about redesigning how decisions happen so the business moves faster and every experience feels more relevant by default.\u201d Rather than layering AI onto existing workflows, Macy\u2019s is embedding intelligence directly into systems that include personalization, search, operational planning, and software development itself.<\/p>\n

The company\u2019s strategy is reflective of a larger shift taking place across retail: moving from isolated AI pilots toward integrated systems designed to compress, as Murugan puts it, \u201cthe gap between the signal and the action.\u201d Early efforts focused on narrow, high-impact use cases like search recommendations and customer engagement, where measurable gains in conversion and reduced friction quickly built internal momentum. \u201cOnce we established the quick wins, scaling was a business decision, not a technology debate anymore,\u201d he says.<\/p>\n

That momentum is now extending into conversational commerce through tools like Ask Macy\u2019s, an AI-powered shopping assistant designed to act more like a personal stylist than a traditional search bar. Whether for a prom, a vacation, or a last-minute event, customers can describe what they need conversationally and receive curated recommendations informed by past purchases, preferences, and context.<\/p>\n

Still, the company sees AI as more of an invisible layer augmenting human judgment than a replacement for it. The long-term vision is retail that feels increasingly seamless, adaptive, and personalized, powered by systems customers may never even notice are there.<\/p>\n

\u201cThe real transformation in this all comes from continuous improvement,\u201d Murugan says. \u201cIt\u2019s about learning from the mistakes, quickly adapting to the newer technology standards that are coming into play, timing, and execution which compound into a meaningfully better customer experience.\u201d\u00a0<\/p>\n

This webcast is produced in partnership with Infosys.<\/em><\/p>\n

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review\u2019s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.<\/em><\/p>","protected":false},"excerpt":{"rendered":"

Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and how retailers respond to customer behavior in real time. As legacy retailers navigate a fragmented and hyper-competitive landscape, AI is becoming an operating philosophy. At Macy\u2019s, that philosophy is more often defined by what senior director of engineering Murali Murugan describes as an \u201cAI-first\u201d approach. \u201cAI first isn\u2019t about adding intelligence on top,\u201d Murugan says. \u201cIt\u2019s about redesigning how decisions happen so the business moves faster and every experience feels more relevant by default.\u201d Rather than layering AI onto existing workflows, Macy\u2019s is embedding intelligence directly into systems that include personalization, search, operational planning, and software development itself. The company\u2019s strategy is reflective of a larger shift taking place across retail: moving from isolated AI pilots toward integrated systems designed to compress, as Murugan puts it, \u201cthe gap between the signal and the action.\u201d Early efforts focused on narrow, high-impact use cases like search recommendations and customer engagement, where measurable gains in conversion and reduced friction quickly built internal momentum. \u201cOnce we established the quick wins, scaling was a business decision, not a technology debate anymore,\u201d he says. That momentum is now extending into conversational commerce through tools like Ask Macy\u2019s, an AI-powered shopping assistant designed to act more like a personal stylist than a traditional search bar. Whether for a prom, a vacation, or a last-minute event, customers can describe what they need conversationally and receive curated recommendations informed by past purchases, preferences, and context. Still, the company sees AI as more of an invisible layer augmenting human judgment than a replacement for it. The long-term vision is retail that feels increasingly seamless, adaptive, and personalized, powered by systems customers may never even notice are there. \u201cThe real transformation in this all comes from continuous improvement,\u201d Murugan says. \u201cIt\u2019s about learning from the mistakes, quickly adapting to the newer technology standards that are coming into play, timing, and execution which compound into a meaningfully better customer experience.\u201d\u00a0 This webcast is produced in partnership with Infosys. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review\u2019s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-99881","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nRepositioning retail for the AI era - 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