Store & Field Operations
Store standards photo review
Colleagues photograph key store areas against a defined checklist. Vision models score the images for standards — cleanliness, ticketing, display execution, stock presentation — and flag exceptions for the area manager.
30–50%
reduction in physical audit visit time
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Moderate data needs. Works with data most businesses hold, but will likely need consolidation, cleaning, or reformatting before use.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Photo-based standards checks change the incentive: colleagues learn to photograph the good aisle. Randomise which areas are requested on the day and timestamp the images to keep the sample honest.
Vision models are reliable on binary questions (is the ticket present, is the shelf empty) and unreliable on subjective ones (does this display look appealing). Restrict the model to the binary questions and leave judgment to the area manager.
This tends to replace part of a visit rather than the whole visit. Position it as freeing area managers to spend visit time on people and trading, not as removing visits.
Experiment starter: Pick ten checklist items that are unambiguously visual. Collect 200 photos across a region, have the model score them, and compare against an experienced area manager's scoring of the same photos. Anything below 85% agreement is not ready.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — RAG and Knowledge Systems: Unlocking Proprietary DataThis is an decision support opportunity — see the relevant playbook section for how to approach it.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.
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