Store & Field Operations
New site selection and catchment analysis
Model the likely trading performance of a candidate site using catchment demographics, competitor proximity, transport flows, and the performance of comparable existing stores.
10–20%
improvement in first-year sales forecast accuracy
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Challenging. Significant technical work required. Likely needs an AI engineer or specialist vendor. Not a quick win.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Moderate risk. Some customer or external exposure. Errors create rework or reputational impact but are recoverable.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
You need a reasonable number of existing stores to train on. Below roughly thirty comparable sites the model is fitting noise, and a good property director with local knowledge will beat it.
Cannibalisation of your own nearby stores is the most commonly missed variable. Build it into the model explicitly or you will approve sites that move sales rather than create them.
Treat the output as a challenge to the property paper, not a replacement for it. The value is in forcing the case to explain why this site differs from the comparable set.
Experiment starter: Backtest against your last ten openings. Feed the model only the data that was available before each opening and compare its first-year forecast against both the actual result and the original property paper forecast. If it beats the paper on more than half, use it as a standing second opinion.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Experimentation FrameworkFeasibility is limited here — the experimentation framework covers how to run a bounded pilot before committing.More in Store & Field Operations
Self-checkout loss and shrink detection
Use overhead camera vision at self-checkout to detect non-scans, mis-scans, and ticket switching in real time. Prompts the customer to rescan or alerts a colleague before the transaction completes.
Store task generation and prioritisation
Convert system signals — low stock alerts, delivery arrivals, planogram changes, temperature exceptions — into a single prioritised task list for each store colleague, written in plain language with an estimated duration.
Queue prediction and till opening prompts
Predict checkout queue build-up fifteen to thirty minutes ahead using footfall, basket, and historical patterns, and prompt the duty manager to open tills before the queue forms rather than after.
AI Transformation Playbook
Ready to assess your own opportunities?
The playbook gives you the full 150-opportunity directory, scoring tools, and 100+ templates for every stage of an AI transformation programme.