AI Couldn't Count Peppermint Syrup—Why Starbucks Withdrew Its Inventory Management AI After 9 Months

Starbucks has ended its AI inventory management tool "Automated Counting" that was implemented in North American stores. This decision came just nine months after its introduction. The much-hyped "AI-driven inventory" may have become more of a stressor than a relief for on-site operations.

This tool was designed to allow staff to automatically read inventory of milk, syrups, and beverage ingredients by simply pointing a tablet device at the shelves. Using cameras and LiDAR, it recognized items on the shelves, counted them, and quickly identified shortages. In theory, it sounded modern and convenient.

Initially, Starbucks had high expectations for this system. By expanding it to company-operated stores in North America, the aim was to reduce the time staff spent counting inventory in the backroom, allowing them to focus on customer service and drink preparation. Essential ingredients for popular menu items, like oat milk, cold foam, and caramel drizzle, would not run out. Customers could order what they wanted, and employees would be freed from monotonous tasks. It was a clear success scenario for AI implementation.

However, real stores are not as tidy as presentation materials.

Milk containers look similar. Shelf angles vary by store. Labels are not always facing forward. Syrup bottles are arranged and filled inconsistently. After busy periods, items might be temporarily placed elsewhere. Inside refrigerators, reflections, condensation, and lighting affect visibility. There is a significant gap between the ideal environment for AI recognition and the actual backroom of a café.

According to reports, Automated Counting sometimes confused similar types of milk or overlooked items. A symbolic moment was when, in a video released by Starbucks, the system reportedly failed to recognize a bottle of peppermint syrup on the shelf. If the AI missed inventory in a demo video meant to showcase accurate inventory tracking, the irony is too strong.

Starbucks explained the discontinuation as a move to standardize inventory counting across stores for more consistent operations. They plan to revert to counting beverage ingredients and milk in the same way as other inventory categories. In other words, at least in this area, manual counting was deemed more reliable.

This withdrawal is too valuable to dismiss as merely an "AI failure." Instead, it clearly illustrates the challenges of introducing AI into retail and food service environments.

Firstly, AI "seeing" is not enough. Inventory management requires more than just image recognition accuracy. It only makes sense when product master data, ordering systems, delivery schedules, store-specific sales, storage conditions (refrigerated or room temperature), shelf sizes, and employee workflows are connected. Even if AI can read "something" from a shelf photo, whether it can be used as accurate inventory data is another matter.

Secondly, tools meant to reduce on-site burdens can sometimes demand additional work from staff. Employees rearrange products for AI to read them correctly, face labels forward, manually input items that are not recognized, and check for errors. If employees end up becoming AI assistants, the meaning of automation diminishes.

Thirdly, AI implementation is also a matter of "trust." A system that makes repeated mistakes is not trusted by the field. Initially, there might be hope for improvement despite some glitches, but if it repeatedly wastes time in daily operations, the sentiment of "it will just make another mistake" spreads. At that point, the tool becomes an adversary rather than an ally for efficiency.

This discrepancy was clearly reflected on social media.

In the Starbucks-related community on Reddit, posts celebrating the end of Automated Counting were prominent. One post shared content from an internal announcement stating that the system was officially discontinued and instructed to remove QR code labels. Responses included "great news" and "closing shifts on Wednesday nights will be better."

In another Starbucks employee community, a post expressed joy over the discontinuation, saying it was "so happy I could cry." In the comments, those who handled closing tasks from the start of the implementation expressed sentiments like "really glad it's gone" and "it won't be missed." Of course, Reddit is highly anonymous, and it's impossible to fully verify from outside whether the posters are actual Starbucks employees. Nonetheless, what was common across multiple posts was not a rejection of AI itself, but rather a sense of fatigue from the fact that "actual work didn't get easier."

Even looking at posts from a few months ago, the complaints were quite specific. The app was slow, crashed, didn't read syrup bottles, registered items as something else, and ultimately required manual counting and input. Some even said, "There isn't much time difference even with manual input." This is the most dangerous evaluation for AI implementation. If an automation tool is perceived as "not convenient," it's one thing, but the moment it's seen as "faster if done by humans," the rationale for continued use rapidly diminishes.

On the other hand, not all reactions were negative. Some posts mentioned that once shelf settings, product orientation, and label preparation were in place, it worked well for certain products. The problem was maintaining those prerequisites across all stores. If aligning the field to AI increases workload, is it really efficiency, or just another form of hassle? This is the fundamental dilemma of Automated Counting.

On LinkedIn, the initial atmosphere was rather positive. Posts by Starbucks affiliates explained the use of AI to enhance coffee crafting and human connections, with comments like "great innovation" and "I want this for my home pantry." As expected on a business social network, many viewed it as a new use case for AI.

This contrast is intriguing. From the outside, AI implementation appears futuristic and rational. For management, investors, and the tech industry, inventory management AI is a clear symbol of efficiency. However, for those using it weekly, what matters is whether the device freezes, whether it can correctly read milk in the fridge, and how long closing tasks take. In the conference room, AI's story is "transformation," but in the store, it's "tonight's work."

On X, this news was received with irony. A representative reaction was a short post spreading the sentiment that "after 9 months of operation, it couldn't count or label." For those skeptical of the AI boom, Starbucks' case became a clear example that "AI can't solve everything."

However, it's too early to conclude that "AI is not suitable for restaurants" based on this withdrawal. Quite the opposite. Inventory management in restaurants and retail stores is an area with significant potential for AI. Demand forecasting, order support, waste reduction, stockout prevention, staffing, and material planning during campaigns are all areas where data can be beneficial. The issue is where to place AI.

If AI is placed in the hands of employees standing in front of shelves, it must withstand the pace of the field. It needs to be quickly correctable when wrong, reflect corrections next time, be clearly faster than manual work, and yield consistent results regardless of who uses it. Otherwise, AI becomes a "ritual" of operations rather than an improvement. The act of using it becomes the goal, and only the numerical adoption rate is pursued.

According to past reports by Reuters, Starbucks' supply chain has long-standing deep issues. Problems like stockouts, deliveries, outdated systems, dispersed suppliers, and limited in-store storage are not confined to store shelves. In this context, Automated Counting can be seen as an attempt to supplement a significant supply chain issue with in-store AI scanning. However, if the entire supply chain remains complex, it's challenging to solve problems with just in-store image recognition.

Starbucks CEO Brian Niccol has been advancing a restructuring plan called "Back to Starbucks" since taking office. Reviewing staffing, operations, menu, supply chain, and store experience to ensure customers can order what they want is a central challenge. Inventory management AI was a technical step towards this goal.

However, this decision is significant in that the company chose to "stop" rather than "continue" certain technologies. In corporate DX, implementation itself is often touted as an achievement. AI was introduced, apps were created, data was visualized, and cloud adoption occurred. But what truly matters is whether it is used on-site, produces results, and continues to improve. Stopping a system that doesn't work well is also part of transformation.

What is particularly striking about this case is that the employee reaction leaned towards "it's hard to take care of AI" rather than "fear of AI taking jobs." During implementation, employees might have felt they were being given a replacement. However, in reality, AI became something that needed to be corrected, assisted, and circumvented by humans rather than replacing them. This is a reality many companies face with AI utilization.

AI seems smart. But once integrated into operations, it suddenly becomes gritty. The system reads labels. Humans organize shelves. AI counts. Humans correct mistakes. AI promises efficiency. Humans are pressed for time during closing tasks. If this gap cannot be bridged, no matter how advanced the technology, it won't settle in the field.

Starbucks' withdrawal is both a cold shower on the AI boom and a lesson for the next AI implementation. AI is not magic. It only functions when the on-site environment, workflows, existing systems, employee trust, and a correction loop for failures are in place.

The AI that missed the peppermint syrup is not just a joke. It encapsulates the difficulty of AI working in the real world. High precision on screen, but misidentifying milk on the shelf. Futuristic in demos, but slow and cumbersome during closing tasks. Efficiency in management materials, but additional hassle on-site.

Starbucks plans to focus on more frequent store replenishments and supply chain improvements moving forward. While this AI withdrawal is a setback on that journey, it is also an opportunity to reassess reality. To ensure customers can order what they want to drink, accurate data, reliable delivery, user-friendly tools, and on-site buy-in are needed more than the word "AI."

The success or failure of AI implementation is not determined by the flashiness of the technology. Ultimately, it depends on whether a tired employee in the backroom before closing thinks, "I can use this."

Source URL

Engadget: Details the reasons behind Starbucks ending its AI inventory management tool after nine months, collaboration with NomadGo, misrecognition issues, and reactions on social media.
https://www.engadget.com/2179029/starbucks-abandons-its-ai-inventory-tool-after-only-nine-months/

Reuters: Initial report on Starbucks ending Automated Counting in North American stores. References internal newsletters, Starbucks comments, misrecognition issues, and the context of performance and supply chain improvement strategies.
https://www.reuters.com/business/starbucks-scraps-ai-inventory-tool-across-north-america-2026-05-21/

Reuters: Related article addressing Starbucks' supply chain issues, AI inventory management tool miscounts, outdated systems, and dispersed suppliers.
https://www.reuters.com/legal/legalindustry/inside-starbucks-supply-struggles-ai-glitches-scattered-suppliers-sandwich-2026-01-27/

NomadGo: Technical explanation of the NomadGo Inventory AI used in Automated Counting. References 3D recognition, inventory counting via smartphones or tablets, and improved inventory accuracy.
https://www.nomad-go.com/news-pr/starbucks-rolls-out-ai-inventory-tool-to-cut-count-times-and-automate-restocking

GeekWire: Report from September 2025 on the introduction. References partnership with NomadGo, use of computer vision, spatial computing, AR for inventory counting, and plans to expand to over 11,000 stores in North America.
https://www.geekwire.com/2025/starbucks-rolls-out-automated-counting-tech-for-inventory-with-help-from-seattle-area-startup/

Reddit r/starbucks: Examples of employee and user community reactions to the end of Automated Counting. References the trend of welcoming comments.
https://www.reddit.com/r/starbucks/comments/1tgkrfe/automated_counting_officially_retired/

Reddit r/starbucksbaristas: Posts and comments on the experience of using Automated Counting. References misrecognition, manual input, dissatisfaction with workload, and voices that it worked under certain conditions.
https://www.reddit.com/r/starbucksbaristas/comments/1q58mq5/lets_talk_about_the_automated_count/

Reddit r/starbucksbaristas: Examples of posts and reactions celebrating the end of Automated Counting.
https://www.reddit.com/r/starbucksbaristas/comments/1tinxx0/rest_in_pieces_automated_counting/

LinkedIn: Examples of initial posts by Starbucks affiliates and positive reactions on the business social network.
https://www.linkedin.com/posts/mikebassani_how-ai-powered-automated-counting-is-brewing-activity-7369058357195767811-JgxZ