After decades of digitization—and years of rapid advances in AI—the industry now stands at the threshold of a far more profound shift: the rise of agentic AI. 

Unlike earlier forms of automation, agentic AI goes beyond task assistance. Intelligent AI agents can plan, reason, and act autonomously collaborating alongside clinicians, care teams, researchers, developers, and all workers from the back office to the front lines. When embedded into everyday workflows, agents transform intelligence from something accessed on demand into something continuously at work—embedding subject matter expertise with human ambition to achieve our highest aspirations.  

But if pervasive agentic intelligence is the destination, how far along is healthcare on the journey?

Measuring healthcare’s readiness for agentic AI 

To answer that question, Microsoft, in collaboration with The Health Management Academy, conducted original research published in the January 2026 issue of the New England Journal of Medicine. Based on surveys and in-depth interviews with senior healthcare executives across provider organizations in the United States, the research offers a grounded, reality-based view of how health systems are progressing along the agentic AI maturity curve—from early experimentation to enterprise level optimization. 

What the research reveals 

1. Agentic AI remains early—but strategic interest is rising
   While enthusiasm is growing, adoption remains nascent. 43% of respondents report piloting or testing agentic AI, yet only 3% have deployed agents in live workflows. At the same time, one-third of respondents indicate no plans to explore agentic AI within the next one to two years—highlighting the gap between experimentation and operational readiness. 
2. Confidence in long-term impact is strong
   Despite limited deployment today, belief in agentic AI’s future impact is clear. 60% of respondents agree or strongly agree that agentic AI will meaningfully improve or disrupt the provider–patient experience, with similar optimism around productivity gains (57%). Nearly half anticipate deeper human–AI collaboration within the next three to five years—reinforcing the view that agents will augment, not replace, clinical and operational roles. 
3. A catalyst for workforce, productivity, and experience transformation
   More than three quarters (77%) expect AI agents to improve backend productivity, while 60% believe they will fundamentally reshape the patient–provider experience. Yet this transformation will require change: 60% cite reskilling and upskilling as a top challenge as ecosystems of AI models and agents expand. 
4. A clear gap between belief and deployment
   Qualitative interviews reveal that leaders increasingly view agentic AI as a strategic end state—one that depends heavily on progress in workforce readiness, governance, and data infrastructure. Moving from promise to sustained value will require deliberate, coordinated investment across all three. 

Why this moment matters: A leadership imperative

The publication of this research marks a shift in the future of work. The question is no longer if agentic AI will reshape healthcare—but how intentionally health systems choose to shape that transformation. 

Healthcare has a rare window to define the role of agentic AI before patterns harden, and expectations are set. Success will be determined not by technology alone, but by how effectively organizations prepare their foundations and empower their people to work alongside digital colleagues in a hybrid workforce. 

Building strong governance frameworks, establishing a trusted data foundation, and developing an AI ready workforce are no longer optional—they are prerequisites for leadership in the organizations on the frontier of the next era of transformation. 

• Read the New England Journal of Medicine article.
• Read the full report: Agentic AI and the Next Frontier of Transformation.
• Explore how other healthcare organizations are already using AI to make tangible impacts.

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The insurance industry stands at a pivotal moment in its digital transformation journey. With deep data reserves, a tradition of analytic decision-making, and a workforce skilled in actuarial and underwriting practices, insurers are uniquely positioned to benefit from the ongoing advances in AI.

However, despite early enthusiasm and pilot projects, only 7% of insurers have successfully scaled AI initiatives across their organizations.¹ The adoption of increasingly powerful AI agents—systems that can support autonomous tasks, help make decisions, and take action under human oversight—offers a promising path forward. By embedding intelligent agents into workflows, insurers can tackle legacy constraints, talent shortages, and operational inefficiencies to unlock transformative value.

Challenges in adopting agentic AI

The broad adoption of agentic AI in insurance is hindered by several entrenched challenges.

First, a persistent talent shortage affects specialized roles such as actuarial analysis and underwriting, which limits the industry’s ability to leverage data effectively. Adding to the challenge is legacy infrastructure, including outdated systems and fragmented data architectures, which can impede integration and scalability.

Financial strain across the insurance sector is another major factor, with catastrophe losses exceeding $100 billion annually for six consecutive years, making high-frequency property losses a structural issue.²

Organizational resistance also plays a significant role; siloed teams, unclear priorities, and a conservative corporate culture slow the pace of AI adoption.

Opportunities with agentic AI

Despite these hurdles, agentic AI presents transformative opportunities. Workforce augmentation is one of the most promising areas. For instance, Sedgwick’s Sidekick Agent, developed in collaboration with Microsoft, enhances claims processing efficiency by more than 30% through real-time guidance and decision support.³

AI also enables personalized customer experiences at scale, using embedded systems to tailor communications and support. Operational efficiency can be improved significantly in some implementations, with end-to-end redesigns yielding 30–40% gains in net efficiency.¹

Furthermore, agentic AI, under human guidance, can enhance quality assurance by improving consistency and helping insurers adhere to compliance processes, which is especially important as seasoned professionals retire and institutional knowledge can be lost.

With agentic AI, chatbots can also be improved to more effectively enhance customer experience. Instead of answering a question and handing a customer off to a queue, an agentic solution can help orchestrate a more end-to-end experience. Potentially, this can include everything from capturing first notice of loss, to requesting missing documentation, updating policy and billing systems, scheduling appraisals, and proactively notifying customers of next steps (all subject to insurer workflows and regulatory review, of course).

This “resolve, not route” approach is already showing measurable impact in claims operations: For example, according to McKinsey, one major insurer rolled out more than 80 AI models in its claims domain, cutting complex-case liability assessment time by 23 days, improving routing accuracy by 30%, and reducing customer complaints by 65%.⁴

For carriers, such outcomes translate into faster cycle times, higher customer satisfaction, and better loss-adjustment expense control—all while preserving necessary human oversight where judgment, empathy, and regulatory accountability are required.

Strategies for success with agentic AI

To successfully adopt agentic AI, insurers must align technology initiatives with customer needs and business goals.

Establishing an AI Center of Excellence (CoE) is a foundational step. A CoE provides governance, strategic direction, and technical expertise, helping organizations avoid fragmented AI adoption and scale responsibly.

Iterative testing is also essential, beginning with high-volume, repeatable tasks that help insurers refine models through feedback loops and production pilots.

Targeting scarce talent areas, such as fraud detection and underwriting, can maximize impact by augmenting roles that are difficult to fill.

Industry accelerators like Cognizant’s Agent Foundry offer prebuilt tools and frameworks that can help reduce implementation time and support compliance efforts. This platform-agnostic solution supports the full lifecycle of agent deployment, from discovery to scale, and integrates seamlessly with existing enterprise systems.

Finally, cultural transformation is critical. Since 70% of scaling challenges are organizational, insurers must foster a culture of change, accountability, and long-term thinking to fully realize AI’s potential.¹

Move to agentic AI with confidence

Agentic AI is not just a technological upgrade, it is a strategic imperative for insurers seeking to thrive in a rapidly evolving landscape. By addressing structural challenges and embracing AI as a catalyst for transformation, insurers can unlock new levels of efficiency, personalization, and resilience.

The path forward requires bold leadership, cross-functional collaboration, and a commitment to continuous learning. Those who invest in scalable frameworks, such as AI Centers of Excellence and industry accelerators, will be best positioned to lead the next era of insurance innovation.

Explore solutions for agentic AI in insurance

• Explore insurance solutions and resources on Microsoft for Insurance.
• To learn about how Cognizant and Microsoft are partnering to help insurance companies advance their cloud modernization journeys, see the Cognizant and Microsoft Partner Page.

¹ Insurance leads AI adoption. It’s time to scale

² 2025 marks sixth year insured natural catastrophe losses exceed USD 100 billion, finds Swiss Re Institute

³ Sedgwick optimizes claim workflows with AI application Sidekick and Microsoft integration

⁴ The future of AI in the insurance industry

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Over the past year, Microsoft engineers have road-tested that combination with executives, technicians, customer support representatives, and developers. Regardless of geography or market, operators voiced three universal priorities: break down operational silos, unlock data’s latent value, increase efficiency, and accelerate innovation without eroding trust. At TM Forum DTW Ignite 2025 in Copenhagen, Microsoft is demonstrating how the complementary relationship between ODA and agentic AI converts those ambitions into measurable business outcomes. 

Microsoft’s next chapter with the Open Digital Architecture 

Microsoft has been a hands-on contributor to TM Forum initiatives for well over two decades, coauthoring Open APIs, chairing working groups, and donating production hardened code that turns standards into deployable solutions. The ODA has become a focal point of that collaboration. By aligning Microsoft Azure cloud-native foundations with ODA’s composable blueprint, Microsoft helps operators assemble best-of-breed solutions without the drag of proprietary silos. 

Engineering teams from Microsoft work with communications service providers (CSPs) and industry suppliers to validate specifications, publish reference implementations, and channel field experience back into the standard. The result for operators is faster interoperability, reduced integration cost, and quicker time-to-value for new digital services. 

Yet a common obstacle remains: fragmented observability. Every vendor captures telemetry differently, leaving operations teams to deploy ad hoc log aggregators and parsers that inflate costs and slow incident response. Microsoft’s latest ODA contribution addresses this head-on. 

• ODA Observability Operator (open source on GitHub)
  The operator prescribes a common logging contract, integrates with Azure Monitor, and exposes health data through TM Forum nonfunctional APIs. In early trials, carriers shrank the meantime to detect billing anomalies significantly, freeing teams to focus on proactive optimization rather than forensic log diving.
• ODA Landing Zone for Azure
  Guidance and a best practice guide on infrastructure-as-code templates that hydrate into an ODA compliant environment—policy, security, and monitoring.

The “Growing B2B with autonomous agents” catalyst project, involving players like Microsoft, Vodafone, and various industry partners, leverages the ODA Accelerator to transform B2B sales for mid-tier enterprise customers by enabling flexible quoting and commerce through generative AI. It enables flexible quoting and commerce, allowing customers to find relevant products using semantic search and create customized solutions that meet their specific business requirements, budgets, and timelines. 

These assets illustrate a simple truth: standards only matter when they migrate from documentation into running code. By operationalizing TM Forum guidance, Microsoft accelerates engineering productivity, slashes integration costs, and strengthens the capabilities of telecoms, as well as providing a feedback loop for continual improvement. 

Empowering network monetization through network APIs 

Through our engagement with CAMARA and GSMA Open Gateway, Microsoft has played a pivotal role in helping operators monetize their networks via a robust partner ecosystem. This ecosystem supports the provisioning, aggregation, and routing of network API requests, enabling seamless integration and enhanced functionality. Our collaboration with industry leaders such as Aduna, Infobip, and Vonage brings aggregated network APIs directly to the Azure Marketplace. This integration grants Microsoft’s global community of developers and enterprises effortless access to essential network functions, including SIM swap detection, phone number verification, real-time device location, and on-demand quality-of-service controls. Standardized through the CAMARA open-source project—co-led by GSMA and the Linux Foundation—these APIs are designed for seamless integration, ensuring that operators can efficiently use network capabilities to drive innovation and growth. 

Giving the network a trusted Copilot 

Anyone who has joined a major incident conference bridge understands the sense of urgency—and the expense. Multiple teams chase clues, minutes feel like hours, and every second of downtime erodes customer experience and brand equity. Network Operations Agents built with Azure AI Foundry offer another path to successful resolutions. As Cristina Moura Rebelo, Head of AI Community and Ecosystem Engagement at MEO, describes it: 

“MEO is transforming into an AI-powered techco, infusing AI into key domain areas and leveraging innovation and technology to create a competitive advantage, business growth, and operational excellency. The first steps made with Azure AI Foundry were key in unlocking the potential of use cases to streamline operations with ChatGOC and the HekaBot, in a scalable, iterative, and agile way, within a very short period of time, delivering outcomes and scaled efficiency to the teams. This is our path to becoming an AI-powered techco.”   

—Cristina Moura Rebelo, Head of AI Community and Ecosystem Engagement, MEO

These AI companions ingest real-time telemetry, topology graphs, historical tickets, and vendor manuals; reason over anomalies; then recommend—or even execute—remediation steps under strict guardrails. Every action is logged, policy checked, and auditable so that safety and compliance are part of the operational flow.

“At a time when pressure to grow has never been greater, data and AI are illuminating the path forward, helping telcos simultaneously achieve three critical goals of growth, efficiency, and security.”

—Praveen Shankar, Executive Vice President I Capgemini 

At TM Forum DTW Ignite 2025, Microsoft will be presenting on how we are transforming telecom operations with agentic AI, and unveiling the Network Operations Agent Framework, a reference architecture and working pilot environment that operators can explore hands-on. The package includes infrastructure-as-code templates, sample knowledgebase content, and step-by-step guidance for integrating Azure AI Foundry with existing telemetry pipelines. With these assets, communications service providers can progress from proof of concept to production in a matter of weeks—and do so with the assurance that every remediation action remains within corporate risk tolerance. 

Unifying data with the Telco Analytics POC Accelerator 

Data is the fuel for agentic AI, yet it often sits stranded across disparate clusters, data marts, and line-of-business applications. The Telco Analytics POC Accelerator removes that friction, deploying a domain specific data estate on Microsoft Fabric complete with service assurance, revenue management, and subscriber 360 schemas; lineage policies aligned to data mesh principles; and guidance to connect your backend data sources. 

Beyond core ingestion pipelines, the accelerator provides predefined tables for service assurance, revenue management, and subscriber 360, alongside sample queries and dashboards that surface quick wins. Built-in sample data allows developers to prototype AI workloads safely—accelerating experimentation while protecting customer privacy.

When operators gain control of their data estate, they monetize faster, govern better, and feed AI models richer context. Microsoft provides the launch pad.

“Fabric let us build on the familiarity, security, and scalability of Azure. It unites data flows, storage, analytics, and machine learning in a single experience.”

—Jerod Ridge, Director of Data Engineering, Lumen

This unified approach empowers operators to achieve real-time insights and smarter decisions, driving business growth and innovation.

Reimagining business support systems for an agentic world 

Business support systems (BSS) are the commercial nerve center of a telco, yet many still feel like 1990s ERP software: dense menus, arcane codes, and labor-intensive workflows. Microsoft’s agentic BSS proof of concept charts a different course. 

At its heart is Microsoft Copilot Studio, which leverages TM Forum Open APIs, the Model Context Protocol, and secure tool registration to let AI agents act on behalf of customer care reps. Consider an agent who says, “Upgrade Alessia’s plan to unlimited data and add a family hotspot.” The AI agent validates entitlements, calculates prorated charges, and triggers fulfilment—no swivel chair required. Subscribers upgrade in the time it takes to sip coffee.

Microsoft is equally optimistic about the potential of an Order Fallout Agent. Up to 3% of orders stall in fragmented fulfilment chains. The agent monitors the queue, diagnoses failure patterns, and either self heals or curates a guided fix. In short, the Order Fallout Agent turns a perennial pain point into an autonomous, closed loop process—freeing care agents to focus on higher value conversations and giving customers the seamless experience they expect.

KPN has extended the use of AI companions to their sales operations with Microsoft 365 Copilot. KPN used Microsoft 365 Copilot to enhance their sales operations, streamlining processes, improving customer engagement, and driving business outcomes.

“From the moment a customer contact becomes an opportunity, we link to that information in Microsoft 365 Copilot for Sales, so we can see all relevant data to prepare for a conversation with the customer,”

—Pierrette de Leeuw-Koumans, Lead Generation Team, KPN

Copilot provides real-time data analysis, predictive insights, and automated workflows, enabling the sales team to focus on strategic activities and deliver personalized experiences. 

These demonstrations illustrate how BSS complexity can melt away, replaced by conversational experiences powered by open APIs and trustworthy automation. The journey is incremental—operators can start with a single fallout queue or upgrade flow and expand outward. 

Momentum stretching from lab to live network 

Innovation without adoption is theatre. Microsoft’s ecosystem partners are translating blueprints into operational gains: 

• Microsoft and leading BSS suppliers are exploring joint proof of concepts that integrate the Telco Analytics POC Accelerator and Observability Operator into next generation revenue assurance workflows.
• PLDT has implemented the Amdocs Customer Engagement Platform, a robust, telco-grade solution jointly engineered by Amdocs and Microsoft elevate customer experience management. “By combining the AI, generative AI, cloud, and deep telecom expertise of Amdocs and Microsoft, PLDT an end-to-end solution that will drive higher agent productivity, operational efficiency, and significantly improve customer loyalty,” said Anthony Goonetilleke, Group President of Technology and Head of Strategy at Amdocs.
• Nokia’s NetGuard Cybersecurity Dome is providing comprehensive security for 5G networks, leveraging AI and automation to detect, manage, and respond to threats in real-time.
• Accenture, Capgemini, TCS, Tech Mahindra, and other global SIs are collaborating with Microsoft on service offerings that accelerate deployment of AI-ready data estates—combining migration expertise, reference architectures, and operator specific best practices. 

The breadth of deployments demonstrates that Microsoft’s approach scales across geographies, regulatory regimes, and network generations. 

Charting the first step 

Building toward autonomous operations seldom begins with a blank slate. The most effective starting point is a business moment that already matters—whether it’s easing congestion at a busy urban cell site or clearing a stubborn order backlog. Instrument that scenario end to end, unify the supporting data, introduce a focused agent, and track the results with discipline. Momentum builds quickly when measurable wins are visible to both engineers and executives. 

Microsoft and its partners stand ready to help, whether through co-innovation blueprints, rapid pilots leveraging the ODA Accelerator for Azure, or structured engagements that blend domain expertise with change management. 

Telecommunications remains, at its core, a human endeavor: engineers who safeguard critical infrastructure, customer care teams who build loyalty, strategists who spot the next market opportunity. Agentic AI amplifies that expertise—it automates repetitive analysis, highlights hidden insights, and executes well understood actions—while judgment, creativity, and empathy stay firmly in human hands. By pairing people with autonomous assistance, operators can scale excellence without sacrificing the personalized touch that defines great service. Microsoft invites the industry to explore that partnership at TM Forum DTW Ignite 2025 and beyond. 

Join the journey 

Learn more by visiting the Microsoft Telecommunications Industry hub, where solution briefs, customer stories, and partner offers provide actionable next steps. Together, the industry can turn aspiration into action and chart the next great wave of telecom innovation. 

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Industrial AI in Action 

Through 31 demos, 53 theater sessions, and three ancillary events, Microsoft highlighted how AI agents are helping manufacturers unlock new levels of productivity, resiliency, and growth. As the new interface to industrial data and operations, generative AI tools allow every worker—from the factory floor to the boardroom—to surface timely, relevant insights that drive decision-making. Test agents built with the power of Microsoft Copilot Studio for yourself. 

In the booth, Microsoft focused on the entire manufacturing value chain: advancing innovation in digital engineering with generative AI, preparing the factory edge for AI, AI agents supporting the development of frontline workers, and finally making intelligent digital threads a reality. Microsoft brought these four opportunities to life through four distinct neighborhoods filled with demos, partners, and customer stories. Highlights included collaborations with Rolls-Royce, Siemens, PTC, Sandvik, Husqvarna, Sight Machine, Sanctuary AI, SymphonyAI, Bridgestone, and Databricks. Microsoft’s Hannover presence garnered incredible media attention, notably several news channel interviews with Anges Heftberger, CEO, Microsoft Germany, and a visit from Roland Busch, CEO, Siemens AG. 

This year, Microsoft’s centerpiece displayed the Rolls-Royce transformation journey from design engineering through the factory to maintenance operations. 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. With help from Siemens and Microsoft, Rolls-Royce is now using AI to streamline production, boost engine efficiency, and predict maintenance needs before issues arise.

Making intelligent digital threads a reality 

Grounded in unified operational (OT), enterprise information (IT), and engineering (ET) data, digital threads connect every phase of manufacturing—delivering timely, actionable insights to every team, from design and production to maintenance and customer support. This continuous, connected flow of data enriches every stage of the manufacturing value chain. 

Without a strong data foundation, manufacturers will struggle to tap into the full potential of AI. Data quality, standardization, and integration are often inconsistent, making insights hard to access and trust. Microsoft Fabric is helping manufacturers overcome these barriers—turning fragmented data into intelligent digital threads that power better decisions, faster innovation, and operational excellence. Alongside Fabric and Microsoft Dynamics 365 demos, Microsoft partners AVEVA, Databricks, Kongsberg, and Parsec displayed how AI is influencing real-time production monitoring and predictive maintenance to fuel resilient, efficient, and sustainable manufacturing. 

Engineering with generative AI 

AI is disrupting design and engineering, unlocking new levels of innovation, speed, and creativity. With generative AI, manufacturers can now rapidly explore a wide range of possibilities, optimizing products for performance, manufacturability, and cost. Microsoft partners PTC, Sandvik, Schneider Electric, Eplan, Rescale, and NTT DATA demonstrated real-world applications of AI reshaping product development and lifecycle—from accelerated design iterations to predictive simulations. The result is higher-performing, more customer-centric products brought to market faster and more efficiently. 

Preparing the factory edge for AI 

AI is redefining factory operations. Manufacturers must integrate industrial edge solutions with the cloud to fully capitalize on their shop floor investments. The Microsoft Azure adaptive cloud approach captures data from industrial equipment assets and devices, normalizing it at the edge, sending insights to the cloud and back. Along with partners Accenture Avanade, Cognite, Litmus, Schneider Electric, Sight Machine, Rockwell, and Tulip, Microsoft showcased how AI at the edge is transforming real-time factory visibility and performance monitoring.  

Supporting frontline workers with AI agents 

AI transformation is reshaping every aspect of manufacturing operations. As the industry grapples with high turnover, upskilling the workforce has become a critical challenge. AI agents are now giving frontline workers real-time guidance to help them make faster, better-informed decisions. AI-powered agents are streamlining industrial environments, allowing operators, production teams, and facility managers to access insights and optimize processes through natural language interactions. By accelerating issue resolution and root cause analysis, the agent improves day-to-day productivity and operational resilience. In addition to Microsoft 365 Copilot and Microsoft Dynamics 365 Field Service demos, partners Sanctuary AI and SymphonyAI highlighted how AI and automation are redefining the future of frontline work. 

Driving AI leadership and industry innovation 

The Microsoft theater was busy this year. Moved in the booth, this space connected business leaders, innovators, and customers to the experts, creating a forum to discuss the unique challenges facing manufacturing and how AI and cloud technologies are helping address them. Here are a few highlights from the theater: 

• “Celebrating women in manufacturing” brought together influential female voices in manufacturing to explore their career journeys, achievements, challenges, and advice to inspire the next generation of talent. Thank you to panelists Elise Hersko, Sandra Anderstedt, and Monica Ugwi.  

• An Industrial AI leadership conversation between Roland Busch, Siemens CEO, and Uli Homann, Microsoft CVP of Cloud and AI, who shared their learnings on leading in AI. Both agreed that success depends on a trusted data ecosystem, responsible AI practices, and a commitment to scaling AI initiatives that start with the customer.  

• Microsoft Intelligent Manufacturing Award (MIMA) showcase, in partnership with Roland Berger, celebrated the winners of the MIMA, recognizing innovation in smart manufacturing across Europe, Middle East, and Africa. The 2025 winners included Continental, Diehl Metering, Philip Morris Manufacturing & Technology, ZEISS Digital Innovation, plus Cereal Docks and MIPU.  

Unlock new possibilities with Microsoft 

Thank you to the customers, partners, and the thousands of attendees who engaged with the Microsoft booth throughout the week. We’re looking forward to HANNOVER MESSE 2026. 

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With a growing demand for housing in this municipality of 200,000 people, the City wanted to ease the burden of obtaining a building permit. As with many government agencies, applications and inquiries used to be handled primarily in person. In 2024, however, the City decided to improve the cumbersome process with technology.  

Using Microsoft Copilot Studio and related cloud technologies, the City created a custom Copilot that reduced the permit approval process from 15 weeks to 5 to 7 weeks—“A giant leap forward,” according to Community Planning Director Jamie Tellier. Critically, with the help of low-code development, the copilot was conceptualized and deployed in only eight weeks.  

It’s just one improvement in a single government agency, but the City of Burlington story reflects a significant global trend. In the year or so since Microsoft’s core generative AI and Copilot offerings arrived broadly in the marketplace, first with Microsoft 365 Copilot and subsequently with developer tools, industry-specific solutions, and autonomous AI agents, the broad impact on governments is becoming clearer.  

At Microsoft for Government, making the most of cloud and AI is central to our focus on helping government agencies and organizations to solve some of society’s biggest challenges. As we work with government customers on a broad array of challenges and solutions, we continue to be amazed at the expanding impact of AI and modern cloud technologies, which are delivering far more than just efficiency gains. 

How cloud and AI are broadly transforming governments 

When it comes to operations and IT, governments worldwide face a set of uniquely difficult challenges. The community expects them to deliver a quality of service and user experience that matches what they get from the private sector. At the same time, governments face specific demands around compliance and security that most other sectors do not. Factor in shrinking budgets, aging workforces, and legacy on-premises systems that add cost and risk, and governments struggle to hold the line, much less to innovate.  

However, the advent of AI and complementary cloud solutions can offer help and advance both cost savings and innovation. For example, Microsoft 365 Copilot played a key role in the successful modernization of communications systems in the UK Home Office.  

A critical government department responsible for national security and public safety, UK Home Office urgently needed to modernize outdated systems to continue meeting national security and public safety demands. Working with Microsoft and technology partners Colt and Netcompany, they were able to drive a smooth migration of 63,000 users in just eight working days and minimized disruption to essential services.  

AI-powered support from Copilot played a crucial role in optimizing workflows, summarizing meetings, generating follow-up tasks, and offering real-time insights. The cost-savings it delivered also allowed the department to allocate resources to more strategic areas, reinforcing its commitment to delivering exceptional value for the public. 

As we look across government customers worldwide, we see three key areas in which cloud and AI are delivering new benefits:

1. Increase productivity and save time with personal assistants
   Productivity benefits are central to the value delivered by agents and AI. The core capabilities of Microsoft 365 Copilot are uniquely attuned to help address the frustrations surrounding repetitive tasks, serving effectively as tireless personal assistants.
   
   Interestingly, while 49% of professionals surveyed by Microsoft said they worry about AI replacing their jobs, 70% said they’d like to lessen their workloads by delegating as much of their work as possible to AI.¹
   
   A good case in point is the Torfaen County Borough Council in Wales, which is using Copilot to help respond to growing service demands even as budgets were reduced. Copilot’s seamless integration with everyday applications like Microsoft Word, Excel, and Teams meant that workflows were not interrupted. Employees then saw significant time savings in things like minute-taking tasks and summary reports. As Chief Executive Stephen Vickers put it, “It’s saving time and it’s delivering a better end product.”
   
   Elsewhere in the United Kingdom, the Buckinghamshire Council in England implemented Copilot to improve productivity and staff wellbeing across selected operations. Employees reported 10 to 20% time savings on tasks such as transcribing meetings, creating reports, drafting emails, and handling customer inquiries. Project managers were able to take on more projects due to an average time savings of 30 hours per month. And customer service workers focused more on providing better assistance. As one put it, “With Copilot transcribing, I can focus completely on what the customer is saying, rather than worrying ‘Did I take that down right?’”
   
   Likewise, in early 2024, the Dubai Electricity and Water Authority (DEWA) introduced Copilot as part of a modernization effort to revolutionize utility services. Internal operations were streamlined dramatically, as processes that had previously taken days, such as research and document drafting, were completed in mere hours. Critically, customer happiness remained consistent at a 98% rating, as internal efficiency soared.  
2. Automate government operations and reduce costs
   An additional category of Copilot benefits is the ability to reduce costs by automating operations and delivering insights and data visualizations that help people make informed decisions quickly. 
   
   Copilot can be integrated into many business systems, including customer relationship management (CRM) and contact center solutions, to provide contextual, AI-powered responses. This means that whether an agency is using Dynamics 365 or another CRM solution, Copilot can seamlessly connect to those systems and enhance existing workflows.
   
   In the United Kingdom, the Driver and Vehicle Standards Agency (DVSA) is evaluating Copilot as an expansion of a highly successful effort to bring its nationwide driving test system in house after decades of outsourcing it. The new solution, integrated with Microsoft Dynamics 365, has improved customer satisfaction rates from 80% to 96%, while saving a projected £15 million within five years.
   
   The agency’s deep investment in the Microsoft platform positions them to readily innovate with generative AI in ways that promise to, for example, power a data-driven approach to understanding drivers and the use of roads. “It’s still early days,” said Digital Operations head Alex Fiddes, “But I think this will help the DVSA respond at a far more rapid pace than it’s done in the previous three decades.”
   
   Copilots can also orchestrate complex, long-running processes with more autonomy and less human intervention. Microsoft Copilot Studio offers a subset of capabilities that allow for deep customization, which lets organizations tailor Copilot to their specific business needs without the need for costly development time or extensive modifications.  
3. Protect your data with secure and compliant enterprise-ready AI
   Security is obviously paramount for government organizations, which are not only among the most attacked sectors in the world but are often the most stressed due to staffing shortages and budget constraints. The good news is that Microsoft Security Copilot offers a powerful way for governments to make dramatic improvements in cybersecurity.
   
   Security Copilot is the first generative AI security product to combine the most advanced AI models with a Microsoft-developed security model. It is powered by Microsoft Security’s unique expertise, global threat intelligence, and comprehensive security products. This helps governments maintain a secure and compliant approach to security and privacy, it applies data classification labels to make sure the right people have access to the right data, and it helps protect unauthorized access with data loss prevention (DLP) strategies.
   
   At Oregon State University, Security Copilot is playing a central role in protecting vital research and sensitive data, including the personal information of students and faculty. After experiencing a major cybersecurity incident in 2021, the university created a new Security Operations Center (SOC) that integrates Microsoft Security solutions, including Microsoft Sentinel and Microsoft Defender. Security Copilot is used for essential tasks to help the security team assess and respond quickly to cyberthreats.
   
   In particular, Security Copilot holds promise in automating processes and addressing vulnerabilities, according to SOC Manager Emily Longman.

“Copilot for Security will boost our automation capabilities and help our analysts—who are college students—learn how to quickly write more Kusto Query Language (KQL), such as threat hunting with more advanced hunting queries, and more workbooks.”

Emily Longman, Security Operations Center Manager, Oregon State University

Our commitment to security above all 

As promising as AI innovation is, we recognize that progress will always depend on world-class security to help ensure safety, privacy, and regulatory compliance. Since Satya Nadella made security Microsoft’s top priority in May 2024, Microsoft has dedicated the equivalent of 34,000 engineers to advance the objectives laid out in the Secure Future Initiative (SFI), a framework that provides a structured, comprehensive approach for enhanced cybersecurity across all Microsoft products and services. 

For governments, this commitment means that agencies and organizations can innovate with confidence in Microsoft advanced cybersecurity measures, compliance support, and risk management tools.  

Learn more 

To help your government organization take the next step in your cloud and AI journey, contact your local Microsoft representative or certified technology partner. They can help explore options, identify use cases, and transform your ideas into meaningful solutions.  

• Visit the Microsoft for Government website and read more of our blogs.
• To explore interactive demos showing how Microsoft 365 Copilot can deliver powerful experiences across many government scenarios, visit Microsoft solutions for government.
• To explore resources to ease deployment and adoption of Microsoft 365 Copilot check out these enablement resources.
• To help users get the most out of Microsoft 365 Copilot, including help with effective prompts, visit the Microsoft 365 Copilot Skilling Center.

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¹ Work Trend Index Annual Report, Microsoft. 

The post The face of government service delivery is changing as AI and cloud capabilities continue to expand appeared first on Microsoft Industry Blogs.

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.

Learn more

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?

The post More human-centered retail with AI appeared first on Microsoft Industry Blogs.

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 Industry 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 Industry 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.

Microsoft Cloud for Retail

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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.

The post AI-powered retail: 3 reasons to start digitalizing your warehouse in 2025 appeared first on Microsoft Industry Blogs.

Technology integration opportunities in sports 

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

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

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

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

Data-driven decision-making 

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

Billie Jean King Cup: Transforming tennis strategy with AI 

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

Key highlights include: 

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

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

NFL: Game-changing technology on the sidelines 

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

Key highlights include: 

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

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

BWT Alpine Formula One Team: Data-powered racing innovation 

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

Key highlights include: 

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

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

Integrated fan engagement 

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

LALIGA: Enhancing fan engagement with data-driven insights 

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

Key highlights include: 

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

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

NBA: Building a next-generation fan engagement platform 

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

Key highlights include:  

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

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

Transforming the sports industry

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

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

Microsoft’s commitment to the media and entertainment industry  

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

Next steps 

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

Microsoft at the 2025 NAB Show

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

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