Enterprise Intelligence Group
The highest-value early AI wins in merchandising aren't models or forecasts — they're assistants that remove preparation drudgery. Five you can stand up in 90 days.
The highest-value early AI wins in merchandising are not forecasting models or pricing engines. They are assistants that remove the preparation and searching drudgery your category managers do by hand every week — low-risk, high-frequency, and quick to prove. Here are five you can realistically stand up in 90 days, in the order most teams should tackle them.
1. Category review preparation
The single best first use case. An assistant assembles the review pack a category manager compiles manually today: the performance narrative from your existing data, what changed since last review and why, current supplier agreements, and the rules and exceptions that apply — every claim citing its source. Humans still decide; the hours move from compiling to thinking.
2. Buyer and supplier knowledge search
Negotiation history, agreed terms, and past commitments retrievable in seconds instead of dug out of email threads. This is where "our knowledge is trapped in inboxes" stops being true — and it's the foundation the other use cases reuse.
3. Range and assortment decision history
"Did we try this before, and what happened?" answered instantly. Most costly assortment mistakes are repeats — a relisted failure, a re-entered segment everyone senior once knew to avoid. An assistant with the decision history stops the organization paying twice.
4. Meeting and action capture
Category reviews and supplier meetings generate decisions that vanish into someone's notebook. An assistant that captures, structures and tracks those decisions turns every meeting into durable knowledge — and feeds use cases 1–3.
5. Policy and SOP answers
Ranging rules, promotional guidelines, own-label criteria — answerable at the point of work, so new merchants get productive in weeks and nobody guesses at policy.
Why these five, and why in this order?
They share four properties that make an early AI program succeed: high frequency (used weekly, so value compounds), low risk (they prepare, humans decide), real time savings, and — crucially — building them forces you to consolidate the category knowledge every later use case depends on. Skip that foundation and even a brilliant model produces confident, unusable answers.
Frequently asked questions
Can we really deploy AI in 90 days? These assistant-style use cases, yes — because they run on knowledge you already have, once it's consolidated and governed. What takes longer is the ungoverned path: teams that skip the knowledge work spend months on pilots that stall on trust.
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