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.

The post Supply Chain 2.0: How Microsoft is powering simulations, AI agents, and physical AI appeared first on Microsoft Industry Blogs.

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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Yet despite broad cloud adoption, migration and modernization remain complex. Legacy architectures, fragmented environments, and persistent skills gaps continue to slow progress, pushing organizations to find ways to migrate faster while minimizing operational risk. 

The IDC study highlights agentic AI as a critical unlock. These intelligent systems automate assessments, orchestrate migration and modernization efforts, and optimize operations across hybrid environments—helping organizations shift from periodic, manual initiatives to continuous, adaptive modernization. This momentum is driving unprecedented growth, with IDC forecasting the public cloud services market will reach USD1.9 trillion by 2029. 

While migration frameworks may be horizontal, their real-world impact is industry-specific. Healthcare, financial services, and manufacturing each face unique constraints shaped by regulation, operational risk, and mission-critical systems. 

In this blog, we explore the key migration and modernization challenges across these three industries—healthcare, manufacturing, and financial services—through real customer stories that highlight the tangible impact cloud adoption is delivering today.

Healthcare: Modernizing securely while powering next-generation clinical experiences

Healthcare faces the toughest modernization headwinds: strict regulations (HIPAA/HITECH, HITRUST), fragmented clinical data across electronic health records (EHRs) and imaging systems, aging on-premises infrastructure resulting in high Capex, and heightened exposure to ransomware.¹ Clinical environments also demand extremely low latency and high reliability.

The IDC study notes that these constraints slow modernization—but accelerate the need for it, as organizations push to scale telehealth, imaging workloads, genomics pipelines, and AI-powered clinical workflows.¹ 

What healthcare organizations need, according to the IDC study: 

• Secure, compliant integration across EHRs, picture archiving and communication systems (PACS), genomics systems, and Internet of Things (IoT) medical devices.¹
• Elastic compute for high-throughput imaging and genomics. 
• Stronger disaster recovery and recovery time performance.¹
• Ambient documentation and AI-supported diagnostics.
• Secure clinician collaboration and modern patient digital front doors.

Customer spotlight: Franciscan Health

Facing aging infrastructure and disaster recovery risks, Franciscan adopted a pragmatic workload placement strategy—moving its Epic EHR to Microsoft Azure.

The results included: 

• $45 million in savings over five years after migrating Epic to Azure.
• 90% faster disaster recovery compared to the prior environment.
• Around a 30-minute failover, reduced from hours.
• $10–$12 million per day in potential downtime risk avoided.

Learn more about Franciscan Health’s journey to migrate its Epic EHR to Azure.

Healthcare’s modernization mandate is clear: reduce operational risk, meet regulatory demands, and harness cloud AI to improve patient outcomes. 

Financial services: Enabling real-time intelligence and automated compliance

Financial institutions operate in one of the most regulated environments, including the payment card industry data security standard (PCI DSS), the Sarbanes-Oxley Act (SOX), the Gramm-Leach-Bliley Act (GLBA), Basel capital frameworks, and know your customer (KYC) and anti-money laundering (AML) requirements, and rely heavily on legacy mainframes that are difficult to modernize. Today, regulatory pressure is intensifying further as new frameworks such as the EU’s Digital Operational Resilience Act (DORA) and the EU AI Act raise the bar for operational resilience, third-party risk management, model transparency, and ongoing compliance monitoring. Under DORA, financial services firms must demonstrate continuous information and communication technology (ICT) risk management, advanced incident reporting, and resilience testing across critical systems and cloud service providers. Meanwhile, the EU AI Act introduces governance requirements for high-risk AI systems, including explainability, data lineage, human oversight, and auditability—with direct implications for fraud models, credit scoring, and customer decisioning platforms.

IDC interviews highlight accelerating demand for real-time risk analytics, fraud detection, digital onboarding, and infrastructure elasticity to support peak activity—capabilities that are increasingly mandated, not optional.¹

Key challenges the IDC study identifies: 

• Strict data residency, model risk governance, explainability, and eDiscovery requirements.¹
• Heightened expectations for operational resilience, cyber defense, and third-party risk oversight.
• Legacy systems and common business-oriented language (COBOL)-based batch processes resistant to change.
• Rapidly evolving regulatory mandates requiring continuous compliance rather than point-in-time audits.

Cloud—especially especially platform as a service (PaaS) and managed services—helps financial institutions shift from static, batch-driven compliance to continuous controls and real-time observability. By reducing batch windows from hours to minutes, modern cloud platforms enable real-time insights, automated evidence collection, resilient architectures, and policy-driven compliance workflows aligned with DORA and AI governance requirements.¹ Learn more about how Microsoft can help financial institutions navigate these requirements. 

Customer spotlight: Crediclub

To accelerate product innovation and meet expectations from Mexico’s national banking and securities commission (CNBV), Mexican fintech Crediclub modernized its databases to a serverless platform as a service (PaaS) architecture and adopted microservices.¹

The impact:

• Uptime improved from around 80% to 99.5%.
• 90% reduction in network latency through Multiprotocol Label Switching (MPLS) and dark fiber.
• Rapid deployment of new financial products via Kubernetes and DevSecOps.

For financial institutions, modernization is no longer just about efficiency—it is foundational to resilience, trustworthy AI, and regulatory compliance at scale. 

Manufacturing: Unifying IT and OT for predictive, data-driven industrial operations

Manufacturers operate in one of the most complex operating environments—defined by legacy and proprietary operational technology (OT) protocols, historically air-gapped manufacturing execution systems (MES) and supervisory control and data acquisition (SCADA) systems, and globally distributed supply chains. Stringent low-latency requirements for safety-critical systems, intermittent connectivity at the edge, and the need to protect intellectual property further compound the challenge. The ability to modernize and unify these environments—without compromising safety, reliability, or performance—represents a critical inflection point for industrial transformation.

Unique modernization challenges according to the IDC study:

• Ultra-low latency requirements for safety-critical operations.
• Massive telemetry ingestion and time-series analytics at scale.
• Operational complexity across global, distributed supply chains.
• Secure protection of intellectual property across edge and cloud environments.

Opportunities unlocked by cloud:

• Predictive maintenance with IoT ingestion.¹ 
• Reduced unplanned downtime and improved overall equipment effectiveness (OEE).
• Digital twins for plants, lines, and products.
• Computer vision for real-time quality and safety. 
• High-performance computing (HPC) simulations for engineering and design. 
• Standardized, global data models.

Customer spotlight: ASTEC Industries

ASTEC unified fragmented systems across its rock to road value chain—from aggregate processing through asphalt production and paving—by adopting Azure, modernizing to timeseries databases, and building a universal connectivity platform using Azure IoT Hub, Azure Events Hub, and Power BI.¹

The results:

• Realtime operational visibility across fleets.
• Predictive maintenance for reducing downtime.
• New digital services supported by connected equipment.

Manufacturing’s modernization imperative: unify OT and IT, scale real-time intelligence, and enable global efficiency. 

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

Microsoft’s strategy is grounded in a simple principle: modernization should be continuous, intelligent, and collaborative. The IDC study emphasizes that successful enterprises adopt a balanced, multipath migration strategy, blending rehost, replatform, refactor, and software as a service (SaaS) substitution based on workload criticality.¹

Microsoft enables this approach through a comprehensive set of tools and offerings, including Azure Copilot and GitHub Copilot. Agentic automation enables:

• Discovery and dependency mapping.
• Security assessment and 6R recommendations.
• Application refactoring, code remediation, and modernization. 

Azure Migrate provides unified discovery, assessment, migration execution, and modernization services. Azure Accelerate complements this with a coordinated framework that includes:

• Guided deployments through Cloud Accelerate Factory.¹ 
• Funding and Azure credits for planning, pilot, and rollout. 
• Expert partners and tailored skilling programs.

The IDC study concludes that organizations using Microsoft Azure for migration and modernization achieve lower operational costs, improved resiliency, faster modernization timelines, and stronger security postures—especially in regulated industries.¹

Looking ahead: Agentic modernization as the foundation for AI-ready enterprises

Across all industries, IDC’s findings are consistent: agentic AI is emerging as the new force multiplier for modernization, enabling organizations to keep pace with rising complexity, regulatory demands, and competitive pressure. 

Healthcare, financial services, and manufacturing each face unique constraints—but cloud modernization remains the foundation for innovation, operational excellence, and enterprise AI. 

Microsoft’s approach gives organizations the unified automation, intelligence, and tooling they need to modernize securely and at scale. 

• Explore how Azure Copilot accelerates cloud migration.
• Discover GitHub Copilot for app modernization.
• See how Azure Accelerate supports transformation.

¹ IDC White Paper, Cloud Migration and Modernization Strategies for Healthcare, Financial Services, and Manufacturing, February 2026.

The post Modernizing regulated industries with cloud and agentic AI appeared first on Microsoft Industry Blogs.

Research we conducted with senior healthcare executives in the United States, published in the New England Journal of Medicine, revealed a growing readiness divide. As some systems build governance, security, and workforce models to scale AI safely, others are still in proof-of-concept mode. The result? Diverging outcomes in productivity, workforce strain, cost-to-serve, and resilience.

The question is no longer whether AI belongs in healthcare. It’s how quickly organizations can operationalize it—safely, responsibly, and at scale.

Microsoft works with more than 170,000 healthcare customers globally to move from pilot to production with enterprise-grade security, privacy, and compliance.

So what does Frontier Transformation actually look like? The following examples show how healthcare organizations are embedding AI into core workflows—moving beyond pilots to deliver real, scalable impact with the governance and trust required in clinical environments.

Accelerating discovery and clinical development with AI

Frontier organizations are reinventing discovery by treating AI as an always-on research partner. It compresses the time it takes to find, synthesize, and act on evidence across functions. The result isn’t just faster tasks; it’s faster decisions and a more scalable path from insight to impact. As these capabilities become table stakes, organizations that can’t industrialize knowledge of work will fall behind in speed-to-trial, speed-to-market, and ultimately speed-to-patient.

UCB: Scaling agent-based AI with a secure internal platform

UCB built SKAI, a secure internal platform on Microsoft Azure for generative and agent-based AI, helping teams apply knowledge faster and operationalize AI with governance built in.

Syneos Health: Streamlining complex data to bring therapies to patients faster

Syneos Health is using AI to help teams analyze large, complex data sets across the clinical development lifecycle. With faster, more consistent synthesis of study inputs and operational signals, biopharma customers can make decisions with greater speed and confidence. Syneos Health reported reducing time for clinical trial site activation by about 10%, helping remove friction from a critical step in getting lifesaving therapies to patients. Enhanced predictive modeling and forecasting tools also allow teams to identify risks earlier, model scenarios, and engage customers and clinical partners more effectively.

Advancing care delivery with AI in the flow of clinical work

In care delivery, transformation happens when AI shows up in the flow of work. It reduces cognitive and documentation load and gives time back to clinicians. Frontier organizations use AI to shift capacity toward patients, not screens, while improving consistency and quality. As patient expectations rise and workforce shortages persist, the ability to deliver more care with the same (or fewer) resources is quickly becoming a differentiator.

Intermountain Health: Rehumanizing care by reducing documentation burden

Intermountain Health adopted Microsoft Dragon Copilot to reduce the administrative load that can pull clinicians away from patients. By supporting clinical documentation and automating routine tasks, clinicians at Intermountain Health reported experiencing a 27% reduction in time spent on notes per appointment, reducing cognitive burden and enabling more meaningful patient engagement by incorporating AI as a core part of their clinical workflow.

Cooper University Health Care: Giving clinicians time back in the flow of care 

Cooper University Health Care is using AI-powered clinical documentation to reduce the administrative burden that pulls clinicians away from patients. By embedding AI directly into clinical workflows, clinicians at Cooper reported saving more than four minutes per patient visit on documentation, experiencing less burnout, and engaging more meaningfully with patients—demonstrating how AI optimized workflows can rehumanize care at scale.

Mercy: Bringing ambient AI to nursing workflows

Nurses are at the center of care delivery and often at the center of documentation burden. Mercy has been using AI capabilities to transform nursing care. By capturing and structuring information in the flow of work, Mercy reported 8 to 24 minutes saved per shift for high-use nurses, a 21% reduction in documentation latency and a 4.5% increase in patient satisfaction from their initial rollout.

Streamlining operations and experiences across the healthcare organization

Frontier Transformation requires more than point solutions. It takes an AI-ready operating foundation that connects people, processes, and data across the organization. Frontier organizations use copilots and agents to standardize work, automate routine interactions, and deliver more consistent experiences at scale. Those that treat AI as isolated experiments often find themselves outpaced by peers who can improve service levels while bending the cost curve.

Bupa APAC: Building an AI-ready foundation to improve customer experiences

Bupa APAC is streamlining operations, automating routine processes, and making customer experiences more seamless thanks to AI. With an emphasis on AI readiness—skills, governance, and secure access to information—Bupa APAC upskilled its workforce with Microsoft 365 Copilot and GitHub Copilot, generating more than 410,000 lines of AI-assisted code, initiating more than 30,000 Copilot chats, and accelerating more than 100 AI use cases to improve care.

CareSource: Scaling compassionate service with cloud and AI

CareSource is applying AI to support operational scale while keeping a human touch. By modernizing platforms and automating processes that can slow service delivery, CareSource reduced documentation time by 75%, saved over USD125,000 on automation, and boosted developer productivity by up to 30%, helping their teams focus on the needs of members, providers, and communities.

Strengthening cyber resilience with AI

Cyber resilience is a transformation prerequisite. As care becomes more digital, AI must help defenders move at machine speed while maintaining trust and compliance. Frontier organizations use AI to triage, investigate, and report faster—reducing risk and freeing experts for the threats that matter most. In a sector where disruption can compromise patient safety, lagging security maturity can erase hard-won gains in digital transformation.

St. Luke’s University Health Network: Saving nearly 200 hours per month with AI-powered security agents

As healthcare expands its digital footprint, cyber defense becomes inseparable from patient safety and trust. St. Luke’s University Health Network is using Microsoft Security Copilot agents to accelerate phishing alert triage and to generate incident reports in minutes instead of hours. The organization reported saving nearly 200 hours per month, freeing security teams to focus on higher-value investigations and improving speed to response across its environment.

Act now to lead the future

If you’re looking at these examples and wondering where to start, focus on a few moves that help you learn quickly and scale safely.

• Start with workflows, not technology: Identify the highest-friction moments (such as documentation, imaging backlogs, complex data synthesis, member service, and security triage) and design AI interventions that measurably reduce time, effort, and risk.
• Get your foundation right, early: Prioritize secure access, identity, and data governance so copilots and agents have the right context, without compromising privacy or compliance.
• Make it real, and make it stick: Operationalize responsible AI (like oversight, evaluation, and human-in-the-loop), measure quality and safety, and invest in change management so adoption scales beyond early enthusiasts.

Start your Frontier Transformation today

These organizations show what Frontier Transformation looks like in practice—embedding intelligence across clinical, operational, and administrative work to deliver faster insights, reduced burden, strengthen security, and create better experiences at scale. The competitive bar is moving quickly. Waiting to act can mean higher costs, slower throughput, and greater strain on already-stretched teams. With deep healthcare experience and a global customer base, Microsoft can help organizations scale AI responsibly from the first workflow to redesign to enterprise-wide adoption.

The post What Frontier healthcare leaders are doing differently with AI appeared first on Microsoft Industry Blogs.

New capabilities include the ability to surface relevant work-related information alongside patient data for customers using Microsoft 365 Copilot; partner-built AI apps and agents available through Microsoft Marketplace that extend intelligence across revenue cycle, clinical insights, and decision support; and expanded role-based experiences for physicians, nurses, and radiologists designed to scale securely across settings and geographies.

Today, more than 100,000 clinicians rely on Dragon Copilot as part of their daily practice—supporting care for millions of patients every month. That kind of adoption doesn’t happen by accident; it happens when technology earns trust, fits naturally into clinical workflows, and proves its value day after day. As healthcare continues to accelerate, the question facing organizations is no longer if AI will be part of care delivery, but how quickly they can equip their teams with tools that scale safely, work across roles, and keep clinicians focused on patients. The new Dragon Copilot capabilities we’re introducing at HIMSS 2026 build on this proven foundation—extending trusted clinical support beyond documentation to meet the growing demands of modern care.

Clinicians need more than access to data—they need an AI assistant that works alongside them, understands context, and supports action across systems and settings. Built on Microsoft Azure, Dragon Copilot delivers this capability with enterprise‑grade security, responsible AI, and cloud scale—giving organizations the confidence to deploy broadly and grow with care teams wherever they work.

“We ultimately went with Microsoft because of the security, the compliance, the scalability, and the fact that they’ve delivered reliable solutions for years.”

—Snehal Gandhi, MD, Vice President and Chief Medical Information Officer, Cooper University Health Care

See what Dragon Copilot has to offer:

Unifying the disparate—so care teams can move faster, with confidence

By unifying information from across systems and sources, Dragon Copilot reduces fragmentation and unnecessary searching—bringing patient data, trusted clinical content, and partner powered AI insights into a single, contextual experience within the clinical workflow.

What makes this approach different is not just access to information, but how intelligence is delivered and applied. Clinicians can naturally query, summarize, create, and act using voice or text—without toggling between tools. Insights are surfaced instantly in one place, enabling care teams to move fluidly from understanding to action while spending less time navigating systems and more time with patients.

That intelligence is grounded in a broad set of trusted sources, including:

• Prebuilt trusted clinical content with citations
• Patient data like diagnoses, labs, medications, and allergies
• Organizational content such as policies, procedures, schedules, and communications

When needed, reliable web information can also be accessed through a safety‑first pathway—ensuring responses remain appropriate for clinical use.

Care delivery depends on more than clinical facts—it also depends on fast access to the work context around care. With Microsoft 365 Copilot, powered by Work IQ and accessible inside Dragon Copilot, clinicians can pull in relevant work-related information from connected apps and enterprise data, right where they’re already working. Work IQ is the intelligence layer that helps Copilot understand how people collaborate across emails, files, meetings, and chats—so responses are grounded in the right context. The result is a more unified experience that reduces time spent searching across tools and keeps momentum inside the clinical workflow.

Dragon Copilot extends clinical intelligence beyond any single system or screen. Instead of being locked into one interface, clinicians can invoke powerful AI capabilities wherever they’re already working—across applications, EHRs, and web pages. By simply clicking or highlighting text, Dragon Copilot can read, understand, and apply its intelligence directly in context, without forcing clinicians to switch tools or reenter information.

For example, a clinician reviewing a note can place their cursor over a sentence and say, “Add more detail about what the patient shared regarding their cardiac history.” Dragon Copilot immediately expands the documentation using the surrounding clinical context—no copying, no pasting, and no workflow disruption—helping clinicians move faster while keeping their focus on the patient, not the screen.

Building on this foundation, Dragon Copilot further unifies innovation through AI apps and agents available in Microsoft Marketplace. Developed by partners such as Canary Speech, Humata Health, Optum, and Regard, these solutions deliver capabilities across clinical insights, revenue cycle management, prior authorization, and clinical decision support. Organizations can easily purchase, deploy, and scale partner innovation—while clinicians experience those insights directly within their existing workflows.

“Sentara Health is integrating Regard’s diagnosis and documentation technology within Dragon Copilot to save time, improve revenue integrity, and most importantly improve care.

By combining Dragon’s ambient conversation capture with Regard’s ability to surface key insights from data, we expect to help our clinicians identify comorbidities and relevant diagnoses in real time without adding steps to their workflow. Our goal is straightforward: strengthen the clinical picture, reduce documentation burden, and support more informed decision-making at the point of care.”

—Dr. Joseph Evans, Vice President, Chief Health Information Officer at Sentara Health

Simplifying the complex—so care teams can be present with patients

Dragon Copilot streamlines clinical documentation and routine tasks, so clinicians spend less time navigating systems and more time focused on patient care. By simplifying physician and nursing charting, notes, flowsheets, and radiology reporting, it reduces rework and cognitive burden—helping care teams work more efficiently and confidently across the day.

This simplification is powered by healthcare-grade AI models built for clinical accuracy, with clinical note quality evaluated using the Provider Document Summarization Quality Instrument (PDSQI9)—an industry standard developed with leading academic and healthcare institutions to ensure clear, consistent, and clinically appropriate outputs.

Beyond documentation, Dragon Copilot automates high friction tasks across the workflow. Persona specific note types, automated referral letters and after‑visit summaries, summaries of prior radiology reports, and proactive coding guidance reduce manual effort and unnecessary toggling—allowing care teams to focus on decisions, not data entry.

New and expanded capabilities include:

• Proactive ICD‑10 specificity suggestions, delivered during note review to support timely, accurate reimbursement.
• Reusable custom clinical documents, created from prompts or examples and managed as templates, allowing clinicians to get additional unique content created automatically, such as custom letters.
• Pull-forward workflow support to jump-start new documentation from prior notes.
• Multilingual conversation capture, connecting with patients in their language. Captures the conversation in 58 languages and automatically converts the encounter into a note written in the primary language used in each country.
• Seamless migration from Dragon Medical One, preserving existing commands, vocabularies, profiles, templates, and AutoTexts.

Scaling across roles, geographies, and devices

Dragon Copilot is designed with role-based experiences that deliver the right capabilities to each clinician, when and where they’re needed. Physicians, nurses, radiologists, and other care team members benefit from workflows tailored to their unique responsibilities—from documentation and care coordination to image interpretation—while organizations maintain consistency, security, and compliance at scale. With a single solution spanning multiple roles, including the only experience built for radiologists and demonstrated outcomes for nurses, healthcare organizations can simplify their technology footprint and drive greater return on investment.

Physicians

Dragon Copilot supports physicians across care settings through EHR‑integrated workflows and a dedicated app available on mobile (iOS and Android), web, and desktop. Physicians can document more efficiently, access timely clinical information, and reduce cognitive load—whether at the point of care or on the go.

Together with partners, Dragon Copilot continues to scale globally and is now available in U.S., Canada, the UK, Ireland, France, Germany, Austria, Belgium, and the Netherlands.

Nurses

Dragon Copilot enhances nursing workflows by ambiently capturing documentation at the point of care and transforming conversations into structured flowsheet entries. With expanded support for all med-surg flowsheet templates and lines, drains, and airways (LDAWs) additions and removals—nurses can document more completely without disrupting care.

Through a dedicated app available on mobile (such as iOS and Android), web, and desktop, nurses can also access information from trusted medical sources, query transcripts to surface key patient details, and create concise summaries—without leaving their workflow—reducing clicks, and keeping focus on patient care.

“Dragon Copilot gives power back to nurses to spend time at the bedside with face-to-face interactions.”

—Stephanie Whitaker, MSN, Registered Nurse, Chief Nursing Officer, Mercy

Nurses using Dragon Copilot have reported reduced cognitive load, faster documentation, and improved patient experience, reinforcing the value of role‑specific AI designed for frontline care. The Dragon Copilot nursing experience is available in the United States.

“I can say that without a doubt, using Dragon Copilot has significantly reduced the time that I’m focused and worrying about sitting down and getting my charting done behind the computer.”

—Christine Dupire, Registered Nurse, Mercy

Radiologists

Paired with PowerScribe One, Dragon Copilot helps minimize repetitive tasks such as reviewing prior reports and automates routine steps in report creation. It surfaces relevant clinical context, integrates customizable AI experiences, and provides intelligent access to credible information—helping radiologists stay focused and deliver high‑quality reports with confidence. The Dragon Copilot radiology experience is currently in preview in the United States.

“As we embrace the next frontier of AI, we know that having cloud-based solutions that work seamlessly with our existing products and systems is paramount. Having Dragon Copilot as a companion for PowerScribe One gives me confidence that I can test and benefit from the latest AI advancements with minimal disruptions and distractions.”

—Sean Cleary, MD, Vice Chair of Informatics for Imaging Sciences University of Rochester Medical Center

Restoring humanity to healthcare through AI

AI will only transform healthcare if it truly serves the people delivering care. Dragon Copilot is built for that purpose—bringing role‑based experiences, hands‑free workflows, and proactive clinical intelligence together in a way that fits naturally into how clinicians work. By unifying information, reducing friction, and extending trusted intelligence across the workflow, Dragon Copilot helps clinicians spend less time managing tasks and more time connecting with patients—restoring focus, confidence, and humanity to the practice of medicine.

Join the more than 100,000 clinicians already using Dragon Copilot

• Visit us at the HIMSS 2026 Booth #2812.
• Get the playbook to learn how clinicians utilize AI to work smarter.
• Download the white paper to explore AI use cases transforming healthcare.

The post Unify. Simplify. Scale: Microsoft Dragon Copilot meets the moment at HIMSS 2026 appeared first on Microsoft Industry Blogs.

Today, a new architectural approach is finally emerging, and it is agentic. The rapid advance of agentic AI represents an evolution from reactive interactions to goal-oriented experiences across all aspects of banking. Unlike traditional keyword-based bots, agentic assistants can understand intent, maintain memory, take initiative, and orchestrate tasks across systems. They can support multi‑step workflows, operate within defined policies, and assist customers in a single, intelligent pane of access.

For banking, these advanced capabilities have finally aligned to ever-higher levels of customer expectations to make agentic AI not only viable but increasingly leveraged by leading banks.

Why banking needs a new model

Most customer-facing automation in banking is now rule-based. Traditional chatbots merely answer questions. They don’t finish tasks, much less resolve important needs. They rely on keyword matching, offer minimal personalization, and they operate as single channel interfaces that usually escalate issues instead of resolving them. Too often, this leads to low containment, long cycle times, and customer frustration.

Agentic AI assistants change the equation. They can integrate deeply into core systems, understand identity and consent policies, and provide end-to-end workflow orchestration that delivers more positive outcomes.

AI models now support multistep reasoning, secure APIs allow policy-aware actions, and cloud environments enable industry-grade identity, consent, and auditability.

The time is now for agentic AI

The rapid adoption of broad-scale agentic AI solutions in banking is the product of the convergence of some powerful trends:

• AI-native experiences have reset customer expectations: Consumers increasingly expect proactive, personalized, and frictionless digital interactions.
• Industry competition is intensifying: Highly innovative banks and financial institutions are scaling customer-facing AI capabilities and raising the bar for the entire market.
• Secure orchestration is now achievable: Banks have built robust foundations for consent, governance, compliance, and identity, all of which are essential for safe agentic actions.
• Models can now execute multi‑step tasks: Banking no longer needs to settle for static flows and limited interactions; assistants can complete complex journeys from disputes to onboarding.

As these factors accelerate, agentic banking is fast gaining momentum. In fact, it is already operational today for many financial institutions.

A three-step blueprint for agentic solutions

Microsoft’s blueprint to help banks develop game-changing innovations includes a structured, deliberate path for adopting agentic AI across internal and customer-facing scenarios. Rather than layering AI onto outdated workflows, institutions must redesign experiences with outcomes in mind. This can be done through the development of three steps of AI innovation:

Step 1: Internal employee assistants

In this step, banks strengthen the maturity of AI innovations internally, by improving employee productivity and supporting back office workflows such as Anti-Money Laundering (AML) routing, document gathering, and payment operations. This phase establishes the organizational readiness needed for external experiences.

Step 2: External customer assistants (owned channels)

In this step, banks introduce customer-facing assistants within their digital properties, such as websites and mobile apps. These solutions initially target a narrow set of journeys to help validate measurable outcomes and build confidence, setting the stage for scale, including deeper transactional use cases.

Step 3: External customer assistants on third-party platforms

Once confident, banks can deliver rich, new AI-enabled experiences beyond their own digital properties, helping to stay foremost in the customer relationship. Even as the front door shifts to non banking platforms, banks can retain primary engagement by anchoring identity and execution within governed, policy driven solutions that can incorporate agentic AI assistants from multiple platforms (ChatGPT, Gemini, Microsoft Copilot, and so on).

Real-world impact in agentic banking is well underway

Across the customer journey, agentic experiences are transforming outcomes. Here are just four areas where we work with customers to deliver measurable benefits.

Disputes and fraud resolution

Disputes and fraud incidents are among the most stressful and urgent customer interactions in banking. These moments demand precision, empathy, and speed —which traditional chatbots usually can’t deliver. Agentic assistants change this experience by understanding transaction context in real time, anticipating customer needs, explaining next steps with clarity, and orchestrating complex actions across compliance, fraud, and operations systems. They help manage escalation intelligently while keeping customers informed with conversational transparency.

Commerzbank’s introduction of an AI-powered assistant called “Ava” demonstrates the impact of this shift. Built with Microsoft Foundry Agent Service, Ava reportedly now resolves about 75% of customer conversations autonomously. The result is a dramatic reduction in response times, more consistent fraud handling, and meaningful relief for human agents who can focus on high complexity cases requiring expertise and judgment.

Product discovery and onboarding

Even when banks offer strong products, customers often struggle to understand differences, evaluate eligibility, or navigate onboarding processes. Static comparison charts and rigid forms create barriers that trigger abandonment. Agentic assistants address this gap by offering contextual, conversational discovery. They can analyze eligibility, financial behaviors, and long-term goals to guide customers toward the most relevant products, compressing the time from interest to completion.

For instance, ABN AMRO’s migration to Microsoft Copilot Studio showcases these benefits at scale. Their customer facing assistant “Anna” now supports millions of customer interactions annually, automating more than half of them. Customers receive tailored recommendations and seamless onboarding, while the bank benefits from reduced abandonment and increased conversion rates across key products.

Payments and money movement

Customers today simply expect that payments should be fast, intuitive, and free of error. Instead, many people frequently encounter multiscreen forms, confusing validation steps, and interfaces that are prone to mistakes. Agentic AI helps eliminate much of this friction. Customers can simply say what they want to do—for example, “send rent,” “transfer to my savings,” “pay my credit card”—and the assistant determines the optimal method, confirms details, and applies safeguards automatically.

A good example of this is Bradesco’s deployment of generative AI into its virtual assistant “BIA.” After integrating Microsoft Azure OpenAI and Data Lake services, BIA reportedly achieved an 82% first level resolution rate and an 89% retention rate in the first week. Response times fell from days to hours, and usage surged. Payments became conversational, secure, and reliable, helping build long term customer confidence while improving operational efficiency.

Financial guidance and servicing

Financial decisions are deeply personal and often complex. Customers want clarity, reassurance, and the sense that their institution understands their broader financial picture. Agentic assistants support this by combining institutional expertise with personalized context. They can remember life events, adapt to changing goals, and help explore scenarios, understand options, and stay informed about their financial commitments.

Virgin Money embodies this evolution through its award-winning assistant, “Redi.” Built with Microsoft Copilot Studio and Dynamics 365 Customer Service, Redi reportedly now supports millions of customers and delivers what they need more than 90% of the time. The guidance feels informed and tailored, strengthening trust and deepening long-term relationships. Employees report smoother workflows, while customers experience consistency and clarity across channels.

Advancing digital transformation with agentic AI

For banks, technology is finally catching up with customer expectations. The shift is transforming digital experiences from reactive support into proactive engagement.

Agentic AI solutions are defining the next generation of customer experiences, and banks that move now can better position themselves to gain durable competitive advantages by modernizing operations from the inside out and engaging customers in ways that were not previously possible.

Microsoft provides an unmatched set of platforms and services that combine data intelligence, orchestration, and observability to help build, deploy, govern, and scale agentic assistants. Our investments in Security for AI, Zero Trust, and AI governance, help banks keep agentic experiences safe and trusted across the AI lifecycle. This means that with the right blueprint banks can navigate this moment with confidence, clarity, and control.

Explore how agentic AI can modernize banking experiences

• Get a practical transformation framework and see how Frontier Firms lead with agentic AI in our Becoming a Frontier Firm: AI in Financial Services e-book.
• Watch Leading at the Frontier: AI in Financial Services to see how leaders in financial services are successfully using AI to transform complexity into opportunity.
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.

The post The agentic moment in banking: A blueprint for better customer experiences appeared first on Microsoft Industry Blogs.

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 Microsoft Industry Blogs.

With advances in AI—especially intelligent agents and the automation patterns emerging from agentic design—insurers worldwide are recasting their most critical operations and offerings. From marketing and customer engagement through underwriting and claims processing, the industry is rapidly evolving, with AI as a central driver.

At Microsoft, we identify organizations that embed AI agents deeply across their operations as Frontier Firms. These are innovation leaders who are blending human judgement with AI agents and who, according to a November 2024 IDC study commissioned by Microsoft, report returns roughly three times higher than slow adopters.¹

Insurers and other financial services companies make up the highest concentration of Frontier Firms, which is not surprising given the competitive nature of the sector and the outsized impact of agentic AI.

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How AI is transforming the end-to-end insurance value chain 

Insurers can potentially realize transformative benefits with AI without needing to replace their core platforms, but rather by augmenting and accelerating them through targeted, extensible, AI-powered capabilities. Through advances such as intelligent agents and the automation patterns emerging from agentic design, insurers are consolidating fragmented workflows into connected, intelligent, adaptive systems.

Consider the impact on claims processing. In 2024, more than 30 million personal auto claims were reported in the US alone.² Each one typically required adjusters one to three days just to gather, read, and interpret documents. The slow, manual nature of traditional claims processing is one of the most labor intensive and high impact functions in insurance. It is also where agentic AI delivers some of the fastest return on investment (ROI). For example, AI can help automate document understanding and summarization for faster and more accurate processing. In policy and coverage validation, it can help reduce back-and-forth queries between adjusters and underwriters and speed the approval of well-qualified claims. In contextual triage and routing, it can help improve the productivity of employees across claims processing by enhancing early fraud detection and reducing delays caused by manual sorting or misrouting. With millions of claims processed annually and cycle times measured in days or weeks, even modest improvements can potentially create significant financial and customer experience gains.

Agentic AI is reshaping much more than claims. Across the value chain, a unified agentic ecosystem can deliver measurable outcomes.

In underwriting, agents can automate information gathering processing to help sales agents submit more complete requests for quotes to underwriters. Agents can help interpret submissions, orchestrate scenarios and catastrophe modeling, and assist in generating proposals aligned to client mandates.

In marketing and distribution, agents can redefine the customer experience by increasing personalization at scale with speed and boosting sales opportunities. Agents can flag top renewals and generate personalized outreach, help prioritize leads, optimize campaigns and prepare tailored client briefs and pitch materials in seconds.

In customer onboarding and service, service become more anticipatory and less reactive. Agents can help validate information across documents automatically and detect missing forms or inconsistencies early. Virtual assistants can answer inquiries around-the-clock with contextual accuracy and trigger proactive outreach if a customer shows signs of churn or claim frustration.

In risk and compliance, teams move from firefighting to orchestrating safe, scalable operations. Under the direction of qualified processionals, agents can help monitor exposures continuously across economic, climate, and portfolio data, read regulatory updates and support assessment workflows, and help detect fraud by surfacing potential issues to the appropriate teams and workflows.

How agentic AI is benefiting insurers worldwide

Already, we’re seeing the impact of agentic AI building on the benefits of generative AI to deliver transformative new benefits for insurers.

For example, Generali France is transforming insurance operations with intelligent agents that empower front‑line workers and experts across the business to achieve a people-centric vision for product and service delivery. The firm has built more than 50 agents with Microsoft Copilot Studio and Azure OpenAI to address a broad range of specialized used cases. These agents do more than generate content, they act across complex information flows, from extracting information from unstructured data and running hyper-personalized marketing campaigns, to assisting with content creation and standardizing responses to requests for proposals (RFPs). These powerful solutions allow experts to focus on judgment and customer care, measurably helping Generali achieve top‑ranked customer satisfaction.

Elsewhere, a major global insurer strengthened its crisis response in near real-time by using AI to rapidly compare property locations with public wildfire evacuation data. Instead of hours of manual analysis, teams quickly generated clear, actionable risk insights, improving situational awareness and enabling faster, more confident communication with stakeholders.

Another insurance and financial services company took a proactive approach to risk mitigation, using AI to scan records for a brittle material linked to structural failures in older buildings, helping to identify and assess risks before losses could occur.

These real-world scenarios are only the tip of the iceberg, giving an early view of the broader transformation that is quickly redefining the competitive landscape. In upcoming blogs, we will share deeper examples and customer‑aligned scenarios across the end-to-end insurance value chain.

The journey to becoming a frontier insurer starts now

To unlock the value of agentic AI, Microsoft offers an end‑to‑end cloud and AI platform that insurers can incorporate powerful agents into their technology ecosystems. Microsoft Foundry provides the developer platform for building, testing, deploying, and orchestrating AI agents and applications, and Microsoft Agent 365 offers a control plane to help govern, secure, monitor, and manage agents across an enterprise, regardless of where they were built. This means that insurers can design, customize, deploy, and integrate intelligent agents across the value chain, with enterprise‑grade governance and a comprehensive suite of AI models and services.

Microsoft further strengthens this foundation with industry‑specific data models, process frameworks, and prebuilt connectors that simplify integration with core insurance systems, analytics environments, and workflow applications. This helps ensure faster time‑to‑value and accelerates modernization of claims, underwriting, servicing, and risk operations.

And critically, insurers also benefit from a deep, global partner ecosystem of trusted technology and solution providers who are well versed in delivering mission-critical solutions on the Microsoft Cloud, combined with our deep, long‑standing expertise in the insurance sector. Together, this ecosystem empowers insurers to innovate confidently, scale securely, and realize measurable impact with agentic AI.

The journey to agentic AI involves identifying high-impact workflows early, creating a unified data platform, addressing governance from the start, and empowering teams with smart change management. By embracing a frontier firm mindset—human led, agent operated—insurance leaders can unlock new value and innovate in the new competitive landscape. To continue your AI journey, contact your Microsoft representative or technology partner.

Next steps on your journey to agentic AI

• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.

¹ IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

² Verisk, ClaimSearch Trends Report, 2024 Year-end Analysis

The post From bottlenecks to breakthroughs: How agentic AI is reshaping insurance appeared first on Microsoft Industry Blogs.

It’s the day before a friend’s birthday. You need a gift under 50 dollars that arrives tomorrow. Instead of opening a dozen tabs, you ask an AI assistant. In a single conversation, it understands your context, evaluates constraints, and recommends a choice you can act on with confidence and feel good about giving.

That moment illustrates a shift underway in retail. ¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products. Shopping is increasingly happening within AI-guided conversations, where agents move beyond listing options to interpret intent, evaluate tradeoffs, and guide decisions in real time.

For CMOs, this is not simply another channel to optimize. It represents a fundamental change in how influence is earned and how brands compete for loyalty, margin, and growth.

What is agentic commerce and how is it changing the shopper experience

For years, the front door to retail was a homepage, a search box, or a category page. Growth came from capturing keywords, driving traffic, and optimizing funnels, often measured through last click attribution.

Agentic commerce changes that. The new front door is the conversation.

Instead of navigating pages and filters, shoppers now express intent in natural language: “I need a sustainable gift under fifty dollars for a coworker who loves cooking. It has to arrive by Friday and feel premium, not generic.” The conversation itself becomes discovery, surfacing unexpected options and sparking new ideas. Through continued dialogue, those possibilities sharpen into confident decisions.

What matters is not just what is recommended, but how that recommendation is shaped using context in the moment of decision.

How AI shopping agents generate insights that drive business growth

The following video shows agentic commerce in action. It demonstrates how a single request can generate value both for the shopper and the brand.

A shopper asks for a coat for a trip to Aspen in February. The agent immediately goes to work, considering weather data, the shopper’s style preferences, available inventory, pricing, reviews, and more.

At the same time, that request also produces actionable insights for the brand. The marketer sees intent signals combined with internal retail data and broader market trends. Patterns emerge, showing rising demand for winterwear. An AI agent recommends a targeted promotion, the marketer approves it, and the offer is delivered back to the shopper.

The shopper receives curated recommendations with a timely promotion, makes a confident choice, and heads to the slopes in the right jacket.

This is agentic commerce in action. The conversation delivers value in the moment while simultaneously generating learning that informs future business decisions. This creates a feedback loop that strengthens with every interaction.

What sets this apart from previous shifts in the industry is when influence occurs. It is no longer applied only before a decision through advertising or merchandising. Influence is now shaped during the decision itself, as real-time signals flow between shopper and brand.

What makes the agentic commerce shift different

Retail has gone through major transitions before from physical stores to websites, from desktop to mobile, and from owned commerce to marketplaces. Each wave opened new channels of demand.

Agentic commerce goes further. It changes how decisions themselves are made. It introduces a decision layer between shoppers and brands. Behind every interaction sits a learning layer, where signals flow back to merchants, informing decisions that shape recommendations, promotions, assortments, and experiences.

The scale of this shift is substantial. ²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion. This is not an incremental growth. It represents a reconfiguration of how products are discovered, evaluated, and purchased.

For CMOs, strategy now centers on guiding decisions at the moment of choice and capturing the intelligence those interactions produce. Brands that earn influence in the moment and compound what they learn over time are better positioned to build lasting advantages in pricing, loyalty, and lifetime value.

How to build competitive advantage in agentic commerce

As agentic commerce scales, two imperatives are emerging, and leading brands are pursuing both.

Ensure discoverability wherever shoppers ask

AI assistants such as Microsoft Copilot are becoming common starting points for product discovery. When a shopper asks, “What’s the best running shoe for marathon training?” or “Find a sustainable laptop bag under $200,” AI agents interpret intent and surface recommendations.

To compete in these moments, brands must ensure their products, attributes, and value propositions are accurately represented in AI platforms. Success depends on being discoverable when consumers ask questions, not just when they type keywords into a search bar.

Build owned agentic experiences that capture learning

Discovery on third party platforms creates awareness. Owned conversational experiences create advantage. When brands deploy AI agents on their own properties, they capture the context behind each decision. That intelligence feeds merchandising, pricing, inventory, content strategy, and personalization. Just as importantly, brands can use these insights to enrich product catalogs with the language, attributes, and use cases that improve discoverability on third-party AI platforms, strengthening AEO and GEO performance over time.

Trust plays a critical role here. According to Bain & Company, while consumers increasingly use AI for research, they currently trust brands’ on-site agents three times more than third-party agents. That trust advantage makes owned conversational experiences more effective at driving conversion.

Why the winning move is doing both

The question for CMOs is not whether to participate in agentic commerce. The shift is already underway. The real question is whether your brand will appear only in third-party recommendation engines, or whether it will also own the intelligence that turns interactions into durable advantages in pricing, loyalty, product development, and lifetime value.

Four actions retail leaders can take now to prepare for agentic commerce

Agentic commerce is taking shape, and the path to readiness is clear.

1. Optimize for AI discoverability. Most product data was built for search filters, not AI conversations. Brands need structured attributes, descriptions that reflect real use cases and brand voice, and accurate information for pricing, availability, fulfillment, and policies. This practice, often called AI engine optimization (AEO) or generative engine optimization (GEO), helps ensure AI can accurately represent your products when shoppers ask questions across search engines and conversational platforms.
2. Launch owned conversational experiences to learn fast. Deploying AI shopping assistants on owned surfaces creates a feedback loop. Each interaction and conversion generates insight into customer intent, preferences, and friction points. Marketers can use these insights to continuously improve product catalog data, strengthening AEO and GEO performance across all platforms.
3. Design for openness and portability. Agentic commerce spans websites, apps, stores, partner channels, and third-party platforms. Product intelligence and brand logic need to travel across surfaces so brands can participate in new ecosystems without losing differentiation or starting over each time an interface evolves.
4. Govern measurement so learning compounds into growth. As AI agents mediate more of the shopper journey, brands risk losing visibility into how decisions are made. Clear expectations around signal sharing, experimentation, and insight ownership helps ensure every interaction strengthens the business.

Trust and privacy run across all four moves. Transparent recommendations, responsible data use, and alignment with brand values shape long term loyalty.

A new growth era for retail CMOs

Agentic commerce does not replace marketers or merchants. It elevates their role, bringing intelligence, context, and action closer to the moment of choice. For CMOs, this is a leadership moment. It is an opportunity to shape how customers discover and choose products, turn data into conversational intelligence, and build trust as a durable source of differentiation.

Those who define how shoppers discover, trust, and choose in an AI mediated world will define the next era of growth in retail and consumer goods.

Prepare your brand for agentic commerce

Optimize your product data and brand content so AI agents can accurately represent and recommend your products across platforms. Access our guide on AEO and GEO to advance your business for AI-driven shopping.

Retail

Accelerate retail growth with Microsoft for Retail

Find AI use cases, customer stories, and more resources on Microsoft for Retail page.

Learn more here

¹Bain & Company estimates that 30% to 45% of US consumers already use generative AI to research and compare products.

²McKinsey projects that by 2030, the US business-to-consumer retail market alone could see up to $1 trillion in orchestrated revenue from agentic commerce, with global projections reaching $3 trillion to $5 trillion

The post How agentic commerce is becoming the new front door to retail appeared first on Microsoft Industry Blogs.

At Microsoft, we see retail moving forward into a world where agents strengthen and streamline human action through automation and intelligence on tap. Cutting-edge agentic technology will enable retailers to move faster and smarter. But the emotional connection with the consumer, the trust, creativity, and empathy that define great brands, cannot be automated. Delivering authenticity at scale will rely on a workforce model that consciously centers and invigorates the collaborative human experience—one that sees agents as force multipliers for workers to deploy in pursuit of their best work. As one retail analyst put it, “Technology alone isn’t enough—real transformation requires rethinking how we work within human-plus-agentic teams.”³ Success in the next chapter in retail will accrue to the organizations who maximize the power of these human-agent teams—what we at Microsoft call a Frontier Firm. 

That’s why we believe the agentic future should be human-led, with automation embedded in key workflows and a return on intelligence that helps people move faster and smarter. Retail executives echo this vision: “AI tools can free up front-line employees to prioritize tasks that require a human touch, such as engaging with customers and designing stores.”⁴ Consumers will continue to seek out and reward authentic, down-to-earth experiences, even as they embrace AI for convenience. The retailers who win in 2026 and beyond will be those who combine the best of both worlds: the speed and precision of agents with the creativity, empathy, and expertise only humans can deliver.

How Microsoft agentic solutions are helping move retail forward

We are excited and proud to be leading the evolution of agentic retail with right-sized development options for every organization—whether they’re looking for a fully-configurable, pro-code development experience or a low-code/no-code, fully-managed environment. 

Microsoft Foundry enables professional developers to build, optimize, and govern sophisticated AI solutions that run at the edge or in the cloud. Retailers seeking deep customization benefit from the widest selection of foundation models on any cloud, including models tailored for the retail industry, open agent frameworks, vast integrations, enterprise scalability, and organization-wide observability and controls. For example, Foundry IQ helps organizations securely ground AI apps and agents on enterprise data stored in any location, while Foundry Tools allows agents to automate workflows and respond with real-time precision across more than 1,400 business systems like SAP, Salesforce, ServiceNow, Shopify, Stripe, Adobe, and Dynamics 365. This enables retailers like The ODP Corporation and Bayer to drive business results with action-oriented and context-aware agents as part of their broader application landscape. 

Organizations looking to rapidly prototype conversational commerce and worker empowerment agents will appreciate Microsoft Copilot Studio easy low-code/no-code configurability and straightforward integrations. And for retailers looking for a turnkey experience with minimal time-to-value, we offer a curated group of managed agent templates in Copilot Studio that will help these organizations deploy enterprise-ready AI agents without starting from square one.

Each template comes preconfigured with proven logic, connectors, and workflows backed by rigorous evaluation, model tuning, and quality controls. Retailers can deploy agents with confidence knowing that Microsoft security, compliance, and lifecycle governance are built in. Native integration across Microsoft 365 and Power Platform makes it easy to embed intelligence into existing applications, automate workflows, and streamline data orchestration.

Together, these capabilities reduce implementation time, lower IT overhead, and accelerate time-to-value—quickly moving organizations from pilots to real, scalable outcomes.

Today, we are introducing three agents built specifically for retail: one for product discovery, one for catalog enrichment, and one for store operations. Each is designed to help retailers tame complexity, improve data quality, and deliver higher-value customer and employee experiences.

Personalize retail journeys with the personalized shopping agent

The personalized shopping agent template serves as a digital expert associate available across retail channels. It goes beyond basic search-and-scroll by delivering guided, natural language product discovery that helps the shopper feel like a knowledgeable store associate is assisting them.

Instead of returning loosely related results, the agent asks clarifying questions, interprets nuance, and offers informed recommendations that reflect real customer needs. Retailers can tune the agent’s voice, language, and style to match their brand, ensuring every interaction reinforces the retailer’s identity and expertise.

Whether customers are searching for a fragrance that evokes a memory, planning an outfit for a themed event, or finding the right running shoes for an upcoming marathon, the agent engages in thoughtful conversation to guide decision making. Built on Microsoft Foundry’s enterprise grade AI stack—including Azure OpenAI in Foundry Models, Azure Machine Learning prompt flow, and Microsoft Fabric—the agent delivers personalized discovery at scale while remaining easy to integrate across web, mobile, and in-store experiences.

Its headless and tailless architecture allows retailers to pull from multiple systems and maintain a consistent, branded experience anywhere it is deployed. Cross-sell and upsell intelligence, financing suggestions, and even in-home service recommendations help retailers drive higher conversion and customer satisfaction.

Leading retailers are already putting this to work. Ralph Lauren’s “Ask Ralph” virtual stylist runs on this agent template, with comprehensive knowledge of the brand’s products, design philosophy, craftsmanship, and history, emulating the immersive experience of a Ralph Lauren flagship store.

Catalog enrichment: From messy product data to meaningful content

For more than three decades, retailers optimized their product catalogs for three-word search terms. But conversational search shines when it can query catalogs full of detailed, multi-dimensional product attributes. Retailers eager to surface the perfect personalized product suggestions to their customers can quickly improve the accuracy and relevance of conversational search results by augmenting their catalogs with a rich set of attributes for each product.

Modernizing and enriching product descriptions is no small task, however. Even if a retailer could resolve all the gaps and inconsistencies in their legacy product catalogs, manual data entry, inconsistent vendor information, and the scale of product refresh cycles promise ongoing operational friction and disappointing conversational search results.

The catalog enrichment agent template solves this by automatically cleaning, completing, and standardizing product information. Designed for simplicity and accessibility, the configured agent integrates directly into familiar environments like Microsoft Teams and Microsoft 365 Copilot Chat, where merchandisers can review flagged items, approve updates, and act quickly as product offerings change.

Because the agent does not rely on a fixed schema or centralized data source, it can ingest product details from images, PDFs, structured tables, or unstructured documents. It then transforms that information into clean, brand-aligned catalog entries. The agent can work independently or alongside existing systems, giving retailers a flexible, modular way to improve data quality without large-scale re-platforming.

Once data is ingested, the agent extracts key attributes, applies brand guidelines, aligns content to the retailer’s taxonomy, and generates consistent product descriptions. It can process thousands of products—flagging low-confidence entries and producing review-ready outputs—before teams even start their day.

As merchandisers provide corrections, the agent learns and improves over time. Whether operating in auto approval mode or with human review, it dramatically reduces manual workload and improves the accuracy of product data. The result is better product discovery, higher customer confidence, and more scalable catalog operations.

“With Microsoft’s catalog enrichment template forming the backbone of our personalized shopping experiences, we can turn product details into meaningful insights that help shoppers discover styles in real time, receive tailored recommendations, and explore complete looks. It’s a powerful step forward in our commitment to delivering service that’s as dynamic our brand.”  

David Torrecilla, Head of Innovation at Guess

Store operations: Turning real world signals into real time action

The store operations agent template is a prebuilt, low-code solution that converts real-world signals into timely, actionable guidance for every store. It continuously monitors external factors—weather, events, holidays, and seasonality—and triggers operational recommendations when conditions change.

Store managers can review, approve, or refine these suggestions, and the agent improves with each piece of feedback. Once approved, the agent orchestrates execution through Microsoft Teams Planner, creating and assigning tasks with clear instructions, due times, and progress tracking. This keeps teams aligned, improves task compliance, and shortens the signal-to-action cycle.

The agent includes integration stubs and templates so retailers can connect to inventory, shipping, workforce planning, and other systems. It can be tailored to specific policies, playbooks, KPIs, and regional requirements, enabling day-to-day operational precision.

Strandbags, the largest specialty luggage and accessories retailer in Australia and New Zealand, is one of the early adopters. Their teams are using the store operations agent template to act more quickly on local insights and stay ahead in a dynamic retail environment.

Murdoch’s Ranch & Home Supply is also deploying the store operations agent template in locations across six U.S. states.

 “Beyond efficiently aggregating data, the agent provides valuable insights into community events, sales trends, and actionable recommendations for improving store performance. A key aspect of this agent is its integration into our ecosystem, allowing us to continuously refine its capabilities and enhance the value it delivers.” 

Steven Potratz, Senior Learning and Development Leader at Murdoch’s

Shirley Gao, Chief Digital and Information Officer of PacSun, shared that the objective of the store operations agent template deployment underway in PacSun’s retail locations is “to embed AI at the core of store operations, equipping associates with predictive intelligence, actionable workflows, and real-time business alerts. This enhances operational visibility and accelerates data-driven decision-making at the store level. Through continuous co-creation and adaptation to our unique processes, we ensure AI delivers meaningful impact for both our business and our people.” 

Transforming retail end-to-end

Every product, every collection, and every campaign is part of a broader narrative retailers use to connect with customers. But delivering those experiences consistently requires precision, context, and agility.

Microsoft’s retail agent templates are designed to make that possible.

• The personalized shopping agent template turns product discovery into a tailored conversation.
• The catalog enrichment agent template ensures foundational data is accurate, complete, and aligned to the brand.
• The store operations agent template transforms signals into action and orchestrates execution at scale.

Together, they form a connected ecosystem that supports the full arc of retail storytelling—from data to discovery to delivery. By pairing agentic automation with human judgment, these solutions help retailers scale their brand, delight customers, and achieve greater return on intelligence (ROI).

All three agents are available today in Microsoft Copilot Studio and Microsoft Marketplace.

Retail is entering a new era—one built on intelligence, speed, and empowered teams. I’m excited to partner with you as we take the next step forward.

Join me on Monday, Jan 12, 2026 on the Big Ideas stage at NRF 2026 for Becoming a Frontier Firm: Unlocking the New ROI—Return on Intelligence as I explore the journey to an agentic future. 

Hear from Kathleen Mitford, Microsoft Corporate Vice President of Industry Marketing in conversation with Adobe and The Coca-Cola Company about the future of retail growth on the Big Ideas stage. Don’t miss Personalization at Scale: Using AI to unlock the next phase of growth on Sunday, January 11, 2026.  

Enjoy an illuminating fireside chat, The future of an icon: Ralph Lauren’s journey of heritage, innovation and partnership with Microsoft on the keynote stage on Monday, January 12, 2026. Featuring Shelley Bransten, Microsoft Corporate Vice President of Worldwide Industry Solutions and David Lauren, Chief Branding and Innovation Officer at Ralph Lauren, this session will explore how iconic brand heritage and digital creativity converge to shape the future of luxury retail. 

Interested to learn how you can jump-start your agentic future? Visit us at Booth #4503 to experience:

• AI-powered shopping journeys.
• Intelligent merchandising and catalog enrichment.
• Dynamic supply chain orchestration.
• AI-assisted workforce solutions.
• Customer stories across throughout in-booth demos and theater that showcase the ROI of AI and agents today.

Explore solutions and more

• Find out how you can start deploying agents across your organization with Microsoft Agent Factory. 
• Learn how agentic AI and Microsoft Dynamics 365 are helping organizations shape the future of retail.
• See Forrester’s Predictions 2026: Digital Commerce.
• Discover how your organization can become a Frontier Firm at a Microsoft AI Tour event near you.
• Explore key retail AI use cases, customer stories, partner solutions, and more on the Microsoft for Retail homepage.
• Follow Microsoft Cloud on LinkedIn.
• Read about how Microsoft is setting the standard for responsible AI practices.

¹ Morgan Stanley, Here Come the Shopping Bots, December 8, 2025.

² Adobe, Adobe: Cyber Monday Hits Record $14.25 Billion in Online Spending with Over $1 Billion Driven by Buy Now Pay Later, December 2, 2025.

³ Slalom, Industry Outlook, Retail Industry Trends 2026.

⁴ Snowflake, The AI Tipping Point: What Retail Leaders Need to Know for 2025, February 4, 2025.

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