In 2016, 7-Eleven began a digital transformation aimed at redefining convenience. The starting point was loyalty. “Step one was to build a product discipline, bring the technology in house, and reduce reliance on third parties,” says Scott Albert, VP and head of store and enterprise products.

Two years later, the Texas-based retailer reapplied the product playbook, now powering store systems across more than 13,000 US and Canadian locations. “We moved from projects — start date, end date — to product: continuous improvement and iteration,” Albert says. “From outputs to outcomes, co-owned with design and engineering.”

Albert knows the terrain. A company veteran who cut his teeth in operations, he led product for loyalty and now oversees digital product for store systems, fuel, restaurant concepts, and merchandising, evidence of how far the model has scaled.

Setting the foundation

The idea was straightforward but the shift wasn’t. “It was tough early on because it meant change,” Albert says. “The business was used to saying, ‘I need X.’ Often that wasn’t the real problem. Our job was to get underneath, understand the problem, design a solution for now and the future, and then iterate.”

It takes several ingredients to solve big problems, like customer research, business process knowledge, data, and technology, so it’s natural that product teams are cross-functional. But that structure can also create competing priorities if not managed correctly. While the setting is convenience retail, the lesson applies to any CIO shifting from project-based delivery to product-driven transformation. “Success depends not on org charts, but on cross-functional trust, buy-in, and commitment,” he says.

That structure set the foundation, and the real breakthroughs came from applying product thinking to their daily work.

Product thinking in action

“For me and my team, the customer is the store associate,” Albert says. That focus shaped priorities to remove low-value tasks, surface just-in-time insights, and let systems work for people, not the other way around.

The team learned this firsthand on midnight store walks. In one New York City visit, they noticed a new associate glued to her phone. “We thought she was distracted,” Albert says. “Turns out she’d recorded her trainer so she could remember.” That single observation sparked a redesign of training to move job aids and how-to videos from a back-room PCs to mobile devices on the floor, embedded in the flow of work.

The same product instinct of watching users, identifying friction, and iterating has carried into 7-Eleven’s AI initiatives. AI-assisted ordering, for example, reduced what was once up to 30 hours a week of manual work to under an hour a day, freeing up associates to focus on customers. At scale, those savings add up to more than 13 million hours reclaimed annually, and test-and-learn pilots tying the changes to about $340 million in incremental sales.

The back office has been transformed as well. After migrating store systems to the cloud with its 7-BOSS platform, 7-Eleven layered in “quick cards” that surface AI-generated insights and let associates act in three clicks or less. A clustering model identifies lookalike stores by sales mix, location type, even seasonality, and pushes tailored assortment recommendations. “With three clicks, you can add an item, forecasting kicks in, and delivery happens in days,” Albert says.

Together, these stories trace a clear pattern of observing the customer (in this case the store personnel), solving for their pain points, then amplifying the solution with data and AI. It’s product thinking at work.

Operating like a product company

Behind the scenes, the mechanics mirror digital natives. Teams run in pods with product, engineering, and design as a three-legged stool. Quarterly planning sets direction, but roadmaps flex. “Tell me everything you’ll do next year — that was the old model,” Albert says. “Now we focus on quarters, but sometimes that’s too long. We plan, then adapt.”

Release cadence has accelerated as well, from two or three big bangs a year to monthly releases.

The cultural shift is ongoing funding for work that never ends. “There’s no such thing as done in product,” he says. “We’re on the fifth iteration of our forecasting model. We’ll keep improving.”

Start small, measure hard

Albert’s advice to other tech executives: start small. “Find a problem that matters, build a cross-functional team, measure success, and validate results,” he says. “Then add a second team, a third, and you’re off.”

And above all, measure. “Pick metrics backed by data so no one can debate the results,” he adds.

Nearly 10 years after its first loyalty decision, 7-Eleven’s product mindset now extends far beyond consumer apps. The store itself has become a living product, updated monthly, informed by data, and built around the associate.

For Albert, the real measure of success is to make the system work for the associate, so they can delight customers. “It’s the same product discipline, now applied to every corner of the store, and it’s redefining what convenience looks like at scale,” he says.