Enterprise Intelligence Group
A practical example of a ChatGPT agent that prepares evidence for SKU add, retain and delist decisions without replacing merchant judgment.
For enterprise retailers, workspace agents are the most relevant ChatGPT pattern for repeatable team workflows — reusable agents that can work across connected applications, operate on schedules, and follow workspace-defined permissions and approval controls. Availability and exact functionality depend on the retailer's plan, connected applications and administrator configuration.
Assortment decisions are rarely based on one number.
A low-selling item may serve an important customer mission. A profitable SKU may duplicate another product. A new launch may be underperforming because it never received the agreed distribution. A slow-moving regional item may be essential in a particular store cluster.
This is why simplistic AI recommendations — "delist the bottom 10%" — are dangerous.
The commercial value is not in automatically removing products. It is in assembling the evidence, history and exceptions required for a merchant to make a better decision.
An Assortment Decision Pack Agent can support add, retain, expand, reduce or delist reviews by preparing a structured evidence pack for every nominated SKU. It does not make the final assortment decision. It makes the reasoning easier to see, challenge and preserve.
Why this is an achievable early agent
Assortment optimization can become technically complex when it includes demand transfer, space elasticity, localization, constraints and predictive modeling. But the first agent does not need to solve the entire optimization problem.
A valuable initial version can: gather the available evidence · apply agreed screening rules · retrieve prior decisions · identify missing context · surface relevant exceptions · compare products consistently · generate a draft decision pack · capture the merchant's final rationale.
This creates value before the retailer attempts a fully automated assortment-optimization platform.
What does the agent actually produce?
For every SKU being reviewed, the agent creates a structured record containing: product and supplier information · sales, units, margin and rate of sale · store and cluster distribution · availability and out-of-stock context · promotional dependency · shelf-space or facing information where available · customer need or assortment role · comparable and potentially substitutable products · launch date and agreed evaluation period · previous add, retain or delist decisions · supplier commitments or contractual considerations · known local-market exceptions · evidence supporting each available option · information still required before a decision is made.
The outcome is a consistent decision-support pack, not an autonomous decision.
A high-level ChatGPT agent setup
1. Establish the decision scope
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