Lowe’s Innovation & Technology Culture

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Our engineering team builds and supports the enterprise cart and checkout platform at Lowe’s – the technology behind every customer purchase across Lowes.com, the Lowe’s app and more than 1,700 stores nationwide. We focus on creating a smooth, dependable and connected checkout experience that lets customers shop however they prefer, whether that’s online, in-store or a mix of both. From syncing carts in real time to keeping transactions secure and fast, we tackle the complex challenges that come with large-scale, omnichannel retail. Every change we make is about making checkout a little faster, smoother and more reliable for both customers and store associates.

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

One of our most impactful initiatives uses AI to help engineers diagnose and resolve production issues faster. We built a tool that connects to existing monitoring systems, pulling in data from logs and performance metrics. Instead of creating a new standalone product, we integrated intelligent prompts directly into the development environment where developers already work. These prompts help identify errors, surface patterns and suggest root causes and next steps without switching tools, giving teams faster insights with minimal disruption to their workflow.

Next, we’re expanding this into an autonomous AI agent that listens for alerts from communication and collaboration tools, automatically analyzing signals, correlating them with system performance data and summarizing potential causes for on-call teams. At the same time, engineers use a code assistant to handle repetitive coding tasks, generating boilerplate code, improving documentation and writing test cases, as well as custom GPTs that turn user stories into feature scenarios and acceptance criteria, streamlining sprint planning.

The Impact of AI By the Numbers

Since launching, Jain’s team has seen a 15 to 20 percent reduction in mean time to resolution for production issues, a 15 percent increase in developer throughput and a 20 percent decrease in manual test-case authoring time.

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Before we integrated AI into our workflows, our engineers spent hours digging through logs, analyzing system metrics and manually connecting alerts across services. Root cause analysis could take three to six hours per incident, often requiring multiple team handoffs, and writing test cases or defining acceptance criteria was just as time-consuming and inconsistent.

With AI now built into our tools, debugging and feature planning take a fraction of the time, and we’ve seen measurable gains in developer velocity, consistency and overall confidence. More importantly, AI has changed how we work. By handling the context-heavy, repetitive parts of engineering, it gives our teams more time to focus on innovation, deeper problem-solving and creating better experiences for our customers.