Lowe’s, one of the largest home improvement retailers in the world, has 1,700 stores carrying a highly complex inventory that serves homeowners, contractors, plumbers, electricians, and professional trades. With more than 300,000 associates, millions of daily customer interactions, and a rapidly expanding digital footprint, Lowe’s business model depends on synchronized, high-quality data across stores, supply chains, and digital channels.

In part 1 of this series, Lowe’s Senior Vice President of Data, AI & Innovation Chandhu Nair explained why the future of enterprise AI depends on mastering structured, unstructured, and uncaptured data — the three foundational layers that power scalable intelligence.

In this second installment, Nair sits down with Rohit Choudhary, CEO of Acceldata, to unpack Lowe’s multi-year modernization journey, the hybrid infrastructure powering its AI stack, and the strategic mechanisms Lowe’s uses to prioritize AI use cases that drive measurable value.

Inside Lowe’s 7-year modernization journey

Lowe’s digital transformation began with a hard reality check: before AI, analytics, and cloud acceleration, the company had to fix the basics. “We had severe technical debt to the point where we couldn’t print our receipts in a store,” Nair recalls. When CEO Marvin Ellison and the CIO initiated the modernization seven years ago, the mandate was simple: stabilize the retail core and rebuild for scale.

Much of the early work focused on revitalizing customer and associate-facing systems.

“We started modernizing our e-commerce by moving it to the cloud and rebuilding our point-of-sale system across all stores,” says Nair. “Our stores are different — it’s a hundred-thousand-square-foot store with 14 different stations. The way you sell appliances is very different from self-checkout or working with plumbers.”

Lowe’s needed a flexible, configurable POS ecosystem purpose-built for home improvement. In parallel, the team modernized the enterprise technology stack end-to-end — infrastructure, data, applications — to support the speed and resilience the business required.

One of the breakthrough accomplishments from this period was the launch of MyLowe’s Rewards, the company’s first home-improvement loyalty program.

“That required not just technology transformation,” Nair emphasizes, “but changing the thinking around a customer-centric approach to everything we do, and then building experiences, platforms, and data layers around it.”

Behind the scenes, an equally critical effort was underway: consolidating data scattered across thousands of stores and systems.

“It was important for us to bring it all together, standardize data management and governance, and create a semantic layer. That work powers everything we’re doing from an ‘unlocking AI’ perspective,” Nair explains.

AI, he notes, has reset the field for every enterprise, and Lowe’s intended to start the race with an advantage: “Given all of our foundational work, the question is: How do we continue to lead the race, without ever having to play catch-up?”

A hybrid strategy for an AI-driven retail enterprise

With modernization as the backbone, infrastructure became the next strategic frontier. Lowe’s intentionally chose a hybrid AI strategy — cloud, on-prem, and edge. “There’s a lot that’s evolving,” Nair says. “It’s important to have your workloads, both training and inference, in the right place.”

Lowe’s uses:

• Google Cloud Platform for e-commerce and large-scale data workloads
• On-premise data centers for high-intensity model training
• Significant edge infrastructure with GPUs inside stores

The edge layer is critical. “We leverage a lot of edge computing for inference,” Nair explains, “including the computer vision work that happens in the stores.”

For a retailer where store-level decisions — inventory, pricing, staffing, fraud detection, safety, and service — directly impact profitability, the combination of centralized intelligence and distributed inference has become essential.

Lowe’s “Lighthouse Model” for measuring value

With a modern architecture and data foundation in place, the next challenge was prioritizing AI use cases with real business impact.

Lowe’s evolved its ROI-based prioritization into a four-dimension evaluation framework: 

• Investment vs. ROI — directly tied to Lowe’s Total Home Strategy
• Leadership readiness — do operational partners have the appetite to co-create with emerging tech?
• Change-management readiness — can the business absorb the operational shifts required?
• Risk and governance profile — what is the brand, compliance, and operational risk?

This led to the creation of six lighthouse areas, where Lowe’s invests deeply and iterates rapidly. Nair explains that they were chosen using a rigorous framework of investment-to-ROI potential, leadership and change-management readiness, and both governance and brand-reputation risk posture.

Because traditional measurement frameworks are too slow or too rigid for AI’s pace, Lowe’s redesigned its value-tracking approach.

“We had to work with our measurement and finance teams to define the right framework,” Nair says. “Some techniques don’t work for AI or take too long. We needed something more dynamic.”

That need for dynamism led to the creation of the AI Transformation Office — an internal command center governing priorities, success criteria, measurement, and readiness.

“It’s a core function continuously looking at use cases,” he explains. “What should go below the line? What should come above the line? What’s changing in the landscape — like agentic AI — and how do we keep this fresh and accountable to the revenue goals we set?”

Lowe’s AI advantage: A foundation built for the future

Across modernization, infrastructure, and measurement, Lowe’s strategy reflects a clear insight: AI leadership depends on foundational strength.

From cloud-native POS to edge GPUs, from semantic data layers to a multi-dimensional prioritization model, Lowe’s is architecting the stack that will power the next decade of retail intelligence.

As Nair puts it, “AI has brought everyone back to the start of the race. Our goal is to keep leading that race, not catching up.”

CDO Magazine appreciates Chandhu Nair for sharing his insights with our global community.