Store & Field Operations
In-store colleague product assistant
A conversational assistant on the store handheld that answers colleague questions about products, stock location, promotions, and policy, grounded in the retailer's own product and process content.
30–50%
reduction in time to answer a customer product question
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.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Walmart has deployed generative AI capabilities aimed at associates as well as shoppers, on the argument that a colleague who can answer immediately is worth more than a customer-facing chatbot (Microsoft).
Ground it strictly in your own content. A model that improvises product specifications in front of a customer is worse than no assistant at all — configure it to say it does not know and offer to find someone who does.
Handheld screens are small and the shop floor is noisy. Answers need to be two sentences, not two paragraphs, and voice input matters more than it does at a desk.
Experiment starter: Collect the 100 questions colleagues most often ask each other in one store format. Build a retrieval assistant over your product data and policy content, and score its answers against a knowledgeable department manager. Ship when it clears 90% on the top 100.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Experimentation FrameworkThis is an augmentation 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.
AI Transformation Playbook
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