Back to Retail Value Chain
AI Assistants · Sell
In-Store Wayfinding Assistant
Conversational copilots that augment a human in the loop with on-demand recommendations and tasks.
Medium impactIdentified
Business objective
Grow conversion, basket size and price realisation across channels.
Value
Meaningful operational lift — productivity, speed or quality gains.
Who uses it
SalesEcommerceStore OperationsPricing
Business outcomes
- Hours saved per user per week
- Higher self-service, lower escalation
- Consistent answers grounded in trusted data
How it works — workflow
Typical ai assistants loop powering this use case.
Step 1
User asks question / takes task
Step 2
Retrieve grounded context
Step 3
Generate response / draft action
Step 4
Human approves or refines
Step 5
Log feedback for improvement
Data required
- • Policies & SOPs (unstructured)
- • Knowledge bases
- • Live operational data
- • User context & permissions
- • Conversation history
Connected systems
- • POS / mPOS
- • Ecommerce platform
- • Pricing engine
- • CMS / PIM
- • Loss-prevention CCTV/CV
Business processes
- • In-store operations & checkout
- • Ecommerce merchandising
- • Pricing & promotions
- • Loss prevention
- • Colleague enablement on the floor
What's required to be successful
- Retrieval-augmented knowledge base
- Identity & permissions
- Conversational surface (chat / copilot)
- Evaluation harness & guardrails
- Change management & training
Underpinned by the Xfuze Foundation — see the five pillars.
Related use cases
Same stage or AI type — useful when scoping an end-to-end ambition.
AI Insights · Sell
Conversion Rate & Funnel Analysis
HighLive
AI Insights · Sell
Basket Analysis & Product Affinity
HighIn Progress
AI Insights · Sell
Pricing Elasticity Modelling
HighIn Progress
AI Insights · Sell
Promotion Effectiveness Analysis
HighIdentified
AI Insights · Sell
Channel Performance Insights
MediumIdentified
AI Insights · Sell
Store Performance Benchmarking
MediumIdentified