Artificial intelligence is changing retail in ways shoppers may not always see. The most important changes are not limited to chatbot storefronts or virtual try-ons. They are happening inside the systems that decide which products appear, how inventory is managed, how teams build software, and how quickly a retailer can respond when customer behavior shifts.
At Macy’s, senior director of engineering Murali Murugan describes this direction as an “AI-first” approach. The idea is not to bolt intelligence onto old processes, but to make AI part of the way retail decisions are made from the start.
Retail AI Is Moving Below the Surface
For many shoppers, AI in retail may sound like a conversational assistant or a recommendation carousel. Those tools matter, but the broader transformation reaches further into the business. According to the source article, AI is influencing search results, supply chain movement, customer engagement, operational planning, and software development.
That matters because digital retail depends on many small decisions happening at speed. A customer searches for an item. A system decides what to show. Inventory information affects what can be offered. Engineering teams need to improve the experience without slowing the business down.
Murugan frames the work around changing the decision process itself:
"AI first isn’t about adding intelligence on top," Murugan says. "It’s about redesigning how decisions happen so the business moves faster and every experience feels more relevant by default."
That statement points to a shift from AI as a feature to AI as an operating model. In this view, the customer may simply experience a smoother path to the right product, while the intelligence behind that path stays mostly invisible.
From Pilots To Integrated Systems
The article describes a broader retail move away from isolated AI experiments. Instead of treating AI as a series of disconnected pilots, retailers are working toward systems that connect signals to action more quickly. Murugan describes this as compressing “the gap between the signal and the action.”
At Macy’s, early work focused on areas where results could be measured and friction could be reduced. Search recommendations and customer engagement are cited as examples. These are practical places to begin because they sit close to the shopping journey and can affect conversion.
Once those early uses showed value, the question changed. Murugan says:
"Once we established the quick wins, scaling was a business decision, not a technology debate anymore,"
That distinction is important. A pilot proves that a tool can work. Scaling asks whether the organization is ready to put that tool into regular decision-making. In retail, that can mean connecting personalization, search, planning, and engineering practices so they reinforce one another rather than operating as separate projects.
Ask Macy’s Shows The Customer-Facing Side
One visible example is Ask Macy’s, an AI-powered shopping assistant. The article describes it as a tool meant to behave more like a personal stylist than a traditional search bar.
The difference is in how a shopper can describe a need. Instead of typing a narrow product query, a customer can explain the occasion or context. The examples given are a prom, a vacation, or a last-minute event. Ask Macy’s can then return curated recommendations shaped by past purchases, preferences, and context.
This is where conversational commerce becomes more than a chat interface. The value is not simply that the customer can type in natural language. The value is that the system can connect that request to relevant product discovery. If it works well, the experience feels less like searching through a catalog and more like getting help from a person who understands the situation.
Still, the source does not present AI as a replacement for people. It describes the company’s view as an invisible layer that augments human judgment. That is a narrower and more practical claim than saying AI will run retail on its own.
The Real Goal Is Continuous Improvement
The long-term picture described in the article is retail that becomes more seamless, adaptive, and personalized. Customers may not know which AI system helped shape the experience. They may only notice that the recommendations are more useful, the journey has fewer dead ends, or the interaction feels more relevant.
Murugan connects the transformation to ongoing iteration rather than a single launch:
"The real transformation in this all comes from continuous improvement," Murugan says. "It's 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."
That emphasis on compounding improvements is central to the AI-first retail idea. Search can improve. Recommendations can improve. Planning can improve. Engineering work can move faster. Each area affects the others because the shopping experience is not one tool; it is the result of many connected systems.
For legacy retailers operating in a fragmented and hyper-competitive market, the implication is clear from the source material: AI is becoming a way to organize the business, not just a way to decorate the website. The most meaningful gains may come when intelligence is embedded into everyday retail decisions, quietly shaping a customer experience that feels more personal by default.