_________________________________________________________________________________________ Predictions for 2026
by continuously monitored performance at the item-location level. Suppose a single SKU at a single store begins drifting off pattern. In that case, agentic systems can detect it and adjust the forecast without affecting the other 19 SKUs that are performing as expected.
Another major unlock will see qualitative feedback transformed into structured, operational actions. US retailers, for example, are inundated with hundreds of emails each week containing input from store managers about missed deliveries, mislabeled products, moldy shipments, and more. These realtime insights are rarely incorporated into the forecasting engine. In 2026, AI agents will be able to process and record this feedback, triggering direct system edits such as reconciling inventory, adjusting shelf-life, and prompting change orders.
As improvements unfold, demonstrated success will feed back into data models in real-time, reinforcing the corrective actions taken and training agents to recognize similar patterns more quickly, with greater accuracy. Some agents will become long-term fixtures, continuously improving as they are used, while others can be deployed quickly to manage new or unusual use cases, serving as temporary stopgaps, that yield immediate improvements without overburdening data teams. non-processed options to food insecure regions. The program has effectively reduced perishable waste, while fresh category sales increased, and offers a framework for greater adoption of automated replenishment solutions.
Looking ahead, there is an opportunity to move toward Distribution Center( DC)- level forecasting derived from a retailergrounded, single source of truth. When DCs plan inventory using the same item-location signals that drive store-level replenishment, the retail supply chain becomes even more synchronized. Tighter alignment between brands, distributors, and retailers can even reverse the notorious bullwhip effect, where local miscalculations amplify into greater imbalances upstream.
As these foundations mature, agentic systems can extend further upstream and downstream, to automate replenishment decisions, reduce waste, and tighten demand signals across channels. The main challenges facing teams between now and this reality will involve stakeholder alignment around a clean and accurate data truth, executing local pilots that prove measurable ROI, and scaling with the guardrails and governance needed to maintain quality as automation expands. ■
Automated replenishment and waste-free supply chains
Over the last few years, Dollar General has scaled automatic ordering for its fresh produce program to over 7000 store locations, delivering healthy,
Austin White-Gaynor www. gocrisp. com
Austin White-Gaynor is the Senior Director of Data Science at Crisp, where he leads work in AI-driven retail supply chain optimization. With six years of experience focused on fresh-category replenishment, he has helped build forecasting, inventory, and shelf-life models that reduce waste and strengthen day-to-day operations for CPG brands and retailers. Austin holds a PhD in Geosciences from Penn State University.
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