Exactly one year ago, we outlined how generative AI is creating a new era of efficiency and innovation for logistics and supply chain. We mapped AI use cases across the value chain, from demand forecasting to AI-based customer service, and introduced two new reference architectures for logistics and supply chains: adaptive cloud and AI‑enhanced experiences, alongside innovations in Microsoft Dynamics 365.

Since then, technology has rapidly evolved. We are now in the agentic era of AI with agents being capable of reasoning, planning, and taking action across complex supply chain workflows. End-to-end agent hosting like in Microsoft Foundry and open protocols such as Model Context Protocol (MCP) have made it easier for AI agents to connect with each other as well as enterprise systems, tools, and data.

Additionally, there have been significant advances in 3D simulations, robotics, and embodied intelligence. Open platforms for physical AI like NVIDIA Cosmos with world foundation models (WFMs) as well as the OSMO edge-to-cloud compute framework on Azure enable machines and humanoid robots to act more effectively in the physical world, resulting in broader automation across warehouses, distribution centers, and transportation. This new article picks up Microsoft’s perspective on supply chain and logistics one year after our previous blog article and explores how our own logistics teams as well as frontier customers and partners use this new wave of innovations together with Microsoft.

Microsoft supply chains: Our own “customer zero” story

Microsoft operates one of the world’s most far-reaching cloud supply chains spanning more than 70 Azure regions, over 400 datacenters, and a network of more than 600,000 km of fiber. Our datacenters are the backbone of Microsoft Azure powering everything from AI infrastructure and collaboration tools to networking and security. Microsoft also runs supply chains for Microsoft Windows and Devices with Surface hardware and PC accessories as well as Xbox consoles and gaming hardware.

All of our supply chains have undergone a fundamental transformation over the past decade, evolving from a reactive, manual environment into a rapidly emerging autonomous, agentic supply chain. In the past, our operations were dominated by Excel-based reporting, limited visibility, and siloed data. In 2018, we consolidated more than 30 systems into a single supply chain supply chain data lake on Azure, enabling predictive analytics and the first generation of cognitive supply chain capabilities. In 2022, we began experimenting with generative AI, followed by the development of an AI platform to operationalize agents at scale. Today, this foundation is accelerating to fully autonomous agents, and more than 25 AI agents and applications have been deployed. Below are three examples:

• The Demand Planning Agent drives AI‑based demand–simulations for non‑IT rack components—improving forecast accuracy and reducing manual reconciliation.
• The Multi‑Agent DC Spare‑Part Space Solver uses computer‑vision‑driven monitoring and multi‑agent reasoning to forecast spare‑part storage needs and proactively mitigates space or stockout risks.
• The CargoPilot Agent continuously analyses transport modes, routes, cost structures, carbon impact, and cycle times—providing optimized shipment recommendations that balance speed, sustainability, and efficiency.

The goal is to operate over 100 agents by the end of 2026 and equip every employee with agentic support. The impact today is already huge: AI in logistics is saving our teams hundreds of hours each month demonstrating how agentic operations are translating directly into efficiency and business value. Both in our own Microsoft supply chain transformation and Frontier customers we work with, we have seen that unifying the data estate is key. Yet, it’s what organizations do next that truly generates value with AI.

In supply chain, we believe real value gets unlocked by driving three elements:

• Enabling AI-powered supply chain simulations.
• Building agentic supply chains.
• Integrating first physical AI innovations.

Simulations: The digital twins of supply chains

As supply chains become larger, more interconnected, and more exposed to global volatility, simulating scenarios before they unfold is becoming a critical capability to reduce risk and increase resilience. Discrete event-based simulations (DES) within supply chains enable the development of a virtual risk-free model to test how a complex system reacts to interventions and variables before implementation. With Microsoft’s advanced modelling tools such as Azure Machine Learning and the new machine learning model in Microsoft Fabric with Power BI semantic models, organizations in supply chain and logistics can simulate demand patterns, shortages, or supply chain disruptions.

Our partner paiqo offers with prognotix an AI-powered Forecasting Platform available on the Microsoft Marketplace. More than 70 algorithms enable supply chain experts to generate and optimize highly accurate demand forecasts directly within their Azure environment. Cosmo Tech offers an AI simulation platform for Advanced Supply Chain Risk Management on Azure, offering enterprise customers dynamic digital twins that simulate how disruptions and decisions impact system-wide performance. InstaDeep uses Azure in high-performance compute for AI-enabling deep reinforcement learning and predictive analytics that optimize last-mile delivery, inventory levels, and fleet utilization.

The next level of simulation combines multiple physical simulations in 3D environments and discrete event-based simulations to enable teams to build comprehensive digital twins of warehouses, distribution centers, production lines, and logistics networks. These virtual environments allow organizations to model both the physical behavior of assets and the dynamic flow of operations. By integrating these simulation methods within a digital twin and applying AI, teams can predict future outcomes, optimize performance, and prescribe actions that drive continuous operational improvements. This can help customers lower capital expenditure, shorten commissioning, and ramp up phases, as well as improve operational key performance indicators (KPIs).

Taking warehouses as an example, customers and partners can build advanced, AI-enabled 3D visualizations for four key scenarios:

• Warehouse planning (such as greenfield and brownfield).
• Warehouse monitoring (like real-time monitoring and people movement heatmaps).
• Warehouse improvement (for example trailer dwell time optimization and collision detection for safety and automation).
• Warehouse maintenance (like asset monitoring in real-time, detect quality issues, and reduce rework).

In collaboration with NVIDIA we offer access to NVIDIA libraries and frameworks including NVIDIA Omniverse™, NVIDIA Isaac Sim™, and NVIDIA Omniverse Kit App Streaming that enable developers to build applications and workflows to simulate and test intelligent machines in digital twins before building or deploying anything in the real world. Applications built on these libraries and frameworks allow developers seamlessly integrate geometry data (such as 2D, 3D, and point clouds), AI capabilities (for example large language models, Volume Shadow Copy Service (VSS), and Solvers), and Internet of Things (IoT) signals across operational technology (OT) environments.

The reference architecture below illustrates how to combine cloud and edge computing using NVIDIA Omniverse Kit App Streaming to visualize warehouse operations in real-time with graphics processing unit (GPU) accelerated Kubernetes clusters natively deployed on Azure to remotely monitor, analyze, and optimize warehouse performance with greater precision and situational awareness.

Inside the physical warehouse, operational data from robotic arms, conveyors, and warehouse sensors are captured on the edge using Azure IoT Operations running on Arc-enabled Kubernetes and using MQTT broker. The architecture adopts the Universal Scene Description format (OpenUSD) to ensure that 2D, 3D, and point cloud geometry from the warehouse can be seamlessly integrated into the digital twin. Microsoft Fabric takes up the data in the cloud to provide a unified analytics foundation. Eventstream and eventhouse capture incoming telemetry as real-time streams or batch data. Microsoft OneLake acts as the governed, centralized data lake that consolidates all warehouse data. Digital twin builder transforms raw IoT signals into a contextualized virtual representation by mapping telemetry to the warehouse’s digital model. Powered by NVIDIA Omniverse, high-fidelity simulation and spatial computing occur creating a real digital twin which is streamed directly to the browser—eliminating the need for high-end local hardware. Tools such as Microsoft Copilot Studio and Microsoft Foundry enable natural language interaction. Across all stages, security is maintained through Azure Arc, ensuring consistent governance, configuration, and policy enforcement across edge and cloud.

SoftServe has proven to be an excellent delivery partner for digital twin applications. Together with Microsoft, they seamlessly integrated AI agents built on NVIDIA libraries and open models into beverage production simulations at Krones, enabling physical-accurate digital twins that reduced cycle times from hours to under five minutes. Similarly, at Toyota Material Handling Europe, SoftServe built a digital twin for simulating autonomous forklifts in virtual warehouse environments, enabling rapid testing, optimization, and safer deployments, helping to reduce the training times of autonomous systems by more than 30%.

TeamViewer’s augmented reality platform Frontline provides an additional simulation angle. Wearables such as smart glasses or wrist-mounted devices bring data seamlessly to frontline workers to get guidance in a hands-free manner for picking and packing as well as AI‑assisted counting. At DHL Supply Chain, TeamViewer’s solution is deployed globally to support vision picking of over 1,500 workers across 25 United States sites with fully hands‑free processes.

Agentic supply chains: The multi-agentic web

Agentic supply chains mark a new era of autonomous AI systems that proactively manage and optimize end-to-end supply chain operations. These agentic systems aim to continuously improve overarching KPIs like operating margin or cash conversion as well as specific KPIs such as lead time or freight cost per unit, ensuring that every agentic action contributes to measurable business impact.

Agentic supply chains are built on today’s human-driven tasks and encode the underlying decision-making logic. They include single purpose agents such as “troubleshooters” that constantly diagnose issues and propose fixes as well as “orchestrator agents” like planners or organizers that coordinate multistep workflows. These agents become functional through modern data fabrics, robust systems of record, and event-driven architectures that provide real-time information and governance.

Below is an overview of supply chain agents we have identified along the value chain through multiple customer and partner discussions.

Frontier Firms have already created value with multi-agentic systems.

• CSX Transportation has deployed a multiagent system that validates customer eligibility, routes complex requests, and supports rail operations with multistage coordination.
• Dow Chemical operates invoice analysis agents that review thousands of freight invoices each day, automatically detecting discrepancies and saving the company millions across its global shipping network.
• C.H. Robinson has rolled out a large fleet of generative AI agents including fast quoting agents that deliver tailored freight quotes and automating key steps along the shipping lifecycle.
• Blue Yonder has created an off-the-shelve Inventory Ops Agent on the Microsoft Marketplace that identifies supply–demand mismatches in real-time and recommends corrective actions such as alternate sourcing or demand swaps to keep inventory levels optimized.
• Resilinc offers an agentic supplier risk platform on Azure with pre-built AI agents (like for disruption, tariffs, and compliance) that autonomously evaluate potential impacts, initiate supplier engagement and recommend mitigation strategies.
• o9’s Digital Brain platform on Azure has been enhanced with various AI agents taking over simple tasks like getting specific data and more complex like creating full demand reviews.
• GEP recently added to their source-to-pay GEP SMART and supply chain solution GEP NEXXE (both built natively on Azure), a portfolio of AI agents that cover sourcing, negotiation, contract lifecycle, spend analysis, and market intelligence.
• Kinaxis offers its Maestro supply chain planning platform including AI agents that sense disruptions, run scenario simulations, and provide prescriptive insights through natural language.

Additionally, several delivery partners have used Microsoft tools like Microsoft Foundry and Copilot Studio to build agents for customers at high speed.

• Avanade, for example, offers concrete off-the-shelf agents including a trade compliance agent, a digital twin agent, and lead-2-cash agents.
• Softserve offers a catalyst with 3 customized agents in 30 days.
• NTT DATA is developing a simulation-driven, agentic AI-powered decision-support system for supply chain network agentic rebalancing platform.
• PwC delivers end-to-end agentic AI consulting services, powered by distinctive industry expertise.
• Capgemini is currently building an agentic end-to-end offering using the latest Microsoft IQ technology to be released at Hannover Messe in April 2026.

Microsoft Work IQ, Foundry IQ and Fabric IQ together form an intelligence layer for supply chains—from demand planning to inventory and customer service—that connects how people work, how the business operates, and what the organization knows. This gives AI agents full enterprise context so that agents can reason, simulate scenarios, and act in line with real-world constraints and KPIs such as inventory turnover to support better decisions.

Together with our strategic partner Celonis we have developed a new reference architecture leveraging Fabric IQ and the Celonis Process Intelligence Graph to transform fragmented supply chain data into agentic workflows. A collaborative stack that integrates raw data at the bottom and creates intelligent, automated actions at the top.

On the System of Record (SoR) layer, data is often siloed and does not “speak the same language,” leading to a fragmented understanding within the supply chain. Microsoft Fabric unifies this data through mirroring, streaming, or multi-cloud shortcuts with the goal to create a zero-copy connection and ensure the data is fresh and accessible without the weight of traditional extract, transform, and load (ETL) processes. Fabric IQ provides a reasoning layer that translates raw, unified data in OneLake into context-aware insights. This is the basis for Celonis’ Process Intelligence (PI) Graph which sits between data and the automation and uses process mining to map out how the supply chain actually runs—generating operational supply chain insights and suggesting improvement potentials from a process point of view. It communicates with Microsoft Fabric through Rest APIs, providing the knowledge and context that AI needs to make sense of the data. The agentic layer is divided into three functions:

• Building: Use Copilot Studio, Microsoft Foundry, and Power Automate to create custom AI agents.
• Orchestrating: Use MCP and Agent2Agent Protocol (A2A) to manage how different agents work together.
• Overseeing: Use the control plane for agents in Agent 365 (generally available in May 2026) to monitor agent activities and ensure performance as expected.

On the top layer, with the help of Microsoft Entra ID, insights and suggested actions are shown in tools employees use, such as Microsoft Teams, Microsoft 365 Copilot, Dynamics 365, Power Apps or in the Celonis UI.

A large global pharmaceutical company is using the above architecture to unify fragmented logistics data, enabling real-time identification of temperature-critical pharmaceutical returns and designing an agentic return process that unlocks multi-million euro annual productivity gains. Uniper automated material and service needs with Celonis and Microsoft. Microsoft Copilot in Teams and Power Automate orchestrate approvals, SAP actions, and replace manual component planning with proactive, agentic workflows that ensure timely material availability.

Physical AI: From warehouse handling to last mile deliveries

Physical AI is the final evolution of supply chain intelligence, building on simulations and agentic AI and embodying that intelligence directly in the physical world. In the near future, humanoid robots and robotic systems will physically take over more and more operational tasks along supply chains and logistics: from trailer unloading and sorting, pallet handling and replenishment, to packing and labelling and autonomous last‑mile deliveries. As intelligence moves from screens into machines, supply chains and logistics may gain a new level of physical agility.

Microsoft is pushing the frontier of physical AI with it’s new Rho‑alpha robotics model that combines natural language, visual perception, and tactile feedback to make robots more adaptive and autonomous. Microsoft has launched an early access research program with selected partners to advance co‑training and domain adaptation and aims to integrate the model in Microsoft Foundry in the coming months. Already today, customers and partners may take the below robotics toolchain reference architecture to train and deploy warehouse robotics with NVIDIA Osmo on Azure.

This toolchain is an open-source, production-ready framework that integrates Azure cloud services with NVIDIA’s physical AI stack, from simulation to training and deployment. It combines Azure Machine Learning, Azure Kubernetes Services (AKS), Microsoft Fabric, Azure Arc, and NVIDIA’s robotics and AI stack. NVIDIA Isaac Sim and Isaac Lab enable high-fidelity simulation and reinforcement learning, while NVIDIA OSMO orchestrates scalable training workflows across cloud and edge environments.

Detailed information can be found here.

Hexagon Robotics has started to deploy this architecture using Azure IoT Operations as well as Fabric Real-Time Intelligence in Microsoft Fabric to provide production-ready humanoid robotic solutions. Their industrial humanoid robot, AEON, combines dexterity, locomotion, and unique spatial intelligence to tackle complex industrial use cases for warehousing and logistics such as inspection and inventory taking.

Figure AI, funded by Microsoft, enables the deployment of their humanoid robots in real-world logistics environments using Azure’s AI infrastructure. Their latest model Figure 03 can take over warehouse tasks such as sorting packages at conveyor belt speeds and help at last-mile delivery with near human-level precision.

KUKA and Microsoft jointly developed iiQWorks.Copilot, an AI-powered assistant that enables natural language robot programming and significantly simplifies automation tasks. By integrating Azure AI services, the solution allows users to design, test, and deploy robot workflows faster and more safely—cutting programming time for simple tasks by up to 80%. This has benefitted all KUKA robotics deployed in warehouses and logistics.

Wandelbots’ NOVA software layer combined with Azure cloud services unifies heterogeneous robots and brings adaptive automation to the shop floor. Wandelbots NOVA streamlines warehouse and fulfillment operations such as palletizing by simplifying robot programming, accelerating deployment, and enabling AI-powered path planning and scaling across multiple robot brands. Together, these capabilities position Wandelbots NOVA as a physical AI platform for orchestrating and scaling AI-powered automation across supply chain operations.

Get in touch with us

Contact us directly at screquests@microsoft.com or go to Microsoft for Manufacturing to explore how Microsoft technologies can transform your supply chain. Join us at Hannover Messe in April 2026 to hear directly from our industry leaders, explore cutting-edge ideas, and connect with peers.

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Manufacturing is moving beyond local optimization toward a closed loop of end-to-end intelligent orchestration. Looking back at CES 2026, we can see that the industry narrative is quiet but fundamentally shifting. 

Across what we’re seeing with customers globally, three shifts stand out. First, the system shift. The operational foundation is evolving from digital to intelligent: more resilient, more real-time, and critically, more governable. Second, the data shift. The digital thread is no longer a static archive. It is becoming a living system—continuously updated and directly powering decisions as conditions change. Third, the work shift. We’re moving from copilots that assist individuals to agents that can collaborate and take on tasks—so the workflows themselves become more self-driving.

Together, these forces are raising the bar. Companies now need an end-to-end intelligent chain that turns AI from isolated point solutions into an organizational capability—reusable, scalable, and auditable. Drawing on Microsoft’s long-term work with manufacturers worldwide, and on how technology is evolving, I’d like to offer a practical framework for building that intelligent chain—so leaders can convert insight into action, and pilots into capabilities that scale.

AI use-case map for manufacturing: End-to-end intelligence from design to service

Scene One: Digital Engineering: Turning R&D into a profit engine

The role of the digital thread is evolving. Traditionally, it served primarily as a system of record—aggregating and archiving data. With AI and a unified data platform, it is becoming a real-time decision backbone spanning design, manufacturing, and service. Knowledge generated at one stage can now be applied immediately to improve outcomes in another. Generative and agentic AI are accelerating the core engineering loop—design, simulation, manufacturability analysis, and engineering change management—shortening iteration cycles and pushing risk discovery earlier in the process. Engineering data is no longer an R&D-only asset; it increasingly informs factory scheduling, quality strategies, maintenance policies, and service feedback loops.

This shift is already visible in practice. HARTING, a leader in industrial connectors, has deployed an AI assistant powered by Azure OpenAI and Microsoft Cloud for Manufacturing, making connector design faster, simpler, and more intuitive than ever before. Customers can describe their requirements in natural language, and the AI translates these inputs into technical specifications, guiding them to the right product within a minute. Customers can also visualize their configurations in 3D, enhancing confidence in their decisions. Siemens DI provides comprehensive cutting-edge software, hardware, and product lifecycle management solutions for industries including automotive and aerospace.

Using Microsoft Azure AI, Siemens DI developed a Microsoft Teams application for its industry-leading product lifecycle management (PLM) solution, Teamcenter. This solution analyzes unstructured voice content in multiple languages, automatically generates summary reports, and delivers information precisely to the relevant design, engineering, or manufacturing experts within Teamcenter. Through this intelligent collaboration mechanism, field issues are resolved faster, and knowledge transfer efficiency is significantly enhanced.

Scene Two: Intelligent Factory: AI is rewriting scheduling, quality, and maintenance

Production, maintenance, quality, and inventory remain the four core modules of factory operations—and that does not change in a smart‑factory context. What is changing is how these modules run. AI is systematically reshaping their operating logic: inventory management is moving from static rules to dynamic optimization based on real-time signals; quality management is shifting toward earlier, more precise judgments through computer vision, time‑series forecasting, and anomaly detection; and maintenance is evolving from after‑the‑fact repairs to predictive maintenance—progressing further toward adaptive process control.

As OT and IT capabilities mature, factories are gaining the ability to reason and respond directly at the point of value creation—on the shop floor, in real time. Frontline teams, empowered by multimodal Microsoft Copilot, can push the boundaries of what they can diagnose, decide, and execute. Over time, this human‑machine collaboration forms operational “agents” that can be deployed into production lines and day‑to‑day routines—turning intelligence into repeatable execution.

Global candy maker Mars operates manufacturing facilities across 124 locations worldwide. To safeguard its global equipment network, Mars partnered with Microsoft to deploy the Microsoft Defender for IoT solution. This enables visual management and threat detection for industrial equipment within stringent air-gapped production environments. Simultaneously, the solution transmits critical security data to a centralized system, enhancing data visibility while ensuring production continuity. International technology group Körber has transformed its market-leading PAS-X MES product into a cloud-based software as a service (SaaS) solution to address the stringent and multifaceted production management demands of the pharmaceutical sector. Using the robust stability of Microsoft Azure, Microsoft for Manufacturing, and Microsoft Azure Kubernetes Service, this solution enables customers to achieve greater flexibility and scalability. Simultaneously, by integrating data from IT and OT systems such as enterprise resource planning (ERP), supply chain management (SCM), and manufacturing execution system (MES), it delivers near real-time, actionable insights from diverse systems to employees. This significantly enhances equipment uptime, employee productivity, product quality, and overall output.

Scene Three: Resilient supply chain: From insight to execution with agentic AI

Early AI in supply chains mostly provided forecasts and dashboards. Valuable as they were, humans still needed to translate insights into action. The next step is agentic AI that executes—coordinating with suppliers, triggering replenishment or re-planning, optimizing inventory, and managing exceptions in logistics. When this happens, the traditional plan–execute–feedback loop transforms into a continuous intelligent system. The result is more than improved service levels—it enhances structural resilience and sustainability, as the system senses disruptions earlier, acts faster, and learns continuously.

A China-based electronics manufacturer, Xiaomi has built a unified after-sales supply chain management platform based on Microsoft Dynamics 365 and Microsoft Power Platform, using Azure for system integration and multilingual support. Utilizing Dynamics 365 Customer Service, Xiaomi has created a work platform that integrates financial processes, data integration, and security authentication across multiple communication channels. This platform also visualizes current inventory and proactively monitors and manages inventory levels in real time, enabling collaborative management between frontline services and backend supply chains. As a global leader in the smart terminal and home electronics industry, TCL is reshaping the industry landscape with its “Hardware + AI + Ecosystem” strategy, building a full-scenario ecosystem spanning multiple devices. Beyond driving innovative applications of Azure cloud and AI technologies in manufacturing, supply chains, and user experiences, TCL has pioneered the integration of Azure OpenAI, multimodal interaction, the intelligent Microsoft Copilot® assistant, and the Artificial Intelligence Generated Content (AIGC) ecosystem into smart TVs, smartphones, tablets, air conditioners, and other home appliances. This enables seamless cross-device connectivity and immersive experiences.

Scene Four: Connected customer: The product doesn’t end at delivery

In an AI-native model, product delivery is no longer the finish line. Customer experience continues through Over-the-Air (OTA) updates, AI-guided diagnostics, predictive service, and personal recommendations. AI enables a true closed loop—from customer feedback to engineering, factory, service, and back—turning experience into a growth driver rather than a cost center. None of this can scale without trust. As AI moves from recommendation to execution, governance becomes essential. Organizations need model governance, data and access control, OT and endpoint security, and explainability with rollback capabilities. This layer underpins all use cases, ensuring AI operates safely and reliably.

Epiroc, a Swedish mining and infrastructure equipment manufacturer, uses Microsoft Azure Machine Learning to build predictive maintenance and equipment performance models, transforming machine data into actionable customer insights. By identifying potential failures in advance and optimizing maintenance planning, Epiroc delivers a more proactive and transparent service experience, deepening customer relationships while opening new service-driven growth opportunities. Lenovo partnered with Microsoft to deploy the Microsoft Dynamics 365 Sales platform, thereby transforming its global customer relationship management (CRM) system.

By consolidating fragmented customer data and standardizing sales processes onto a unified digital platform, Lenovo achieved end-to-end visibility from lead management to opportunity tracking. The transformation improved collaboration efficiency, strengthened data-driven decision-making, and reinforced a more customer-centric operating model. In the “Hyper-Competition in High Dimensions” of the smart electric vehicle industry, NIO significantly boosts R&D efficiency by generating 610,000 lines of code daily through its intelligent GitHub Copilot® copilot, achieving an acceptance rate of up to 33%. The in-vehicle assistant NOMI, built on Azure OpenAI, enhances driving safety and user experience through precise contextual interaction. Simultaneously, Microsoft security solutions safeguard NIO’s complex IT environment and hybrid AI platform, automating daily threat detection and enabling cross-device security coordination.

Scene Five: Trust, safety, and OT security: The non-negotiable foundation

None of these AI use cases can scale without trust. Once AI moves from a recommendation system to an execution system, governance becomes essential. Manufacturing organizations need four core trust capabilities: model governance (ModelOps and Responsible AI), data and access control (Zero Trust architecture and industrial data protection,) OT and endpoint security, and explainability with controllability and rollback, so decisions can be understood, constrained, and safely reversed when needed. This is not a separate chapter; it forms the operating layer beneath all use cases, ensuring AI operates safely and reliably across the organization.

Ford, a longstanding automotive manufacturer synonymous with innovation, has deployed Microsoft solutions—including Microsoft Defender, Microsoft Sentinel, and Microsoft Purview—across its global operations. This initiative enhances visibility, automates responses, and strengthens data governance within its hybrid environment as companies worldwide face escalating cybersecurity threats. AI models learn from every interaction to improve detection capabilities and reduce false positives. With a unified security platform, Ford can focus on business strategy while reducing complexity and boosting operational efficiency. Smart pet device leader PETKIT is currently upgrading its systems on the Azure platform to achieve standardized device connectivity, telemetry data aggregation, and global compliance and security for users worldwide. Microsoft’s products and services not only enhance the company’s technological depth but also provide a cloud-plus-AI platform for global market replication.

2026: The inflection point when AI shifts from “more” to “different”

Once an end-to-end intelligent chain is in place, AI’s role inevitably shifts from offering advice to executing processes—and manufacturing moves from isolated efficiency gains toward full system redesign. In this sense, 2026 will be the year this transformation is proven on a scale. It will be a demanding moment for industry, but also a rare opportunity for leaders to make a true step change. This shift is becoming visible across several dimensions.

In 2026, AI in manufacturing will no longer exist as a collection of pilots. Instead, it will function as an enterprise nervous system—continuously sensing, learning, and coordinating decisions across functions. Organizations will move from experimenting with AI to running with AI, shifting from exploratory adoption to responsible, repeatable execution at scale.

Second, the ability to scale AI will become a key competitive differentiator. AI should not be confined to isolated applications but integrated into cross-departmental and cross-business collaboration to unlock its full potential. In other words, the gap between enterprises no longer lies in whether they deploy AI, but in their ability to achieve scalable implementation across the entire end-to-end value chain. Research from MIT and McKinsey suggests that leading enterprises can achieve up to four times the impact in half the time by building unified data and governance foundations.¹

Third, technical readiness will help define 2026. Edge inference, OT and IT integration, industrial networking, and model governance have matured to the point where AI can operate directly where value is created—on the plant floor, in real time, and within the flow of work. AI is moving beyond general content generation toward deep operational integration, spanning equipment, processes, quality, and logistics, and becoming an integral part of closed-loop industrial control.

Beyond technology, people, governance, and culture will emerge as true differentiators. In 2026, the primary constraint for many manufacturers will be organizational readiness—the ability to share data responsibly, collaborate across silos, and build AI literacy and operating rhythms that sustain change. Research on scaling AI highlights the “10–20–70 rule”: roughly 10% of success comes from algorithms, 20% from technology and data foundations, and 70% from people and processes.¹ Scaling AI effectively therefore requires building skills, accountability, and safety-and-governance capabilities in parallel with the technology itself.

Finally, the maturation of industry standards and ecosystems will accelerate broader AI adoption. Manufacturers face converging pressures—from geopolitics and cost to compliance and supply chain resilience. According to public records, 81% of manufacturers cite fear of falling behind as a primary driver of adoption.² The implication is clear: the question is no longer “Do we need AI?” but “Can we afford not to evolve?” As industrial data semantics, standardized APIs, reference architectures, and increasingly packaged solutions mature, time-to-value will shorten and complexity will fall—making AI feasible for a much broader set of manufacturers.

From insight to action: A 2026 checklist for manufacturing leaders

At this point, the question is no longer abstract: can your organization turn AI capabilities into sustainable, day-to-day operations—rather than pilots and demos? In conversations with manufacturers around the world, this question consistently separates leaders from laggards:

• Strategic clarity: Have you defined the core business problems AI must solve, beyond simply “adopting AI”?
• Data foundation: Can your data platform support real deployment, not just proof-of-concept results?
• Operational readiness: Are your factories and supply chains prepared for AI-powered routines in daily execution?
• Workforce capability: Does your workforce have the baseline skills to work effectively with AI systems?
• Ecosystem usage: Do your partners and platforms support continuous upgrades and rapid scaling?
• Governance and security: Is governance strong enough for AI to move from recommendation to execution?
• Resilience impact: Is AI measurably strengthening operational resilience?

We can already see the direction of travel toward the future. But trends alone do not create leaders. Execution does. The real differentiator will be who can turn AI from concept into action, from tool into capability, and ultimately from capability into resilience.

Advancing intelligent manufacturing with Microsoft

Manufacturing is entering a new phase—powered by actionable data, increasingly autonomous systems, and a more empowered workforce. Companies that unify their data, drive autonomy across planning and execution, and integrate the value chain through digital threads and digital twins will be best positioned to convert operational excellence and innovation into sustained growth.

Against this backdrop, Microsoft continues to work closely with manufacturers to expand what is possible across design, production, supply chain, and service. By combining cloud, data, and AI platforms that are advanced yet practical to deploy, we aim to help organizations build end-to-end intelligent operations—accelerating innovation while maintaining security, responsibility, and scale.

¹ KPMG, Intelligent manufacturing A blueprint for creating value through AI-driven transformation.

² businesswire, Ninety-Five Percent of Manufacturers Are Investing in AI to Navigate Uncertainty and Accelerate Smart Manufacturing, June 2023.

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AI is delivering real, measurable returns for telecom

AI is already delivering measurable business impact across industries, and telecom is among the leaders. A recent IDC study shows operators are achieving 2.8 times return on generative and agentic AI investments, with many leading companies reaching up to 5 times return. Frontier telecoms are realizing even greater returns from AI by making it foundational to how their business operates—from employees and core workflows to the end‑to‑end value chain. These leaders are moving beyond incremental efficiency by using an end-to-end AI platform and unified data approach that embeds AI into everyday operations, enabling faster decisions, tighter execution, and continuous performance improvements across their organization.

With more than 80% of the Fortune 500 building active AI agents, Microsoft Copilot is rapidly becoming essential to how employees think, collaborate, and deliver results. As AI proves its value, telecoms are moving beyond pilots to connected intelligence that elevates customer experiences, replaces manual workflows with autonomous operations, hardens and self‑heals networks, and drives new revenue opportunities. Connected intelligence will differentiate fast-moving telecoms across every area of business operation.

Read more about Frontier telecoms here.

Return on intelligence and trust

For telecoms, achieving value from scalable AI depends on two factors: intelligence and trust. Built from three complementary IQ elements—Work IQ, Fabric IQ, and Foundry IQ—Microsoft IQ is the intelligence layer that connects AI, data, and context across the business. It gives AI agents deep awareness of how people work, how the business operates, and how decisions are made. This intelligence layer accelerates decisions, improves customer experiences, automates operations and networks, and unlocks new ways to monetize AI‑based services. Trust is built through Microsoft’s carrier‑grade control plane, which provides built‑in monitoring and governance across the entire AI platform, including AI agents from our partner ecosystem, to allow telecoms to innovate responsibly, support regulatory compliance, and scale AI with confidence.

At MWC 2026, Microsoft is announcing new technologies that will help telecoms move forward with AI and use intelligence to drive the business.  Microsoft delivers this through a single platform that brings AI, unified data, trust, and governance together to enable telecoms with connected, actionable insights to accelerate innovation and growth.

Building the sovereign, AI-ready edge for telecom

For telecoms, thriving in the era of agentic intelligence begins with a resilient foundation. Today, we’re advancing Microsoft Sovereign Cloud with fully disconnected operations, extending cloud capabilities and AI-ready infrastructure deeper into operator networks than ever before. As demand accelerates for low-latency services, real-time processing, and stronger assurances around data sovereignty and regulatory compliance, the edge has become a critical extension of telecom networks and foundational layer of modern digital infrastructure. This is especially true for regulated industries and mission-critical scenarios where operational resilience and control over data are paramount.

To support these confidential environments, Azure Local offers full stack capabilities that support customers across connected, intermittently connected, and fully disconnected modes. This is essential for sovereign environments where uninterrupted access to operational, network, or customer-facing systems is non-negotiable. Azure Local disconnected operations keeps critical services running securely without connectivity to the cloud. At the same time, Foundry Local will be able to offer modern infrastructure and support for large AI models. Using the latest graphics processing unit (GPU) infrastructure from partners like NVIDIA means customers with sovereign needs will now be able to run models locally on their own hardware, inside strict sovereign boundaries enabling powerful, local AI inferencing in fully disconnected environments.

Customers can deploy and govern workloads inside their own datacenters, using familiar Microsoft Azure experiences and consistent policies, without depending on continuous connection to public cloud services.

AT&T uses Azure to support its cloud and edge strategy, enabling consistent operations across a distributed network footprint. By applying Azure’s management and governance capabilities across environments, AT&T can bring compute and data processing closer to where services are delivered while maintaining strong security and operational oversight.

“As we expand our network edge capabilities, Azure plays a key role in helping us apply cloud-native principles across our distributed infrastructure. The scalability and flexibility of Azure’s adaptive cloud approach allow us to deploy services closer to our customers, maintaining control while providing the reliability and performance they expect from AT&T. This long-standing partnership enables us to innovate and deliver next-generation experiences at the edge.”

—Sherry McCaughan, Vice President, Mobility Core and Services

Azure’s cloud-native management capabilities and global platform enable organizations like AT&T to modernize and scale edge environments, supporting next-generation services while maintaining consistent governance, security, and operational control. Native management capabilities and a global platform enable organizations like AT&T to modernize and scale edge environments, supporting next generation services while maintaining consistent governance, security, and operational control. 

We’re also investing in multi-rack deployment capabilities for Azure Local, extending scale points to support large-scale infrastructure for the most demanding, mission-critical workloads. Customers will be able to expand from single-node and cluster deployments to multi-rack environments designed for high availability, fault isolation, and operational simplicity at scale. Multi-rack deployment on Azure Local is currently in preview and will be available in the coming months.

Microsoft is collaborating with telecom operators to deliver sovereign cloud platforms and managed services that combine hyperscale innovation with local control, enabling enterprises to meet data residency, regulatory, and security requirements while accelerating trusted digital and AI transformation.

Agentic customer experiences that drive growth

With this foundation in place, telecoms can move beyond isolated use cases to scale intelligence across experiences and growth models. The same agentic capabilities that transform customer engagement also unlock new ways to monetize services, reduce cost to serve, and create differentiated value.

Today, telecom customer journeys are fragmented. Customers often switch channels to complete tasks, driving abandonment and cost. AI agents turn customer intent into end‑to‑end action across systems. Microsoft is now offering a telecom agentic store reference framework to replace click‑based journeys with natural‑language interaction. Coordinated AI agents handle discovery, sales, service, billing, and partner offers in the background—customers state their goal and agents deliver the outcome. The result is higher digital completion, faster resolution, and better experiences. This framework also creates a new monetization platform, enabling federated AI marketplaces with built‑in identity, billing, and sovereign deployment for trusted ecosystem commerce at scale. Telecoms are already working with Microsoft and system integrators to adopt this architecture—unifying sales and service, reducing cost‑to‑serve, and creating a scalable foundation for partner‑led innovation. 

FiberCop modernizes edge cloud and contact center

FiberCop runs Italy’s most advanced, far-reaching and pervasive digital network infrastructure. FiberCop recently announced that it has integrated Azure Local into its network, transforming the access infrastructure into an edge cloud platform capable of delivering cloud-native services, virtualized network functions, and advanced workloads while meeting sovereignty and compliance requirements. Today, FiberCop announces that it is accelerating its agentic transformation, moving to an AI‑first contact center model where autonomous AI agents, Copilot, and human expertise work together. By adopting Dynamics 365 Contact Center, FiberCop has begun modernizing customer engagement with unified data, intelligent routing, and AI‑powered self‑service and assisted service that delivers more efficient operations and better customer experiences at scale.

Introducing Ericsson Enterprise 5G Connect to reimagine customer experience

Ericsson announces ongoing collaboration with Microsoft introducing the Ericsson Enterprise 5G Connect solution—validated on Microsoft Surface 5G Copilot+ PCs and built on top of Windows 11’s Enterprise Cellular Managed Connectivity (ECMC) capabilities. This new offering enables enterprises to centrally manage secure, seamless 5G connectivity for mobile and hybrid employees, using automatic network switching and robust policy enforcement to enhance productivity and security. IT teams gain scalable management and control, while end users benefit from uninterrupted, AI-powered experiences across private and public 5G networks. The solution is currently being piloted by Ericsson and is in private preview. To learn more, visit our Windows IT Pro blog.

Intelligent business operations, built for telecom

Delivering connected customer experiences depends on what happens behind the scenes. Telecom operations require trusted, governed access to network and customer data. That’s why operators are moving from legacy data warehouses to a modern lakehouse that unifies business and network data.

Microsoft Fabric provides a single, policy‑governed data foundation for real‑time, operational, and analytical data to speed AI insights at scale. Building on this foundation, today we’re announcing Azure Databricks Lakebase will be available in March 2026, giving telecom operators a managed PostgreSQL environment with next generation separation of storage and compute for transactional data, providing instant availability, instant clones, and scale-to-zero. This brings online transaction processing (OLTP) capabilities to the Databricks Data Intelligence Platform on Azure designed for developer performance with low total cost of ownership (TCO), eliminating the traditional gap between operational systems and the lakehouse.

Partners are building on this data foundation as well. For example, Nokia integrates its data suite with Fabric to securely unify network telemetry and reduce AI use case development time by up to 80%.

MTN transforms fraud prevention with AI

In today’s rapidly evolving digital landscape, identity theft and first-party fraud are escalating at alarming rates, posing significant risks to individuals and businesses across South Africa. MTN has made a bold move to transform its fraud management approach by harnessing advanced Microsoft technologies. Shifting from traditional, reactive methods to a proactive, AI-powered ecosystem, MTN is not only protecting its customers and strengthening revenue defenses but also reinforcing national digital resilience and contributing to a safer, more secure digital economy for all.

Amdocs powers intelligent business operations

Amdocs is making several announcements with Microsoft that deepen integration to deliver next-generation solutions. The first is AI-powered application modernization through the Amdocs Agentic Services platform, embedding Microsoft AI solutions such as Azure OpenAI and Microsoft Foundry into end-to-end modernization and migration to Azure. Second, Amdocs Cognitive Core platform built on amAIz, offering prebuilt agent libraries, cross-domain insights, and telecom-specific AI that integrates with any business or operating system stack and runs securely on Azure. Colt Technology is working with Amdocs and Microsoft to streamline operations and accelerate service delivery with real-time insight.

To learn more about transforming the OSS/BSS with agentic AI, read this blog.

Power autonomous networks with built-in trust and control

As intelligence is embedded across data and operations, the next frontier is the network itself. Agent-driven operations enables networks to move from reactive management to autonomous executions that respond faster, reduce risk, and improve resilience at scale.

To help operators move from pilots to production at scale, Microsoft is evolving its network operations agent (NOA) reference architecture—a proven framework shaped by real world deployments, industry collaboration, and learnings from Microsoft’s NetAI program.

NOA is built for today’s telecom realities: exploding event volumes, rising complexity, and persistent skills gaps. The latest evolution deepens integration with Microsoft AI and collaboration platforms, strengthens alignment with open standards, and delivers a modular, production-ready path to autonomy—without compromising telco-grade safety, governance, or human oversight. Operators engage AI directly through Microsoft 365 Copilot and Microsoft Teams, while Microsoft Foundry and the Microsoft Agent Framework provide a governed, observable runtime for multi-agent orchestration at scale. Expanded support for TM Forum Open APIs helps ensure interoperability across existing business and operations support systems, making NOA an open, secure foundation for autonomous networks. Read more about NOA.

Leading operators such as Far EasTone Telecom and Vodafone are already applying this blueprint to modernize network operations, reduce human error, accelerate recovery times, and enable engineers to focus on higher value work.

Far EasTone Telecom (FET) is turning agentic AI into real operations impact 

FET exemplifies how leading operators are turning this architecture into real operational impact. FET is using the NOA framework to redefine cloud native network operations by embedding agentic AI across its NOC and change management workflows. Today, nearly 60% of its NOC operations are AI-assisted, with about 10,500 operational tasks executed per month, including incident summaries, automated ticket closure, network checks, and proactive voice notifications. AI agents now handle largescale alarm correlation and root cause analysis in seconds, supporting nearly 7,000 monthly operational queries with an average response time of 16 seconds, and enabling most maintenance actions to complete within one minute. This shift has significantly reduced human error, accelerated recovery times, and allowed engineers to focus on higher value work.

Vodafone’s journey toward intelligent network operations

Vodafone is working with Microsoft to apply this proven AI‑powered blueprint for autonomous network operations across transport infrastructure and field‑force management. The collaboration combines Vodafone’s deep network expertise with Microsoft Foundry and the NOA framework to modernize how large‑scale telecom networks are operated.

This blueprint is built on Microsoft’s own experience running autonomous agents across its global Azure transport network, where AI continuously monitors performance, identifies root causes, and autonomously manages more than 65% of fiber‑break field dispatches—improving time to repair by up to 25% and accelerating root‑cause analysis by 80%. By applying these proven capabilities to Vodafone’s transport network, the two companies are accelerating the shift toward intelligent, automated transport network operations across the telecom industry.

“By working with Microsoft, we’re combining deep network expertise with proven AI‑powered operations to create something greater than either could achieve alone. Together, we’re building intelligent, automated transport network operations that empower our teams and deliver faster, more resilient connectivity networks for our customers.”

—Alberto Ripepi, Chief Network Officer, Vodafone

Other operators, including AT&T, T-Mobile, Telefónica, and MEO, are adopting Microsoft Foundry as a blueprint for scaling agentic AI across complex, multi-vendor networks. 

Today, Kenmei announces it is collaborating with Microsoft to help operators accelerate their path toward autonomous networks by combining Kenmei’s telecom intelligence offer with Azure and Microsoft Fabric to enable scalable analytics and agentic AI–powered operations. Already in use at leading operators like Telefónica and Etisalat (e&), this collaboration brings proven deployments into a broader cloud and AI ecosystem designed to reduce manual effort, speed decision‑making, and unlock new levels of network automation.

Microsoft achieves global milestone to extend internet access with Starlink

As telecoms scale intelligence across networks, operations, and experiences, connectivity remains the starting point. Because AI only delivers impact where access exists, expanding internet access is foundational to an intelligent and inclusive telecom future.

Today, 2.2 billion people around the world remain unconnected.¹ To help overcome this barrier, Microsoft pledged to bring access to 250 million people by 2025. We are pleased to share that we’ve expanded internet access to 299 million people through the power of technology and partnerships in communities around the globe. But we know there is more work to do to support unconnected communities and enable global participation in the AI economy.

In support of this on-going effort, we are unveiling a new collaboration with Starlink designed to bring Microsoft’s experience with governments, local operators, and community partners together. With more than 9,000 satellites in low-Earth orbit, Starlink will extend digital infrastructure to rural, agricultural, and hard-to-reach communities. You can read more about how we met this milestone and are continuing to extend AI-enabled connectivity aligned with community needs.

Join us at MWC 2026 to learn more

Frontier telecoms are already proving what’s possible when AI, data, trust, and governance come together on a single platform to power faster operations, autonomous networks, intent-driven engagement, and real return on intelligence.

Join Microsoft At MWC 2026 to see how operators and partners are moving from AI promise to production through real deployments, live demos, and customer stories.

¹Facts and Figures 2025, ITU.

The post Microsoft accelerates telecom return on intelligence with a unified, trusted AI platform appeared first on The Microsoft Cloud Blog.

We’re thrilled to announce the public preview of the Product Change Management agent template—an AI-powered solution that transforms how manufacturers manage the process of change across equipment, products, processes, and more. Built on Microsoft Copilot Studio, the agent automates workflows and connects systems, helping teams cut approval times from weeks to days, reduce errors, and bring innovations to market, faster.

Learn how Copilot Studio can help build and customize agents that work for your operations.  

Reenergizing change management with AI

Engineering change management (ECM) is how manufacturers manage change without causing production chaos. Changes move through a complex, controlled path with requests reviewed, approved, and rolled out to multiple stakeholders and systems. Whether responding to market shifts, regulatory updates, or quality improvements, manufacturers initiate millions of change requests each year.

Today, ECM is slow. While highly collaborative, the process is easily bogged down by siloed information, manual steps, and disconnected processes. When it breaks down, costs from scrap, stoppages, and delayed product launches pile up.

The Product Change Management agent template addresses these pain points by infusing intelligence, automation, and orchestration into this otherwise manual process. The agent provides a managed solution that can be tailored to specific business needs—accelerating deployment while ensuring consistency and governance. Connecting people, data, and systems together with Microsoft AI, it simplifies execution—cutting approval times to days, improving uptime, and ensuring change traceability.

Powering AI change management end-to-end

The Product Change Management agent template is an AI-powered orchestrator, built in Copilot Studio. It autonomously manages engineering change processes through a series of specialized sub-agents, collaborating with your team to ensure every change is executed efficiently and accurately. Using Microsoft 365 Copilot and Microsoft Teams, the agent shifts manual tasks to focused reviews and refinement—delivering faster, safer changes with fewer errors and less review turmoil, while maintaining compliance and alignment. maintaining compliance and alignment.

Some key capabilities set it apart:

• Automated workflow orchestration. Accelerate approvals by coordinating the entire change process, from request to closure, autonomously. Embedded into Microsoft 365 Copilot and Teams, the agent initiates impact analysis, routes approvals, and updates records—keeping stakeholders informed.
• System of record synchronization. Keep engineering and operations systems aligned. The agent ensures updates are consistently reflected across product lifecycle management (PLM) and enterprise resource planning (ERP) platforms, eliminating rework and maintaining alignment from design through delivery.
• Collaborative stakeholder engagement. Simplify communication across engineering, quality, and operations with natural language interfaces and intelligent routing. This ensures that the right people are engaged at the right time, reducing bottlenecks and accelerating approvals.
• Data-driven impact analysis. Evaluate proposed changes across inventory, suppliers, and production. The agent surfaces real-time insights to guide decision-making and flag potential risks early—empowering teams to act.
• Built-in compliance and traceability. Document and audit changes at every step. The agent enforces governance policies, tracks decisions, and supports regulatory compliance without adding complexity.

In short, product change management lays the agentic foundation for manufacturing digital threads—enabling agility, transparency, and reliability for every stakeholder.

Transforming change management at Coca-Cola Beverages Africa

Coca-Cola Beverages Africa (CCBA) is the eighth largest authorized Coca-Cola bottler in the world by revenue, and the largest on the continent—operating in 14 countries. Serving more than 800,000 customers, CCBA accounts for 40% of all Coca-Cola ready-to-drink beverages sold in Africa through a host of international and local brands.

With thousands of stock keeping units (SKUs), multiple packaging formats, and a relentless focus on sustainability, CCBA runs one of the most complex beverage supply chains in Africa. Agility is critical especially when managing formulation and packaging changes that ripple across multiple production lines, inventory systems, and financial models.

Every year, CCBA makes more than 1,000 changes to its bottle molds alone, often driven by material availability or sustainability initiatives. Historically, these changes relied on manual workflows: engineers drafting requests, planners emailing spreadsheets, and multiple handoffs across departments. This process was slow, error-prone, and risky. A single misalignment could mean production downtime, inaccurate cost data, or compliance gaps. The Product Change Management agent template from Microsoft is transforming this process.

Acting as a digital orchestrator, the agent brings intelligence, speed, and reliability to the CCBA change lifecycle. Here’s how:

• Smart initiation. When a planner or engineer triggers a change, such as switching a supplier or updating a packaging component, the agent immediately identifies all affected products and plants. It auto-drafts the request, applies the standard template, and fills in known details like part numbers and descriptions—eliminating repetitive manual work.
• Automated routing. The agent ensures the request moves to the right reviewers in the correct sequence, removing guesswork and delays. Notifications flow through familiar tools like Teams and Outlook, alerting stakeholders when action is required.
• Instant system updates. Once approvals are complete, the agent updates Microsoft Dynamics 365 in real time, syncing bill-of-material data. It confirms changes immediately, rather than days of manual checks.

The Product Change Management agent is streamlining equipment management across CCBA’s capital assets and products, enabling faster identification of impact areas and responsible individuals, and improving operational efficiency

—Joshua Motsuenyane, Chief Information Officer, CCBA

While this strategic collaboration is still new, CCBA is already seeing results. Actions that once took days of back-and-forth now happen in hours or less. Product change management also represents a major milestone in its Frontier Firm journey—making change management a focus across one of Africa’s most dynamic manufacturing networks.

Creating the future of change management in manufacturing, together with partners

AI-powered change management is now imperative. As changes proliferate across more assets and systems, manufacturers need governed, AI-guided workflows to maintain speed and quality. Discrete manufacturers—building complex products from computers to cars—feel it the most: disconnected systems and manual handoffs slow adoption, raise error rates, and suppress productivity. PTC and Microsoft are changing that.

Together, we’re building an agentic architecture that bridges operations and engineering systems, enabling faster decisions with enterprise visibility. Enabled by technologies such as model context protocol (MCP), native PLM agents in Windchill and ERP agents in Dynamics 365 interoperate to surface problem reports, collate data from multiple systems, and drive automation in PLM workflows such as change impact analysis, where data governance rules are established, ensuring AI agents work in the right context and within the right controls.

Ready to simplify change and accelerate execution?

Product Change Management agent template

Join the public preview

In combining Microsoft for Manufacturing with expertise from partners, we can deliver better, more comprehensive industry solutions. As we expand this ecosystem, manufacturers will gain even broader interoperability, deeper insights, and more resiliency across their value chain.

Shaping the manufacturing Frontier

With the Product Change Management agent template, manufacturers gain a trusted technology partner in navigating every engineering change. Part of a broader vision to enable intelligent digital treads across manufacturing, product change management is about empowering teams to innovate with confidence, backed by data and AI automation.

Industrial AI can accelerate product design and engineering outcomes. Learn how with our latest Signals Report.

The post Accelerate innovation with AI: Introducing the Product Change Management agent template appeared first on The Microsoft Cloud Blog.

This milestone comes at a time when AI is fundamentally changing how healthcare organizations operate and serve patients. AI is powering self-service interactions, guiding service representatives in real time, and autonomously handling end-to-end workflows. As these capabilities expand across the care journey, trust and data protection are becoming just as important as innovation.

Healthcare leaders are asking: How do we harness AI to improve patient experiences without compromising privacy? What safeguards are needed as both AI and human representatives engage in sensitive interactions across channels? How do we build future-ready systems that are both innovative and compliant?

A secure, HITRUST-certified platform helps provide the foundation to confidently address those questions.

Learn more about Dynamics 365 Contact Center

HITRUST: Setting a new bar for secure, AI-led patient engagement

HITRUST, governed by healthcare industry representatives, developed and maintains the Common Security Framework (CSF). This certifiable framework builds on the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health (HITECH) Act—United States healthcare laws that define requirements for the use, disclosure, and safeguarding of individually identifiable health information, and enforce penalties for non-compliance. HITRUST provides a standardized compliance framework, assessment, and certification process that cloud service providers and covered health entities can use to measure their compliance.

As AI becomes foundational to patient engagement, from summarizing service interactions and routing inquiries based on patient intent to helping agents triage across digital and voice channels, this level of assurance is critical. With HITRUST certification, Dynamics 365 Contact Center delivers the governance patients, providers, and regulators expect, while enabling innovation in the agentic AI era.

Helping healthcare organizations achieve more

Dynamics 365 Contact Center is an AI-first Contact Center as a Service (CCaaS) solution that unifies AI agents, channels, real-time analytics, and human service representative support in a secure, extensible, and composable platform. With HITRUST certification now in place, healthcare organizations can confidently apply AI in ways that drive real operational impact:

• Automate with assurance: Use AI voice and digital agents for scheduling, triage, and intake while protecting protected health information (PHI).
• Empower human service representatives securely: Give service reps compliant escalation paths, governed knowledge retrieval, and smooth handoffs.
• Streamline operational workflows: Use autonomous agents to automate back-end processes and help reduce manual effort.

This certification builds on our earlier HIPAA compliance announcement and adds to Dynamics 365 Contact Center’s broader set of assurances including FedRAMP, System and Organization Controls (SOC), Payment Card Industry Data Security Standard (PCI DSS), and multiple International Standards Organization (ISO) standards.

Built for what’s next

As healthcare organizations modernize their service operations, unify data across systems, and adopt agentic AI to improve business processes and patient experiences, Dynamics 365 Contact Center helps ensure that trust and compliance remain central to every interaction.

To learn more about our compliance journey and explore resources, visit the Dynamics 365 Contact Center compliance page. Additionally, explore the capabilities of Dynamics 365 Contact Center and sign up for a free trial.

Dynamics 365 Contact Center

Deliver intelligence, automation, and efficiency across channels.

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The post Raising the bar for healthcare security: Dynamics 365 Contact Center achieves HITRUST certification appeared first on The Microsoft Cloud Blog.

When 70% of Microsoft 365 Copilot users report that the integration of generative AI into their everyday applications and tasks makes them more productive,¹ firms can see how AI can fundamentally revolutionize their businesses—starting by empowering the people who keep their companies running.

Historically, technology innovations have often not focused foremost on the needs of the average worker. Rather, they were often focused on empowering executives or driving loftier business goals such as enhancing competitiveness and profitability or powering new products and services. Generative AI is distinct in that it is tailored to benefit employees first. 

Helping banks and other financial institutions take full advantage of AI is central to our work at Microsoft. In the past two years, we’ve worked intensively with firms around the world to explore new avenues of AI innovation, with use cases that span an incredible range of opportunities. None have been more noteworthy than employee empowerment.

For many users, this happens initially with Microsoft 365 Copilot, which is embedded into apps like Word, Excel, PowerPoint, Outlook, and Microsoft Teams to integrate AI directly into everyday work tasks. 

Copilot removes drudgery and empowers employees 

When every employee has an AI assistant that helps them work better and faster, the sky is the limit on innovation. And it couldn’t come at a better time. According to the Microsoft 2025 Annual Work Trend Index, 53% of leaders say their company’s productivity needs to increase, yet 80% of the global workforce reports lacking the time or energy to do their job.¹

This “capacity gap” is why 82% of leaders expect to use digital labor to expand their workforce in the next 12 to 18 months. For many, the journey starts with Copilot and related cloud solutions that remove the drudgery of work and help people do the same work better and faster. 

4 ways Copilot is delivering immediate impact in financial services 

Enhanced productivity is the broad term for an important set of benefits that generative AI can deliver. For financial services firms, the specific use cases and benefits span many areas but we will concentrate here on four in particular: summarization, content creation, process optimization, and real-time insights.

1. Summarization
One of the most valuable features of Copilot is summarization—the ability to instantly produce a customized summary of anything from a recently conducted meeting to a transcript of a customer conversation, to summaries of whitepapers and PowerPoint presentations. In a fast-paced environment where analysts, advisors, and other professionals juggle multiple tasks, having a tool that can immediately extract key takeaways and follow-up actions is invaluable.

A good example is the experience of Hargreaves Lansdown, a leading United Kingdom financial services company that was early to embrace Copilot. Until recently, advisors had to manually take notes for customer meetings and later transfer them into a branded document, a process that could take up to four hours. With Copilot summarization and Microsoft Teams Premium, the process is being cut to as little as one hour through the automatic generation of meeting summaries, documentation, and action items.

Copilot not only speeds summarization, “it’s also good quality information,” says Systems Operations Manager Daniel Toman. “We know nothing is being missed.”

2. Content creation
The process of drafting emails, building presentations, and writing important client documents can be time-consuming and frustrating. Copilot eases that burden by identifying relevant source materials, using natural language processing to create messages and documents, and pulling information from across the Microsoft Graph (an API that connects all Microsoft 365 data, documents, and users.) The benefits for financial services include improved client engagement, scalable workflows, and AI-powered insights.

Content creation is delivering major benefits to Bank of Queensland (BQQ), which adopted Copilot to enhance collaboration and productivity. It has helped decrease the time required to draft internal manuals by 99%, marketing content by 88%, and human resource document drafts by 75%. Those gains are credited with improved customer service and operational efficiency—plus greater innovation in a competitive market.

“Copilot puts the power in the hands of the employee to be able to find efficiency.”

—Hayley Watson, Head of Enterprise Capability, BOQ

And to offer another customer example, in the United Kingdom, Floww, a financial infrastructure platform provider, increased employee efficiency by up to 20%, using Copilot to process massive quantities of data spanning technical documents, regulatory compliance requirements, and financial information, then condensing and delivering reports in easily accessible, shared formats.

3. Process optimization
Too many important processes in financial services are still dependent on manual tasks that can slow productivity and drain resources. Copilot solves this by automating processes and enhancing collaboration. The net benefits for firms include streamlined operations, fewer errors, and more time for employees to focus on high-value work.

For example, Dutch wealth management firm Van Lanschot Kempen wanted to help advisors focus more on personal connections with clients and found that too much time was being spent on unautomated tasks. So, they enlisted Copilot to improve common workflows and processes. Copilot now helps save them time by drafting emails (in multiple languages) in response to prompts, taking notes in meetings, and identifying and automatically assigning action points. An added benefit is that it reduces the language barrier and increases the quality of emails and documents.

“Having to take notes and structure action points and recaps accounted for around 40% of my time, Copilot is now my assistant during and after meetings.”

—Johanna Albert, Digital Adoption Specialist

Elsewhere, LGT, a Liechtenstein-based international private banking and asset management group, is using Copilot in their legal and compliance departments—for instance, to simplify reviews of lengthy contract documents. What used to take up to four hours can now be done in about 30 minutes. And global payments platform Paysafe cut the amount of time spent building technical documentation by up to 50%, automating meeting documentation, information retrieval, and document creation.

4. Real-time insights
Copilot is redefining how financial services professionals work by providing real-time insights that empower employees and enhance decision-making. Imagine, for example, a mortgages operation manager who needs to ensure efficient loan processing while maintaining compliance and optimizing customer satisfaction. Throughout the process, Copilot instantly retrieves and summarizes critical data, improving both speed and accuracy. Across firms and roles, real-time insights help with everything from research and predictive analysis to collaboration, workflow automation, and decision-making.

The scope of the transformation this represents is reflected in Microsoft’s strategic partnership with Moody’s to co-create new products and services for research and risk assessment. Built on a combination of Moody’s robust data and analytical capabilities and the power and scale of Azure OpenAI Service, the partnership creates innovative offerings that enhance insights into corporate intelligence and risk assessment. One early offering is a new copilot, “Moody’s CoPilot,” deployed to Moody’s 14,000 global employees, which helps drive firm-wide innovation and enhance employee productivity in a safe and secure digital sandbox. 

Beyond Microsoft 365 Copilot: A new breed of financial services agents 

Microsoft 365 Copilot is really just the start. Microsoft also offers a range of Copilot solutions tailored for businesses sectors, including Microsoft Copilot for Finance, which connects to financial systems such as Dynamics 365 and other leading financial management platforms to assist with tasks like analysis, reconciliation, collections, and communications.

For firms who want to do more, Microsoft Copilot Studio makes it easy to build custom AI agents without requiring extensive coding expertise. It also includes a powerful new Researcher Agent that helps with complex, multi-step research. It delivers deep insights with greater quality and accuracy than previously possible to advance important tasks like market analysis, opportunity identification, and key reporting. 

Set the stage for new waves of AI innovation 

Employees who use copilots are not just more productive, they’re more likely to be engaged and innovative. For many, their embrace of AI will become career accelerators. Empowered employees will help drive the reinvention of even the most established firms, and make endless contributions to the upstarts and even those we can’t imagine today.  

As the rapid evolution of AI continues, we will soon see the adoption of new classes of AI agents that won’t simply provide assistance but will execute tasks and orchestrate action autonomously. The future is approaching rapidly: our research suggests that global business leaders expect their teams will be training (41%) and managing (36%) AI agents within five years.¹  

Soon, human-agent teams will upend the org chart, and every employee will become an agent boss. Now is the time to get them up and running. 

Learn more 

• Visit Microsoft for financial services to learn more about how Microsoft empowers financial services organizations to achieve more.
• Learn more about using Copilot in Financial Services and download the Financial Services scenarios kit.

Reinventing productivity

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¹ The 2025 Annual Work Trend Index: The Frontier Firm is born.

The post 4 ways Microsoft Copilot empowers financial services employees appeared first on The Microsoft Cloud Blog.

Build AI agents to drive business processes with Model Context Protocol servers
To provide some context, generative AI is fundamentally reshaping the way organizations work, introducing a new way of interacting with technology—using natural language to simplify and accelerate tasks. This innovation is driving unprecedented productivity gains, streamlining complex processes that once required manual effort and specialized tools. As this technology matures, we’re entering the next phase: the autonomous enterprise, where organizations and people use technology, particularly AI and automation, to operate and adapt in an age of rapid transformation and innovation. Where there once was “an app for that,” there will now be “an agent for that”.

This transformation isn’t just about automation—it’s about people. By putting intelligent agents in the hands of every employee, organizations are empowering individuals to focus on higher-value work, make decisions faster, and drive innovation. Sales teams can deepen customer relationships without being bogged down by administrative tasks. Finance professionals can move from manual reconciliation to strategic forecasting. Marketers can go from idea to execution, and product managers can orchestrate complex workflows with clarity and speed.

The Autonomous Enterprise is the future of business. Business applications will work with agents built by Microsoft and our partners. In this new era, organizations aren’t just streamlining operations, they’re amplifying human potential and accelerating their journey to the autonomous enterprise.

This is why we’re so excited about the Dynamics 365 ERP and CRM MCP servers. These servers help eliminate data and application silos, allowing agents to work seamlessly across processes and enable new autonomous scenarios for improved business functionality and productivity.

Dynamics 365: Agent-ready business applications
Agentic AI is an AI system that can take actions generated by the system, with very limited or even no direct human intervention. Autonomous actions built into agents operating across various business processes, industries, and segments, can make businesses more efficient and responsive. Designed not just to support tasks, but to operate autonomously, AI agents can intelligently orchestrate workflows and make context-aware selections. But how do you create a context-aware agent when data, information, and processes are ever-changing?

MCP standardizes how applications provide context to language models, enabling seamless integration with different data sources and tools. This open standard connects AI assistants and agents to various systems where data resides, such as content repositories, business tools, and development environments. An MCP-compliant agent uses rich contextual information to act efficiently, unlike a non-MCP-compliant agent, which lacks necessary context.

Using the MCP server, makers can easily connect agents to existing knowledge sources and APIs, enabling them to interface directly with Dynamics 365 applications. Actions and knowledge synchronize automatically, facilitating real-time updates and the evolution of functionality. This model significantly simplifies agent development and minimizes ongoing maintenance efforts.

Diagram illustrating how different agents and clients connect to an MCP-compliant server to access data and actions from Dynamics 365 and other business applications.
Central to this innovation is Microsoft Copilot Studio, which provides a standardized protocol for agents to seamlessly interact with Dynamics 365 applications, helping to ensure consistency, reliability, and scalability. Security and governance are also prioritized from the start as Dynamics 365 MCP servers require authentication and enforce authorization. Agents that access Dynamics 365 through the MCP server must authenticate as a valid Dynamics 365 user, helping to ensure the benefits of Entra ID identity protection. This also prevents escalation of privileges, meaning the agent will only be able to perform the MCP actions that they are authorized to do. The MCP servers are also made available to Microsoft Copilot Studio using connector infrastructure. This means they can employ enterprise security and governance controls such as Data Loss Prevention controls and multiple authentication methods. 

For partners and customers, MCP standardization dramatically reduces complexity, accelerates development, and increases time to value.

MCP-compliant agentic AI
At Microsoft, we bring a deep understanding of critical business processes for small and medium business (SMB) as well as large enterprise organizations through our market-leading Dynamics 365 ERP and CRM business solutions—combined with our industry-specific expertise delivered through our Microsoft Cloud for Industry solutions. This combination of experience and expertise uniquely positions us to deliver on the needs of customers across size, business process, industry, or region.

Our newly introduced set of MCP servers enable multiple scenarios across business processes. Below are a few examples of what’s possible with Dynamics 365, Microsoft Cloud for Industry, and our broad ecosystem of partners.

Sales and service
Custom agents and AI assistants can now be connected to Microsoft Dynamics 365 Sales, Microsoft Dynamics 365 Customer Service, and Microsoft Dynamics 365 Business Central applications through MCP servers. Agents can retrieve and update CRM data, create quotes, and complete orders. They can also complete orders, get order/case summaries, and email drafts. These MCP servers open endless possibilities in automating tedious jobs in sales and service functions, irrespective of company size or industry.

For example, telesales representatives can use intelligent assistants, such as Claude, connected to Dynamics 365 MCP servers to prioritize leads, qualify them, generate quotes, and send personalized emails—without needing to switch contexts or rely on complex integrations. And when customers encounter an order issue, service representatives can resolve it quickly by using Dynamics 365 Customer Service data to retrieve/update case information and create replacement orders in real time.

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Supply chain and finance
The AI procurement agent illustrated below efficiently validates purchase requisitions against company policies, existing inventory, and delivery records to identify a suitable supplier that meets the criteria for cost, speed, sustainability, and reliability. It further consolidates multiple items from the same supplier into one purchase order and sends it for purchase. The agent can significantly enhance efficiency in procurement processes, where timely and budget-conscious supply delivery is critical.

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Business Central
For small and medium size businesses, for example, looking to optimize sourcing information and vendor compliance, the custom agent demonstrated here can quickly identify shipments containing materials that require compliance checks. The agent provides guidance on recycling requirements and updated sourcing standards, reads supplier contracts, and suggests next steps like confirming vendor certifications and updating shipment checklists. A solution like this could streamline the compliance process, which can help customers gain a competitive advantage.

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Partners using the MCP server
Our partners play a crucial role in driving innovation and delivering value to customers. We’re dedicated to making Dynamics 365 MCP servers accessible, enabling our customers and partners to develop diverse agent scenarios across industries and business processes, regardless of their business application vendor. Today we’re also announcing the creation of an “AI business applications” coalition with XX and XX as founding members. These organizations along with Microsoft are committed to delivering MCP standard compliant solutions.

With MCP server becoming the standard of the future for agents, partners can use it to more quickly and efficiently orchestrate headless business services in ERP and external systems. It turns simple intent into action, automating procurement for faster, efficient, and resilient supply chain operations. Our ecosystem of partners has started using MCP server for Dynamics 365 to create a host of industry-specific agents.

Avanade, an early adopter of Microsoft 365 Copilot for Sales and a leading Microsoft partner, is excited to use MCP servers for Dynamics 365 to enrich their AI-powered request for proposal (RFP) Insights agent. This agent helps sellers summarize, evaluate, and respond to RFPs using historical Dynamics 365 data, further streamlining proposal generation. While initially for internal use, Avanade is exploring deployment for clients in engineering, construction, and professional services.
Emission AI agent by Fellowmind will use AI and MCP servers for Dynamics 365 to automatically classify and organize purchase transactions to prepare it for Greenhouse gas (GHG) emission accounting purposes by categorizing spend-types (e.g. office supplies, raw materials, travel expenses) through data extraction, classification, algorithms, taxonomy mapping, and real-time feedback and learning. The agent provides support to procurement and environmental, social, and governance (ESG) professionals, helping them streamline their processes and achieve more accurate results.
HSO’s PayFlow Agent improves invoice payment efficiency in accounts payable. Streamlining timely payments and reducing inquiries that require manual intervention leads to faster resolutions and enhanced supplier relationships. Using MCP server for Dynamics ERP MCP, PayFlow processes seller payment inquiries, identifies invoice statuses, matches them against buyer receipts, and retrieves tracking information to notify responsible parties to either remit payment promptly or set an expectation of when payment can be received. 
JourneyTeam is enriching its Strategic Account Manager agent that accesses MCP servers for Dynamics 365 to optimize lead engagement. The agent summarizes historical services and projects, compares lead summaries and interests, compiles recommendations, then, after manual reviews, will initiate next steps by utilizing MCP servers, Microsoft Azure AI Search, and Document Intelligence.
MCA Connect is building a smart sourcing agent that accesses MCP servers for Dynamics 365 to automate requisition processing, supplier assignment, and workflow submission. The MCP servers give the agent access to actions like getting open requisitions, approving vendors, and assigning suppliers based on supplier performance metrics without the need to create new APIs and integrate with Dynamics 365.
Publicis Sapient Hummingbird is building an agent to improve lead management using MCP servers for Dynamics 365 to access data that will streamline the process of managing business-to-business leads. This agent automates lead qualification, scoring, and personalized engagement, accelerating hot leads to quotes faster and nurturing warm leads through a series of targeted emails. This innovative approach enhances efficiency, improves customer experience, and drives higher conversion rates and revenue growth.
RSM is building intelligent, secure, and context-aware agents that accelerate workflows, improve decisions, and expand capabilities by embedding them directly into real-world business processes. These agents, developed using Microsoft Copilot Studio, will access MCP servers for Dynamics 365 to support humanitarian logistics by coordinating critical supply chains, helping to ensure timely delivery of life-saving equipment, and automating procurement tasks.
TTEC is building a post-service upselling agent that accesses MCP servers for Dynamics 365 to prospect for warranty plans after a purchase, turning each sale into an upsell opportunity. The agent will help drive personalized sales and service conversations at scale by using the knowledge, tools, and actions from the MCP server.
As we look ahead, the convergence of intelligent agents, standardized platforms, and deep domain expertise will define the next frontier of business transformation. The ability to harness autonomous capabilities will define tomorrow’s market leaders. Businesses that act now will gain a decisive competitive edge and chart a course toward sustained success. The autonomous enterprise is no longer a vision of the future—it’s here, built with Microsoft and its partner ecosystem.

Join us at Microsoft Build 2025 to explore how MCP servers are transforming Dynamics 365 and the broader Microsoft Cloud–MCP server focused sessions at Microsoft Build 2025.

Let’s shape what’s next, together.

The post The autonomous enterprise: How generative AI is reshaping business applications appeared first on The Microsoft Cloud Blog.

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.

Microsoft 365 Copilot

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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 The Microsoft Cloud Blog.

What are OSS and BSS?

Learn how to streamline processes and drive growth

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Modernizing OSS and BSS: From reactive to agentic AI

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

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

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

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

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

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

Agentic AI in action: From insight to autonomous operations

Faster time-to-market for new services

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

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

Why run OSS on the public cloud?

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

Self-optimizing networks and beyond

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

Embracing open standards and ecosystem collaboration

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

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

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

Achieving scale with cloud-native AI

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

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

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

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

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

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

Key value streams include: 

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

Positioning for revenue impact and the autonomous future

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

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

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

Ready to transform your operations?

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

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

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

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

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

Benefits of generative AI in product engineering  

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

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

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

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

Establishing a secure engineering data foundation  

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

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

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

Accelerating product engineering and R&D 

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

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

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

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

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

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

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

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

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

Microsoft in manufacturing and mobility industries 

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

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

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

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