The coffee giant confirmed to Fortune it has made an operational decision to move to a single model of counting inventory following an announcement in September to deploy its automated counting tool. 

The app, provided by NomadGo, took inventory of beverage components like milk and syrups in order to keep track of item shortages. In February, Reuters, which first reported the discontinuation of the tool this week, cited Starbucks sources who said the app often miscounted or mislabeled items, failing to identify the presence of bottles on shelves.

“We test ideas in our coffeehouses, listen closely to partner feedback, and make changes to deliver a better, more consistent experience,” a spokesperson told Fortune in a statement.

NomadGo didn’t immediately respond to Fortune’s request for comment.

Carl Addison, a Starbucks shift supervisor of nine years based in Shoreline, Wash., told Fortune the automated counting app required stores to rearrange back-of-house storage, which was a time-intensive process. The app’s inaccuracies made employees’ workflow more challenging, he said. If the system counted too much of the product, it wouldn’t send enough of a product a store was running low on. If the system counted too little, it wouldn’t ship enough of a needed product.

“It started off not particularly accurate and got less accurate over time,” Addison said.

Starbucks sent Fortune a handful of barista responses to the automated counting tool expressing that it improved inventory processes and the interface to view inventories. 

“Thanks for discontinuing Automatic Counting! The thought behind it was great, but the execution was proving difficult,” one comment read.

Brian Niccol’s ‘back to Starbucks’ plan

Starbucks has implemented a host of AI tools as part of its “back to Starbucks” plan under CEO Brian Niccol to improve slumping sales and streamline operations. The coffeehouse’s recent AI deployments include Green Dot Assist, an app on the store’s iPads able to provide recipe cards and appropriate ingredient substitutions, as well as troubleshoot issues with machinery. It has a Smart Queue tool that sequences orders to improve order speed and efficiency. Former CEO Laxman Narasimhan said in early 2024 customers were abandoning mobile orders because of long wait times and product availability.

So far, Starbuck’s turnaround strategy, which also includes adding cozier seating and paring back menu items, appears to be working. Last month, the company reported a 7.1% increase in quarterly comparable U.S. sales last quarter, beating analysts’ expectations of a 4.5% increase. Quarterly revenue increased 9% to $9.5 billion.

Retail’s automation challenges

Starbucks’ decision to revert back to its previous inventory system also reflects broader growing pains in how the retail industry has deployed AI. Earlier this month, a major Pizza Hut franchisee sued the chain over its AI program. The franchisee claimed Pizza Hut’s Dragontail Artificial Intelligence system gave gig workers increased visibility to internal systems that enabled them to leverage AI systems for their own benefit, like selecting orders with larger tips and bunching orders, resulting in delayed deliveries and “cascading operational breakdowns.”

As global restaurant automation is expected to balloon into a $28 billion market this year, the pressure is on for these technologies to deliver.

At this point of AI’s development, the challenges retail spaces are facing in scaling the technology has led Santiago Gallino, a Wharton professor of operations, information, and decisions, to this conclusion: “Right now, there is more hype than actual benefit.”

“Many retailers feel the pressure to say they are doing AI-related things and AI-related innovations and running these things before they’re ready to give concrete and real returns,” he told Fortune.

Gallino applauded Starbucks’ decision to walk back its use of the automated counting tool. Inventory management is an ongoing issue in retail, he said, and while technology has advanced to allow companies to improve non-trivial inventory challenges, optimization tools are not a panacea to these issues.

Other companies like Zara, have spent years trying to refine its use of algorithmic technologies. The fast-fashion retailer implemented a microprocessor-based tagging system more than a decade ago, tagging inventory with Radio-Frequency Identification (RFID) that ultimately improved the accuracy of inventory and made it easier to track items across its system.

According to Gallino, the Zara case study is less an argument about technology generating universal benefits to retailers, but rather an example of a company doing the research and iterating a technology’s use to fit its specific needs. While the onus is on retailers to leverage budding technology, AI as a whole will only become a sustainable technology for these companies if it offers a return on investment.

“One general theme that to me is still a little bit perplexing, is how, on many levels, [return on investment] seems to be not a main consideration—the promise that down the road all this is going to make sense,” Gallino said. “That is something that can be out of focus in the middle of the hype.” 

Addison, the shift supervisor, said at this juncture, barista’s workflows are not best served by the technology.

“I would love AI if I felt like it worked, but have to say…I just don’t feel like it’s a solid fit for a retail environment, where accuracy and speed are both really important,” he said. “And it just doesn’t feel like it can really deliver on those fronts for us.”