AI Agent for Retail Inventory: Is the Data Ready?

The retail inventory problem starts in the data

It is easy to reduce the subject to „AI will predict demand”. Even a good model will not fix a process in which till sales do not match the stock level, returns are booked late, product variants are duplicated under different SKUs, and the supplier's delivery time was entered once and never updated. In that setting an agent produces elegant suggestions on skewed data.

So every day the agent first combines the data and flags inconsistencies: SKUs with negative stock, products that sell although they are out of stock, differences between the online shop and the warehouse, orders without a confirmed date, goods with high stock and low turnover. The forecast shows a base scenario, a cautious one and one with a promotion. An order proposal has a basis: sales, stock, orders on the way and the delivery time.

What an AI inventory agent costs and when it pays back

The count covers the work around each case: searching, reading, writing and handing over. What remains is the time to check the result, 30% of today's work in the scenario. Before the quote we measure both times on 20 to 100 cases the team has already handled, in a free test that takes 5 working days.

The count includes only the buyer's time. We measure fewer stock-outs and lower stock separately in the pilot, because they also depend on promotions, the season and the buyers' decisions.

When a forecast is not the first step

If the data from the tills, the warehouse and the online shop does not match, the first stage is to establish which data is reliable and who is responsible for exceptions. McKinsey describes predictive demand planning as an area where analytics uses many variables, and that does not guarantee a result in a particular shop. You count the cost of running the agent per unit, for example per order proposal, as FinOps describes. Approval by a person stops the run before an order goes to a supplier, for example with human-in-the-loop in LangChain.

Where to start

The pilot covers one product group and a few shops, compared with the period before the pilot. The wider warehouse scope is described on AI in warehousing. We build this kind of system as part of our AI agents. For the free process review, a description of one case from the last few weeks and the tools it went through is enough. You do not need to send confidential documents, and you receive a written plan within 2 working days.