Manufacturers are rapidly modernizing by using Microsoft cloud and AI solutions to transform key operations—streamlining product testing with cloud-based analytics, accelerating R&D through generative AI, and optimizing factory operations with intelligent automation.

For instance, BMW has enhanced the driving experience for over 13 million active users by integrating digital services within its vehicles. The MyBMW app, modernized with Cloud services, connects drivers to a range of digital features designed to enhance convenience and engagement. For BMW Group, this approach not only streamlines repetitive processes but also provides an open-source platform to support future scalability.

The time is now for manufacturers to modernize with AI in mind

BMW Group showcases its ongoing digital transformation in manufacturing. What began with a few sensor clusters has evolved into the Industry 4.0 revolution—the coming together of IT and operational technology to solve perennial challenges in manufacturing and heavy industry. Frontier leaders have achieved outcomes like Emirates Global Aluminum subjecting 97.5% more products to quality inspection, DEXIS reducing on-site service needs by 30%, and Fischerwerke construction enhancing the service life of structures. 

Rockwell Automation describes today’s opportunity:

“Our customers are looking to us for faster delivery, new functionality, reduced time-to-value, and new ways of working[…] It’s a perfect time to bring the power of modern IT—including the cloud—to the factory floor.”

— Brian Shepherd, Senior Vice President for Software and Control at Rockwell Automation  

The next wave of transformation—the AI and automation wave—has arrived. We’re seeing early adopters achieve significant business outcomes by integrating these technologies into their daily lives and work. The following section will highlight some powerful manufacturing success stories—each in key impact areas where leaders are modernizing with AI in mind.

Transforming the product testing lifecycle

Let’s talk again about BMW, this time overviewing how it has digitally transformed their product lifecycle through Internet of Things (IoT) cloud modernization and AI. 

Cloud modernization: BMW was experiencing slowdowns due to the enormous amounts of data sent by its 3,500-car test fleet, To address this, it developed an IoT data recorder connected to Microsoft Azure cloud platform, using Azure AI services, Azure App Service, Azure Kubernetes Service (AKS), and Azure Data Explorer. This combination was able to handle the massive amount of IoT data while modernizing the apps that monitor, manage, and analyze it. The upshot? 10 times faster data delivery and analysis handling twice the volume.

Adapting and infusing AI: BMW wanted to democratize and scale the impact of this test data, so it connected with Microsoft to see how it could adopt a generative AI chat experience. By using Azure AI services, BMW made its data available through natural language queries while Microsoft Power BI provided data visualizations to empower decision-makers across all business roles. “We can put very complex raw data into an understandable and comprehensive web interface so many BMW employees who aren’t engineers are also able to access it,” explained Heinz Gebhart, co-creator of BMW’s IoT data recorder. “Azure is the turbocharger for delivering the right data to the right person on a large scale.” 

Augmenting R&D innovation

For cutting-edge products to deliver real ROI, they need to address in-demand customer use cases. That’s why Denso, a leader in automotive parts manufacturing, embraced modernization to prepare the apps powering its advanced robotics for real-world applications. 

Cloud modernization with AI: Denso, Japan’s largest automotive parts manufacturer, is exploring new markets for expansion (like advanced automotive safety features and connected driving to factory automation and agriculture). To transform its own operations and enhance customer satisfaction, it turned to autonomous robots powered by generative AI. However, this approach would have to look different than the apps and functions that ran its traditional robotics.

“Conventional robots are inflexible machines that act based on the movements and instructions they are given. In contrast, we are developing control technology in order to realize a human-like robot that acts according to human language and can also easily correct its errors in judgment when a human points them out.”

—Keitaro Minami, Project Assistant Manager of Automation Innovation Section, Business Innovation Department, Cloud Services R&D Division, Denso Corporation

Denso rearchitected its control program to interface with generative AI using Azure OpenAI, Github Copilot, and Azure App Service. This let them significantly streamline development and allowed a container approach to address future customer use cases where robots cannot always be connected directly to the cloud. 

Optimizing factory operations

After a major company shift, powertrain solution provider Aurobay needed to rebuild their digital environment, including apps and infrastructure that ran mission—critical operational devices on the factory floor.

Cloud modernization: Aurobay collaborated with Microsoft to implement a hybrid cloud architecture powered by Microsoft Entra ID and Azure Arc. This initial migration prepared it to better manage its operational applications both on-premises and in the remote cloud using Azure Virtual Machines and Azure Kubernetes Service. 

“A specialist team from Microsoft helped us set up the full Azure tenant with all the landing zones, subscriptions, and group policies, which was critical in getting this right from the start.”

—Carol Wittgren, Head of Digital Acceleration at Aurobay

Adapting and infusing AI: With new software and architecture leadership in place, Aurobay is continuously embracing innovative methods of data modeling and management to maximize its data, taking its capabilities to the next level with Azure AI services, Azure Machine Learning, and Azure high-performance computing (HPC).

Modernize your manufacturing

Microsoft cloud and AI offerings provide end-to-end solutions for manufacturing, supporting interoperability, scalability, and modernization from backend to frontline. Azure enables manufacturers to modernize efficiently while minimizing risks and maximizing the benefits of AI-powered tools. 

We invite you to learn more about partnering with Microsoft to unlock real-world manufacturing modernization, including how to innovate by accelerating cloud and AI adoption. 

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¹Cloud Radar: Manufacturing Industry Report, Infosys, April, 10 2024.

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“Potholes not only tell you about the state of your infrastructure,” noted a Harvard researcher in 2019, “they also tell you about the nature of participation in your city.”¹ A city that fixes a pothole promptly is not just responsive, its constituents feel empowered to engage with government. 

In recent years, expectations have only risen on what governments need to deliver, leaving many cities struggling to deliver services in ways people prefer while also running a gauntlet of budgetary, regulatory, and societal challenges. On the one hand, citizens want to access great city services on par with how they do their banking or shopping—that is, secure, personalized experiences on smartphones and computing devices, rather than exclusively in-person. On the other hand, governments face unprecedented pressures in terms of funding, regulations, staffing, and cybercrime. 

To bridge this gap, more and more city governments are looking to use the force-multiplying power of generative AI. Its ability to converse in natural language and reason over vast stores of data, then find answers, compose messages, and orchestrate actions is not only solving longstanding modernization challenges, it’s also opening incredible new frontiers in city services. 

Helping city governments evaluate, explore, and successfully deploy high-impact solutions with AI is now the primary focus of our work at Microsoft for government. In cities around the world, we have seen dramatic acceleration in generative AI innovation, with new solutions that are helping cities to: 

• Deliver personalized services.
• Empower the professionals who serve the public.
• Derive better insights and greater value from data.

The future of the smart city is already here—in Abu Dhabi 

The President of the United Arab Emirates (UAE), His Highness Sheikh Mohamed bin Zayed Al Nahyan, launched an ambitious drive around 15 years ago to make government services more accessible and service-oriented. In Abu Dhabi, the nation’s capital, those efforts took a giant leap with the advent of AI, accelerating innovation that led to the launch of a new AI-powered government services platform in October 2024. 

Aptly called TAMM—which in Arabic translates to “consider it done!”—the platform began as a centralized portal several years ago and was revised to expand service offerings. With the application of new AI capabilities, it is now a one-stop digital hub, offering access to nearly 950 government services for citizens, residents, visitors, and investors. 

Built on Microsoft Azure OpenAI service, TAMM uses advanced AI to deliver new classes of benefits. The platform offers real-world examples of how AI can transform smart cities by unifying services and inviting engagement in new and powerful ways. 

Here are three noteworthy ways that TAMM improves city service delivery. 

1. Serve people as they like, with personalized interactions 

TAMM is designed to remove barriers between government services and the people who need them. In many cases, that means no longer forcing them to go to government buildings to get things done. 

The new TAMM includes a generative AI assistant that provides every day service, offering personalized access to services such as license renewals, utility bill payments, permit applications, healthcare, and more. There’s even a new photo reporting app, where people can take a snapshot of a problem they come across (including, yes, a pothole) and the assistant helps to fill out a report and later sends updates on the progress of repairs. 

TAMM also helps to untangle bureaucracy to simplify common yet complicated tasks. The process of registering a car, for instance, was dramatically simplified. What previously required days of visiting buildings and standing in lines can now be done quickly through the app—which also recommends the right type of insurance policy and synchronizes it with registration. 

2. Deliver better results with a more energized and empowered workforce 

Because TAMM handles so many more routine tasks than before (such as responding to basic questions on services issues or applications), city employees can focus more on high-value service delivery. With live services including video and audio options, agents can deliver high-touch assistance while still maintaining user privacy. 

A good example of this is the case of a foreign worker who lived in Abu Dhabi for 10 years and was told by an immigration agent that she couldn’t leave the country due to visa issues. In tears, she opened the TAMM app on her phone and was connected to a helpline, where an agent quickly eased her anxieties. “I said I don’t know what to do, and the agent was literally amazing,” the woman said. “[The agent] said, ‘Don’t worry, it’s getting updated now’—and I was on my way.” 

The approach to innovation behind TAMM also reflects an important trend: equipping public servants to work like product teams so that city services evolve like platforms. The TAMM organization operates in a unique “factory” in Abu Dhabi that operates like a startup—agile, data-driven, and obsessed with user satisfaction. The city’s employees don’t just execute services; they co-innovate with citizens and stakeholders to create them. Real-time dashboards, productivity-enhancing agents, and a culture of continuous iteration are driving success and proving that empowering the workforce is the foundation of smarter cities. 

3. Keep cities moving with services that listen, learn, and protect 

TAMM is designed to help people better navigate government services by understanding and responding to user needs almost instantly. It recognizes multiple languages and offers the option for spoken conversations, intelligently walking people through a broad range of complex processes. For example, for a family with a person who has a disability, TAMM can help navigate special services, significantly streamlining a qualification process that previously took weeks.  

TAMM not only remembers previous conversations and knows the status of an issue or process, it is also deeply integrated across major government entities in the city. Service can be coordinated with in-person service centers or agencies who help housebound people in their homes.  

The TAMM platform is powered by Microsoft Azure OpenAI service and G42 Compass 2.0, a next-generation enterprise AI platform that provides sovereign cloud services. It also uses open-source models, including JAIS, a high-performing Arabic Large Language Model, and Azure OpenAI GPT-4.

The TAMM app now assists Abu Dhabi’s 2.5 million citizens to conduct than 10 million transactions a year. Helping to protect data and ensure privacy within these transactions is the world class cloud security provided by the Microsoft platform—reflecting our commitment to security above all in delivering AI services, as codified in our Secure Futures Initiative.

Keys for building a foundation of success with AI 

The noteworthy innovation happening in Abu Dabhi is a great example of a city realizing the transformative potential offered by generative AI. Many others are following the trend, and the results are exciting. 

As we look across the global landscape, we note a set of common factors that consistently underpin successful AI adoption. We would advise every city to consider the following: 

• A mission-first mindset drives smarter AI adoption. Cities that anchor AI initiatives in clearly defined public outcomes—such as reducing response times to citizen queries or improving access to social services—are better equipped to prioritize high-impact use cases and rigorously measure results. Aligning AI innovation with policy goals improves clarity and can also boost community trust.
• AI literacy must span the entire workforce. Successful implementations include investments to build AI literacy across all levels of the public workforce—from IT and data science teams to case workers and city clerks. With effective training and a culture of learning and sharing, cities have more empowered workforces and enjoy better outcomes.
• Strong data foundations are critical. Cities that proactively clean, integrate, and govern their data estates—including structured, unstructured, and semi-structured data—are better able to operationalize AI faster, more securely, and at scale. A modern data platform emphasizes robust privacy and access protections as prerequisites for AI success, helping civic leaders avoid common pitfalls such as model bias, incomplete datasets, or compliance gaps. 

Learn more about AI technology for governments

To help your city government make the most of modern cloud and AI technology, contact your local Microsoft representative or certified technology partner. Together, we can help you explore options, identify use cases, and transform your ideas into meaningful solutions.

• For in-depth guidance and resources on designing, deploying, and sustaining AI-powered solutions in city government, visit the Public Sector Center for Digital Skills.
• For workforce development and training resources and guidance tailored to cities and other government organizations, visit Microsoft Learn for Government.
• For more on how Microsoft is helping to empower governments with AI, read our blogs. 

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Sources:

¹ Harvard Griffin GSAS, “Pothole Politics”, January 2019.

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As the mining industry undergoes its digital and AI transformation, Microsoft remains committed to delivering innovative and secure solutions. From adopting AI and agents to streamlining business processes and unlocking efficiency to moving legacy systems to the cloud—we’re dedicated to working together towards a powerful and sustainable future of mining.

AI transformation for a more resilient future of mining

As we are seeing across the energy and resources industry, the mining sector is facing growing pressure to support the global energy transition, with AI emerging as a prominent solution. With demand for critical minerals expected to quadruple by 2040³, AI can help mining companies locate and extract resources more efficiently, with studies showing potential reductions of 20% to 30% in the time and cost of mineral discovery.⁴

From early stage exploration to downstream processing and logistics, AI has the potential to be embedded throughout the mining value chain. In upstream operations, it can enhance mineral prospectivity mapping, resource estimation, and production planning. Downstream, it can optimize ore blending, recovery, and processing. Even side streams like supply chain logistics are beginning to see gains, as AI-powered efficiencies ripple across operations. And in exploration, AI unlocks insights from vast geoscientific datasets—both legacy and real-time—enabling faster, more accurate decision-making.

The possibilities for AI use cases in the mining sector are abundant, and there are ways for organizations embarking on their digital transformation journey to get started today—such as with workforce productivity. AI adoption in this context is a powerful step towards the future of work, and Ma’aden, a mining company in Saudi Arabia, is a prime example of that. Ma’aden used Microsoft 365 Copilot, Microsoft Copilot Studio, and Microsoft Azure OpenAI Service to help employees be more productive in daily tasks, like getting quick answers on policies, summarizing content, and drafting presentations, emails, and meeting minutes. Ma’aden saw enhanced productivity, with Copilot users saving up to 2,200 hours monthly.

In addition to workforce productivity, Microsoft AI solutions are also enabling operational transformation, as seen in Sandvik’s approach to equipment optimization. Sandvik created a cloud-based service solution that uses data and AI to generate insights on the state of their machines to support the optimization of the operation of equipment. Powered by Microsoft Azure Cloud and its analytics and AI services, the solution uses data to produce actionable insights into equipment performance and status—helping to drive transformation across its business.

Foundations for AI-driven transformation in mining

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Unlocking potential: Bringing the cloud to mining operations

As the mining industry advances efficiency, safety, and sustainability goals, the adaptive cloud has emerged as a critical piece of this journey. Microsoft’s adaptive cloud approach uses cloud-native and AI technologies across hybrid, multi-cloud, edge, and Internet of Things (IoT) environments. By making operational technology (OT) cloud-enabled, mining organizations can unlock real-time insights, streamline operations, and enhance resilience. This union of cloud and OT supports smarter decision-making and predictive maintenance, and lays the foundation for innovation and scalability.

Boliden offers a compelling example of how cloud infrastructure can modernize mining operations at scale. The Swedish mining company needed to automate and centralize data collection, increase visibility across processes, and add new ways to analyze information. Boliden monitors the Garpenberg site with a network of 500 cameras that give management teams oversight of the mines, wells, and operations, helping to keep an eye on productivity and safety. The company now uses a combination of Microsoft Azure IoT Edge and Microsoft Azure IoT Hub to connect the cameras with other Boliden systems and the rest of its IoT network, which consists of thousands of sensors above and below ground, along with other devices. By working with a flexible, fully featured cloud infrastructure, the company can now bring more productivity and safety to all their sites.

Emirates Global Aluminium (EGA) also exemplifies how adaptive cloud infrastructure can overcome the limitations of traditional on-premises environments to support scalable, intelligent operations. EGA deployed a hybrid environment that connected private cloud services through on-premises datacenters. Deploying a hybrid environment helped to optimize latency, support advanced AI and automation solutions, offer sustaining commercial savings by applying intelligence at the edge, and streamline processing for massive amounts of real-time readings from sensors, machinery, and production lines.

Learn more about energy and resources solutions with Microsoft

No matter what your organization’s digital transformation may look like, Microsoft is committed to helping to drive progress in the mining industry and working to grow sustainable, secure, AI-powered businesses. Microsoft has always been built on trust and a robust security suite, and is committed to prioritizing security in the design, build, and operation of our products and services. To take a deeper dive into cybersecurity in the age of generative AI and building a foundation for AI-powered transformation in mining, read our latest e-book.

• Explore the possibilities of AI transformation with Microsoft.
• Read the AI Transformation in Mining whitepaper.
• Visit Microsoft for energy and resources and Microsoft for the mining industry.
• Read the new International Energy Agency report, Energy and AI.
• Learn more about Microsoft Security for enterprise. 

¹ Tracking the Trends 2025 | Deloitte US, Deloitte 2025

² 2025: The Year the Frontier Firm Is Born, Microsoft, April 2025

³ The energy transition will need critical minerals and metals. Here’s how to mine responsibly, World Economic Forum, June 2024

⁴ Now is the time to invest in sustainable mining technologies. Here’s why, World Economic Forum, September 2024

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Agentic AI as the hero 

At MWC 2025, Microsoft spotlighted how agentic AI is driving real industry transformation—turning innovation into impact for telecoms. Throughout the event, telecom leaders cited agentic AI as a game-changer, exploring new revenue streams, and automating complex tasks. From hyper-personalized marketing campaigns to proactive network management, these applications are already delivering measurable business value. AI is no longer a pilot or proof of concept; it’s a proven technology, a necessity, for operators to deliver transformative customer experiences at-scale. 

Attendees witnessed demos and theater sessions that showed how AI can assist in the automation of complex tasks, anticipate network bottlenecks, and enhance customer experiences at-scale. Industry leaders consistently emphasized that building trust in AI is paramount, especially as AI becomes more important in supporting decisions previously handled by humans. Microsoft’s approach—rooted in transparency, security, responsible AI and governance—stood out. Microsoft highlighted how integrating Azure’s robust compliance features offers operators and their customers peace of mind, showing that trust and AI innovation are complementary. 

Booming booth and demo showcase 

The Microsoft booth welcomed more than 11,500 unique visitors—interested in learning how Microsoft’s AI portfolio helps drive innovation. The energy was high around our experiential demos, bringing to life real-world applications. Attendees learned how Microsoft Fabric and Azure analytics unifies data to optimize network operations, and powers immersive applications. These showcases highlighted the practical benefits of modern AI-powered technologies. 

Packed theater sessions 

The energy in Microsoft’s theater sessions was electric—overflow crowds packed the aisles to hear how telecom operators are igniting innovation and growth with AI sessions like “Korea Telecom Accelerates AI Adoption” and “AI Ignites Innovation and Growth in Telecom,” where speakers spotlighted how agile customers and partners, such as Accenture, Nokia, and Amdocs, are taking decisive leaps forward in their AI journeys. Each of these sessions echoed a consistent message: whether it’s network modernization or harnessing data, customer and partner momentum is accelerating digital transformation with real business results.

Announcements and customer success stories 

In addition to the success stories shared onstage, Microsoft unveiled news and announcements at MWC 2025 (see my LinkedIn post for details) that reinforce our commitment to accelerating telecom innovation. Microsoft introduced extended agentic AI capabilities for proactively detecting and self-healing network anomalies, demonstrating how operators can achieve near-zero downtime while reducing operational expenses. Another focus was low-code expansion across Microsoft Dynamics 365 and Microsoft Power Platform, empowering telecoms to rapidly build and deploy AI-powered workflows—everything from personalized marketing funnels to simplified customer onboarding experiences. Microsoft also introduced two AI-powered sales agents—Sales Agent and Sales Chat—within Microsoft 365 Copilot to help sales teams close deals more efficiently by automating lead management, customer engagement, and sales tasks. ​ 

Microsoft showcased deepened partnerships with industry leaders like Amdocs and Nokia, geared toward modernizing network infrastructure and elevating customer touchpoints. These collaborations highlight Microsoft’s efforts with partners to tackle the sector’s biggest challenges, such as automating network rollouts, augmenting cybersecurity with AI defenses, and simplifying data monetization. Together, these new announcements reinforce how AI—and especially agentic AI—will continue to transform telecom, enabling telecoms to deliver faster, smarter, and more secure connectivity.

Attendee excitement also came in the form of customer success stories. Telecoms spoke about how agentic AI and other Microsoft AI solutions have already begun reshaping their business: 

• Vodafone described how adopting AI-powered network maintenance cut outages and boosted satisfaction scores.
• KT provided a real-time case study on scaling AI from niche pilot projects to a core tenet of corporate strategy. 

Each story illustrated tangible business value, resonating with attendees seeking proven outcomes.

Partner ecosystem and shared success 

One of the most energizing aspects of MWC 2025 was connecting with Microsoft’s dynamic ecosystem of partners. At this year’s customer and partner reception, conversations buzzed with ideas on how to unlock greater value by combining Microsoft’s cloud and AI capabilities with solutions from trusted partners like Amdocs, Accenture, Nokia, and more. 

The partner spotlight was on innovation—from co-developed analytics dashboards that simplify 5G rollouts to integrated AI modules that accelerate service provisioning. These collaborations underscore how Microsoft’s partnerships with leading telecom Software Development Companies (SDCs) and System Integrators (SIs) are delivering tangible business outcomes. Startups were also a key part of the story. Microsoft for Startups highlighted a new wave of innovative startup SDCs that are helping telecom customers improve customer experience, data insights, and operational efficiency. 

By bringing together Microsoft’s technological depth with the domain expertise of partners, the company is addressing some of the telecom industry’s most pressing concerns—reducing operational overhead, maximizing customer lifetime value, and accelerating innovation. This spirit of community sets the tone for continued innovation.  

What is next 

The momentum behind AI adoption in telecom is accelerating. According to a recent McKinsey report, the focus on scaling AI from one-off pilots to enterprise-wide initiatives will only intensify as data volumes grow and networks become more complex. Microsoft stands ready to help telecoms navigate the next phase through delivery of telecom-specific AI innovations that drive business results: 

• Telco data model in Microsoft Fabric 
  Provides a unified data framework enabling telecoms to run advanced agentic AI workflows across service assurance, customer care, and revenue management.
• Autonomous networks and self-healing operations 
  Leveraging Azure OpenAI Service and the Microsoft generative AI and agentic AI platform, Microsoft envisions proactive, self-healing networks that detect, diagnose, and remediate issues in real time—reducing truck rolls, lowering OPEX, and raising customer satisfaction.
• Open and intelligent Radio Access Network (RAN) optimization 
  Now open source, Project Janus enables dynamic service models and real-time telemetry in Open Radio Access Network (O-RAN) environments—empowering telecoms to build intelligent, AI-optimized RAN architectures using Microsoft’s agentic AI framework to enhance performance, flexibility, and innovation.
• Hyper-personalized customer experiences 
  AI agents within Dynamics 365 Customer Insights, Microsoft 365 Copilot and Amdocs Customer Engagement Platform can unify behavioral and usage data to deliver precise, timely engagements. Telecoms can reduce churn and boost average revenue per user (ARPU) by offering the right products and solutions.
• Expanding revenue beyond connectivity 
  With the embedding of AI-powered interactions directly into customer and developer ecosystems, telecoms can open new business models—such as vertical-specific APIs and AI marketplaces that monetize network APIs, network analytics and agentic automation. 

Microsoft is deeply committed to partnering with telecom operators to co-create the future of connectivity. By aligning agentic AI capabilities with the industry’s most urgent challenges, Microsoft helps telecoms unlock new value—securely, ethically, and at scale. Through strategic collaborations with partners like Nokia, Amdocs, and leading SDCs and SIs, Microsoft delivers scalable, trusted solutions that position telecoms as pioneers in the AI-powered era. 

Until next time, Barcelona 

The transformation of the telecom industry is accelerating—driven by bold investments in AI, deepened partnerships, and a shared commitment to innovation. As the industry moves beyond MWC 2025, one thing is clear: telecom operators are poised to lead in an era defined by intelligent networks, data-driven agility, and customer-centric growth. 

With Microsoft’s agentic AI capabilities, trusted cloud infrastructure, and collaborative partner ecosystem, telecoms can unlock new efficiencies, monetize new services, and deliver seamless experiences that generate lasting business value.

Learn more about how Microsoft is powering the next wave of telecom transformation with AI and agentic automation by visiting our Microsoft for telecommunications Industry Page.

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At Microsoft, prioritizing sustainable practices with suppliers has also helped us uncover new opportunities for innovation and cost optimization.  

Our new guide, Reduce Risk, Create Resilience: Advancing Supply Chain Sustainability, outlines how data intelligence and collaboration can transform supply chains to be more agile, sustainable, and resilient. We offer lessons from our own experience as we strive to meet our ambitions to be carbon negative, water positive, and zero waste by 2030, all while protecting ecosystems. And we provide actionable insights and practical steps to help customers address reporting pressures, mitigate risks, and seize opportunities for innovation, ultimately driving resilience and long-term value.

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Aligning data and systems for the road ahead 

Organizations are facing growing regulations and customer expectations to prioritize sustainable practices. To make meaningful change, organizations and their suppliers must share data and work together to address challenges across the ecosystem.  

Advanced analytics and AI are transforming supply chain sustainability at Microsoft. By leveraging these technologies, we accelerate day-to-day work, gain real-time insights, and drive co-innovation to meet sustainability reporting requirements as well as business continuity needs. 

For instance, our procurement team used Microsoft Cloud for Sustainability to centralize supplier emissions data and streamline processes. By integrating AI-powered automation, such as automated review of supplier assurance letters, we reduced survey processing time by 92%. 

Eckes-Granini, a European producer of name-brand fruit juices, is committed to ensuring that all its raw ingredients are sustainably sourced by 2030. The company used Microsoft Intelligent Data Platform including Microsoft Azure and Microsoft Power BI to connect to essential data sources and create precise visualizations of suppliers’ progress. This enables Eckes-Granini to track risks and follow up with suppliers accordingly. Now almost 70% of Eckes-Granini’s juice ingredients meet sustainability standards, and the company is better prepared to respond to Germany’s Supply Chain Due Diligence Act.

Choosing carbon-free electricity solutions 

Carbon-free electricity (CFE), such as wind, solar, and hydroelectric power, can be a powerful lever for decarbonization through improved fuel efficiency while helping to reduce exposure to varying fuel prices. These solutions can play an integral role in industry decarbonization goals, such as the International Maritime Organization’s target of reducing a 50% of their absolute CO₂ emissions from 2008 levels, by 2050.^{1 2} Organizations can help scale these efforts and the benefits of CFE in supply chains by launching supplier enablement programs.  

To help meet our own carbon reduction targets, Microsoft now requires suppliers to transition to 100% carbon-free electricity for the goods and services delivered to Microsoft by 2030. We’re making this easier for suppliers through our Supplier REach Portal, co-created with 3Degrees, to streamline access and procurement of CFE; and Zettawatts’ Supplier CFE Program, to provide assistance for reaching our CFE requirement, from understanding CFE procurement to discussing goals, developing plans and budgets, and reviewing agreements. 

Turkish energy company Enerjisa Üretim established a round-the-clock remote operation center that receives more than 50,000 signals per second from its large network of hydropower, wind, and solar plants. It processes the data using an Azure-based solution including Azure IoT Hub, Azure Digital Twins, and Azure Machine Learning. The solution delivers real-time monitoring and data analytics on power plant performance—all in one centralized location. It also uses Microsoft Azure OpenAI Service to forecast future outcomes, predicting average daily production for turbines for up to two months.

Creating new value from resource optimization 

The World Economic Forum estimates that adopting circular business models could unlock up to USD4.5 trillion in value by 2030.³ By adopting circularity, companies can not only help meet regulatory requirements but also drive new innovation, enhance their brand reputation, and differentiate their business. 

With a focus on the long-term value of resources, companies can uncover ways to reduce environmental impact while also increasing value for the business. Supply chains are central to this opportunity. 

To help meet our goal of becoming a zero waste company by 2030, Microsoft set a target of reusing or recycling 90% of our cloud hardware by 2025. We not only reached that target a year early—we exceeded it. In 2024, we reached a 90.9% reuse and recycling rate of our cloud servers and components.

Within our global datacenters, Microsoft Circular Centers are foundational to this work, enabling us to process and route decommissioned servers and hardware components to their next useful lives, such as internal reuse, other electronic supply chains, or academies that train datacenter technicians. 

Reinventing supply chain logistics  

Supply chain logistics challenges are bigger than any one company. To meet growing regulatory and market pressures, organizations need to leverage data and AI technology across their ecosystems and industries. Sharing data at a more granular level, they can identify opportunities to improve infrastructure and boost sustainability across whole logistics networks.  

Applying this principle, Microsoft has moved our cloud supply chain to renewable diesel in our road freight operations in Europe and California while keeping existing equipment in use. 

We’re also advancing aviation decarbonization by integrating sustainable aviation fuel (SAF) into shipments of cloud hardware. Through multi-year agreements, we’re working to reduce air freight emissions and help scale the adoption of SAF across the industry.³ 

Results so far include: 

• 50% lower emissions for road freight operations in Europe and California, using renewable diesel.
• 17,000 metric tons of carbon dioxide equivalents (mtCO2e) saved through SAF, compared to conventional transportation fuels.
• 73% lower relative carbon intensity of our cloud logistics supply chain since 2022 through lower-carbon transportation and logistics consolidation. 

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Get started mapping your sustainable supply chains 

Supply chain sustainability is unique for every organization. Take the first steps by exploring the ins and outs of using data technology and collaboration to drive environmental reporting compliance, adaptability, customer and stakeholder satisfaction, and innovation.  

• Dive in with the guide: Reduce Risk, Create Resilience: Advancing Supply Chain Sustainability. 
• Explore Microsoft Cloud for Sustainability.
• Read about AI transformations for sustainability.

¹ The Intergovernmental Panel on Climate Change (IPCC) defines decarbonization as: “The process by which countries, individuals or other entities aim to achieve zero fossil carbon existence. Typically refers to a reduction of the carbon emissions associated with electricity, industry and transport.” (IPCC)

² Decarbonized supply chains are resilient supply chains, McKinsey, 2022.

³ Circular Transformation of Industries: Unlocking Economic Value, World Economic Forum, 2025.

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Modernizing OSS and BSS: From reactive to agentic AI

OSS and BSS have long been the operational and commercial backbone of telecoms. Generally speaking, OSS manages network operations—provisioning, inventory, and fault detection—while BSS handles transactional functions like billing and customer management. Traditionally, these environments have remained fragmented, hindering a unified view spanning the customer, the network, and the business.

Thanks to advances in data management, AI and generative AI, these systems can now move beyond reactive troubleshooting to automated, predictive, and—even more significantly—agentic solutions, in which AI autonomously orchestrates tasks end-to-end. Whether it’s proactively responding to service degradations or autonomously managing resolving customer issues, agentic AI promises unprecedented cost mitigation, efficiency, and agility. 

However, effectively harnessing the proactive benefits of agentic AI requires telecom providers to establish a unified source of data truth through seamless data accessibility, rather than trying to consolidate all data onto a single platform. By enabling unified access to network, operational, and business data through a singular data catalog—such as Microsoft Fabric, which utilizes shortcuts and mirroring—telecoms ensure AI-powered insights are accurate and comprehensive. Without cohesive access to high-quality data, AI-powered insights risk becoming fragmented or misleading, limiting the transformative potential of autonomous decision-making and potentially leading to inaccurate, risky decisions. 

The critical importance of data accessibility and cohesion is exemplified by AT&T’s migration to Azure Databricks, highlighting tangible benefits: 

1. Unified data access and operational visibility: Instead of traditional consolidation, unified data access through platforms like Microsoft Fabric provides comprehensive context, enabling AI algorithms to generate precise, actionable insights. AT&T’s migration to Azure Databricks illustrates how improving accessibility to quality data across silos empowers technical staff, enhances analytical capabilities, and improves decision-making accuracy—dramatically reducing the risk of overlooking critical dependencies or making suboptimal decisions.
2. It enables closed-loop intelligence: Agentic AI extends beyond merely analyzing data; it proactively acts in near real-time. A cohesive data access approach, like the one implemented by AT&T, facilitates rapid anomaly detection and automated corrective actions within network and revenue systems. This closed-loop intelligence is crucial for next-generation AIOps, enabling seamless and automated responses across the entire telecom infrastructure. 
3. It accelerates new revenue opportunities: Providing cohesive access to operational and business data creates agile, scalable monetization pathways. AT&T’s adoption of Azure Databricks accelerated its ability to launch new services by automating complex data processing and analytics tasks. Similarly, telecoms leveraging unified data access solutions can rapidly provision and monetize services such as customized 5G and 6G experiences or on-demand network slicing—shifting from manual processes to dynamic, programmable offerings.

A modern, agentic, cloud-native OSS and BSS environment built on public cloud principles doesn’t just serve the operator; it also creates a frictionless platform for third-party and ecosystem partners to plug in. Whether it’s Internet of Things (IoT) device vendors, over-the-top content providers, or enterprise service integrators, cloud-native OSS with open APIs allows rapid partner onboarding and co-creation. In turn, operators can easily expand their portfolio with new revenue streams—bolstering the business to business to everything (B2B2X) model—while still maintaining centralized oversight and robust security at scale. 

Agentic AI in action: From insight to autonomous operations

Faster time-to-market for new services

Traditionally, launching a new offering in telecom could take upward of 50 weeks, hindered by lengthy approvals, hardware provisioning, and siloed systems. In a cloud-native environment, operators can test, iterate, and deploy new products—like on-demand network slicing or advanced IoT bundles—in days or even hours. This speed is a game changer for operators transitioning from ‘telcos’ to ‘tech-cos,’ where continuous experimentation and rapid scaling of successful pilots are essential to staying competitive. Coupled with agentic AI that autonomously manages tasks, cloud-based OSS and BSS ensures you don’t just move faster—you move smarter. Leading telecoms are already laying the groundwork for agentic AI by adopting:

• Predictive analytics for network health: For instance, AI-powered anomaly detection can preempt equipment failures, but true autonomy means the system itself orders the replacement part, dispatches a technician, and reroutes traffic in the meantime—all driven by integrated data across OSS and field service management. 
• Proactive policy and billing: In a unified data environment, usage spikes or new IoT device activations can trigger dynamic policy updates in real time—while simultaneously adjusting billing parameters. This end-to-end automation requires that the network layer (OSS) and the revenue layer (BSS) share data instantly and accurately. 

Why run OSS on the public cloud?

As service catalogs explode and customer demands evolve more rapidly, operators need elastic, scalable infrastructure to shorten time-to-market and accommodate fluctuating loads. Public cloud delivers on-demand compute and storage, reducing capital expenses and enabling rapid innovation with built-in AI and machine learning services. Moreover, the global reach and reliability of platforms such as Microsoft Azure allow telecoms to replicate, secure, and manage their OSS across regions far more easily than traditional on-premises setups. By shifting OSS to a cloud-native model, operators can pivot from lengthy, monolithic upgrade cycles to nimble, iterative releases—critical for accelerating 5G and 6G services, IoT offerings, and B2B2X monetization scenarios.

Self-optimizing networks and beyond

While self-optimizing networks (SON) currently manages aspects of radio access networks, next-generation AI solutions extend self-optimization to the entire telecom domain. Microsoft Project Janus is an early example of how real-time AI-powered telemetry can proactively detect network anomalies, predict service degradations, and dynamically optimize network resources—laying the foundation for fully autonomous network operations. Telefónica España, for example, leveraged Azure AI and machine learning to achieve significant improvements in network performance and efficiency. By incorporating AI and big data technologies, Telefónica España is developing more intelligent networks capable of self-optimization and adaptation. This intelligence allows for a reduction in time to market for new solutions, enabling the company to swiftly implement innovations that enhance network performance and customer satisfaction. With advanced generative AI, AI-powered instructions can autonomously fine-tune network configurations, adapt capacity, and realign resources based on live traffic patterns. This orchestration is feasible only when AI has an enterprise-wide view of network, business, and operational data.

Embracing open standards and ecosystem collaboration

Just as critical as data consolidation is ensuring interoperability and flexibility. Many telecoms are turning to TM Forum’s Open APIs and adopting Open Digital Architecture (ODA) principles. These frameworks reduce vendor lock-in, streamline data exchange, and allow AI solutions to operate across heterogeneous environments. 

For example, TM Forum’s collaboration with Microsoft has accelerated the adoption of carrier-grade, open-source ODA canvases. By aligning Azure’s robust cloud capabilities with ODA standards, operators are now better equipped to innovate rapidly, simplify complex integrations, and significantly reduce the operational hurdles associated with legacy systems.

Microsoft plays a pivotal role in supporting these open standards, providing a cloud-native, modular approach fully aligned with ODA. A practical illustration is Sure Telecom’s adoption of Azure, where leveraging Microsoft’s open API framework allowed them to consolidate disparate data sources and achieve enhanced customer insights and operational efficiency. Microsoft’s platform delivers out-of-the-box integrations and open APIs that empower operators to harness AI-powered analytics and intelligent automation workflows, minimizing friction traditionally encountered during legacy system modernization. 

Achieving scale with cloud-native AI

A robust, cloud-native foundation is essential for scaling AI across complex telecommunication environments. Containerized microservices, DevOps practices, and serverless compute reduce operational overhead, allowing teams to focus on innovating rather than managing infrastructure. Within such environments: 

• Azure AI services streamlines the training, deployment, and monitoring of AI models across OSS and BSS workloads. 
• Microsoft Fabric fosters seamless data ingestion, orchestration, and transformation—critical for building that unified data estate necessary for agentic AI. 

By converging data and AI workloads in the cloud, telecoms can more quickly test and deploy innovative services that leverage advanced analytics for both operational efficiency and new revenue streams.

In addition to the operational and technical upsides, running on public cloud offers a more predictable and flexible cost model. Instead of large capital expenditures tied to peak capacity, operators pay only for what they consume. This shift in economics not only aligns with sporadic traffic spikes—common in modern usage-based and event-driven architectures—but also frees up budget to invest in strategic AI initiatives. By reducing hardware overhead, maintenance, and upgrade costs, telecoms can reinvest in higher-value activities such as AI-powered product innovation and partner ecosystem growth. 

Microsoft’s unique value: Building a telecom foundation for agentic AI

Microsoft combines a partner-centric approach with end-to-end technology solutions—bringing actionable capabilities to telecoms that want to realize AI-powered OSS and BSS at scale.

Key value streams include: 

1. Telecom-specific cloud and data services: Telecom-optimized solutions from Microsoft and its partners help unify network, operational, and customer data into a single source of truth. 
2. First-party AI agents: Microsoft’s growing suite of autonomous agents, such as those integrated within Dynamics 365, automate complex business processes—enhancing efficiency and decision-making across various telecom operations. 
3. Alignment with industry standards: Microsoft’s active support for TM Forum and ODA ensures an open, interoperable environment. Operators can adopt AI without overhauling existing infrastructure or incurring vendor lock-in. 
4. Security and compliance: As AI-powered automations become central to business functions, Microsoft provides enterprise-grade security and governance—critical for protecting sensitive network and customer data. 
5. Partner ecosystem: Collaborations with leading vendors—such as Amdocs, CSG, Blue Planet, ServiceNow, Netcracker, and system integrators—create end-to-end workflows that accelerate modernization and reduce complexity. Through these partnerships, Microsoft’s AI tools seamlessly integrate with telecom-specific applications.

Positioning for revenue impact and the autonomous future

When OSS and BSS data is unified and AI-powered processes take over routine tasks, telecoms can prioritize innovation that directly impacts the bottom line. Whether rolling out new network services or offering real-time network slicing for enterprise customers, the ability to act on consolidated data in an autonomous fashion sets operators apart in a hyper-competitive market.

Short-term gains include faster time-to-market for new services, reduced operational costs, and improved customer experiences. Longer term, fully autonomous, self-healing networks that optimize themselves and require minimal manual intervention, unlock new revenue streams through AI-powered insights. Project Janus is already demonstrating this shift—showcasing how AI-powered network intelligence moves beyond predictive analytics into autonomous, self-optimizing operations that reduce operational overhead and ensure peak performance with minimal human intervention.

Project Janus demonstrates how AI-powered network intelligence can move beyond predictive analytics into autonomous, self-optimizing networks—reducing operational overhead and ensuring peak performance with minimal human intervention. 

Ready to transform your operations?

The industry is moving beyond point solutions toward a future where agentic AI and unified data estates power autonomous operations. For telecom leaders, now is the time to ensure OSS and BSS modernization strategies align with open standards, prioritize data consolidation, and prepare for the emergence of fully autonomous networks.

Microsoft and its partners are here to guide you on this journey—from building robust cloud-native foundations and consolidating your data estate to delivering intelligent, revenue-focused transformations across OSS and BSS. By embracing this approach today, you’ll ensure your operations not only keep pace with evolving market demands but lead the next era of telecommunications innovation. 

Learn more about our AI and generative AI solutions for telecommunications and discover how we can help you lay the groundwork for the agentic AI revolution—starting with your most strategic asset: your data.

The post The transformative impact of AI and generative AI on OSS and BSS in telecommunications appeared first on The Microsoft Cloud Blog.

Manufacturers face existential challenges around several key and often conflicting goals; the need to increase revenue whilst at the same time reducing costs across the value chain—spanning engineering, manufacturing, and supply chains, starting with product design and engineering. These challenges have impacted everything from product requirements and capabilities to product development all the way to sourcing and production. A recent IDC report highlighted how for product managers, investing more in engineering and research and development (R&D) correlates with lower cost of goods sold (COGS) and higher revenue growth for manufacturers, suggesting that investments in product engineering investments drive financial success.¹    

Benefits of generative AI in product engineering  

As product complexity and connectivity has continued to increase, engineers’ roles have become multi-disciplinary, requiring interaction with various data sources and tools, such as product lifecycle management (PLM), computer-aided design (CAD), computer-aided manufacturing (CAM), application lifecycle management (ALM) for software requirements, and computer-aided engineering (CAE). In addition to manufacturability, engineers need to incorporate aspects such as sustainability, regulatory compliance, quality, materials, and supplier and supply chain considerations much earlier in the product design process. The many lines of software code now prevalent in physical products and the growth in software requirements, also pressures traditional manufacturing information technology (IT) to support a proliferation of software tools, data, and infrastructure.      

Generative AI is transforming product engineering and R&D to enable manufacturers to realize these benefits:  

• Cost reduction: Optimizing product designs for cost, sustainability, and manufacturability can reduce product development and production costs.  
• Better decision-making: Facilitated through data analysis and scenario simulation, generative AI provides valuable insights for informed decisions that can enhance product development, improve product quality, and better meet customer demands.  
• Productivity and skills gap: Helps experienced designers automate tasks they do often, and inexperienced designers to get up to speed quickly and avoid errors with best practice guidance.  Assists with analysis and optimization of existing designs and can even generate new designs with user input.   
• Efficiency: Reduce the time taken by engineers to both search across, and interact with, product data from various sources across the product lifecycle.  
• Faster time-to-market: Shorter product development cycles mean products can reach the market faster to capitalize on new opportunities more quickly. 
• Innovation: Continuously analyzing product-related data from various sources, customer feedback, and learning from it with generative AI can suggest innovative solutions that might not be more readily apparent.   

Microsoft partners play a pivotal role in transforming product engineering and R&D by building industry-specific solutions that integrate data unification and contextualization capabilities with Microsoft technologies which, combined with the Microsoft Cloud, are revolutionizing engineering functions.    

Establishing a secure engineering data foundation  

Product engineering and R&D involve handling many types and modalities of data, including CAD files, technical specifications, product data and configurations, requirements, and process data. Manufacturers commonly use a range of systems, including PLM, ALM, and enterprise resource planning (ERP) systems, to manage this complex data. These form a secure data foundation on which transformation of product engineering is built upon, and sensitive IP can be protected.     

The following are examples where generative AI is helping to deliver value in a secure, engineering data foundation with AI on the Microsoft Cloud.  

• Siemens has integrated Microsoft Teams, Microsoft Azure OpenAI Service, and Siemens’ Teamcenter PLM solution into an app to facilitate real-time communication and collaboration among frontline workers and engineers.
• Aras has introduced AI-assisted search and an intelligent copilot, using Azure OpenAI Service and Microsoft Copilot Studio on Azure, enhancing user interaction with PLM data, facilitating quicker access, analysis, and action on critical information through scalable search and conversational AI, user interaction with PLM data, facilitating quicker access, analysis, and action on critical information through scalable search and conversational AI.
• PTC Codebeamer Copilot focuses on requirements authoring and analysis for the flagship Codebeamer Application Lifecycle Management (ALM) solution. This AI-powered agent, being used by Volkswagen Group, improves the efficiency of the design phase, helping to ensure potential issues with system requirements are identified and addressed early in the process and a productivity boost as users manage complex hierarchies of requirements.
• Bluestar PLM are leveraging Microsoft Copilot for Dynamics 365 to automatically generate summaries for an engineering object based on data both from Dynamics 365 and Bluestar PLM, and automatically generating item descriptions in multiple languages to make it easier to generate quotes, bills-of-materials (BOMs), invoices, and other documents in different languages.  

Accelerating product engineering and R&D 

Engineers use a range of complex solutions in product engineering when producing product designs from CAD, CAM, and CAE applications. This also involves creating and using many different data types, from 3D CAD and CAM files, to CAE simulation datasets, documents, specifications, and various knowledge repositories.   

The following are examples where customers and generative AI-powered partner solutions are helping to deliver value in accelerating product engineering and R&D with AI on the Microsoft Cloud: 

• HARTING reduced design time from weeks to minutes by introducing an AI-powered assistant fueled by Azure OpenAI Service and Microsoft Cloud for Manufacturing, interoperating with Siemens NX CAD for rapid design. This solution reduced configuration time by 95%, a significant improvement in efficiency and the rapid creation of custom electrical connector prototypes that are speeding up time-to-market.
• Hexagon AI-powered automated CAM programming solution, ProPlanAI, reduces the time taken to program factory machine tools by 75%. This solution is part of Hexagon’s cloud-based Nexus connectivity and collaboration platform for discrete manufacturers, and is powered by Azure OpenAI Service, Microsoft Azure Cosmos DB, and Microsoft Azure Databricks.
• Siemens copilot for NX X software uses an adapted industry AI model to help users ask natural language questions, access technical insights, and streamline design tasks for faster product development. It provides CAD designers with AI-powered recommendations and best practices to optimize the design process within the CAD experience, aiding engineers in implementing best practices quickly, ensuring high-quality results from design to production.
• Rescale is transforming engineering innovation by integrating AI-powered tools with Microsoft technologies to enhance simulation data workflows with Rescale Automations, automating data processing for real-time insights, improving decision-making and collaboration with AI models including Phi-4 to reduce cycle times and costs while maximizing simulation insights.
• Siemens has announced an industrial foundational model (IFM) to enhance the productivity of engineering and automation tasks across the industrial sector. For example, it will help engineers automate CAM programming with context-aware recommendations, support Structured Control Code (SCL) generation and accelerate the creation of Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&ID). The IFM is built on Microsoft’s Azure platform. 

The next step: Unlock innovation in product engineering with AI-powered digital threads 

The next stage in revolutionizing product engineering and R&D sees the addition of multi-agent AI systems that can orchestrate, collaborate, and scale across complex enterprise workloads, including product engineering solutions, supply chain, manufacturing execution systems, customer relationship management, field service, and enterprise resource planning.   

Microsoft, along with partners like PTC, Autodesk, and Aras, believe that digital threads are becoming a reality for industrial customers due to unified data foundations and generative AI. Unified data foundations make data usable by securely sourcing it from various systems and automating contextualization. Generative AI agents use this data to provide insights and take actions, unlocking numerous use cases across the manufacturing value chain, including product engineering, all through unified data foundations and generative AI.  

The following are several such examples of innovations that are fueling the emergence and promise of AI-powered digital threads: 

• Aras InnovatorEdge is a new low-code API management framework for extending product digital thread ecosystems, which will also integrate with Microsoft Fabric, Microsoft 365 Copilot, and Microsoft Cloud for Manufacturing, enabling seamless connectivity for advanced analytics and AI-powered insights.
• Autodesk Fusion connects people, data, and process through the product development lifecycle. Autodesk Data Solutions in Fusion Manage and Microsoft Fabric enable data management and process optimization. Additionally, Autodesk’s digital twin offerings through Tandem, factory simulation through FlexSIM, and factory operations management with Fusion Operation all benefit from this collaboration across the IT and operational technology (OT) ecosystem.
• PTC is collaborating with Microsoft on an enterprise data framework and agentic model for PLM scenarios in PTC Windchill within Microsoft Fabric to accelerate manufacturers digital thread strategies and unlock insights and workflows across the value chain using AI-powered agents.
• Toyota is deploying AI agents to harness the collective wisdom of engineers and innovate faster and more efficiently in a system named “O-Beya,” or “big room” in Japanese. The “O-Beya” system currently has nine AI agents—from a Vibration Agent to a Fuel Consumption Agent, bringing together numerous functional experts.  

By using Microsoft Cloud for Manufacturing and AI-powered solutions from our partner ecosystem, manufacturers can securely unlock new levels of impact. The integration of AI-powered solutions and AI agents unlocks innovation, reduces costs and improves operational efficiencies, meaning manufacturers are better equipped to navigate challenges and seize opportunities.    

Microsoft in manufacturing and mobility industries 

Learn more about Microsoft Cloud for Manufacturing and Microsoft for automotive, and how companies are using Microsoft AI capabilities in Microsoft AI in Action. 

Learn more about the unique use cases and solutions driving innovation in product engineering and R&D from our presence at Hannover Messe 2025.

Microsoft Cloud for Manufacturing

Drive innovation with an AI-powered digital thread

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¹ IDC Research, Investing in Product Engineering — Increase Revenue and Decrease Cost, Doc # US51892224, February 2025. 

The post Shaping the future of product engineering and research and development with generative AI appeared first on The Microsoft Cloud Blog.

Technology integration opportunities in sports 

Microsoft technology helps drive the quality of the game and create new business opportunities for organizations by:

• Enhancing performance with real-time data insights and analytics for data-driven decision-making. 
• Improving operational efficiency through streamlined workflows, increased collaboration, and seamless data integration. 
• Elevating fan engagement with AI and real-time customer insights to create a comprehensive ecosystem of personalized experiences.   
• Unlocking broadcast and media integration opportunities by using advanced cloud and AI technologies to scale content operations and reach more audiences.  
• Supporting secure data storage and processing by implementing advanced cloud technologies to secure content with high-speed data storage and processing. 

Whether it’s supporting Formula One engineers to make split-second race decisions, empowering tennis players with AI-assisted match analysis, or delivering personalized experiences to fans, Microsoft technology is redefining the future of sports—making organizations faster, smarter, and more connected than ever before.  

Learn more about Microsoft’s technical solutions through key partnerships below. 

Data-driven decision-making 

In high-performance sports, every decision can alter the course of the game. From AI-powered analytics that provide real-time insights for athletes to cloud-based solutions that optimize operations, learn more about how Microsoft technology is driving data-led decision-making and reshaping how teams compete in the Women’s World Cup of Tennis, the NFL, and Formula One. 

Billie Jean King Cup: Transforming tennis strategy with AI 

The Billie Jean King Cup uses Microsoft AI and cloud technologies to provide players and coaches with data visualizations and real-time insights during matches.  

Key highlights include: 

• Match Insights App: Azure hosted application that delivers critical gameplay data, such as player movement, ball trajectories, and shot accuracy, to coaches and players in near real-time. 
• AI-powered analytics: Microsoft Azure OpenAI Service analyzes vast datasets to provide actionable rally and serve insights, helping coaches anticipate opponent strategies and make informed decisions. 
• Secure data management: Microsoft Azure Cloud Services help to ensure the secure storage and processing of high-volume data generated during matches. 

Read more about how Microsoft and the Billie Jean King Cup are elevating competition through data-driven insights.

NFL: Game-changing technology on the sidelines 

The NFL uses Microsoft hardware and software to enhance game-day operations and team collaboration. 

Key highlights include: 

• Microsoft Surface Sideline Viewing System (SVS): Hardware and software solution that provides coaches and players with near real-time, high-resolution images of plays, enabling rapid strategic adjustments. 
• NFL Combine App: Application that streamlines talent evaluation by providing real-time access to key performance metrics. 
• Enhanced collaboration: Microsoft Teams and Azure facilitate seamless communication and collaboration among NFL teams. 

Read more about how Microsoft and the NFL are changing the game with new levels of operational efficiency.

BWT Alpine Formula One Team: Data-powered racing innovation 

BWT Alpine Formula One Team uses advanced AI and Azure’s robust cloud infrastructure to unlock new capabilities in data insights, regulatory compliance, and business operations.  

Key highlights include: 

• AI-powered race strategies: Azure Computer Vision and Multi-Agent Resourcing Optimization (MARO) reinforcement learning allows Alpine to optimize race day strategy and car setup based on real-time telemetry. 
• High-speed data processing: Azure provides secure, high-speed data storage and retrieval, allowing split-second decisions during races. 
• Regulatory compliance: Azure AI Search and Microsoft Copilot Studio streamline compliance processes, helping to ensure adherence to Formula One regulations. 

Read more about how Microsoft and BWT Alpine Formula One Team are maximizing performance on and off the track.

Integrated fan engagement 

In today’s digital world, sports leagues are expected to meet fans at multiple touchpoints with highly personalized and easily accessible content. Learn more about how leagues such as LALIGA and the NBA are using Microsoft technology to redefine the sports and entertainment industries and take the fan ecosystem to the next level. 

LALIGA: Enhancing fan engagement with data-driven insights 

LALIGA uses real-time data processing and AI-powered analytics with Azure to deliver match insights and personalized digital experiences across platforms. 

Key highlights include: 

• Beyond Stats: Fan-facing data and insights platform powered by Azure that captures and analyzes more than 3.5 million data points per match to provide engaging content for fans across multiple platforms including social media, broadcast, and the LALIGA app. 
• Data Sports Platform (DSP): Comprehensive system powered by Azure that unifies fan interaction data across touchpoints to generate tailored content and products to match fan preference. 
• Seamless infrastructure: Azure’s high-performance infrastructure helps to ensure reliable content delivery and enhanced fan experiences across digital platforms. 

Read more about how Microsoft and LALIGA are personalizing the experience for fans around the world.

NBA: Building a next-generation fan engagement platform 

The NBA integrates Azure and AI technology to provide fans with personalized content, real-time insights, and tailored experiences across digital platforms.  

Key highlights include:  

• AI-integrated platform: The NBA Insights and Top Performances platforms within the NBA App provide real-time game updates and AI-generated highlights to enhance the fan experience by utilizing Microsoft AI technology. 
• The reimagined NBA App: Powered by Azure, the NBA App offers personalized content recommendations, real-time game insights, and a social-style video experience. 

Read more about how Microsoft and the NBA are deeply engaging fans at every level.

Transforming the sports industry

Microsoft innovative technologies are transforming the sports industry, driving performance, enhancing fan engagement, and streamlining operations. From the racetracks of Formula One to the courts of the NBA, Microsoft’s partnerships are setting new standards for excellence in sports. As technology continues to evolve, the future of sports looks brighter than ever, with Microsoft leading the way in this exciting journey.

Learn more about how Microsoft is transforming sports and other media and entertainment organizations around the world through our customer stories page. 

Microsoft’s commitment to the media and entertainment industry  

Microsoft allows media organizations to achieve more through a trusted and secure platform, built to empower content creators and distributors, enhance the viewer experience, and reimagine monetization strategies. More information can be found on the Microsoft media and entertainment industry solutions website. 

Next steps 

Microsoft will be showcasing some of these case studies and more at our upcoming exhibition with NAB Show, April 5–9, 2025, in Las Vegas. Go through a journey of interactive demos that illustrate the capabilities needed to deliver fan-focused content and that highlight key aspects of the transformation process required to implement cutting-edge technologies for enhanced performance and fan engagement.

Microsoft at the 2025 NAB Show

See how Microsoft is helping to shape the future of broadcast and entertainment

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The post How Microsoft is transforming sports with cutting-edge technology appeared first on The Microsoft Cloud Blog.

Historically, logistics has lagged other industries in digital transformation. More than 75% of industry leaders acknowledge that their sector has been slow to embrace digital innovation. Instead of prioritizing digital transformation, companies have traditionally focused on incremental improvements in operational processes. But in today’s fast-moving market, this approach is no longer enough as customer expectations have also evolved dramatically. A staggering 91% of logistics firms report that their clients now demand seamless, end-to-end logistics services from a single provider.¹

AI has become a game-changer for the industry to help overcome challenges and fulfill customer expectations. From enhancing customer experiences—such as shipment planning and service requests—to driving productivity in core supply chain operations and demand forecasting, AI presents a massive opportunity. AI can also improve safety, sustainability, and workforce reskilling, giving employees more time to focus on customers. The numbers speak for themselves: AI-powered innovations could reduce logistics costs by 15%, optimize inventory levels by 35%, and boost service levels by 65%. Over the next two decades, AI adoption in logistics could generate between $1.3 trillion and $2 trillion per year in economic value.² 

In fact, the AI revolution in logistics is already underway, and Microsoft is at the forefront, empowering businesses with Azure’s cloud capabilities and cutting-edge AI solutions.

With this article, Microsoft releases two new reference architectures—Adaptive Cloud for Logistics and Supply Chains and AI-enhanced experiences for Logistics and Supply Chain, enhanced functionalities in Dynamics 365 for Supply Chain, and showcases partner-led offerings.  

The use cases 

What are the use cases that AI can impact in logistics? The answer is simple: Almost everywhere along the value chain. This covers inbound logistics and outbound logistics as well as supporting activities, see the overview below: 

Inbound logistics 

One of the most critical use cases in supply chain optimization is demand forecasting. Accurate predictions by AI can serve as the foundation for downstream activities—driving efficiency and enhancing overall optimization. For example, precise demand forecasts play a key role in inventory management and storage optimization within warehouse management systems.  

SPAR Austria, a leading food retailer with over 1,500 stores, has significantly improved its demand forecasting capabilities through AI-powered solutions built on Microsoft Azure in collaboration with Microsoft partner Paiqo. This advanced implementation has achieved more than 90% forecast accuracy, leading to a 15% reduction in costs by minimizing waste.

AI-based route optimization can lead to significant fuel cost reductions, which is a substantial cost-saving measure for logistics companies and contributes to sustainability goals. Load management algorithms and real-time data analytics maximize space utilization in trucks, vessels, and warehouses. 

Additionally, AI-based scheduling, booking, shipping, and invoicing have significant impact on flexibility, cost, and operational process efficiency.  

Dow Chemical, a global leader in materials science, faced significant challenges with its existing freight invoicing system, which involved up to 4,000 daily shipments and various types of invoices. A newly developed invoice agent built with Microsoft Copilot Studio streamlines the company’s freight invoicing process by monitoring incoming emails for attached invoices, structures the data for analysis, and scans for billing inaccuracies. This automation helps Dow manage its logistics spending more efficiently, reducing potential overpayments and improving operational efficiency.

Outbound logistics 

AI and robotics play a crucial role in optimizing picking and packing processes. Additionally, advancements in technology enhance order processing and returns management—streamlining operations and driving cost reductions. 

Cutting-edge technologies like natural language processing (NLP) and machine learning are transforming customer interactions by reducing handling times and associated costs. With the deployment of virtual assistants and every day customer support, businesses can improve response times significantly.  

Global sports retailer Decathlon, in partnership with Microsoft partner Parloa, has successfully leveraged AI to enhance customer service. By implementing AI-powered solutions, the company has reduced the number of calls forwarded to live agents by 20%, demonstrating the power of automation in improving efficiency and streamlining customer interactions.

Supporting activities 

AI and emerging technologies are transforming key support processes across the logistics value chain. In procurement and pricing, for example, AI-powered agents streamline the request for proposal (RFP) and request for quote (RFQ) process. Additionally, dynamic pricing capabilities optimize revenue management, while AI-powered advancements enhance traditional finance and controlling functions. AI also plays a crucial role in simplifying customs management and ensuring seamless regulatory compliance. 

Below is an overview of solutions from independent software vendors (ISVs) and partners that can be leveraged out-of-the-box for selected use cases: 

• Wandelbots: Robotics for picking and packing
• Paiqo: Demand forecast
• InstaDeep: Load optimization
• Fareye: Route planning and optimization
• Parloa: Customer service
• Coneksion: Messaging
• CH Robinson: Mail AI agents
• Cosmo Tech: Supply chain simulation

3 building blocks for a digitized state-of-the-art logistics 

From a Microsoft perspective, there are three key building blocks for logistics and supply chain companies to build an AI-ready platform that can enable a variety of use cases: 

1. Adaptive Cloud: The modular base infrastructure 
   Microsoft adaptive cloud unifies siloed teams, distributed sites, and sprawling systems into a single operations, security, application, and data model across hybrid, multi- cloud, edge, and Internet of Things (IoT) environments. 
2. Microsoft Dynamics 365 suite: Microsoft’s comprehensive business suite 
   Dynamics 365 suite, including Supply Chain Management, offers comprehensive solutions to enhance visibility, streamline procurement, optimize fulfillment, and improve planning. 
3. AI and agentic AI: Solutions to automate business processes 
   Microsoft offers advanced agentic AI solutions which enable the creation and orchestration of agent- and multi-agent systems for enhanced productivity and automation. 

Adaptive cloud: The modular base infrastructure 

Within the logistics domain, adaptive cloud can address multiple areas for increased efficiency such as quality control, warehouse operations, damage detection, or robotics automation. With the capabilities of the full Azure stack on the edge, IoT operations, and a data fabric, adaptive cloud is the essential lever for improving business both in the cloud and on the edge. 

Adaptive cloud shifts organizations from a reactive posture to one of proactive evolution, enabling people to anticipate and act upon changes in market trends, customer needs, and technological advancements ahead of time. This strategic foresight enables businesses to pivot quickly, embrace continuous improvement, and integrate new technologies smoothly. By building resilience into their operational models, businesses can optimize resource usage and mitigate risks before they manifest. 

The adaptive cloud can be adapted or selectively applied to multiple customer scenarios. We map how commitments and promises are realized by system skills and capabilities below:

• Operate with AI-enhanced central management
  Elevate IT capabilities and focus on strategic work by abstracting resources from distributed locations into one operations and management layer with AI assistance and automation. 
  Critical capabilities:
  • Universal AI assistant, portal, and tools
  • Consistent configuration management
  • End-to-end observability
  • Governance at scale
  • Built-in security and control
• Rapidly develop and scale applications across boundaries
  Bridge OT and IT gap to transcend legacy system constraints with composable cloud-native tool chains, containers, and data services everywhere.
  Critical capabilities:
  • Kubernetes everywhere
  • Hyperscale cloud services to the edge
  • Central application deployment
  • Global orchestration and resiliency
  • Streamlined DevOps integration
• Cultivate data and insights across physical operations
  Supercharge physical operations with a unified data foundation, enabling efficient workflows, predictive insights, and cost-effective resource utilization from edge to cloud.
  Critical capabilities:
  • Common data foundation
  • Actionable insights with AI Coordinated workflow orchestration from edge to cloud
  • Contextualized data to information
  • Centralized device management

Dynamics 365 suite: Microsoft’s comprehensive business suite

The Dynamics 365 Supply Chain Management portfolio outlines various stages and components of the supply chain process, divided into several categories, from design to decommission to record to report. The table below indicates the bandwidth of the suite capabilities: 

Microsoft Copilot enhances supply chain management by leveraging AI and automation. By integrating Dynamics 365 with AI-powered Copilot, organizations can significantly enhance their supply chain management processes. The combination of advanced AI capabilities and comprehensive business applications ensures that supply chain operations are efficient, responsive, and adaptive to changing conditions.  

The new Warehouse Management Only Mode is a specialized feature within Microsoft Dynamics 365 Supply Chain Management (and can also be used standalone) designed to cater specifically to warehouse management processes. This mode allows businesses to set up a legal entity dedicated solely to warehouse operations, providing warehousing services to other legal entities within Supply Chain Management or even to external enterprise resource planning (ERP) and order management systems. 

AI and agentic AI: Solutions to automate business processes 

The new AI-enhanced reference architecture for logistics brings it all together—from the connection to existing data systems to AI-enhanced experiences for various user groups like end-customers, warehouse managers, fleet managers, or customer service: 

The user-facing applications layer describes some of the common front-end experiences that can be built using Microsoft services. End users require mobile and web applications built using services such as Azure API Management, Azure App Service, and Azure Functions. Developers create AI-powered user experiences leveraging services such as Azure OpenAI Service. These applications can be deployed in Azure tenants and can scale to millions of users.  

Business users leverage Dynamics 365 (including Dynamics 365 Customer Service, Dynamics 365 Finance, Dynamics 365 Project Operations, and Dynamics 365 Customer Insights) to manage business operations such as claims, promotions, and ticketing. Dynamics 365 has built-in custom agents for many common business use cases such as customer service, sales, finance, field service, and customer insights.

Front line workers are fully integrated into the business with customized workflows and automated operations with custom AI, tailored to their needs and the ergonomics of their workplaces, whether fixed terminals, mobile devices, or augmented reality.  

Microsoft Copilot Studio facilitates the creation of custom AI agents to support their work. Power Apps enable the creation of custom user interfaces, while Power Automate enables the creation of business workflows. With Microsoft 365 Copilot, employees can collaborate and communicate using Microsoft products such as Microsoft Teams, SharePoint, and Outlook. 

The operation of supply chain and logistics generates large amounts of data. The data storage and analytics layer describe how to securely store business data to support operations and create insights. 

Microsoft Dataverse is a scalable data platform that securely stores and manages business data. The data model is a structured framework that organizes data in tables with relationships. It is possible to use industry models to harmonize and integrate business data across multiple applications.  

Microsoft Fabric is an end-to-end data and analytics platform that includes real-time analytics capabilities. OneLake is a unified logical data lake that centralizes and simplifies data management, with multiple analytical engines and workspaces. Fabric enables organizations to process and analyze data for timely insights and decision-making. Supply chain and logistics are established businesses. It is important to integrate existing data systems, such as connected assets as well as existing systems. 

Messaging services on Azure enable connectivity to assets and devices using standardized communication protocols such as Message Queuing Telemetry Transport (MQTT) with Azure Event Grid, or data streams like Apache Kafka using Azure Event Hubs. Serverless solutions like Azure Functions provide compute to process messages. 

Get in touch with us 

Customers can work directly with Microsoft Industry Solutions teams on custom projects that offer a short go-to-market time. Whether you choose ready-to-deploy partner solutions or bespoke projects with Microsoft partners or Microsoft Industry Solutions, we provide the expertise and support to ensure your success.  

Visit Microsoft for travel and transportation or contact our team to learn more and take the next step in your Microsoft AI journey.

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¹ Accenture, Freight and Logistics: Finding the right path to digital transformation, 2023.

² McKinsey, Digital logistics: Into the express lane?, December 2024.

The post The future of logistics: How generative AI and agentic AI is creating a new era of efficiency and innovation appeared first on The Microsoft Cloud Blog.

Microsoft is at the forefront of this transformation, dedicated to developing and implementing responsible AI technologies. By fostering innovation and collaboration through Microsoft Cloud for Healthcare, we continue to reinforce how responsible AI can enhance healthcare delivery and improve outcomes for patients worldwide. Building on this commitment, we’re excited to introduce new features in our AI healthcare portfolio that will further drive industry efficiencies, and better patient outcomes. 

Advanced AI models and integrations for healthcare 

As medical technology advances, improvements in medical imaging are critical for better diagnosis of disease and improved patient care. In 2024, we announced the launch of healthcare AI models, a collection of cutting-edge multimodal medical imaging foundation models available in Azure AI Foundry. Designed for precise image segmentation, MedImageParse 2D model covers many imaging modalities, including x-rays, CTs, MRIs, ultrasounds, dermatology images, and pathology slides. It can be fine-tuned for specific applications such as tumor segmentation or organ delineation, allowing developers to test and validate the ability to leverage AI for highly targeted cancer and other disease detection, diagnostics, and treatment planning.  

Today, we’re excited to share the MedImageParse model is now optimized for 3D medical imaging data. MedImageParse 3D can handle complex 3D datasets produced by advanced imaging, such as MRI and CT scans, providing a more comprehensive view into patients’ conditions. The enhanced ability to visualize and interpret anatomical abnormalities and structures provides for much more accurate diagnosis that may have been missed by 2D analysis. MedImageParse can also support healthcare researchers with comprehensive image analysis and a more streamlined workflow for radiologists, improving overall efficiency and reducing human error. MedImageParse 3D can soon be found in the Azure AI Foundry model catalog.  

In partnership with Microsoft Research, the Microsoft Health and Life Sciences model catalog will also feature several new and updated multimodal medical foundation models including TamGen for protein design, Hist-ai for pathology, and ECG-FM for electrocardiogram (ECG) analysis. 

Leveraging multimodal AI for improved health insights 

Today, we are excited to announce new functionality in healthcare data solutions that allows customers to orchestrate multimodal AI insights directly into Microsoft Fabric. Now in public preview, orchestrating multiple modalities (e.g., text, image, audio, video, and other forms of sensory input) of health data within Fabric allows healthcare organizations to generate a robust set of insights that help faster decision-making and improved patient outcomes. 

Customers can leverage Fabric to orchestrate multimodal AI insights by connecting their healthcare data to a variety of AI services and models. These AI-generated insights are then integrated back into the healthcare data estate to enable various use cases like creating targeted outreach and care plans by enriching clinical conversations with social determinants of health (SDOH) and sentiments. Another possible scenario is deriving quick insights and disease progression trends for clinical research by creating image segmentations and combining it with imaging metadata through Microsoft Power BI reports. 

The orchestration capability includes five out-of-the-box examples to help customers connect and integrate to AI models: 

1. Text analytics for health in Azure AI Language to extract medical entities from unstructured data such as diagnoses and medications, and the relations between entities.  

2. MedImageInsight AI model in Azure AI Foundry to generate medical image embeddings from imaging data.  

3. MedImageParse AI model in Azure AI Foundry enables segmentation, detection, and recognition from imaging data across numerous object types and imaging modalities.  

4. Sentiment analysis with Azure OpenAI Service to score sentiment for categories such as doctors’ services, staff services, facilities, and cost from conversational data. 

5. SDOH extraction with Azure OpenAI to extract social determinants of health data from conversational data based on the Centers for Medicare and Medicaid Services’ defined categories. 

To further enhance data accessibility, we’re pleased to share the general availability of additional functionality that enhances the existing capabilities within our healthcare data solutions offering. These include:   

• Centers for Medicare and Medicaid Services Claim and Claim Line Feed Files (CMS CCLF) claims data ingestion: Customers can bring CMS CCLF claims data into Fabric where they can assess the effectiveness of their care management programs, monitor population-level trends and usage as well as measure their performance against benchmarks to reduce overall claim expenses and improve patient care.  

• Social determinants of health public datasets transformation: Now healthcare organizations can ingest, persist, harmonize, and use SDOH public datasets to identify risks and health-related social needs, ultimately creating more equitable healthcare for all patients and communities. 

• Care management analytics: By using unified healthcare data and care management analytical templates, healthcare providers can enhance patient care by identifying high-risk individuals, optimizing treatment plans, and improving care coordination. This empowers organizations to deliver personalized, efficient, and proactive care.  

• Patient outreach analytics: Healthcare providers communicate with their patients more effectively by orchestrating personalized journeys across patient touchpoints. This capability simplifies the process by bringing data from different sources into Fabric, transforming it into an industry data model, and serving it to a Power BI report. 

• Dragon Copilot ambient AI integration: Dragon Copilot’s AI-powered, voice-enabled capabilities reduce the administrative workload of clinicians by automatically documenting patient encounters. With integration into Fabric, this new capability brings conversational data into Fabric OneLake. This integration enables customers to access, store, and manage the raw data generated. The data is stored in a lakehouse, organized in a hierarchical structure by date, which lets customers view each file and its content. When used in conjunction with healthcare data solutions, customers can combine their conversational data with their clinical data to learn more from patient interactions. 

“There is a lot of unrealized value in patient physician interactions. OSUMC is aiming to leverage conversational data along with multimodal AI insights in healthcare data solutions such as social determinants of health extraction to improve patient outcomes.”  

—Ravi Dyta, Director of IT at Ohio State University Wexner Medical Center

Achieve more with AI you can trust

This week’s Microsoft Cloud for Healthcare announcements underscore our commitment to transforming healthcare through advanced AI models and data integrations. By leveraging these cutting-edge technologies, we’re empowering healthcare organizations to deliver better care, help improve patient outcomes, and drive innovation in the industry. 

Connect with us in the Microsoft booth #2221 at HIMSS 2025 to immerse yourself in the latest advancements in data and AI from Microsoft and our partners.  

• Meet Microsoft Dragon Copilot: Your new AI assistant for clinical workflow

• Learn more about our recent releases in healthcare data solutions in Microsoft Fabric 

• Explore the latest with healthcare AI models

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Medical device disclaimer: Microsoft products and services (1) are not designed, intended or made available as a medical device, and (2) are not designed or intended to be a substitute for professional medical advice, diagnosis, treatment, or judgment and should not be used to replace or as a substitute for professional medical advice, diagnosis, treatment, or judgment. Customers/partners are responsible for ensuring solutions comply with applicable laws and regulations.  

Generative AI does not always provide accurate or complete information. AI outputs do not reflect the opinions of Microsoft. Customers/partners will need to thoroughly test and evaluate whether an AI tool is fit for the intended use and identify and mitigate any risks to end users associated with its use. Customers/partners should thoroughly review the product documentation for each tool. 

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