Enterprise Intelligence Group

How to Build a Category Review Preparation Agent in ChatGPT

See how retailers can use a ChatGPT agent to prepare category reviews, retrieve decision history and return time to category managers.

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.

Category managers rarely begin a category review with a clean, complete view of the business.

They begin by searching.

They retrieve the previous review deck, download performance reports, locate promotional results, check supplier correspondence, review assortment changes and ask colleagues why earlier decisions were made.

The information usually exists. The problem is that it is spread across multiple reports, folders, inboxes and individual memories.

A Category Review Preparation Agent is one of the most practical first AI agents a retailer can introduce. It does not replace the category manager or make the commercial decision. It performs the repetitive preparation work required before the category manager can apply judgment.

That makes it a strong early use case:

What does the agent actually do?

For a selected category and review period, the agent assembles a draft category-review briefing containing:

The result is not an automated category strategy. It is a better-prepared category manager.

A high-level ChatGPT agent setup

1. Define the agent's job

The agent needs a narrow, unambiguous purpose. A useful instruction could be:

Prepare an evidence-based category-review briefing for the nominated category and period. Retrieve relevant performance information, previous decisions, supplier context and promotional history. Cite the source for every material statement. Identify missing or conflicting information. Do not make final ranging, pricing, promotional or supplier decisions.

This establishes the distinction between preparing a decision and making a decision.

2. Connect the approved knowledge sources

Depending on the retailer's systems and ChatGPT workspace configuration, the agent might be given controlled access to: SharePoint category folders · previous review presentations · approved supplier-meeting notes · promotional calendars and post-event reviews · assortment-change records · merchandise policies · planning assumptions · approved exports from reporting or business-intelligence systems · category-specific spreadsheets · product and supplier master data.

Retailers should restrict access to only the sources required by the workflow and maintain approval controls for consequential write actions. The first implementation does not need every source. A pilot might begin with: previous category-review decks · a weekly SKU-performance export · promotional results · supplier-meeting notes · the current assortment file. That is often enough to prove the workflow before deeper integrations are considered.

3. Define the workflow

A typical run could follow eight steps.

Step 1: Confirm the request. The user specifies category, review period, store group or channel, purpose of the review, and required output date.

Step 2: Retrieve the approved evidence. The agent locates the relevant reports, documents, meeting notes and previous decisions.

Step 3: Check completeness. It confirms whether the required periods and sources are available. Missing information is reported rather than silently ignored.

Step 4: Build the performance story. The agent identifies the largest movements in sales, margin, volume, distribution, availability and promotional performance.

Step 5: Retrieve decision history. It finds previous decisions concerning major SKUs, suppliers, promotions or assortment changes and extracts the recorded rationale.

Step 6: Identify exceptions. It highlights results that may require attention, such as: strong sales but declining margin · high productivity but poor availability · promotional growth followed by post-event decline · duplicate products serving the same customer need · new products without an agreed evaluation window · previous delist decisions that were never completed.

Step 7: Generate the review pack. The agent produces a standard briefing using the retailer's preferred structure.

Step 8: Request merchant review. The category manager validates the content, adds judgment and decides what should enter the formal category review.

Example output

A useful output could contain six sections:

- Executive summary — the five developments that matter most. - Category performance — sales, units, margin, volume, distribution and availability. - Assortment movements — new products, exits, distribution changes and performance against expectations. - Promotion and supplier context — recent events, commitments, funding and unresolved actions. - Decisions requiring attention — questions the category manager needs to resolve. - Evidence and sources — links or citations showing where each material point originated.

This last section is critical. A retailer should be able to distinguish a supported statement from an AI-generated interpretation.

What should the agent not do?

The first version should not: approve or remove a SKU · change prices · alter forecasts · send supplier communication · update a production assortment · treat incomplete data as definitive · present recommendations without showing supporting evidence.

Any external changes should remain behind explicit human approval and approved application permissions. Current enterprise application controls let organizations determine when actions require confirmation and constrain what connected tools may do.

How should the retailer measure the pilot?

Useful measures include: hours spent preparing each review · percentage of required sources successfully included · number of previous decisions retrieved · number of unsupported statements identified during review · category-manager satisfaction · percentage of the briefing accepted without material correction · time from data availability to review-ready output.

The objective is not to prove that AI can create a presentation. It is to prove that the retailer can prepare a more complete category conversation with less manual searching and compilation.

How EIG helps

Enterprise Intelligence Group does not begin by connecting an agent to every available folder. We first work alongside the category team to determine: how category reviews are actually prepared · which reports merchants genuinely trust · which unofficial files are essential · where previous decisions are recorded · which exceptions require human judgment · what the review pack must contain · who owns and validates each knowledge source.

We then define the agent's instructions, knowledge architecture, workflow, approval points, output template and success measures. The technology is only one part of the solution. The real work is giving the agent access to the right retail intelligence, in the right sequence, with the right controls.

Ready to test a Category Review Preparation Agent?

EIG can help you scope a focused pilot around one category, one review process and a controlled set of knowledge sources. Book a Retail Intelligence Discovery Workshop and identify what your first category-management agent should do — and what it should never be allowed to do.

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