Enterprise Intelligence Group
Most consulting pages tell you what a firm believes. Here's what a Retail Intelligence Discovery Workshop actually produces — walked through end to end via a clearly-illustrative example. Not a real client; no invented results.
What a Retail Intelligence Discovery Workshop Actually Produces
Most consulting pages tell you what a firm believes. Very few show you what you actually receive. So here it is — the concrete output of a Retail Intelligence Discovery Workshop , walked through end to end.
A note on this example. Everything below is an illustrative, composite scenario — a mid-sized specialty retailer we've invented to show the shape and quality of the work. It is not a real client and contains no real numbers. We don't publish client details, and we never invent results. What's real is the method and the deliverables; the scenario is there so you can see them clearly.
"Meridian Outdoor" (invented) runs 40 stores and a growing e-commerce channel. Their category reviews take weeks, the logic behind ranging decisions lives with two long-tenured buyers, and leadership wants to know where AI could actually help — without a rip-and-replace program. That's a typical Workshop starting point: one high-value area, real pressure, knowledge trapped in people.
Over the engagement we embed with that team — sitting in the reviews, shadowing the buyers, watching how decisions are really made — and turn what we observe into four assets Meridian keeps, whether or not they ever build anything else with us.
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. Real criteria (captured from the buyers): vendor reliability in-season headline margin; a "protect the hero SKU" rule that overrides the planogram; a regional weighting nobody had written down. Where AI helps: assembling the review pack and flagging exceptions — the buyer still makes the call.
The value isn't the diagram. It's that a decision which lived in two people's heads is now explicit, teachable, and ready for a system to support.
Deliverable 2 — a Buyer Knowledge Library entry
The Decision Map shows the flow; the Knowledge Library captures the judgment — the exceptions, the "we tried that in 2019," the supplier quirks. Governed, owned by Meridian, and structured so it can later feed an AI assistant without leaking or going stale.
Deliverable 3 — the AI Opportunity Portfolio
A prioritised, honest list of where AI would create real value in this area — and where it wouldn't (yet). Each opportunity is scored on value, feasibility on their existing systems , and readiness.
Illustrative shape (not real scores): | Opportunity | Value | Feasibility now | Verdict | |---|---|---|---| | Category-review prep assistant | High | High | Start here | | Assortment exception flagging | High | Medium | Next | | Autonomous range changes | High | Low (knowledge not yet governed) | Not yet — foundation first |
That last row is the point of the whole firm: we'll tell you what not to automate yet, and why.
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