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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Subtle and complex in nature, social inflation impacts profitability by driving up claims payments. Outpacing economic inflation by 1.7% in the United States from 2017 to 2022,¹ social inflation drove a 57% surge in liability claims over the past 10 years,² and led to a USD20 billion increase in commercial auto liability payouts from 2010 to 2019.³ 

In response, insurers are increasingly turning to technology, particularly AI, to help predict trends, enhance underwriting processes, and automate workflows. And now, with the potential application of a cloud-based solution that lets companies explore insights collaboratively, insurers can have more options.  

Helping insurers forge effective long-term technology strategies is core to our vision for intelligent insurance and our work with Microsoft Cloud for Financial Services. In the case of social inflation, cloud and AI can help insurers mitigate the incidence and impact of unpredictable outcomes. 

How cloud and AI can help solve the social inflation challenge 

To improve competitiveness amid a volatile landscape, most insurers have invested in cloud modernization over the past decade. This provides an essential foundation for many critical benefits. For example, solutions built on Microsoft Power BI can track and analyze key performance indicators (KPIs), perform predictive analytics, and generate real-time insights.  

Generative AI is now expanding upon these core benefits to deliver dramatic improvements in productivity, operations, and enhanced workflows. For many firms, the first step in realizing value from generative AI is to adapt Microsoft 365 Copilot, which is integrated seamlessly in Microsoft productivity applications. Drawing on the full scope of Microsoft 365 data within the firm (such as emails, Word and Excel documents, Teams communications, and more) Microsoft 365 Copilot can, for instance, help a claims analyst generate a report that draws on the best available information from research reports, emails, calls, and external data sources.  

Many firms are also building custom agents to address specific use cases on challenges such as streamlining processes, enhancing claims processing workflows, and improving overall productivity. Additional value is also being unlocked as firms transform their unstructured data into structured formats, which enables even more robust predictive models and other advanced data analytics.  

To help address social inflation, generative AI and agent functionality can be used to gain important new insights, such as the following:  

• News monitoring: AI-powered tools can continuously scan and analyze news articles from myriad sources. Thanks to natural language processing (NLP), these tools can identify relevant news items, summarize key points, and highlight trends or significant events. This helps businesses stay updated on industry developments, competitor activities, and market shifts.
• Market insights: AI can process vast amounts of market data, such as economic indicators, consumer behavior patterns, and stock prices. With the power of machine learning algorithms, AI can detect patterns, predict market trends, and offer actionable insights. An AI agent can then present these insights seamlessly in daily workflows, helping decision-makers to make informed choices. 
• Marketing campaigns: AI can track the performance of marketing campaigns by analyzing data from multiple channels such as social media, email, and web analytics. It can measure engagement, conversion rates, and return on investment (ROI), providing real-time feedback on campaign effectiveness. AI agents can also assist in generating reports and suggesting optimizations based on the data. 

Looking forward, these capabilities will be further enhanced with agentic AI—autonomous systems that can plan, adapt, and act to achieve goals, requiring minimal human input as they interact with other tools and environments. For insurers, agentic AI can help in ways such as the following: 

• Automating data collection: Agents can autonomously gather data from multiple sources, helping ensure that information is always up to date.
• Providing real-time alerts: Agents can monitor and respond to specific events or changes in data and send real-time alerts to stakeholders to help ensure prompt responses.
• Generating insights: By continuously analyzing data, agents can identify patterns, correlations, and trends, helping businesses to stay ahead of the curve. 

Confidential computing: Opening new opportunities while protecting sensitive data 

To gain new benefits from more sensitive data, such as claims or confidential information, an additional layer of security and confidence is now offered by Azure confidential computing.

Azure confidential computing creates a protected environment called Azure Confidential Clean Rooms that lets different teams within a company or across multiple companies perform joint data analysis and develop risk models, fraud detection models, and more, using advanced encryption techniques to anonymize data.

Across the financial services industry, confidential computing is increasingly being enlisted to help unlock new opportunities. For example, financial messaging provider Swift is using it in an innovative anomaly detection model, enabling the model to be trained on distributed datasets without copying or moving data from secure locations. Beyond regulated industries like financial services and healthcare, confidential computing is also being used for solutions in retail, manufacturing, and energy sectors. 

In addressing social inflation, confidential computing can help insurers understand the hidden drivers that contribute to rising costs, distort risk assessments, and influence claim outcomes. This involves identifying patterns and connections between contributing factors, such as litigation trends or the influence of social media and viral campaigns that can amplify public sentiment.  

With the power of AI combined with confidential computing, actuaries, underwriters, and claims professionals can use natural language prompts to ask questions of data, semantic search can recognize their meaning and intent, and the system can write coherent responses and deliver appropriate resources. This offers an entirely new and transformative way where, by analyzing interconnected factors, insurers can identify high-risk cases and insights that suggest propensity towards social inflation claims, enabling insurance to develop proactive strategies such as early settlements or policy adjustments in high-risk areas. 

Of course, bringing teams or organizations together to consider the adoption of a broad solution approach like confidential computing involves more than just technology. It also requires structure, guidance, cooperation, and leadership. Microsoft is here to help for the long run and enable such collaboration starting with the industry leading Microsoft Azure cloud platform, and our ongoing commitment to security and responsible AI, and our long-term leadership in insurance and financial services.  

We are excited to partner with insurers and the industry at large to help innovate new solutions and business opportunities through cloud and AI. To get started with your business, reach out to your Microsoft representative and we’ll be happy to explore the possibilities. 

Learn more 

• To learn more about our solutions for insurers, visit our Microsoft Cloud for Financial Services website.
• For a deeper look at confidential computing, read the Azure Confidential Computing Blog.  

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¹ Swiss Re Institute, Social inflation: litigation costs drive claims inflation, September 2024.

² Risk and Insurance, Social Inflation Drives 57% Surge in US Liability Claims Over a Decade, September 2024.

³ Boston Consulting Group, P&C Insurance topics for 2024.

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By using more intuitive, natural interfaces to knowledge and information, retailers can start addressing some of retail’s age-old challenges—like finding and retaining the best talent, getting them up to speed quickly, and simplifying store operations so associates can focus on delighting and assisting shoppers.

No matter the size of the retailer, choosing which AI technologies to prioritize and where to start can be challenging. However, there are many ways retailers are now using AI to deliver measurable value and real return on investment (ROI). Research shows that for every $1 a company invests in generative AI, the ROI is 3.7 times across industries and regions (compared to 3.5 times in 2024).¹ Top leaders using generative AI are realizing significantly higher returns, with an average ROI of $10.30—nearly three times more.¹

To build a foundation for AI success, focus on your business strategy—how AI supports your business goals. Start by identifying the business outcomes you’re aiming for and how AI can help you achieve them.

Here’s a glimpse into how you can start making gains with your AI investments today by focusing on store operations and the frontline.

The frontline is first in line with AI

As the face of retail, frontline workers play a crucial role in the shopper experience. According to recent research by McKinsey, there is a strong relationship between the employee and customer experience, as empowered employees are more likely to deliver superior customer service.² Yet many frontline workers spend too much time searching for information, and this is one of the top five reported obstacles to their productivity.³

Generative AI offers significant potential for enhancing frontline productivity and wellbeing, with evidence that most frontline workers think it could help, and they would be comfortable using AI for administrative tasks.³ Generative AI can automate routine tasks, allowing associates to engage more with customers. This shift can lead to a more stimulating work environment, which leads to higher job satisfaction and can help retailers combat ongoing challenges with employee turnover, seasonal hiring, and training.

At a more macro level, generative AI can also allow retailers to continuously learn and feed insights back into their business processes and to grow their products, services, and competitive differentiation. Retailers can do that by identifying patterns in recurring employee questions so they can get to the root cause of operational challenges and address key gaps in training and store processes.

Here are some other ways retailers are using generative AI today:

• Swedish retailer Lindex created Lindex Copilot to offer tailored support to store associates and better understand store needs. Generative AI facilitates this bidirectional learning.
• MediaMarktSaturn lets associates to have voice conversations with generative AI, accessing details for every product, service, and warranty while staying engaged with the in-store customer, maximizing conversion and increasing customer satisfaction—all while wearing an earbud.
• Store associates at gourmet chocolatier Venchi use detailed product knowledge and customer insights to address the diverse chocolate preferences of shoppers, achieving a customer satisfaction score of 4.9 out of 5.

While generative AI technologies are still relatively new, these examples offer a glimpse of what’s possible, and help retailers build an AI foundation for more powerful capabilities emerging with agentic AI.

Agents are revolutionizing retail operations

Investing in generative AI is crucial for retailers looking to reinvent customer engagement, empower store leadership and employees, and stay competitive—and now that opportunity has skyrocketed with agents.

Agents use AI to automate and execute business processes, working alongside or on behalf of a person, team, or organization. Now retailers can leverage agents to help their teams work more efficiently and effectively by giving them faster access to information so they can better support customers and be more productive.

Agents vary in levels of complexity and capabilities depending on the need. Agents can help frontline workers with a variety of time-saving tasks—from quickly surfacing real-time product information or details about store policies and procedures to support Q&A or troubleshooting. In addition to helping speed information retrieval, agents can help frontline workers with more advanced features like automated task creation or even advising and summarizing information—such as listing open tasks for a shift handover or flagging missed communications. Agents can also operate independently to dynamically plan, orchestrate other agents, and learn to improve over time. For example, an automated stock transfer agent might scan sales velocity across multiple stores and automatically transfer goods between locations if one store is oversupplied while another is understocked, minimizing manual intervention.

Find in-the-moment answers fast

One important way to get business value from agents is to help store associates find information about company policies or procedures when a customer is waiting for an answer.

SharePoint agents can help store associates find quick answers from internal company sources in seconds. Using the power of natural language, associates simply ask what they’re looking for on their tablet or mobile device and the agent responds in natural language with a link to the policy documentation for reference.

These agents go beyond information retrieval to also generate step-by-step instructions, synthesize product information, and support frontline managers to create and smart-assign shifts, and auto-validate task completion.

Agents can help associates reduce customer wait time, increase information accuracy, and possibly facilitate sales.

Simplify store processes

Complex business processes are another ongoing operational challenge and opportunity for custom agents to help improve productivity.

Custom-built agents can help retailers connect to external data sources and systems so store associates can find information such as product inventory availability in or near their store, shipping status, or how to initiate a return.

Frontline workers simply ask, “Help me initiate a return,” and the agent guides them through the process by clarifying the worker’s intent and providing them with next steps, all through a chat interface.

Custom agents are best suited to also streamline complex workflows like task management, that often involves multiple steps. Using custom agents built with Microsoft Copilot Studio, frontline workers can easily create a task and send it through a task management system that sends automatic alerts as needed, all from a single pane of glass.

Meeting you where you are on your AI journey

Microsoft offers AI solutions that you can customize to meet your unique needs and scale. There are several ways agents can be deployed, from no code to low code and pro code. Here are a couple options available today.

Microsoft 365 Copilot Chat is a new offering that adds pay-as-you-go agents to our existing free chat experience for Microsoft 365 commercial customers. Copilot Chat empowers retailers to get started on their AI journey today and includes querying the public web (such as a retailer’s website) for free. To enhance Copilot Chat, retailers can also build custom agents using Copilot Studio and SharePoint agents that enable access to retail systems such as enterprise resource planning (ERP), customer relationship management (CRM), and product information management (PIM), and to documents on SharePoint. These paid agents are available on a metered basis, so you only pay for what you use.

Store Operations Agent is a pre-built agent available on Copilot Studio enabling retailers to get started fast with a prebuilt solution that acts as an “associate” to your store associate. With this agent, retail employees can:

• Access data from LOB systems: Look up product inventory, check order status, find customer information and compare products.
• Access store policies and procedures: Quickly find answers to questions from knowledge bases such as SharePoint, websites, and across select internet portals.
• Raise incidents for quick resolution: Connect to incident management tools by using more than 1,000 connectors in Power Platform to raise incidents and alert store teams.

Using Store Operations Agent, employees at leading Nordic retailer Kappahl can quickly and securely surface product information, store policies and procedures, and more, increasing store associate productivity and upleveling the shopping experience for customers.

A new era of retail fueled by AI, powered by people

The range of potential gains with AI extends across retail operations—from people to processes to customers, helping make retail more human at every step of the way. From delighting shoppers to helping associates feel more supported and productive, AI can boost store operations efficiency, creating an environment where both shoppers and workers thrive.

Savings achieved using AI can be reinvested to create a better employee experience, fostering a work environment where employees are enthusiastic ambassadors of the brand, bringing the life of the store to customers every day.

Microsoft is the proven leader for AI transformation with the full technology stack and portfolio to help retail and consumer goods organizations power their business with AI. We can help you assess your agent environment, ideate on agent use cases, and establish success criteria for evaluating ROI so you can decide what agent is best for you.

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Learn more about how these forward-thinking companies are driving ROI with Microsoft 365 Copilot and agents—and illuminating the path ahead for every organization.

• Microsoft’s responsible AI principles
• How CMOs can personalize marketing and optimize spend with AI
• How merchants can drive velocity with AI
• Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems
• How unifying data improves shopper experiences

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¹ Generative AI delivering substantial ROI to businesses integrating the technology across operations: Microsoft-sponsored IDC report – Middle East & Africa News Center.

² How retailers can build and retain a strong frontline workforce in 2024.

³ Work Trend Index: Will AI Fix Work?

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What are OSS and BSS?

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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 Microsoft AI Blogs.

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.

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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 Microsoft AI Blogs.

Why does this matter? For every $1 an organization invests in generative AI, they are realizing an average of 3.7x return.¹

In recent research we commissioned through Devoncroft Partners, it became clear that media and entertainment customers are at different stages of adoption.

“We were a launch customer for a customer service focused AI technology company. We expected it to drive tremendous savings versus historical, labor-intensive customer service groups. It did. What is beyond belief is the fact that customer satisfaction goes up. The customers are happy about it. They get a better response. So, it’s not just about defraying costs. It’s about the fact that we can actually defray costs at the same time, we’re providing a better product or service to our customer. It’s definitely an industry shift.”

OTT Streaming Company

“I have my team run AI tests across a variety of use cases (production, processing, distribution … ) every six months. AI isn’t ready to replace our existing processes. However, every six months the tests are showing double digit improvement. I have never seen this in any other technology…and the trajectory has not flatted out. It’s really impressive.”

Media Conglomerate

Technology decisions made today will dictate the direction and capabilities of the media industry for the next 10 to 15 years, significantly impacting workflows and strategic opportunities.

We are committed to working with our media and entertainment customers and partners to advance AI while addressing some of the key obstacles to adoption. Here are some valuable resources for you to have on hand, wherever you are on your journey.

How you can advance AI in your organization

Discover how our media and entertainment customers are optimizing operations, empowering content creators, and unlocking new monetization opportunities:

Universal Destinations & Experiences

Universal Destinations & Experiences’ award-winning theme park destinations are adopting predictive maintenance across many of its state-of-the-art attractions to help further improve guest satisfaction, using Microsoft Azure and Azure Data Explorer. This has resulted in streamlined operations and maintenance efforts at this attraction, with a 66% reduction in labor hours on work orders related to its blaster network.

HyperCinema

The creative minds behind New Zealand startup HyperCinema had a revolutionary idea: What if a show could be generated in real time as you walk into a venue and immerse you more deeply than ever in the story world of the brand or attraction using generative AI? The company puts 50,000 images and 6,000 30- to 45-second videos through its HyperEngine every day to deliver automated and hyper-personalized in-person storytelling experiences in real time, all powered through Azure and Azure OpenAI Service.

Promo.com

Promo.com has revolutionized its video creation services with Azure and Azure OpenAI Service.

Evolving from a browser-based video editor, Promo.com now helps small businesses and agencies effortlessly produce a month’s worth of marketing videos in minutes with PromoAI. By generating videos in just two minutes, Promo.com has increased its user engagement and grown its PromoAI product, adding over 1 million USD in new annual recurring revenue just five months after launch, while enhancing customer retention by 40%.

LALIGA

LALIGA’s partnership and move to Azure has transformed fan engagement, driven revenue growth, and created new opportunities for sports clubs, leagues, and federations.

Beyond Stats, a fan-facing data and insights platform powered by Azure 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. This partnership is one of many that is showcasing how Microsoft is helping transform sports with cutting-edge technology.

For more media and entertainment customer stories visit our repository online and search by product, industry, business need or organization size.

What holds some media and entertainment companies back from adopting AI, and what you can do about it

Data and security concerns along with lack of skilling are two key barriers to AI adoption for all industries, according to the IDC.² Media and entertainment executives are understandably concerned about protecting creators and content and many are looking for ways to provide employees with skilling and the opportunity to safely experiment with AI while avoiding risk to their core business.

Here are a few resources to take advantage of, depending on the challenge you may be facing:

Protecting creators and content

Andrew Jenks, Microsoft’s Director of Media Provenance and Executive Director of the Coalition for Content Provenance and Authenticity (C2PA), speaks to what Microsoft and other companies are doing to “fight the fakes” in this article from Technology Record.

As well, 80% of leaders across all industries cited leakage of sensitive data as their main concern. Our pledge to our customers and our community is to prioritize your cyber safety above all else. We’re continuously applying what we’ve learned from incidents to improve our methods and practices, ensuring that security is paramount in everything we create and provide, through our Secure Future Initiative (SFI).

Skilling

Microsoft recently announced a strategic collaboration with Pearson, the world’s lifelong learning company, to help address one of the top challenges facing organizations globally: skilling for the era of AI.

The partnership will focus on providing employers, workers, and learners with new AI-powered products and services to help prepare the current and future workforce across industries for the era of work in an AI-powered economy. By combining Pearson’s expertise in learning and assessment with Microsoft cloud and AI technologies, this partnership will play a foundational role in helping organizations realize the full value of AI through reskilling.

For additional resources to brush up on AI skills, visit Microsoft Learn where you can start with a single course such as the Fundamentals of generative AI or create your own personalized plan with AI. While you are there, register for the Microsoft AI Skills Fest to unlock your future with 50 days of AI discovery and learning.

Low-stakes opportunities to experiment

Copilots are infusing AI into every function and role—and are one easy way to experiment with generative AI. And many companies are doing more than experimenting. In fact, more than 85% of Fortune 500 are using Microsoft AI, with 70% of Fortune 500 companies already using Microsoft 365 Copilot.

Copilot can be woven in to help streamline media workflows to allow people to continue to be at the center of creative and strategic work. Here are a few easy-to-use Copilot use cases to get you started:

Empower content creators

• Idea generation: Use Copilot to brainstorm different ideas that may appeal to certain audiences. Copilot can analyze trends and audience preferences to suggest fresh and engaging content ideas.
• Storyboarding: Have Copilot come up with a storyboard to get to the next stage quicker. Copilot can outline scenes, suggest dialogue, and create visual elements to help bring your ideas to life.

Optimize operations

• Campaign development: Work with Copilot to develop strategic and personalized campaigns. Copilot can analyze customer data to create targeted marketing messages and suggest the best channels for promotion.
• Collaboration management: Task Copilot with taking organized and effective notes during Microsoft Teams meetings that can be actioned upon quicky. Copilot can help you stay on top of action items and make the collaboration experience easier than ever.

Unlock monetization

• Data synthesis: Use Copilot to gain quicker, more actionable insights from your data. Copilot can analyze large datasets to identify trends, patterns, and opportunities, allowing everyone to synthesize insights quickly and make informed decisions.
• ⁠Personalized recommendations: Copilot can use predictive analytics for ad campaigns, subscriber churn identification, and personalized recommendations to improve user satisfaction and retention, optimizing advertising revenue.

Find these Copilot use case scenarios and more in the Microsoft Copilot Scenario Library.

Explore these additional resources to see how generative AI can transform your business

• Read the Devoncroft white paper for a deeper understanding of how the media and entertainment industry is adopting AI technologies.
• How real-world businesses are transforming with AI — with more than 140 new stories.
• Coming soon: The Media and Entertainment AI Toolkit—designed specifically for media and entertainment professionals—provides insights, strategies, and recommendations to help you use AI responsibly and effectively. You’ll see a range of AI technologies, including Copilot and Azure AI Foundry, and how they’re already providing benefits to organizations all over.

We are incredibly excited about what our customers and partners are doing with AI. We love to hear about what’s working, what’s challenging, and how we can help. And we can’t wait to share more success stories and lessons learned with you.

Join us at NAB to learn more

If any of the resources, topics, or new content in this blog have piqued your interest, come see us at NAB Show, where you can experience some of our latest AI solutions for yourself.

From theater and keynote sessions to cutting edge demos, let’s reimagine media with AI together. Learn more about Microsoft’s NAB Show presence, including our Microsoft Experience Room, sports floor AI-powered transformation exhibits, and keynote session showcasing the latest innovations from Microsoft, our customers, and partners. 

Microsoft’s commitment to the media and entertainment Industry 

Microsoft enables 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.

Microsoft for media and entertainment

Transform the future of creativity, content, and experiences

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^1,2 IDC, Business Opportunity of AI | Generative AI Delivering New Business Value and Increasing ROI, 2024

The post Microsoft helps media customers capitalize on AI for greater ROI appeared first on Microsoft AI Blogs.

• Data collection and processing at the edge: Providing secure, reliable, and low-latency data transfer and processing in highly sensitive and distributed environments.
• Secure communication and interoperability: Ensuring seamless integration and communication across different domains and platforms.
• Data security: Protecting sensitive information from cyber threats and unauthorized access.
• Real-time analytics: Providing real-time insights and analytics across a fusion of many different data types, to support decision-making.

By solving these challenges, decision-makers can act on near real-time updates and intelligence, enhancing situational awareness and enabling mission success.

How Microsoft Cloud helps solve legacy system challenges

Microsoft is well placed to respond to these challenges through the hyperscale cloud capabilities of Microsoft Azure, encompassing a global network of data centers, servers, and networks that power cloud services, including:

• The Microsoft Adaptive Cloud approach, which lets organizations use cloud-native and AI technologies across hybrid, multi-cloud, edge, and Internet of Things (IoT) environments. This helps defense organizations ensure consistent operations by extending cloud services to on-premises and multi-cloud environments, and it simplifies operations with centralized management, enhanced security, and seamless integration across diverse and complex environments. Additionally, it allows for easier application deployment and a common data foundation across environments.
• Azure Local, enabled by Azure Arc, which is a specialized offering designed to bring cloud computing capabilities directly to the edge, closer to where data is generated, and decisions need to be made. For defense and intelligence customers, this means enhanced security, reduced latency, and improved operational efficiency by processing data locally rather than relying solely on centralized cloud services. This approach is crucial for defense and intelligence operations, where timely and secure data handling significantly impacts mission success.

Adaptive Cloud and Azure Local solutions in action

By way of illustration, consider a joint task force assigned to secure a national border as part of a multi-domain operation (MDO). The objective is to identify and address potential threats, including unauthorized crossings, smuggling activities, and aerial incursions. This is achieved by using advanced technologies, which can potentially benefit the following warfighting functions:

• Land forces patrolling the coastline
• Naval units monitoring the sea lines of communication
• Air units conducting intelligence, surveillance and reconnaissance (ISR) collection
• Cyber units ensuring secure communication and protecting against cyber threats
• Space units ensuring satellite availability for communications and geospatial intelligence collection

Let’s look at some specific scenarios and how technology can help achieve success:

Real-time data collection and edge processing

IoT data collection

Data is collected and processed directly from IoT devices in real-time, close to the source, reducing latency and enhancing security.

How it works:

• Ground sensors and drones equipped with cameras and motion detectors monitor coastline activities.
• Buoys and unmanned surface vehicles (USVs) collect data on maritime traffic and environmental conditions.
• Drones and aircraft equipped with radar and cameras provide aerial surveillance.
• Azure IoT operations deployed on Azure Local securely process and normalize this data at the edge.

Edge processing

The data collected from sensors is processed and transmitted to Azure Local instances deployed at mobile command centers.

How it works:

• Local AI inferencing, such as Azure AI Video Indexer, allows the processing of data at the source. By conducting real-time analysis directly within an environmental context, defense organizations can respond faster and more accurately to emergent situations using AI and machine learning models to analyze patterns, detect anomalies, and provide actionable insights to field commanders.
• Azure Local supports both legacy systems and modern containerized applications, allowing the defense organization to run a mix of applications needed for the mission, from traditional command and control systems to advanced AI-powered analytics.
• Through edge processing, critical information can now be filtered prior to its transmission to the cloud—for instance, identifying potential threats, such as unidentified aircraft or submarines, and alerting the command center for appropriate action.
  • For all tactical units, where traditional terrestrial connectivity is limited or unavailable, low earth orbit (LEO) satellite connections provide connectivity to remote and mobile units, such as ships at sea, aircraft in flight, or land-based command and control nodes. Satellite communication can ensure continuous and secure data transmission, critical to information sharing in a joint operation.
  • Forward operating bases (FOBs) process data on Azure Local, securely transmitting it to the cloud using Azure ExpressRoute, which provides a private connection between the edge and Azure, bypassing the public internet and supporting encryption technologies like MACsec and IPsec to ensure data confidentiality and integrity.

Command and control (C2) situational awareness

The task force sustains a thorough and current operational overview by using data transmitted to Azure from Azure Local. With cloud technologies, command and control data flows seamlessly from collection to actionable insights. The C2 node assesses the situation and determines the appropriate response such as route planning, resource allocation, and threat assessment.

How it works:

• Real-time intelligence managed with Microsoft Fabric, a unified AI data and analytics platform, enables a C2 node to swiftly analyze data from the edge using technologies like Azure Event Hubs and AMQP for data ingestion, and Microsoft Power BI for visualization. The real-time hub provides a unified interface for managing streaming data sources, allowing for rapid decision-making and enhanced situational awareness. Data is further processed and made available to Azure AI Foundry, for use in advanced AI applications.
• AI Foundry uses this data to deploy AI models assisting commanders in analyzing battlefield data and suggesting optimal strategies—for example, using AI models to perform sentiment analysis on communication data from the field. By analyzing the sentiment of messages, AI can identify potential stress or urgency in communications, providing valuable insights to commanders. Additionally, AI can detect patterns and anomalies in the data, such as unusual movements or activities, and alert the command center for further investigation.
• Units can then swiftly adapt to the updated operational plan. Analyzed data and directives from the C2 node are sent to Azure Local. Military applications running on Azure Local Virtual Machine receive directives from the C2 node. The units reconfigure their routes based on the optimized path provided, ensuring efficient movement and resource utilization. They allocate resources as per the new directives, prioritizing critical areas identified by the C2 node. Additionally, the units enhance their threat assessment protocols, incorporating the latest intelligence to mitigate potential risks.

By using Azure Local, the joint task force’s multi-domain operation not only addresses immediate threats but also establishes a robust framework for ongoing border security enabling seamless coordination and integration across land, sea, air, cyber, and space domains. By extending Azure services and security to distributed locations, apps and data are better safeguarded against advanced threats, ensuring reliable protection and operational efficiency.

• Real-time situational awareness: Rapidly assess and respond to emergent situations ensuring the border remains secure.
• Enhanced security: Secure communication channels and robust cybersecurity measures protect sensitive information from cyber threats. This ensures that all units can communicate effectively and securely, maintaining the integrity of the operation.
• Efficient decision-making: Advanced analytics and AI-powered insights enable quick and informed decision-making. The command center can process vast amounts of data in real-time, allowing for swift and accurate responses to emerging threats.

Benefits of Microsoft Adaptive Cloud and Azure Local in defense operations

Enhanced security

• Hardened security posture: Azure Local instances are configured with secured-core settings and automatic data encryption by default, protecting sensitive military communications and intelligence data from cyber threats.
• Microsoft Defender for Cloud Integration: Azure Local integrates natively with Microsoft Defender for Cloud, offering comprehensive monitoring and advanced threat protection. This ensures that potential security breaches are promptly detected and mitigated.
• Network Security Groups (NSGs): NSGs in Azure Local manage and secure network traffic within the Azure environment, allowing for control of inbound and outbound traffic to virtual networks, subnets, and network interfaces with defined security rules. These rules can permit or deny traffic based on various criteria such as source and destination IP addresses, ports, and protocols.
• Trusted launch: Enhance protection against sophisticated threats such as malware-based rootkits and bootkits with Trusted launch security. This includes secure boot, which guarantees that only trusted software is loaded during the boot process, and a virtual trusted platform module (vTPM), which securely stores keys, certificates, and secrets.

Operational flexibility

• Disconnected operations: In areas with limited or no connectivity, Azure Local supports disconnected operations, allowing joint forces to maintain situational awareness and make informed decisions even when not connected to Azure. Data can be synchronized with the C2 node once connectivity is restored.
• Flexible hardware options: Azure Local’s extensive catalog supports rugged hardware suitable for harsh environments, ensuring reliable performance even in extreme conditions.
• Scalability: In support of mission needs, additional Azure Local instances can be quickly deployed to new locations, providing the necessary computing and storage resources to support expanding operations.

Explore Microsoft for defense and intelligence

Learn how Microsoft Cloud can help achieve mission outcomes to promote stability and security:

• Contact your Microsoft representative or technology solution partner.
• Visit the Microsoft for defense and intelligence website

Microsoft for defense and intelligence

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The post Microsoft Adaptive Cloud: Advancing edge computing in the defense sector appeared first on Microsoft AI Blogs.

Agility helps retail and consumer goods supply chains:

• Quickly switch suppliers, develop more flexible sourcing strategies, and mitigate disruptions from potential tariffs¹
• Adapt product offerings and pricing strategies to combat the lingering effects of inflation
• Adopt more real-time demand forecasting tools and flexible warehousing solutions to keep up with shopping patterns
• Augment human labor with automation to improve productivity and address labor shortages

Retail and consumer goods organizations that develop greater agility will catapult themselves forward by using insights from their supply chains as a critical enabler.

Nonetheless, many retailers’ supply chains struggle with agility because warehouse data is often still on-premises—and that’s holding them back from the latest technologies. Because data is central to all business processes, it’s data that either fuels or inhibits supply chain growth. Reliance on on-premises data and legacy systems likely inhibits supply chain growth because it:

• Causes latency that slows decision-making since leaders lack access to real-time data and often rely on outdated snapshots of old data
• Prevents visibility and collaboration since data is often fragmented and siloed
• Limits scale because systems can’t efficiently process increased data volumes and fluctuating demand
• Impedes flexibility when systems can’t adapt quickly to shifting market conditions and demand
• Impairs adoption of new technologies and processes when existing platforms aren’t adaptable

The warehouse is the ideal starting place for increased digitalization because investments made at the warehouse create value that extends to other parts of the supply chain and enterprise.

Digitalizing the warehouse enables operational excellence and innovation through:

• Data-driven decision-making through real-time insights that help managers make more informed decisions and get teams unified around the same information so retailers can get ahead of demand.
• Reduced operating costs related to warehousing operations through enhanced efficiencies gained by automation and robotics—and improved warehouse throughput through layout optimization, labor efficiencies, and automation. This includes reduced time and labor required for tasks such as picking, packing, and shipping.
• Seamless integration throughout supply chain systems, such as enterprise resource planning (ERP) and warehouse management systems. It also sets the stage for other powerful capabilities, such as intelligent stores.
• More scalability, making it easier for retailers to handle seasonal demand fluctuations or rapid growth without disrupting operations.

Agility helps supply chain leaders drive operational excellence and innovation. Nothing enables that level of agility like the cloud. Here are three compelling reasons to start digitizing your warehouse today with Microsoft and its partner ecosystem.

1. Help warehouse managers drive operational excellence with agentic AI

The role of the warehouse manager is pivotal in the supply chain ecosystem, yet warehouse managers are overloaded with information from multiple sources, making it hard to parse what’s relevant and useful.

Blue Yonder’s warehouse manager AI agent offers an easy-to-digest, interactive report designed to help warehouse managers stay up to date with the most important data and information. The agent delivers those key insights when they’re needed, helping ensure operational excellence every day.

Instead of sifting through hundreds of charts and dashboards, pages and pages of report analysis, or piecing together fragments of information from their teams, warehouse managers get a simplified view of what’s happening, what caused the issue, and what to do about it.

It’s like having a personal analyst working alongside the warehouse manager who knows all about their role, their company, and warehouse. That partnership helps the manager move much more quickly from information overwhelm to clear, decisive action.

Blue Yonder expects more developments coming soon, including more data highlights, summaries, and suggested actions, as well as an expanding list of tasks the agents can perform with human guidance.

2. Optimize warehouse design, planning, and operations with simulation

Today’s customers expect retailers to have what they want and deliver it fast to their store or home. Warehouses are critical nodes in the supply chain where optimizations can improve growth and profitability. From receiving shipments to sorting, picking, and packaging, every step of warehouse operations is being modernized with AI that analyzes changes in the physical world.

Simulating facility designs and layouts, processes, and discrete events in fulfillment and distribution centers helps retail and consumer goods enterprises make more informed and faster decisions without the need to physically install systems to evaluate use cases. Simulation also lets enterprises create and use synthetic data to orchestrate between manual labor and automation systems applying AI, machine learning, robotics, sensor technology, management systems, cloud platforms, and data analytics. How can warehouses achieve operational excellence at every step of the orchestration?

NVIDIA Omniverse is a platform for developing and deploying physical AI and simulation applications for industrial digitalization. Developers use Universal Scene Description (OpenUSD) to build solutions on a platform that enables warehouse scale, digital twins, and simulations to optimize layouts and achieve operational efficiencies. These digital twins also serve as virtual training grounds for autonomous systems and robotic fleets that increasingly operate inside these facilities.

Today, leading retailers and consumer goods companies use applications and solutions built on NVIDIA Omniverse to design and simulate greenfield and brownfield warehouses from scratch, establishing an optimal layout and process flow all in a physically accurate digital space. They can evaluate technologies like robotic shelving systems, robotic grid-based storage, or vertical lift modules (VLM) for high-density storage.

Solutions built on Omniverse let retailers integrate data from different enterprise and industrial systems to create, test, and measure design, process, and operational twins before spending precious capital or stepping foot in the building. For greenfield sites, this means a fully optimized virtual version of the entire design before construction begins. For brownfield sites, retailers can seamlessly integrate new automation technologies with existing systems, ensuring the entire warehouse achieves its operational benchmarks and performs as one cohesive unit.

Applications developed with the Omniverse platform also allow supply chain leaders to understand the impact of discreet events that impact efficiency so they can make decisions that improve key performance metrics like warehouse throughput without the risk of costly physical trials.

In the fast-paced world of commerce, time to value is everything. But platform technologies are never the end-all, be-all. That’s why collaborating with the right partners and experts is crucial for retail and consumer goods enterprises. By bringing together integration partners like Accenture to simplify the development and implementation of end-to-end advanced automation and robotics solutions and services, Microsoft’s powerful cloud solutions, and NVIDIA’s cutting-edge accelerated computing, AI, and simulation platforms, retailers can accelerate warehouse transformation and realize value faster than ever.

3. Boost productivity and collaboration with robotics-enabled automation and intelligent orchestration

Warehouse managers have traditionally relied on manual processes and human labor to keep their operations running smoothly. But labor shortages and rising operational costs are making it increasingly difficult to maintain efficiency and productivity. Additionally, the complexity of managing inventory and ensuring timely order fulfillment often leads to bottlenecks and errors.

Advancements in robotics can help supply chains augment staffing, improve employee safety, and drive warehouse productivity. New capabilities are emerging every day and startups are the ones embracing these new capabilities.

Intelligent orchestration and sortation with Unbox Robotics

The last mile can be a significant chunk of the cost in getting the supply chain right. Unbox Robotics is one of hundreds of startups Microsoft works with to deliver retail supply chain solutions. Unbox Robotics can help automate the last mile process by using robots and swarm intelligence that mimics what a swarm of bees or ants do by carrying goods from one place to another. These robots pick items, sort them, and put them in one lot lightning fast so they can easily be picked up and delivered. And because robots can work around the clock, Unbox Robotics can help retailers offset labor challenges with “always on” reliability.

Smart redistributions with YDISTRI—a new era in inventory optimization

Even the best demand forecasting systems can’t fully prevent real-time overstock and understock issues. YDISTRI doesn’t compete with these systems—it complements them by providing an AI-based reactive inventory redistribution solution. For example, in a supermarket chain, YDISTRI analyzes sales patterns, local demand, and product turnover to identify overstocked items—such as specialty foods or seasonal goods—and moves them to stores where they will sell faster at full price, reducing markdowns and waste.

By weighing transfer costs against the risk of discounts or write-offs, YDISTRI helps retailers maximize revenue from existing stock, improving inventory efficiency without relying on heavy markdowns.

Bend the curve on innovation by digitalizing your warehouse in 2025

Improving agility gives retailers the ability to future-proof their business, flex and scale their operations, and be more responsive and adaptive to consumer demands. Supply chain leaders can achieve operational excellence and catapult themselves forward with generative AI, digital twins, and robotics.

Microsoft partners with Blue Yonder, an organization that provides complete solutions across the entire supply chain, and with hundreds of today’s most innovative startups to complement a retailer’s existing technologies. Start using your supply chain as a business enabler by digitalizing your warehouse in 2025 and gain more agility for years to come.

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• Microsoft’s responsible AI principles

• Enhancing supply chain efficiency in the retail and consumer goods industry with agentic systems

• Delivering your supply chain copilot: Prioritizing areas of ROI

• Delivering your supply chain copilot: Getting started on ideation

¹ “Tariffs: What Retailers Need to Know,” Bain & Company, January 2025.

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Yet even with all this progress, for decades fragmented systems and heterogenous environments have kept digital threads within the industry largely aspirational, preventing most organizations from achieving synchronized operations. A persistent inability to connect modern technology solutions with aging infrastructure has also slowed the collaboration long promised to manufacturers. Together, unified data and AI are now enabling organizations of all sizes to break through these barriers, transforming digital threads from static, disconnected datasets to dynamic networks. With AI agents serving as the interface, every worker can surface the overall equipment effectiveness (OEE), total cost of ownership (TCO), and return on investment (ROI) insights necessary to drive decision-making.

At Hannover Messe 2025, Microsoft is showcasing how new AI agents and partnerships with leading software vendors can help manufacturers deliver secure, scalable innovation from the shopfloor to the boardroom. Attendees will experience firsthand how data-driven intelligence and AI-enabled solutions will reshape manufacturing.

AI agents supporting the development of frontline workers 

Manufacturing transformation is reaching into every aspect of operations. Frontline workers now have access to AI agents providing them with enhanced guidance needed to make informed decisions. To expand this modern toolbox, we announced back at Ignite 2024 the public preview of Factory Operations Agent in Azure AI Foundry. An AI-powered assistant, Factory Operations Agent streamlines operations—enabling operators, production, and leaders to quickly access insights and optimize manufacturing processes through natural language querying. In doing so, the agent accelerates issue resolution and root cause analysis to improve productivity within day-to-day manufacturing operations.

As the industry struggles with turnover, worker skilling is an ever-present challenge. The World Economic Forum found that 63% of industry leaders believe skilling to be a significant barrier to growth.¹ Manufacturers need no-code and low-code options that democratize the power of AI without the need for extensive coding. With this in mind, we are announcing the same Factory Operations Agent now available in Copilot Studio in public preview, where with one-click it can be easily integrated into products like Microsoft Teams.

Finally, we’re announcing the public preview of Factory Safety Agent in Copilot Studio. This low-code, customizable solution provides workers with answers to occupational health and safety (OHS) questions and guidelines. It can also streamline safety inspections and personalize workforce training. 

Also, at Hannover Messe 2025, we will be showcasing state of the art technologies that will enhance the future of frontline work, like with our customer Sanctuary AI.

Sanctuary AI is shaping the future of frontline work, ushering in the era of autonomous labor with the power of Microsoft Azure. As frontline labor shortages intensify, manufacturers can explore deploying advanced general-purpose robots with dexterity-driven physical AI to automate repetitive, complex, and unsafe tasks to enhance operational efficiency. With Azure’s high-performance graphics processing units (GPUs), Sanctuary AI can train machine learning models at scale, pushing the boundaries of dexterous intelligence. 

Advancing innovation in digital engineering with generative AI 

Manufacturers shape their market leadership through digital engineering and design. By accelerating development and prototyping, and reducing time-to-market, AI-powered generative design is empowering manufacturers to create new high-performing, customer-centric products.

GenAI use cases to modernize manufacturing

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Aras is introducing Aras InnovatorEdge, a low-code application programming interface (API) management framework embedded in the Aras Innovator® platform. This solution simplifies API creation and integration, enabling secure, scalable data connectivity and enhancing collaboration, operational efficiency, and decision-making across enterprises. It integrates seamlessly with Microsoft Fabric, unlocking deeper insights and optimizing decision-making across the digital engineering landscape. 

Autodesk and Microsoft collaborate to create an AI-powered digital thread to help manufacturers gain efficiencies, reduce costs, and compete smarter. Autodesk® Fusion, the industry cloud for manufacturing, connects people, data, and process through the product development lifecycle. Autodesk Data solutions in Fusion Manage and Microsoft Fabric will enable efficient data management and process optimization. Additionally, Autodesk’s digital twin offerings with Tandem, factory simulation through FlexSIM, and factory operations management with Fusion Operations all benefit from this collaboration, ensuring that these tools work seamlessly across the IT and OT ecosystem. 

Windchill by PTC is a crucial platform for engineering and manufacturing teams globally. To support manufacturers aiming to integrate AI across their value chains, PTC and Microsoft are partnering to develop an enterprise data framework and multi-agentic model within Microsoft Fabric. This collaboration extends digital thread capabilities beyond traditional product lifecycle management (PLM), integrating data from enterprise resource planning (ERP) and manufacturing execution system (MES) systems, and enabling AI-powered insights and workflows. 

Preparing the factory edge for AI  

AI is redefining factory operations, but to fully capitalize on shop floor investments, manufacturers need to integrate their on-premises industrial edge solutions with the cloud. 

A core component of the Azure adaptive cloud approach, Azure IoT Operations is built on industry standards. Capturing data from industrial equipment assets and devices, Azure IoT Operations normalizes it at the edge—sending operational insights to the cloud and back.

Husqvarna is leveraging Azure IoT Operations and AI tools to digitally transform its factory floors. Their AI Vision Companion enhances visual quality control for chainsaw production, while AI chatbots assist night workers with troubleshooting, improving efficiency, and reducing downtime. With these new capabilities, Husqvarna expects to double their in-market connected devices and boost robotic lawn mower sales, expanding Azure IoT Operations from two to 40 factories globally by summer 2025. 

Siemens and Microsoft have expanded their partnership, Siemens Industrial Edge works seamlessly with Microsoft Azure IoT Operations, making OT and IT data planes fully interoperable for manufacturing. This joint effort streamlines data flow between edge and cloud, enhancing machine performance, product quality, and maintenance efficiency, and enabling manufacturers to adopt AI and digital twin technologies for more adaptive, optimized production.

Making AI-powered digital threads a reality for manufacturers 

The nervous system of industrial operations, digital threads weave together critical information, processes, and people across manufacturing segments. Grounded in unified operational (OT), information (IT), and engineering (ET) data, the electronic frameworks can empower individuals with relevant, timely insights. From initial concept to customer support, this continuous flow of data connects and enriches every aspect of manufacturing.

For over a century, Rolls-Royce has been a force for progress; powering, protecting, and connecting people everywhere. Today, with digital transformation at the forefront, the company is redefining how its world-class products are designed, built, and maintained. Hannover Messe 2025 visitors will see firsthand how AI and cloud technologies are shaping the future of aerospace. With the help of Siemens and Microsoft, Rolls-Royce is leveraging AI to streamline production, boost engine efficiency, and predict maintenance needs before issues arise. Rolls-Royce is also helping provide more efficient, reliable, and low-emissions energy solutions, powering everything from critical infrastructure to data centers. Rolls-Royce isn’t just keeping up with the digital revolution—it’s driving it.

Without AI, manufacturing data is difficult to navigate. Data quality, standardization, and integration have been unreliable. Microsoft is helping manufacturers make sense of their data to unlock the AI opportunity. With Microsoft Fabric, manufacturers can integrate data across different departments and teams. Traditionally, this data is trapped within separate systems. Parsec and Tulip integrations mark another major step in our ability to drive operational intelligence, enhancing shop floor and frontline execution. 

Parsec, developer of the industry-leading MES, TrakSYS™, today announced an upcoming integration with Microsoft Fabric and the factory operations agent in Azure AI Foundry solution to help deliver generative AI to manufacturing organizations. Dubbed TrakSYS IQ, this industry-defining functionality will enable users to retrieve and analyze factory data through a conversational user interface, bolstering productivity and data-based decision-making. 

Tulip, a leader in frontline operations solutions, announces its integration with Microsoft Fabric. This integration enables the Tulip Frontline Operations Platform to deliver scalable analytics across multiple factories, leveraging rich datasets for machine learning to provide real-time feedback and alerts to supervisors and operators. 

See Industrial AI in action at Hannover Messe 2025

The future of manufacturing is powered by AI. This year, at Hannover Messe 2025, attendees will have the opportunity to experience how Microsoft and its partners are supporting the industry transformation—from digital engineering, on factory floors, with frontline workers, and through digital thread. Join us at Hall 17, Stand G06. 

Thanks to all partners and customers joining Microsoft at Hannover Messe 2025: ABB, Accenture Avanade, Autodesk, AVEVA, AVL, Bayer, Blue Yonder, Bosch, Bühler Group, C3.ai, Capgemini, Cognite, Databricks, EPLAN, Hexagon, Husqvarna, Kongsberg Digital, Litmus, MTEK, NVIDIA, NTT Data, o9, Parsec, PTC, PWC, Rescale, Rockwell Automation, Rolls-Royce, Sanctuary AI, Sandvik, Schneider Electric, Siemens, Sight Machine, Symphony AI, TeamViewer, TCS, and Tulip. 

¹WEF: Skill Gaps are the Biggest Barrier to Transformation, Skillsoft.

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Over the last decade, this end-to-end view, has been the promise of digital threads in the industrial space, a holy grail of data touchpoints that provide a real time view of the entire lifecycle of a product or a specific process, from design all the way to end of life. This has largely out of reach for most industrial companies for two key reasons:

1. The data problem: Fragmented, siloed, and uncontextualized mountains of data across a heterogenous stack of technologies and modalities, that require prohibitive investments in data science techniques to be able to leverage for a specific use case, with little scalability.
2. Return on investment (ROI): Traditionally, it has been difficult to prove ROI for digital thread initiatives, partly due to the challenges presented by the data problem, and partly because of the complexity to action on insights, from cultural resistance to skills gaps, to mention a few factors.

Microsoft, alongside partners like PTC, believe we are at the pivotal moment where digital threads are becoming an attainable reality for industrial customers due to two key innovations. First, the rise of unified data foundations that make data usable by securely sourcing it from systems like customer relationship management (CRM), product lifecycle management (PLM), enterprise resource planning (ERP) and manufacturing execution system (MES), and automating the contextualization aligned to any given standard or custom data model.

Secondly, the rise of generative AI, specifically, AI agents that reason using this unified data foundation and provide insights or take actions—unlocking thousands of use cases across the manufacturing value chain.

The role of AI agents

AI agents are sophisticated software systems designed to automate complex analyses, support decision-making, and manage various processes. They are productivity enablers who can effectively incorporate humans in the loop through the use of multi-modality. These agents are designed to pursue complex goals with a high level of autonomy and predictability, taking goal-directed actions with minimal human oversight, making contextual decisions, and dynamically adjusting plans based on changing conditions. AI agents can assist in various business processes, such as optimizing workflows, retrieving information, and automating repetitive tasks. They can operate independently, dynamically plan, orchestrate other agents, learn, and escalate tasks when necessary, however, AI agents are only as good as the data used to train the models that power them, and the current landscape of AI agents in the industrial space is domain specific, so these agents are confined to exclusively operate within the constraints of a single data domain, for example a CRM agent or an MES agent.

A leading example of domain specific agent is PTC’s Codebeamer Copilot. The Codebeamer Copilot supports software development process for complex physical products, like software-defined vehicles. Codebeamer Copilot leverages the Codebeamer data graph, for a connected and comprehensive view into the product development process. From requirements management to testing to release, the Copilot provides rapid insight into key areas of application lifecycle management (ALM). The result is automated requirements handling, enhanced quality control, and boosted productivity due to drastically reducing the time it takes for engineers to write and validate requirements.

Application Lifecycle management is just the beginning. The AI-powered digital thread provides agents with the combined knowledge of the entire manufacturing data estate, with multiple domains: removing their previous limitations confining them to one function.

Real-world applications of AI-powered digital threads

The era of AI and digital threads has arrived, and it’s delivering real value for the world’s leading manufacturers today.

Schaeffler

A manufacturer of precision mobility components faced a need to modernize data management, as its data previously took days to decode. Their goal was clear: find a scalable solution to uncover factory insights faster. An agent was implemented to allow frontline workers to immediately uncover detailed information when faced with unexpected downtime. This allows operators to get the line running again faster, reducing costly delays in production.

Bridgestone

The world’s largest tire and rubber company leverages manufacturing data solutions in Microsoft Fabric to accelerate the productivity of their frontline workforce. As a private preview customer, in collaboration with a Microsoft partner, the company uses digital thread and AI technology to address key production challenges, like yield loss. The query system solution enables frontline workers, with various levels of experience, to easily interact with their factory data, and efficiently uncover insights to improve yield, and enhance quality.

Toyota O-Beya

Toyota is leveraging AI agents to harness the collective wisdom of its engineers and accelerate innovation. At its headquarters in Toyota City, the company has developed a system named “O-Beya,” which means “big room” in Japanese. This system consists of generative AI agents that store and share internal expertise, enabling the rapid development of new vehicle models. The O-Beya system currently includes nine AI agents, such as the Vibration Agent and Fuel Consumption Agent, which collaborate to provide comprehensive answers to engineering queries. This initiative is particularly crucial as many senior engineers are retiring, and the AI agents help preserve and transfer their knowledge to the next generation. Built on Microsoft Azure OpenAI Service, the O-Beya system enhances efficiency and reduces development time.

The road ahead

The journey to fully realizing the potential of AI-powered digital threads involves phased implementation. Starting with identifying the right use cases aligned to business goals, where AI agents can play a role. Secondly, identify if the right data is available and in the right standards for usability. Lastly, quickly proving value by implementing a set of initial use cases with a minimum viable digital thread and measuring and socializing its results. Achieving the AI-powered digital thread with the Microsoft Cloud for Manufacturing capabilities:

• Azure adaptive cloud approach to source data from the edge, while supporting application modernization following cloud patterns.
• Partner applications as systems of records, like PTC Windchill.
• Microsoft first party manufacturing agents, like Factory Operations Agent in Azure AI Foundry, to unlock high-value factory use cases.
• Microsoft AI platforms like Azure AI Foundry and Microsoft Copilot Studio to support development and orchestration of custom AI agents.
• Partner applications with agentic AI capabilities embedded, for example PTC ServiceMax AI.

Learn more

• Read the PTC whitepaper on AI-powered digital threads authored in partnership with Microsoft.
• Watch the Microsoft video series with Forrester and NVIDIA on AI-powered digital threads.
• Join us at Hannover Messe 2025 to see the AI-powered digital thread come to life.

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