There is a moment many retail planners will recognise. It is Sunday evening, and a major retailer has unexpectedly pulled forward an order. A promotion is performing ahead of forecast, a supplier has flagged a delay, and inventory is already tight across several stores. The week’s plan is suddenly outdated.

Within hours, someone is rebuilding the plan in a spreadsheet, cross-referencing demand forecasts, inventory, supplier availability, warehouse capacity, transport constraints and customer commitments.

By the time they finish, the assumptions they started with have already changed.

This is not the planner’s failure. It is a failure of the model.

Traditional planning frameworks were built for a more predictable retail environment. Today, retailers face volatile demand, shorter product lifecycles, frequent promotions, supply disruption and increasingly high customer expectations.

Planners are expected to balance availability, inventory, cost and service levels while responding to constant changes across the network. The result is too much time spent reacting rather than orchestrating, while decision-making becomes slower and more difficult.

The question is no longer whether retail planning needs to change. It is what to change it to.

What ‘multi-agentic’ planning actually means

One emerging answer is multi-agent AI: Multiple specialised AI agents working together to solve complex planning tasks, with each agent responsible for a defined role.

One agent might interpret demand, customer orders and promotional activity. Another assesses inventory across stores and distribution centres. A third evaluates supplier constraints, lead times and replenishment rules. A solver generates feasible options, while a verification agent checks outputs and flags anomalies.

The opportunity is not simply greater speed. It is a new architecture for decision-making. Agents can reconcile multiple inputs, identify conflicts, assess trade-offs and escalate uncertainty, creating a more dynamic approach to planning.

Real-time re-planning and scenario simulation

One of the most immediate benefits is the ability to compress the re-planning cycle.

Consider a retailer facing an unexpected surge in demand for a promoted product. Traditionally, a planner may spend hours assessing inventory, supplier capacity, replenishment options and the impact on other products or locations before developing scenarios.

With a multi-agent system, changes in demand or supply can trigger re-planning as conditions evolve. Multiple scenarios can be generated and assessed simultaneously.

What happens if demand is 20 per cent higher than forecast? If a supplier shipment is delayed by three days? If inventory is redirected between distribution centres? Or if availability is prioritised for a particular region?

Instead of manually modelling every scenario, AI can provide a ranked set of options, allowing planners to focus on the commercial decision rather than the data gathering.

Real work, real constraints

The potential is not theoretical. IRIS by Argon & Co has been deploying agentic AI planning systems in complex operational environments, including a leading fresh produce cool store and packing operation.

In this environment, products are perishable, planning windows are narrow, and workforce availability, sequencing, geographic coverage and product-specific requirements all interact.

IRIS designed a multi-agent planning architecture integrating demand, workforce, sequencing and business constraint data. Specialist agents processed individual data streams before a solver generated a feasible schedule. A verification agent then checked the output before it reached the planning team.

The result was a working demonstrator producing schedules directly usable by planners, alongside an explainable summary showing what the system had decided and why.

Human-in-the-loop: AI does not replace planners

Perhaps the most important consideration is the role of the planner.

Multi-agent systems are not autopilots. They are amplifiers.

Retail planners bring knowledge that no system can fully encode: Supplier relationships, customer priorities, promotional nuances, seasonal patterns and commercial context. The right model is therefore human-in-the-loop.

AI handles the computational burden – integrating data, applying constraints, generating scenarios and checking feasibility. The planner provides judgement, challenges recommendations, applies context and makes the final decision.

The future role of retail planning teams

The retail planners who thrive in an AI-augmented environment will not simply execute plans. 

They will orchestrate decisions.

They will define the rules of the system, challenge recommendations, understand trade-offs and ensure planning decisions reflect both operational realities and commercial priorities.

That is a more strategic role than spending hours rebuilding spreadsheets every time demand or supply changes.

The technology is ready. The opportunity is to redesign retail planning around what is now possible – creating faster, more resilient and more intelligent decision-making across the retail supply chain.

Whether you are looking to improve demand and replenishment planning, accelerate scenario analysis or build greater resilience into your retail operations, our team can help.

• Get in touch to explore the practical applications of AI-driven planning for your business.