Use Case Ranking

The Indiana Secretary of State transformed over 20 million historical government records into searchable intelligence using Google Cloud's Gemini and Vertex AI, enabling rapid data insights and modernized legacy systems.,They deployed bilingual AI-powered conversational agents Liz and Otto to provide 24/7 resident support in English and Spanish, significantly increasing team capacity and user access.,Additionally, the office launched a Notary Learning Management System (LMS) powered by AI on Google Kubernetes Engine, training over 50,000 statewide notaries with interactive micro-learning, leading to an award for Best Application Serving the Public at the State Level.

Blue Origin, a leading aerospace company, accelerated lunar hardware development by leveraging AI agents and advanced AWS services.,The company built BlueGPT platform with over 2700 AI agents using Amazon Bedrock, Amazon Bedrock AgentCore, Amazon EKS, Amazon EC2, Amazon OpenSearch, and Strands Agents SDK.,BlueGPT enables autonomous iterative design loops, complex GPU-accelerated physics simulations, and hierarchical AI agent orchestration, drastically reducing hardware development time from years to days.,This AI-powered approach democratized AI use across 70% of employees, improving productivity and enabling the delivery of the world’s first AI agent-designed lunar hardware ready for Moon deployment.

Microsoft has transformed its supply chain by consolidating over 30 systems into a single data lake on Azure enabling AI-driven autonomous workflows. Over 25 AI agents handle tasks such as demand planning, spare-part space optimization, transport optimization, and invoice analysis. The system integrates simulations, digital twins, and AI-powered robotics to optimize warehouse and logistics operations.,The platform uses technologies including Azure Machine Learning, Microsoft Fabric, Azure IoT Operations, Microsoft 365 Copilot, Microsoft Foundry, NVIDIA Isaac Sim, Azure Kubernetes Services, Microsoft Power Automate, and Celonis Process Intelligence Graph. It hosts AI agents able to reason, plan, and act, improving operational KPIs and saving hundreds of work hours monthly across Microsoft and partner warehouses.,Physical AI robotics like humanoid robots are deployed for warehouse tasks and last-mile deliveries enhancing operational agility. Partners such as SoftServe and Celonis have implemented agentic AI and digital twin solutions, achieving significant productivity gains in pharmaceutical logistics and warehouse automation.

Vxceed, a global SaaS provider for consumer packaged goods sales and distribution, built a near real-time AI platform on AWS to improve store-level visibility and execution for retail-focused organizations.,The platform interprets store signals, produces actionable guidance for commercial teams, and generates AI news bulletins with audio and video.

Forvis Mazars, a global business advisory firm, implemented agentic AI and automation to drive digital transformation across industries, notably real estate, healthcare, legal, and biotech.,Leveraging Microsoft technologies including Azure Functions, Azure Logic Apps, Copilot Studio, Power Platform, Microsoft 365 Copilot, Microsoft Fabric, Purview, Defender, and Entra ID, the firm enabled AI agents to autonomously execute business processes with minimal human intervention.,Their maturity journey spanned from rules-based RPA pilots to multi-step agentic automation, embedding business intelligence, compliance, and responsible AI principles throughout.,A flagship implementation automated payroll auditing for real estate clients, reducing processing time from three weeks to one hour, and ensured compliance with local regulations.,Modernization efforts included secure cloud migration and automatic incident response in healthcare, real-time dashboards for legal contract analytics, and personalized member engagement for biotech organizations.,Governance frameworks and an AI Center of Excellence ensured safe roll-out, with metrics/measurable ROI tracked via Microsoft Fabric dashboards.,The project is a leading example of large-scale enterprise agentic AI delivering dramatic productivity, compliance, and operational resilience gains via the Microsoft AI stack.

Land O'Lakes, a leading US agricultural cooperative, expanded its partnership with Microsoft to launch Oz, an AI-powered digital assistant built on Azure AI Foundry. Oz gives retail agronomists instant access to two decades of proprietary crop data, supporting rapid, informed decisions for cost-effective farming interventions. The alliance aims to relieve economic pressure on US farms facing rising input costs, margin squeezes, and climate-related challenges.,The collaboration represents a broader modernization initiative, with the majority of Land O'Lakes' IT migrated to Azure Cloud and Copilot tools tailored to farm management. Additional digital platforms, like Digital Ag and Digital Dairy, enable farmers to track soil health, optimize herds, and align outputs with consumer demand.,Oz is being positioned as a precision tool for agronomists and demonstrates how AI and cloud can advance food security, climate-resilient farming, and operational efficiencies in agriculture.

Fastagger, an African deep-tech startup, has developed a mobile AI business assistant to empower small and medium businesses (SMBs) in Africa. Addressing the challenge of unreliable internet and limited digital infrastructure, Fastagger’s solution runs AI models efficiently on affordable smartphones without requiring a continuous connection. The AUNI system is embedded into Safaricom’s M-PESA Business App, supporting over 2,500 MSMEs in Kenya. This on-device agent enables customer engagement automation, loyalty management, and business coaching.,The AI assistant reduces previously manual processes from eight steps to just two, driving operational efficiency and customer retention. Microsoft’s Africa Transformation Office and NVIDIA have supported Fastagger with mentorship and resources, accelerating the deployment of this mobile-based AI. Key partnerships and the integration with tools like Azure AI, Azure IoT, and GitHub enable robust on-device machine learning. Real-world results underscore substantial improvements in SMB engagement, financial tracking, and growth opportunities, enhancing digital access for underserved populations.,By decentralizing access to AI, Fastagger brings business intelligence and automation to bandwidth-constrained and rural environments—making it easier for SMBs to track customer data, streamline marketing, and make data-driven decisions. The SaaS product costs just $1 to $5 per month, making advanced technologies affordable for local entrepreneurs.,The project illustrates how Microsoft technologies and strategic partnership can close Africa’s digital divide, offering repeatable digital business models to other regions. Fastagger’s infrastructure is available globally via Azure, expanding its impact beyond Africa. Participant feedback validates the significant value of democratized, mobile-first AI for emerging markets.

A leading Canadian commercial real estate capital firm partnered with ALIANDO to overhaul its underwriting, compliance, and finance operations. Facing significant manual processing bottlenecks and siloed data, the firm worked with ALIANDO, a Microsoft partner, to develop a unified AI strategy centered on coordinated copilots. This project leveraged Microsoft Fabric, Azure AI, Power Platform, Azure OpenAI Service, and Copilot Studio to automate document summarization, risk scoring, regulatory validations, audit preparation, and provide real-time analytics. The solution integrated multiple departments’ workflows, increased collaboration, and positioned the company for scalable future innovation. The immediate outcomes were notable improvements in speed, throughput, and revenue, showcasing the value of AI-driven modernization in the finance industry.

Tata Consultancy Services (TCS) provides an AI-native platform for insurance claims processing that transforms a linear claims workflow into an agent-driven ecosystem.,The solution uses Amazon Bedrock Agents with specialist agents to handle document and image ingestion, data extraction, NIGO checks, triage, anomaly detection, and auto-adjudication across workers compensation and disability claims, including live audio transcript generation and dynamic FNOL form filling.

Tangram Therapeutics is a UK biotech company focused on solving human disease through computational RNA interference (RNAi) medicines.,To accelerate drug target discovery and evaluation, Tangram built LLibra OS, an agentic AI platform that unifies proprietary, licensed, and curated public datasets for research and target-indication assessment.,The platform supports retrieval augmented generation, web search, and text-to-SQL to help researchers identify novel targets, evaluate therapeutic potential, and design medicines.

Druva built DruAI, an agentic AI solution to help customers complete cyber investigations and recovery workflows.,The solution uses a multi-agent system on AWS with AgentCore Memory, AgentCore Code Interpreter, Amazon Bedrock access to Claude models, voice capabilities with Amazon Nova Sonic, and Bedrock Guardrails.,In production, DruAI coordinates 8 to 10 specialized agents across investigation, help, and recovery workflows.

Trend Micro enhanced its Companion AI assistant in its Vision One security software with long-term and short-term agentic memory capabilities.,The company used Amazon Neptune as a knowledge graph and experience layer to connect threat intelligence, alerts, investigations, and successful remediation patterns so analysts can get more explainable and personalized security recommendations.,Trend Micro combined Amazon Bedrock, Amazon Textract, and Amazon Managed Streaming for Apache Kafka to orchestrate agentic workflows, clean and enrich user conversations, and feed metadata into the graph for long-term memory and correlation analysis.

Schneider Electric, a global leader in energy and industrial automation, faced growing operational complexity and rising energy demands in AI-driven industrial environments. To maintain its leadership and advance sustainability, Schneider Electric developed an AI-native ecosystem centered on its EcoStruxure platform and powered by Microsoft Azure AI Foundry and Azure OpenAI. Strategic alliances with Microsoft and NVIDIA enabled the integration of AI throughout energy, automation, and sustainability applications. Schneider Electric now delivers industrial AI copilots, end-to-end AI-ready infrastructure for high-density data centers (including liquid cooling with Motivair), predictive maintenance, and a data-driven 'self-healing' supply chain. The architecture enables seamless connection from sensors and hardware to cloud AI services, driving outcomes like lower costs, accelerated delivery, and massive reductions in energy and carbon footprint. Schneider Electric has achieved a €130M+ supply chain value, reduced inventory/delivery times, and scaled recurring AI software revenues. Its open ecosystem and vertical integration make it a dominant industrial AI partner globally.

Banco Bradesco, one of Brazil's largest private financial groups, built a scalable generative AI platform called BRIDGE on Microsoft Azure Red Hat OpenShift.,The platform integrates multiple AI models, supports modular multi-agent creation for business teams, and connects to Azure OpenAI while embedding governance, security, and regulatory controls.,BRIDGE serves internal support, development, operations, technology, and customer-service use cases across the bank.

Axpo, Switzerland’s leading renewable energy producer, undertook a digital transformation to enhance power grid operational efficiency. Facing challenges with decentralized, slow asset information retrieval over a 2,400 km high-voltage grid with 90,000+ assets, Axpo developed the web-based 'Insights' platform. Leveraging Microsoft Azure Cognitive Search, Azure Maps, Power BI, Azure IoT Edge/Hub, and Azure Data Factory, the platform provides real-time asset data, AI-based drone image analysis, SCADA integration, and incident management, enabling faster and safer asset monitoring and predictive maintenance.,Key integrations enabled engineers and maintenance teams to visualize assets geographically, rapidly locate faults, and streamline both manual and drone inspection processes. The Incident Management Module improved crew response and communication. Results include a 99% reduction in asset information search times, safer line inspections via drones, and stepwise progress toward predictive maintenance, supporting grid reliability for Switzerland and Liechtenstein.

Bankdata, a consortium of Danish banks, faced the challenge of modernizing their vast COBOL-based mainframe legacy systems to cloud-native platforms due to growing technical debt, rising maintenance costs, and limited access to legacy experts. The organization aimed to retain more control over project costs and intellectual property, moving away from traditional approaches heavily reliant on global system integrators. Leveraging state-of-the-art Microsoft technologies, Bankdata and partners developed a modular, agent-based migration factory that uses multiple orchestrated AI agents to analyze, convert, and test COBOL code into maintainable Java running on modern platforms. This system underpinned the transition by extracting business logic, visualizing and mapping dependencies, and ensuring that legacy business processes are accurately transformed. Sophisticated orchestration with Microsoft Semantic Kernel enabled precise management of worker agents, intelligent handling of code context, and conversion consistency. Using GPT-4, GitHub Copilot, and Azure OpenAI, the framework delivers robust code translation, dependency mapping, call chain analysis, and quality assurance through test suite automation. The project significantly reduced manual workload, improved code quality and maintainability, and accelerated transformation timelines, all managed in-house at Bankdata.

Infosys Topaz offers an AI-first suite of services, frameworks, and platforms built on Microsoft Azure, Azure OpenAI Service, and Azure Cognitive Services. The solution integrates 12,000+ AI assets and 150+ pre-trained AI models to accelerate organizations’ AI adoption and ROI across industries.,Real implementations include the Goods and Services Tax Network (GSTN), which reduced tax fraud via advanced fraud analytics processing 20 billion transactions. Molina Healthcare enhanced digital transformation and member service through AI-infused cloud technologies, while Siemens leveraged Topaz for digital workforce upskilling and personalized learning, and Booking Holdings benefited from AI-first cybersecurity threat management.,These transformations, led by Infosys in partnership with Microsoft, utilized AI to automate complex processes, optimize operational efficiencies, and strengthen regulatory compliance. The solutions provide responsible, ethical, and secure AI embedded into business operations, purpose-built to create enterprise value and customer trust.,Independent recognition includes awards for enterprise adoption of AI and strategic market leadership. The platform also includes agentic functions and has launched over 200 AI agents for various enterprise tasks. The AI-driven approach helps enterprises unlock efficiencies at scale, future-proof their investments, and build robust connected ecosystems.

KPMG International modernized its global audit process with KPMG Clara AI, a cloud-based smart audit platform used across member firms worldwide.,The platform analyzes whole datasets instead of relying on sampling, automates documentation and substantive audit procedures, and grounds agents in enterprise data for compliant, secure use across regulated jurisdictions.,KPMG built the solution on Azure AI Foundry, Azure AI Search, Azure OpenAI in Foundry Models, Azure AI Content Understanding, Azure App Service, Azure Cosmos DB, and the Microsoft Agent Framework.

NSF, a nonprofit scientific and regulatory auditing organization, needed to organize, verify, summarize, and synthesize tens of thousands of documents across country-specific rules for medical audits.,The solution uses Azure Document Intelligence, Azure OpenAI, Azure Model Context Protocol (MCP) tools and servers, Azure Blob Storage, Azure Python SDK, Azure Cosmos DB, Microsoft Entra ID, and Azure RBAC to automate document validation, structured sorting, version tracking, and summary drafting.,Staff review and refine the AI-generated summaries, while the workflow remains inside Azure cloud controls and private tenant security.,The implementation reduced audit turnaround time from 4–6 weeks to about 2 weeks and was reported to deliver near-perfect accuracy for the proof of concept.

Novus Hi-Tech enhanced FleetGPT to improve fleet safety operations as its deployments expanded.,The system assesses unsafe events in context, determines next-best actions, automates routine follow-up, and escalates higher-risk cases while human teams remain the validation layer.,Amazon Bedrock powers the reasoning layer across video, telemetry, and driver-behavior data; AWS IoT Core, Amazon Kinesis Video Streams, Amazon S3, and AWS Step Functions support ingestion, storage, and workflow orchestration.

Overdose Digital, an AI-first consultancy and digital commerce agency, centralized ecommerce data in BigQuery and used Looker as a governed semantic layer to ground generative AI and reduce hallucinations.,The company built an agentic workflow where Looker-defined metrics trigger Vertex AI and Gemini-based agents to generate hyper-localized creative briefs and assets with human oversight.,It also used BigQuery AI to detect creative fatigue and automatically trigger a synthesis loop that refreshes creative strategy near real time for nearly 300 clients.

TrueLook built an AI-powered construction safety monitoring system on Amazon SageMaker AI that automatically detects PPE and unsafe conditions from jobsite camera images.,The workflow uses SageMaker Processing, SageMaker Training, SageMaker Model Registry, SageMaker Pipelines, MLflow, TensorBoard, Amazon S3, and real-time endpoints to support a multi-stage fine-tuning and active-learning loop.

Ericsson describes an AWS-hosted rApp as a Service solution that uses agentic AI to automate RAN operations and optimization for communications service providers.,The platform integrates with Ericsson Intelligent Automation Platform and O-RAN interfaces to coordinate specialized agents for anomaly detection, root-cause explainability, cell shaping, and uplink performance optimization.,It is delivered as a multi-tenant SaaS offering through AWS Marketplace.

IBM Consulting and Microsoft co-hosted their third annual hackathon with more than 900 participants, bringing together public sector organizations to develop real-world AI agent solutions. State of New Jersey Department of Community Affairs and State of Arizona Department of Child Safety were among the winning teams, focusing on modernizing data entry, compliance, and case management. New Jersey created 'Checkmate,' leveraging Microsoft Azure AI, Dynamics 365, and Power Automate to automate inspection reporting, using conversational AI and speech-to-text for mobile workforce efficiency. Arizona's solution enhanced child welfare service tracking and improved reporting accuracy for critical service records, enabling better support for families. The hackathon demonstrated large-scale industry collaboration and rapid prototyping to overcome digital transformation barriers in government. Both solutions are positioned to improve compliance, reduce errors, and optimize employee productivity, and are considered scalable real-world cases demonstrating innovation in public services.

Fujitsu faced time-consuming and manual sales proposal creation processes, hampering staff productivity and slowing the onboarding of new salespeople. To address these challenges, Fujitsu developed an orchestrated multi-agent AI system using Microsoft Azure AI Agent Service, Azure AI Foundry, and Semantic Kernel, allowing them to automate data gathering and proposal drafting. The new system integrates with Fujitsu's existing workflows and leverages multiple specialized AI agents to dynamically retrieve, synthesize, and tailor sales proposals from dispersed enterprise data sources. As a result, Fujitsu boosted sales team productivity by 67%, enabling staff to focus on customer engagement, faster onboarding for new hires, and delivery of more accurate, data-driven, and current proposals.