In this blog, we present a tapestry of authentic stories from real Azure customers. You will read about how AI-empowered applications are revolutionizing enterprises and the myriad ways organizations choose to modernize their software, craft innovative experiences, and unveil new revenue streams. We hope that these stories inspire you to embark upon your own Azure AI journey. Before we begin, be sure to bookmark the newly unveiled Plan on Microsoft Learn—meticulously designed for developers and technical managers—to enhance your expertise on this subject.
Use Case #1: Transform customer service
Intelligent apps today can offer a self-service natural language chat interface for customers to resolve service issues faster. They can route and divert calls, allowing agents to focus on the most complex cases. These solutions also enable customer service agents to quickly access contextual summaries of prior interactions offer real-time recommendations and generally enhance customer service productivity by automating repetitive tasks, such as logging interaction summaries.
Prominent use cases across industries are self-service chatbots, the provision of real-time counsel to agents during customer engagements, the meticulous analysis and coaching of agents following each interaction, and the automation of summarizing customer dialogues. Below is a sample architecture for airline customer service and support.
This particular setup above contains two interfaces, a customer-facing online support portal and an internal customer support system app. The Azure Front Door provides a single entry point for multi-region architecture. Raw data from Flight booking systems, luggage tracking, customer account data, and travel policies are stored in Azure Cosmos DB. Azure Kubernetes Services hosts web UI and integrates with other components. In addition, this app uses RAG, with Azure AI Search as the retrieval system, and Azure OpenAI Service provides LLM capabilities, allowing customer service agents and customers to ask questions using natural language.
Air India, the nation’s flagship carrier, updated its existing virtual assistant’s core natural language processing engine to the latest GPT models, using Azure OpenAI services. The new AI-based virtual assistant handles 97% of queries with full automation and saves millions of dollars on customer support costs.
We are on this mission of building a world-class airline with an Indian heart. To accomplish that goal, we are becoming an AI-infused company, and our collaboration with Microsoft is making that happen.” — Dr. Satya Ramaswamy, Chief Digital and Technology Officer, Air India
In this customer case, the Azure-powered AI platform also supports Air India customers in other innovative ways. Travelers can save time by scanning visas and passports during web check-in, and then scan baggage tags to track their bags throughout their journeys. The platform’s voice recognition also enables analysis of live contact center conversations for quality assurance, training, and improvement.
Use Case #2: Personalize customer experience
Organizations now can use AI models to present personalized content, products, or services to users based on multimodal user inputs from text, images, and speech, grounded on a deep understanding of their customer profiles.
Common solutions we have seen include conversational shopping interfaces, image searches for products, product recommenders, and customized content delivery for each customer. In these cases, product discovery is improved through searching for data semantically, and as a result, personalized search and discovery improve engagement, customer satisfaction, and retention.
Three areas are critical to consider when implementing such solutions. First, your development team should examine the ability to integrate multiple data types (e.g., user profiles, real-time inventory data, store sales data, and social data.) Second, during testing, ensure that pre-trained AI models can handle multi-modal inputs and can learn from user data to deliver personalized results. Lastly, your cloud administrator should implement scalability measures to meet variable user demands. A sample architecture for a personalization and product recommendations app is shown below.
ASOS, a global online fashion retailer, leveraged Azure AI Foundry to revolutionize its customer experience by creating an AI-powered virtual stylist that could engage with customers and help them discover new trends.
Having a conversational interface option gets us closer to our goals of fully engaging the customer and personalizing their experience by showing them the most relevant products at the most relevant time.” — Cliff Cohen, Chief Technology Officer, ASOS
In this customer case, Azure AI Foundry enabled ASOS to rapidly develop and deploy their intelligent apps, integrating natural language processing and computer vision capabilities. Enabled ASOS to rapidly develop and deploy their intelligent app, integrating natural language processing and computer vision capabilities. This solution takes advantage of Azure’s ability to support cutting-edge AI applications in the retail sector, driving business growth and customer satisfaction.
User Case #3: Accelerate product innovation
Building customer-facing custom copilots has the promise to provide enhanced services to your customers. This is typically achieved through using AI to provide data-driven insights that facilitate personalized or unique customer interactions, to enable customer access to a wider range of information, while improving search queries and making data more accessible. You can check out a sample architecture for building your copilot below.
In this scenario, customers of a retail bike store interact with an AI agent to ask questions ranging from product recommendations to information on customer profiles and sales orders. The solution is designed to also enable fast updates to data, demonstrating how adding or updating an item in the product catalog can be used in near real-time by the AI agent.
DocuSign, a leader in e-signature solutions with 1.6 million global customers, pioneered an entirely new category of agreement management designed to streamline workflows and created Docusign Intelligent Agreement Management (IAM). The IAM platform uses sophisticated multi-database architecture to efficiently manage various aspects of agreement processing and management. At the heart of the IAM platform is Azure AI, which automates manual tasks and processes agreements using machine learning models.
We needed to transform how businesses worked with a new platform. With Docusign Intelligent Agreement Management, built with Microsoft Azure, we help our customers create, commit to, manage, and act on agreements in real-time.” — Kunal Mukerjee, VP, Technology Strategy and Architecture, Docusign
The workflow begins with agreement data stored in an Azure SQL Database and is then transferred through an ingestion pipeline to Navigator, an intelligent agreements repository. In addition, the Azure SQL Database Hyperscale service tier serves as the primary transactional engine, providing virtually unlimited storage capacity and the ability to scale compute and storage resources independently.
Use case #4: Optimize employee workflows
With AI-powered apps, businesses can organize unstructured data to streamline document management and information, leverage natural language processing to create a conversational search experience for employees, provide more contextual information to increase workplace productivity and summarize data for further analysis.
Increasingly we have seen solutions such as employee chatbots for HR, professional services assistants (legal/tax/audit), analytics and reporting agents, contact center agent assistants, and employee self-service and knowledge management (IT) centers.
It’s essential to note that adequate prompt engineering training can improve employee queries, and your team should examine the capability of integrating copilot with other internal workloads; lastly, make sure your organization implements continuous innovation and delivery mechanisms to support new internal resources and optimize chatbot dialogs.
Improving the lives of clinicians and patients
Medigold Health, one of the United Kingdom’s leading occupational health service providers, migrated applications to Azure OpenAI Service, with Azure Cosmos DB for logging and Azure SQL Database for data storage, achieving the automation of clinician processes, including report generation, leading to a 58% rise in clinician retention and greater job satisfaction. With Azure App Service, Medigold Health was also able to quickly and efficiently deploy and manage web applications, enhancing the company’s ability to respond to client and clinician needs.
We knew with Microsoft and moving our AI workloads to Azure, we’d get the expert support, plus scalability, security, performance, and resource optimization we needed.” — Alex Goldsmith, CEO, Medigold Health
Use case #5: Prevent fraud and detect anomalies
Increasingly, organizations leverage AI to identify suspicious financial transactions, false account chargebacks, fraudulent insurance claims, digital theft, unauthorized account access or account takeover, network intrusions or malware attacks, and false product or content reviews. If your company can use similar designs, take a glance at a sample architecture for building an interactive fraud analysis app below.
This technical pattern enables the identification of fraudulent transactions, combining transactions and meta information with the context provided by LLMs. Payment data are processed at scale by Azure Cosmos DB. Transactional data is available for analytics in real-time (HTAP) using Synapse Link. All the other financial transactions such as stock trading data, claims, and other documents are integrated with Microsoft Fabric using Azure Data Factory. This setup allows analysts to see real-time fraud alerts on a custom dashboard. Generative AI denoted here uses RAG, with Azure OpenAI Service of the LLM, and Azure AI Search as the retrieval system.
Fighting financial crimes in the gaming world
Kinectify, an anti-money laundering (AML) risk management technology company, built its scalable, robust, Microsoft Azure-powered AML platform with a seamless combination of Azure Cosmos DB, Azure AI Services, Azure Kubernetes Service, and the broader capabilities of Azure cloud services.
We needed to choose a platform that provided best-in-class security and compliance due to the sensitive data we require and one that also offered best-in-class services as we didn’t want to be an infrastructure hosting company. We chose Azure because of its scalability, security, and the immense support it offers in terms of infrastructure management.” — Michael Calvin, CTO, Kinectify
With the new solutions in place, Kinectify detects 43% more suspicious activities achieves 96% faster decisions, and continues to champion handling a high volume of transactions reliably and identifying patterns, anomalies, and suspicious activity.
Use case #6: Unlock organizational knowledge
We have seen companies building intelligent apps to surface insights from vast amounts of data and make it accessible through natural language interactions. Teams will be able to analyze conversations for keywords to spot trends and better understand your customers. Common use cases can include knowledge extraction and organization, trend and sentiment analysis, curation of content summarization, automated reports, and research generation. Below is a sample architecture for enterprise search and knowledge mining. This technical pattern enables customers to conduct large-scale knowledge mining to find information and gain valuable insights.
H&R Block, the trusted tax preparation company, envisioned using generative AI to create an easy, seamless process that answers filers’ tax questions, maintains safeguards to ensure accuracy, and minimizes the time to file. Valuing Microsoft’s leadership in security and AI and the longstanding collaboration between the two companies, H&R Block selected Azure AI Foundry and Azure OpenAI Service to build a new solution on the H&R Block platform to provide real-time, reliable tax filing assistance. By building an intelligent app that automates the extraction of key data from tax documents, H&R Block reduced the time and manual effort involved in document handling. The AI-driven solution significantly increased accuracy while speeding up the overall tax preparation process.
We conduct about 25 percent of our annual business in a matter of days.” — Aditya Thadani, Vice President, H&R Block
Through Azure’s intelligent services, H&R Block modernized its operations, improving both productivity and client service and classifying more than 30 million tax documents a year. The solution has allowed the company to handle more clients with greater efficiency, providing a faster, more accurate tax filing experience.
Use case #7: Automate document processing
Document intelligence through AI applications helps human counterparts classify, extract, summarize, and gain deeper insights with natural language prompts. When adopting this approach, organizations are recommended to also consider prioritizing the identification of tasks to be automated, and streamline employee access to historical data, as well as refine downstream workload to leverage summarized data. Here is a sample architecture for large document summarization. This technical pattern enables users to conduct knowledge mining to gather information in aggregate over large repositories of documents.
Volve Group, one of the world’s leading manufacturers of trucks, buses, construction equipment, and marine and industrial engines, streamlined invoice and claims processing, saving over 10,000 manual hours with the help of Microsoft Azure AI services and Azure AI Document Intelligence.
We chose Microsoft Azure AI primarily because of the advanced capabilities offered, especially with AI Document Intelligence.” — Malladi Kumara Datta, RPA Product Owner, Volvo Group
Since launch, the company has saved 10,000 manual hours—about 850-plus manual hours per month
Use case #8: Accelerate content delivery
Using generative AI, your new applications can automate the creation of web or mobile content, such as product descriptions for online catalogs or visual campaign assets based on marketing narratives, accelerating time to market. It also helps you enable faster iteration and A/B testing to identify the best descriptions that resonate with customers.
Sample architecture for a product description generator
This pattern generates text or image content based on conversational user input. It combines the capabilities of Image Generation and Text Generation, and the content generated may be personalized to the user, data may be read from a variety of data sources, including Storage Account, Azure Cosmos DB, Azure Database for PostgreSQL, or Azure SQL.
JATO Dynamics, a global supplier of automotive business intelligence operating in more than 50 countries, developed Sales Link with Azure OpenAI Service, which now helps dealerships quickly produce tailored content by combining market data and vehicle information, saving customers 32 hours per month.
Data processed through Azure OpenAI Service remains within Azure. This is critical for maintaining the privacy and security of dealer data and the trust of their customers.” — Derek Varner, Head of Software Engineering, JATO Dynamics
In addition to Azure OpenAI, JATO Dynamics used Azure Cosmos DB to manage data from millions of transactions across 55 car brands. The database service also empowers scalability and quick access to vehicle and dealer transaction data, providing a reliable foundation for Sales Link.
More customer stories from around the world and across industries:
Revolutionizing customer service with Azure OpenAI
CarMax, a leading United States used car retailer, utilized Azure OpenAI Service to create an AI-powered research assistant. This innovative tool streamlined the car-buying process, providing customers with instant, accurate information and improving overall satisfaction.
Our initial goal was customer review summaries for 5,000 car pages, and we figured that using our existing manual process it would have taken about 11 years of content generation. With OpenAI Service, we hit that goal in just a few months. — Sean Goetz, Director, Application Systems, CarMax
The implementation of Azure OpenAI Service allowed CarMax to process vast amounts of automotive data efficiently, demonstrating Azure’s capability to handle complex, industry-specific AI applications. This case highlights the potential of AI-powered apps to transform traditional business models and enhance customer experience.
Empowering personalized banking with Azure
Ally Bank is a digital-first financial institution that uses insights from Azure AI to help customers manage their finances more effectively while streamlining the bank’s operations. By deploying intelligent apps through Azure, Ally introduced personalized banking services, offering customized recommendations based on user behavior and financial patterns.
The goal was to free up associates to truly focus on the customer and support that customer-obsessed mentality they have.” — Wendy Dempsey, Executive Director of Customer Experience, Ally
This implementation showcases Azure’s strength in supporting AI applications in the highly regulated banking sector, balancing innovation with security and compliance.
Optimizing real-time mapping with Azure
TomTom, a global leader in location technology, turned to Azure to modernize its map-making processes. By building intelligent, cloud-native apps with Azure AI and Azure Data Services, TomTom improved the accuracy and speed of its real-time mapping services. The integration of Azure AI services allowed TomTom to process massive amounts of geospatial data efficiently, demonstrating Azure’s scalability and performance in handling data-intensive AI applications.
[Together with Azure,] we have developed TomTom Orbis Maps, which are built on TomTom’s AI-based mapping platform, and TomTom Digital Cockpit, an in-vehicle digital infotainment solution with cloud analytics.” — Paul Hesen, Vice President of Product Management, TomTom Group
With Azure, TomTom significantly increased the efficiency of its mapping operations, reducing response times for driver queries from 12 seconds to as little as 2.5 seconds in a matter of months. This allowed the company to deliver faster, more precise location services to its global user base, enhancing both productivity and customer trust.
Automating audits with Azure AI
KPMG Australia used Azure AI to integrate intelligent apps into their audit process. By leveraging Azure’s natural language processing (NLP) capabilities, KPMG built a solution called KymChat that automates document analysis and enhances data extraction. This transformation reduced the time required for auditing tasks, improving both accuracy and efficiency.
We had experience with Azure Cosmos DB, which gave us confidence in its potential capabilities, but Microsoft’s support for vCore was instrumental in ensuring smooth operations as we increased the scope and capabilities of KymChat.” — Robert Finlayson, Senior Product Manager, KPMG Australia
With Azure AI, KPMG significantly improved the productivity of its auditors, freeing them from labor-intensive, repetitive tasks. The effort not only sped up the audit process but also increased the quality of insights provided to clients, showcasing the power of intelligent apps in professional services.
Accelerating innovation with Azure Kubernetes Service
Manulife, a leading financial services group, leveraged Azure Kubernetes Service (AKS) to accelerate the development and deployment of innovative apps. By modernizing its legacy systems with Azure Kubernetes Service, Manulife was able to create cloud-native, containerized apps that streamlined operations and improved scalability.
With Azure, our customers can submit more documentation online, and we can then use document analysis to extract information and conduct processing to accelerate workflows, ensure accuracy, and improve user experiences.” — Len van Greuning, Chief Information Officer, John Hancock (as Manulife is known in the United States)
Azure’s robust infrastructure enabled Manulife to reduce operational complexity and increase speed-to-market, enhancing its ability to innovate in a competitive industry. Modernization with AKS empowered Manulife to remain agile and responsive, driving higher productivity and delivering better services to its customers.
Closing thoughts
From innovative solutions to heartwarming successes, it’s clear that a community of AI pioneers is transforming business and customer experiences. Let’s continue to push boundaries, embrace creativity, and celebrate every achievement along the way. Here’s to many more stories of success and innovation! loud-native app development and try Azure AI Services for free by visiting our homepage.