Enterprise Intelligence Group
What a Retail Intelligence Discovery Workshop Actually Produces
Explore an illustrative category-decision map, reviewed knowledge record, options table and next-step plan. Actual Workshop outputs and scope are agreed before work begins.
A Retail Intelligence Discovery Workshop can turn a defined business question into records a team can inspect and use. This example shows four possible outputs and how they connect; the agreed scope determines the actual outputs and their depth.
Illustrative example, not a client case study. Meridian Outdoor, its operating details and the records below are invented to explain the approach. They are not EIG delivery evidence, observed customer findings or measured results.
The setup
In this invented scenario, Meridian Outdoor operates 40 stores and an e-commerce channel. Its leaders want to document how two experienced buyers prepare category reviews and consider where AI might help. This example uses that specialist question; another Workshop could address a process, requirements or data problem without an AI project.
For this example, the proposed work combines permitted observation of category reviews, interviews and review of selected records. Participation, access and methods would be agreed before starting. The four outputs below illustrate the work, and every asset the Workshop produces belongs to the client whether or not further work is commissioned.
Deliverable 1 — the Category Decision Map
A single view of how a category decision actually gets made: the inputs, the steps, who decides what, and — critically — the unwritten rules that never made it into a system.
Illustrative excerpt: Decision: seasonal range for the hydration category. Illustrative criteria for this invented scenario: vendor reliability in-season > headline margin; a "protect the hero SKU" rule that overrides the planogram; a regional weighting nobody had written down. Potential AI support to evaluate: assembling the review pack and flagging exceptions — the buyer still makes the call.
The map makes the proposed reasoning inspectable: reviewers can identify the evidence, question an exception and agree who owns the decision. A documented rule still needs validation before anyone relies on it or implements it in a system.
Deliverable 2: a reviewed knowledge record
The Decision Map shows the flow. The knowledge record documents the reasoning, source, owner and review status behind selected rules and exceptions. Conflicting accounts and unverified information remain visible. Discovery does not, by itself, deliver a maintained knowledge system or establish that an AI assistant can use the material safely.
Deliverable 3: prioritized options and the evidence they need
Compare process, data and technology options against the agreed business question. This invented scenario includes AI options because its leaders asked about them. The table records hypotheses and review needs, not measured benefits, validated feasibility or a commitment to build.
| Illustrative option | Evidence to examine | Decision before proceeding |
|---|---|---|
| A consistent category-review record | Missing inputs, conflicting definitions and reviewer responsibilities | Agree the information and ownership the review needs, whether or not AI is used. |
| An assistant that prepares a review pack | Permitted sources, representative questions and examples of incorrect answers | Define evaluation and human review before deciding whether to test an assistant. |
| Automated range changes | Decision authority, exception handling, consequences and recovery requirements | Keep the merchant accountable; documenting a decision does not authorize automation. |
Deliverable 4: a next-step plan
Record the proposed action, owner, dependencies and acceptance checks for each selected option. Identify what needs further investigation and what can be scoped for delivery. Timing, responsibilities and any subsequent fees are agreed separately; the plan is not a 90-day implementation commitment or an assumption that the next step is an AI assistant.
What discovery establishes, and what it does not
Discovery can clarify a decision and the evidence required to move it forward. Its outputs do not certify an AI application, guarantee commercial results or require a technology purchase. The team may choose a process change, requirements work, further investigation or no implementation at this stage.
Every asset the Workshop produces is yours to keep, whether or not you commission further work from EIG.
For scoped discovery, explore the Retail Intelligence Discovery Workshop. If your immediate consulting need is already clear, the service and project links below provide a direct route. A Workshop or AI assessment is not a prerequisite.
EIG brings practical consulting expertise across retail, CPG and distribution, connecting people, process, data and technology.
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EIG helps retail, CPG and distribution teams clarify business requirements, evaluate technology choices and plan implementation. The starting point is the decision you need to make—not a requirement to adopt AI.
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