New AI capability connects operational data across warehouse functions to explain disruptions, recommend next-best actions and support decisions while keeping managers in control.
IFS has introduced Fulfillment Flow, a new AI capability from IFS Softeon designed to embed context-aware intelligence into warehouse operations and help teams move from reactive problem-solving to coordinated, adaptive execution.
The capability addresses a growing challenge for warehouse operators: converting vast amounts of data generated across orders, inventory, labour, equipment, automation and fulfilment processes into timely decisions. While conventional AI tools often optimise individual functions, such as replenishment or wave planning, those decisions can create bottlenecks elsewhere in the facility.
Fulfillment Flow takes a broader approach by combining real-time operational context with AI-driven decision support. Warehouse teams can use natural-language queries to understand what is happening, identify the factors behind an issue and receive recommendations on what action to take next.
At its core are specialised AI agents, described by IFS Softeon as digital workers, covering six operational areas: inbound, wave management, labour, automation, slotting and replenishment, and outbound. Each agent draws on a common set of intelligence pillars, including facility conditions, live operations, interdependencies between warehouse functions, site-level goals, workforce availability and automation capacity.
“Warehouse AI needs context. The real value comes from understanding what is driving a situation, what other operational factors are involved, and what can be done about it.”
— Mark Fralick, CTO, IFS Softeon
These agents share operational insights with a central intelligence agent that evaluates the impact of decisions across the entire facility. This is intended to prevent local optimisation from undermining broader warehouse performance, including activities connected to receiving yards and manufacturing line feeds.
A key element of the design is its human-in-the-loop approach. Rather than independently making operational decisions, the digital workers identify issues and propose actions to the central agent, which works alongside the warehouse manager. Managers retain authority over priorities and final decisions, while the system follows those decisions and continues to learn across shifts.
Mark Fralick, CTO of IFS Softeon, said the objective is to make AI an active part of warehouse execution rather than another isolated analytics tool.
The launch reflects a broader shift in supply chain technology: from automation of individual tasks towards intelligence that understands operational dependencies. For warehouses facing tighter delivery windows, complex order profiles and rising service expectations, the ability to coordinate decisions across the facility could become a significant differentiator.
