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AI adoption: Retail & Quick Commerce

AI adoption in Retail & Quick Commerce

Retail margins are won on availability and waste. AI helps most in forecasting demand, placing stock and keeping store teams informed.

Book an assessmentBook an assessment
Use cases

Where AI pays off first

  • 01

    Demand forecasting

    Store and item-level forecasts that account for weather, events and promotions.

  • 02

    Replenishment and pricing

    Order suggestions and markdowns that reduce stock-outs and waste.

  • 03

    Store operations

    Assistants that answer staff questions on stock, process and policy.

The path

The path for Retail & Quick Commerce

Four stages, each ending with a decision. The first line of every stage is specific to your industry.

Step 1: Assess

Find where AI is worth it

  • Audit sales, stock and promotion history by store and item: forecast quality depends on it
  • List the work that is repetitive, slow or stuck in documents
  • Score each use case by value, feasibility and risk

Use case scoring

12 assessed
Use caseValueFeasibleRiskDemand forecastingReplenishment and pricingStore operations

Owner: Category manager, dairy

Measure: stock-outs and waste

Step 2: Pilot

Prove one use case

  • Forecast one category in a handful of stores and compare stock-outs and waste with the current method
  • Build one narrow use case with real users and real data
  • Write evaluation tests before the first prompt

Pilot: demand forecasting

Week 4

12 stores, 6 weeks

Forecast error, one category

Baseline31%
Pilot14%

Decision: continue

Reviewed with the owner

Step 3: Integrate

Put it where work happens

  • Feed forecasts into replenishment and dark-store slotting with planners able to override
  • Connect to the systems people already use, not a separate tool
  • Add access control, logging and data protection

Connected systems

  • POSSales by store
  • ERPPurchase orders
  • Store appPick and replenish tasks
Access controlLoggingData protectionUsage policy
Step 4: Scale

Make it routine

  • Roll out across categories and add dynamic pricing and delivery-time prediction
  • Reuse the same platform, patterns and guardrails for the next use case
  • Move from assistants to agents where the evidence supports it

AI use cases

This quarter
  • Demand forecastingLive
  • Replenishment and pricingLive
  • Store operationsPilot

18% less waste

Across fresh categories

Retail & Quick Commerce

One customer, every channel, delivered in minutes.

Shoppers move between store, app and website without thinking about it, and expect delivery the same hour. We connect point of sale, inventory and fulfilment so the business can keep up.

What we buildWhat we build

Find your first AI use case

Tell us about your business and where the time goes. We will come back with the use cases we would assess first and how we would test them.

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Your project plan

Ready five days after the first call

Share plan

Approach

Fixed scope
  • Start with sign-up and onboarding, the riskiest flow
  • Ship to a pilot group every week from week three
  • Add the invoice reading agent once data is flowing
First release
Week 4
Demos
Weekly
Code
Yours

Team

5 people
  • PLProduct lead
  • SESenior engineer
  • SESenior engineer
  • PDProduct designer
  • AIAI engineer

Timeline

10 weeks
Discovery
Design
Build
Launch