Inside Target’s AI-Powered Turnaround Plan

Generative AI couldn’t have come along at a better time for Target.

That’s the assessment Target’s tech chief, Prat Vemana, told Business Insider after newly minted CEO Michael Fiddelke spent the morning laying out an ambitious turnaround strategy at the retailer’s Minneapolis headquarters.

The company had just reported its 13th consecutive quarter of weak sales — a stretch that saw big box competitors like Walmart and Costco gain market share — and leaders from across the organization were assembled to make the case that it was a new day at Target.

“I couldn’t have asked for a better time for AI to show up, because now we have a need. We have a bold agenda ahead of us,” Vemana said.

By the numbers for the coming year alone, Target’s agenda is indeed bold: $1 billion in additional capital investments, $1 billion more in new operational commitments, 30 new stores, 130 remodels, overhauls to as much as 75% of the assortment in certain categories, and blisteringly fast product releases.

Fiddelke said the work is already paying off with an acceleration in February sales, and the company expects to report sales growth in every quarter of 2026.

Underpinning the success or failure of nearly every aspect of that strategy are a host of AI-powered tools developed by Vemana’s teams.

“Tech is a through-line in everything that we do,” Vemana said.

Target has a long tech legacy, but the competition is as fierce as ever

Target has long based marketing and operational decisions on vast troves of data, and it was an early mover in applying machine learning to its business.

The company’s predictive analytics department even became the subject of controversy in 2012 when it was revealed that buyer behavior could be used to infer something about a shopper so intimate as a pregnancy. (Whether or not it actually did is unclear.)

A decade-plus later, that almost seems quaint in the age of Meta AI glasses, Ring doorbells, and hackable robot vacuums. Indeed, an entire industry now generates astronomical revenues by anticipating users’ tiniest impulses.

And in just the last few years, generative AI technologies started running laps around what their ML predecessors could do. Meanwhile giants like Amazon and Walmart are reaping the advantages that come with scale.

In other words, the competition caught up, and machine learning that was once such an advantage for Target is arguably less of a leg up in the hyper-paced world of AI.

But as companies around the world cite AI in trimming their tech workforces, Vemana said he needs his teams in the US and India to match the output of an even larger company.

“Generative AI actually opened up a completely other dimension of growth,” he said.

Target is investing in tools to help teams save time — and predict trends

For Target’s apparel teams, a new Trend Brain platform combines visual analysis of fashion photos with social media sentiment analysis in an effort to predict what styles will take off in the next season.

That lets designers pull a new designs together in a matter of weeks rather than months, positioning Target to rotate through collections nearly twice as fast as before, said Gena Fox, Target’s head of apparel.

It also gives the company an early alert for products that might not sell as strongly.

“We saw really early that polka dots were trending,” she added, referring to a line of swimsuits. “So we were actually able to go back and buy into that more and then move out of styles that weren’t performing as well.”

Trend Brain offers another advantage when it comes to handling the endless details of designing a fashion campaign.

“After a few hours of this, you’re going to skip a few things, right?” Vemana said. “AI doesn’t get tired.”

And as that product arrives in stores, managers and associates now have a new suite of Target-designed tools on their handheld Zebra scanners that simplify everything from planning displays, to prioritizing restocking tasks, to getting immediate support without having to go find a desktop computer and fill out forms.

“We really care about human touch — that’s what matters,” Vemana said. “Anything that gives time back to the team, they give back to the guest, and guest is happy.”

Meeting customers where and how they shop

Agentic shopping grabbed headlines last fall, and Target is among the first retailers to offer multi-item baskets through ChatGPT. It’s also one of Google’s partners in developing the open commerce protocol on Gemini.

But beyond those more obvious applications, Vemana said AI is helping the Target team deliver improvements to how customers shop with its app.

For starters, the executive said the way generative AI handles information required a complete overhaul of the app’s code base — a task that AI coding companions made much faster.

“We’ve rewritten the entire app already — what would have taken years and years — in, like, 18 months,” he said.

And with a third of in-store shoppers using the app during their visits, the changes aren’t just for chatbots.

The app itself now has several customer-friendly features, like shopping list scanner tool that converts handwritten notes into a shoppable list, to a store mode that pins list items on a map of an actual location.

Target didn’t break out the full size of its AI investment, and a big question for companies implementing AI is whether the capital required to deploy it will generate a positive return on investment.

Vemana emphasized that Target views the tech as essential for its growth plan.

“It’s not just doing AI for the sake of doing AI,” he said. “We are foundationally strengthening so that we have the right kind of AI applied in the right places.”