What challenges did Coles face before AI implementation?

Scaling edge AI capabilities across a large footprint of retail stores. Speeding up data annotation time for AI training models to enable faster development and deployment.

Source

MicrosoftProductionEvidence: Medium50/100

Coles Group modernizes logistics and retail using Microsoft Azure AI

Use case typeIndustrial inspectionUpdated Jun 13, 2026

Coles Group expanded its Azure Stack HCI footprint from 2 to over 500 stores, implementing edge AI capabilities to improve logistics and retail operations across 1,800 stores. The company also adopted Azure Machine Learning to train and develop edge AI models, accelerating data annotation for AI training models by 50%. This implementation led to a six-fold increase in the pace of application deployment, enhancing operational efficiency while enabling rapid innovation without disrupting workloads.

Organization: Coles
Industry: Retail
Location: Australia
Published: January 2025

Reported outcomes

−50%

timeTime & speed

Strategic outcomes

Scale & capacityScaled edge AI across hundreds of storesNew product / capabilityDeveloped AI models for retail logisticsSpeed & agilityAccelerated application deployment paceCustomer experience & trustImproved customer experience across stores

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 50% decrease

Microsoft BlogJan 28, 2025Blog postInferred claimMedium evidence strength

Reduced data annotation time for AI training models by 50%, enabling faster model development and innovation.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Coles
Provider: Microsoft
Maturity: Production
Linked source: Microsoft Blog

This implementation led to a six-fold increase in the pace of application deployment, enhancing operational efficiency while enabling rapid innovation without disrupting workloads

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 2 of 2

1AI and Machine Learning
2Edge Computing

Scaling edge AI capabilities across a large footprint of retail stores.
Speeding up data annotation time for AI training models to enable faster development and deployment.

Deployment of Microsoft Azure Stack HCI to scale edge AI capabilities from 2 stores to over 500 stores.
Use of Azure Machine Learning to train and develop AI models specifically for logistics and administrative applications in retail.

Technologies

Azure Stack HCI Azure Machine Learning

Achieved a six-fold increase in application deployment speed, accelerating business transformation.
Reduced data annotation time for AI training models by 50%, enabling faster model development and innovation.
Significantly enhanced operational efficiency and improved customer experience across 1,800 retail stores.

Sources & evidence1

Evidence: Medium50/100Evidence strength