Analyst Insight: Artificial intelligence is already one of the most powerful tools that retailers and suppliers have to cut food waste and emissions while still keeping shelves full.

The efficiency and resource optimization that AI promises in retail holds a critical sustainability advantage. When it comes to meeting and prioritizing sustainability goals, companies should look to AI to process and compute the necessary information to strike the optimal balance between empty shelves and marked-down waste or spoilage.

An estimated one-third of global food production is lost or wasted each year, much of it stemming from a decades-long disconnect across retail supply chains. Now that we’re in an era defined by data interconnectivity, with retailers increasingly investing in SKU- and store-level intelligence to boost supplier performance, the alignment between demand and supply can be tightened and continuously optimized. Supply chain automation fueled by clean AI-ready data provides an additional opportunity to make these adjustments quickly and confidently. In turn, organizations can reduce environmental footprints while staying agile amid heightened competition and economic headwinds.

Following are three ways to turn AI-powered data and automation into tangible reductions in food waste across retail supply chains.

Use AI for smarter store-level ordering. Manual ordering at the individual store level struggles to balance availability with waste, especially in fresh and short-shelf-life categories. While under-ordering risks missed sales and a poorer shopper experience, over-ordering incurs greater emissions and landfill waste. 

Data automation and machine learning can accurately predict demand at the SKU–store–delivery-period level by factoring in the multitude of key metrics that human teams struggle to connect daily. Historical sales and fill rate data takes into account seasonality, holidays and local events. These are hyper-local dynamics that AI-powered store-level intelligence can recognize, while centralized planning may miss them, tailoring order quantities to each store’s true selling patterns instead of applying broad rules.

Envision AI-powered replenishment that can intelligently align with retailer goals — for example, to maximize prepared deli food sales even if some temporary waste is required — while quantifying tradeoffs and tuning order logic toward explicit ESG and growth targets. These systems are continuously learning and can make proactive recommendations to keep hitting sales targets and sustainability goals.

Intelligently optimize distribution from DCs to stores. When suppliers are not delivering direct to store (DSD), they’re typically moving product through a distribution network, quantifying the cases needed to keep distribution centers adequately for downstream store orders. AI provides a crucial extra pair of eyes on this valuable stock by accounting for expiration dates and optimizing the distribution of products from DCs to stores, preventing spoilage that drives additional emissions and waste.DCs also handle a constant stream of discontinued products, whether it’s an item, pack size, seasonal SKU, or packaging change. AI can identify these outgoing products and direct them to the stores with the best chance of selling through. For example, a pallet of salsa set to be discontinued in two months can be routed to the locations most likely to move it before expiration.Connected data visibility between DCs and stores will empower AI agents to anticipate surplus, flag at‑risk inventory earlier, and recommend targeted reallocation plans that protect both ESG outcomes and service levels.

Operate from a shared ordering forecast. Transparency is crucial in AI-powered systems – both in terms of the logic they use and how it can be validated and shared broadly across teams. AI‑driven collaboration between retailers, distributors and suppliers promotes trusted partnerships where waste reduction efforts compound across channels every day.With connected AI, upstream partners can make smarter purchasing and production decisions to prevent ingredient spoilage and reduce overproduction, optimizing the overall emissions footprint embedded in the products moving through the network. When all parties operate from a shared, AI‑generated forecast they can trust, they can right‑size everything from processing to packaging and transportation.By predicting orders over a longer time horizon – with all the complexity of holidays, assortment changes, local trends, and even weather events – AI enables end‑to‑end supply chain optimization, ensuring that food and other resources move from production through to sale with minimized waste at every stage.

When retailers and suppliers can accurately decide what to make, where to send it, and when to move it, supply chains become faster and far more resource-efficient. The power of AI to continuously learn and improve yields dividends in reduced waste, and stronger margins. There is more opportunity than ever for organizations to define ambitious sustainability goals, systemically work toward them, and measure their progress with AI. We’ill see the effects of those choices compound across the industry in the years ahead.