How Retailers Are Rebuilding Operations Around AI, Not Just Adding It On
AI is changing retail from the inside out: search rankings, inventory, and code shipping speed. Here is what that means for how legacy retailers compete.
LUMIEN4 min read
Artificial intelligence is changing how retailers operate, and according to MIT Technology Review, the biggest shifts are not customer-facing gimmicks. At Macy's, senior director of engineering Murali Murugan says the company has adopted an "AI-first" approach that focuses on redesigning how decisions are made, not just layering intelligence onto existing systems. The areas seeing the most movement: product search rankings, supply chain inventory flow, real-time responses to customer behavior, and how fast engineers can ship code.
What happened
MIT Technology Review published a piece on June 25, 2026, examining how AI is reshaping retail operations from the inside. The central argument: the most consequential changes are not the ones consumers see in store apps or chat windows. They are happening in back-end systems.
Macy’s is one of the named examples. Murali Murugan, the company’s senior director of engineering, describes the retailer’s posture as “AI-first.” His framing is worth noting directly: “AI first isn’t about adding intelligence on top. It’s about redesigning how decisions are made.”
The specific operational areas the piece calls out include:
- How products surface in search results
- How inventory moves through supply chains
- How fast engineers ship code
- How retailers respond to customer behavior in real time
The piece frames AI not as a product feature but as an operating philosophy for legacy retailers trying to stay competitive in a fragmented market.
Why it matters
Most retail AI coverage focuses on the consumer layer: virtual try-ons, chatbot assistants, personalized recommendations. Those features are real, but they are also relatively easy for competitors to copy. The harder and more durable work is rebuilding the decision-making layer underneath.
Search ranking is a good example. When a retailer’s internal search is driven by AI, the rules for which products appear first change. Suppliers and brands that understood the old merchandising logic now face a different system. That has real consequences for what sells.
Supply chain decisions are similar. Real-time AI-driven inventory management means fewer manual forecasting cycles and faster responses to demand signals. For a large retailer like Macy’s, even small efficiency gains across thousands of SKUs add up quickly.
Developer velocity matters too. When engineering teams use AI tools to write, review, and ship code faster, the retailer can iterate on digital products at a pace that was not previously possible with traditional staffing models.
For legacy retailers specifically, this is where the competitive pressure is sharpest. Newer players built their stacks with these assumptions baked in. Older retailers are retrofitting.
Our take
The Murugan quote is the most useful thing in this piece. “Redesigning how decisions are made” is a clean way to describe what actually separates serious AI adoption from a press release. A lot of companies have bolted AI onto existing workflows and called it a transformation. That is not what is being described here.
We work with clients across e-commerce and service businesses, and the pattern holds: the operators getting real value from AI are the ones who asked “what decision does this replace or improve?” before buying any tool. The ones who are frustrated bought a chatbot and stuck it on their contact page.
That said, this piece is light on specifics. We do not get numbers, timelines, or before-and-after metrics from Macy’s. “AI-first” is still a framing, and framings can be aspirational. The test is whether the decision-making architecture has actually changed, and we do not get enough detail here to verify that.
Watch for follow-up reporting that includes measurable outcomes: inventory carrying costs, search conversion rates, deployment frequency. Those are the numbers that tell you whether the philosophy is producing results.
What to do about it
If you run an e-commerce operation or supply a retailer, think about which decisions in your own business are still manual and repetitive. Prioritize those before building customer-facing AI features. The back end is where the leverage is, and it is also where your competitors are least likely to copy you quickly.
Start with one decision, measure the before state, apply AI, and measure the after. That is the only way to know if the philosophy is working.
