The AI agents participants are designing during Remix have one thing in common: they solve the everyday challenges that consume planners' time. Rather than chasing futuristic ideas, teams focus on practical applications that reduce manual work, uncover insights faster, and help planners make better decisions. Although every proposal is unique, several clear themes have started to emerge.
Smarter demand planning
Demand planning generates some of the strongest ideas. Many participants focus on improving the consensus forecasting process by having an agent compare forecasts from sales, marketing, and statistical models, explain significant variances, and recommend a consensus forecast with a confidence score.
Others teams explore agents that compare forecast runs using different data inputs, helping planners understand the value of additional signals before deciding which forecast to adopt. Several teams also automate repetitive forecasting activities, including data preparation and post-processing, to simplify the overall forecasting workflow.
Earlier visibility into supply risks
Another common theme is helping planners identify problems before they become disruptions.
Participants design agents that continuously monitor for demand surges, assess the downstream impact on production, inventory, and customer orders, and highlight where action is needed. Others extend this concept by focusing on gating supplies, where critical materials or constrained resources become bottlenecks as demand changes. Instead of manually searching for these constraints, planners receive alerts alongside recommended mitigation strategies.
Optimizing inventory with context
Inventory optimization is another area where participants see significant opportunity for AI agents.
Rather than applying static inventory policies, one team designs an agent that adjusts safety stock recommendations based on factors such as product value, supply risk, and sourcing flexibility. Another proposal explores how agents identify excess inventory across the network and recommend transfers to locations where it better supports demand, helping reduce inventory while maintaining customer service.
Making planners more productive
Not every idea center on planning decisions. Many participants focus on making planners more productive.
One concept allows users to describe the analysis they want using natural language, with an agent automatically generating the filters, lookups, or expressions needed to build it. Another concept uses planning metadata to answer questions such as who changed a forecast, when a product was last updated, or what planning changes have occurred over the past month.
A common thread
Whether improving forecast accuracy, identifying supply risks, optimizing inventory, or simplifying day-to-day tasks, the ideas all point in the same direction. Participants aren't looking for AI agents to replace planners. They want agents that gather information, perform analysis, and recommend actions so planners can focus on making decisions.
That's what makes these ideas compelling. They aren't demonstrations of what AI could do someday. They are practical solutions to the challenges supply chain teams face every day.
