Artificial intelligence is rapidly changing the math behind the grocery store produce department, proving to be one of the most effective weapons against the nearly 70 million tons of food waste generated in the U.S. annually. However, a new assessment warns that the technology is hitting a wall, not because of bad code but because of human habit.
According to “The Food Operating System: How AI is Being Deployed Across the Food System to Reduce Waste,” a May 2026 report published by food-waste nonprofit ReFED in partnership with The Spoon, retail fresh categories are yielding some of the strongest evidence of AI’s real-world impact.
Food retail is one of the most visible and economically critical points of food waste in the system. ReFED estimates that U.S. retail generated 3.98 million tons of surplus food in 2024, about 5.7% of total surplus food, valued at $26.9 billion (2026). Retail waste occurs mainly in fresh categories, with produce accounting for the largest share by tonnage.
Afresh CEO Matt Schwartz describes the impact AI makes on grocery store inventory levels in visual terms: “Before Afresh, you see a very full backroom. The produce and meat coolers will have a lot of product in them. Afterward, when we deploy the system, the floor will be full, but we’ll see much leaner backrooms.”
The core challenge in retail produce has always been a delicate balancing act: keeping shelves looking abundantly full without letting highly perishable items rot. Traditional ordering systems rely heavily on historical sales, but fresh produce requires deep contextual awareness — factoring in everything from rapidly shifting weather patterns to hyperlocal economic conditions and complex shelf-life variables.
As of March 2026, Schwartz claims the company has prevented more than 200 million pounds of food loss on a projected annual basis.
“Grocery is a pennies business. And when you’re making thousands of decisions per store per week, being approximately 10% off at each decision point adds up to millions and millions of pounds of waste,” he says.
AI platforms tailored specifically for fresh inventory, such as Afresh, are closing this gap. In a pilot featured in the report with a U.S. value grocer, the deployment of Afresh’s predictive ordering platform projected an impressive $2.7 million in annual savings. The system succeeded by minimizing product shrinkage, noticeably improving on-shelf freshness and drastically reducing the manual hours store teams spent managing orders.
Yet, despite these clear technical and financial victories, ReFED’s findings spotlight a critical bottleneck: organizational culture and human behavior.
The report points out that technically accurate, AI-driven ordering recommendations are frequently overridden by store managers and floor operators. In the grocery industry, reducing overstock to cut down on waste directly spikes the risk of out-of-stocks — the dreaded empty shelves that can lead to immediate lost sales and frustrated shoppers. Because store performance metrics traditionally penalize missing stock much more severely than they penalize backroom spoilage, human workers routinely default to overordering.
Furthermore, integrating these anticipatory systems forces changes to standard retail workflows and labor allocations that many legacy grocery chains are simply not structured to support.
The takeaway from ReFED’s extensive assessment is clear: AI is not a plug-and-play silver bullet. While predictive data models are successfully mastering the unpredictable nature of fresh produce, their long-term success relies on grocers restructuring their internal incentives. To truly design waste out of the food system, corporate retail must ensure that store managers are rewarded, rather than penalized, for trusting the data.
