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There is often a disconnect between "what is working and what is hype" when it comes to AI in food waste, according to ReFED and The Spoon.
Most AI tools in food waste currently focus on measurement and decision support, rather than widely deployed generative AI. Investors are advised to prioritize AI tools that can effectively make waste 'visible' and enable improved decision-making aligned with human incentives at farm, store, or restaurant levels. Crucially, high-quality data is essential for these tools, requiring businesses to potentially update data collection practices or legacy software. ReFED recommends investing in data infrastructure that enhances accuracy and affordability of food waste monitoring platforms, or systems that clean and integrate food system data. Examples include Strella, which uses sensors and machine learning to predict produce ripeness, and Afresh, which uses AI to optimize fresh produce, dairy, and non-perishable food ordering recommendations for stores based on various factors.
Historically, food waste was seen as a standard business cost, but AI tools are changing this perception. Computer vision and sensor-based technologies have been integral in making waste visible in real time, followed by AI-based forecasting, and now generative AI and advanced analytics for better recommendations and even automated decisions. The report highlights tangible results: commercial foodservice operations have reduced waste by 20–53% using AI-powered measuring tools. In food retail, AI-driven demand forecasting prevented approximately 200 million pounds of food loss across 26 countries. Furthermore, AI-powered storage-monitoring systems saved around 20 million pounds of apples across 1,500 storage rooms through more precise inventory decisions.
The consumer household sector faces greater challenges in adopting effective AI food waste tools compared to commercial settings. While AI could theoretically help consumers understand their waste, its causes, and solutions, changing individual behavior proves difficult. Practical barriers include the high cost and complexity of integrating AI tools into existing kitchens or new appliances, making such renovations financially unrealistic for many. Despite these hurdles, companies like Mill are making inroads with compact, in-home food recyclers and companion apps that provide households with data on their specific waste patterns. The ultimate success for AI in this sector hinges on bridging the gap between simply knowing about food waste and actively taking steps to prevent it, by seamlessly integrating tools into daily kitchen workflows.