Dentsu built a predictive analytics copilot leveraging Azure AI Foundry and Azure OpenAI Service to enable conversational access to media insights for campaign planning and budget optimization. They implemented the solution using loosely coupled microservices on Azure Kubernetes Service with API-based integration and GitHub GenAIOps Prompt Flow templates, embedding it within their existing business applications. The system was governed through shared modules to ensure responsible AI deployment and streamline media analytics workflows.

Coles uses Azure Stack HCI and Azure Machine Learning to build an intelligent edge backbone, improving management of applications and AI/computer vision use cases across 500+ stores.

Mars, a global petcare company, uses Microsoft's Azure AI model catalog, choosing Mistral for its model as a service offering, to accelerate the development and deployment of its AI-powered solution RapidRead. RapidRead interprets pet radiology scans quickly, assisting veterinarians in diagnosing medical conditions faster. By employing Azure Machine Learning, Mars can process millions of images and reports, supporting veterinarians with AI-enhanced tools.

Virtual Dental Care (VDC), a teledentistry provider, developed an AI application called Smart Scan using Azure Machine Learning. Smart Scan analyzes dental photos and x-rays to detect common dental issues, such as decay and gum inflammation. Built in the Azure ecosystem, it easily scales for large group screenings, reducing administrative paperwork by 75% in a school study and allowing dentists to focus more on patient care.

Oncoclinicas implemented a comprehensive digital portal and mobile app to automatically scan, extract, and digitize clinical exam data using Azure Cognitive Services’ Computer Vision for OCR and Text Analytics for Health. They integrated backend services including Azure Storage, Functions App, Service Bus, Cosmos DB, and Azure Machine Learning to centralize and structure medical records, deploying the solution over three months with onsite training at 129 care units to streamline clinical workflows.

Network Rail implemented an AI-driven analytics platform called "insight" leveraging Azure Databricks and Azure Machine Learning to process vast amounts of data from its extensive railway infrastructure. The solution integrated diverse data streams—including track geometry, ultrasonic testing, and weather data—using Azure Data Factory and delivered visual insights through Power BI to enable predictive maintenance and efficient asset management. Agile collaboration with Microsoft architects streamlined maintenance scheduling and transformed labor-intensive processes into near real-time, cost-effective decision-making workflows.

Boyner uses Azure OpenAI Service and Azure Machine Learning to analyze data faster and generate product descriptions automatically from product photos.

National Bank of Greece built an AI-powered document processing solution using Azure AI Document Intelligence. They analyzed 50 million pages to create a comprehensive dataset and employed Azure Cognitive Services for Greek language recognition, integrating the solution into six customer-facing digital banking applications. This implementation automates document categorization and data extraction, streamlining both customer service and regulatory processes.

Cognizant uses Microsoft Azure Machine Learning to enhance its performance management system GoPerform by integrating AI to improve manager feedback, ensuring employees receive meaningful, actionable feedback.