Return on intelligence and trust

For media and entertainment organizations, unlocking value from AI at scale depends on two things: intelligence and trust.

Built on three complementary elements—Work IQ, Fabric IQ, and Foundry IQ—Microsoft IQ is the intelligence layer that connects AI, data, and context across the media value chain. It gives AI agents a deep understanding of how creative teams work, how content moves through production and distribution, and how business decisions are made. This shared intelligence accelerates content creation, personalizes audience engagement, streamlines operations, and opens new paths to monetization—all while keeping human intent and creativity at the center.

None of this works without trust. Media organizations operate under intense intellectual property (IP), regulatory, anti-piracy, and contractual constraints. Frontier transformation depends on intelligence that is secure, governed, and observable by design. Microsoft delivers this through an AI control plane, with Agent 365 providing unified governance, identity, and observability across agents—ensuring they are discoverable, auditable, and policy‑controlled as they operate across creative, operational, and business workflows. Combined with Microsoft’s end‑to‑end security and compliance stack—spanning Microsoft Entra, Microsoft Purview, Microsoft Defender, Fabric, and Foundry—media organizations can scale AI confidently while protecting creative IP on a global scale. 

At NAB Show 2026, Microsoft is showcasing how media companies can move beyond experimentation to real business impact with AI. Through a single, unified platform that brings together AI, data, intelligence, and governance, Microsoft enables connected, actionable insights that help media organizations use intelligent work, AI-powered creation, agentic operations, and new growth with AI.

Read more to see where frontier media organizations are already achieving this.

Intelligent work

Frontier media organizations start by transforming how people work. Instead of being spread across dashboards and systems, intelligence shows up directly in the flow of work through Copilot, agents, and Microsoft IQ. See how a few examples of frontier media organizations are embedding intelligence into everyday work:

• Publicis Groupe announced it is expanding its partnership with Microsoft to enable intelligent, agent‑driven work for its more than 110,000 employees while powering the future of agentic marketing for its customers worldwide. Publicis is rolling out Microsoft 365 Copilot across its workforce to embed AI into daily work. Additionally, Publicis Sapient’s Slingshot framework will use Microsoft’s cloud, while Sapient AI solutions will integrate Microsoft Copilot Studio, Microsoft Agent 365, and Microsoft IQ, enabling customers to embed AI directly into core business processes. Sapient’s Bodhi platform will then allow organizations to deploy and scale secure, enterprise-grade AI agents across the business. The partnership is anchored in Epsilon, Publicis’ IP intelligence layer. AI agents built on Microsoft Fabric and powered by Epsilon will be able to reason, decide, and act on trusted, real-world data, to deliver impact that extends beyond model performance to sustained business value.
• The New York Jets are using technology to turn one of the most high‑pressure moments in sports—the NFL Draft—into an example of intelligent work in action. Through their Microsoft powered Titan platform and Copilot enabled tools, coaches, scouts, and front office leaders bring together film, analytics, historical data, and real‑time insights to make faster, better‑informed decisions. By augmenting human expertise with AI and cloud intelligence, the Jets show how intelligent work helps teams operate with speed, alignment, and confidence when every decision matters.

Together, these organizations show how intelligent work starts by meeting people where they already work and embedding intelligence directly into daily media workflows.

AI-powered creation

For creators and content teams, intelligence must move as fast as the moment. Frontier media organizations connect content, audience signals, and creative context in real-time, so insight immediately translates into action. See how frontier media organizations are using AI-powered creativity to scale their content:

• Collective Artists Network is working with Microsoft to support creators with AI-native content systems that keep human storytelling at the center. By embedding intelligence into filmmaking workflows, the collaboration aims to help teams iterate faster while preserving director-led creative vision.

We’re using technology being developed here in India to take our culture and history to a global audience, at a scale that wasn’t possible earlier. For us, this is a long-term priority, building stories that are rooted in who we are, but can travel anywhere in the world.

—Vijay Subramaniam, Founder and Group CEO, Collective Artists Network

• The NBA uses Microsoft Azure AI to power dynamic highlights, real‑time stats, and in-game insights embedded directly into fan touchpoints like the NBA App—bringing fans closer to the action through personalized, data‑driven experiences.

Microsoft has also announced new Microsoft AI models in Microsoft Foundry and Microsoft AI Playground to help media organizations further accelerate this shift. MAI-Transcribe-1 delivers state-of-the-art speech-to-text transcription across the top 25 most-used languages.¹ MAI-Voice-1 generates natural, realistic speech, that preserves speaker identity even across long-form content. MAI-Image-2 was created with photographers, designers, and visual storytellers, delivering natural lighting, accurate skin tones and texture, and clear in-image text for diagrams, layouts, and graphics.

Empowering creators is not about adding AI features. It is about orchestrating intelligence across content, data, and delivery—so creativity becomes action in real time.

Agentic operations

The most profound transformation in media today is operational. Frontier organizations are embedding intelligence across the entire media supply chain—from production and post to rights, distribution, and monetization—using agentic systems to replace manual handoffs with coordinated, end-to-end workflows.

• Penguin Random House is using agentic AI to modernize accessibility at scale, embedding governance and human oversight into core publishing workflows to improve efficiency and compliance.

Penguin Random House leverages Azure AI to scale the creation of high‑quality, context‑aware Alt-Text content across our e-book catalog. This initiative advances our accessibility commitments while materially reducing manual effort, cost, and operational complexity. By embedding Azure OpenAI into our accessibility workflow with a human‑in‑the‑loop governance model, we can generate image descriptions at scale, strengthening regulatory compliance and enabling a more accessible and efficient publishing process. 

—Christopher Hart, CIO Penguin Random House 

• The International Tennis Federation (ITF) is using Microsoft Azure and AI orchestration to power a real‑time intelligence platform that unifies match telemetry and delivers instant, on‑court insights to coaches and players. By processing more than 700,000 data points per match and generating over 1,500 statistical combinations in real time, the ITF is enabling teams to make faster, data‑driven decisions during play through applications like Match Insights, helping standardize access to advanced analytics across more than 140 competing nations regardless of their resources.

• Kantar is using Microsoft Copilot Studio to deploy teams of AI agents that automate complex data preparation tasks across its global operations. By breaking down manual workflows such as translating documents, validating policies, and organizing HR content into smaller subtasks handled by specialized agents, Kantar enabled its People Team to clean, tag, and structure 4,000 artifacts into 400 policy documents in just six weeks, laying the operational foundation for scalable, agent‑driven workflows that support employee queries across 60 countries.

With Foundry IQ and Fabric IQ, agents now operate with shared context across data, workflows, and knowledge—allowing operations to scale without chaos and intelligence to move end-to-end.

Additional partner solutions continue to enable agentic operations:

swXtch.io will introduce swXtch.ai and the swXtch AI Router, a platform that integrates with Microsoft Fabric and NVIDIA AI to enable real-time AI in live media workflows through a simple chat-driven interface, reducing the need for custom pipelines or specialized expertise.

New growth with AI

The clearest signal of frontier leadership is how media organizations innovate. Instead of experimenting at the edges, leaders are building AI‑native platforms that unlock entirely new creative and commercial opportunities.

See how some of these frontier organizations have experienced new growth with AI:

• Microsoft recently announced a partnership with the Mercedes‑AMG PETRONAS Formula 1 Team to apply cloud and enterprise AI across race strategy, team operations and business intelligence, transforming massive volumes of telemetry into real‑time intelligence from the factory to the circuit. With each car generating more than a million data points per second, Microsoft technology helps turn complex race data into faster insights that power smarter decisions and more effective strategies in the moments that matter most. Together, the companies are harnessing data as intelligence to drive performance and strategy, enabling teams to move from raw information to sustained competitive advantage both on and off the track.
• Art Basel is using Microsoft Foundry to power the Art Basel Companion app, unlocking new digital pathways for audience growth and artist discovery across its global fairs. With AI‑powered features such as personalized recommendations and instant artwork recognition through the Art Basel Lens, the platform creates new opportunities for deeper visitor engagement—helping attract new audiences, increase return visits, and expand how collectors and fans interact with galleries through AI‑enabled discovery.
• The Premier League is using Azure AI and Foundry to unify decades of match statistics, editorial content, and video into real‑time, personalized digital experiences for its global fanbase. By enabling rapid innovation through agentic AI and real‑time personalization, the League has unlocked new forms of fan engagement across its owned platforms, driving a 20% year‑over‑year increase in engagement and activating more than 60 million users in the early months of rollout.

Additional partner solutions continue to unlock new growth with AI:

SymphonyAI’s Revedia is an AI‑first platform supporting over $40B in industry content revenue, rapidly ingesting and normalizing third‑party data to deliver accurate revenue and viewership insights at scale. Beyond data management, the Revedia Suite provides prescriptive intelligence—recommending actions and forecasting outcomes to maximize distribution performance and revenue. Revedia is trusted by a broad cross‑section of the media industry, including major studios, broadcasters, cable networks, and Direct-to-Consumer (D2C) platforms.

The Microsoft and MediaKind partnership continues to accelerate, with MK.IO emerging as the proven cloud-native streaming platform for live sports. Built on Azure, MK.IO supported DAZN’s delivery of the FIFA Club World Cup 2025, streaming 63 matches to audiences across over 200 markets with consistent, broadcast-quality performance. It reflects a broader industry shift toward platforms that combine reliability with the agility of API-driven services. A transformation MediaKind is showcasing at NAB 2026 through MK.IO’s self-serve platform and large language model (LLM)-optimized documentation, with live demonstrations in Microsoft’s booth highlighting AI-assisted workflows in action. This momentum continues to grow through MediaKind and Microsoft’s collaboration on some of the most prestigious sports ecosystems in the world, including ongoing work supporting top-tier football experiences such as the Premier League. 

Join us at NAB Show 2026

Frontier media organizations are already proving what is possible when intelligence, data, and trust come together on a single platform. Join Microsoft at NAB Show 2026 to see how Copilot, agents, Microsoft IQ, Foundry, and Fabric come to life through real deployments, live demos, and customer stories shaping the future of media.

¹ Top 25 languages by Microsoft product usage

The post Powering intelligent media: How frontier organizations realize a return on intelligence with Microsoft appeared first on The Microsoft Cloud Blog.

This evolution reflects a broader shift in how healthcare approaches security. Rather than responding to risk after the fact, organizations are embedding security across identity, data, infrastructure, and applications—building resilience as a foundational capability that supports innovation at scale.

For some organizations, AI is being adopted faster than traditional governance structures can keep pace. According to Microsoft’s 2026 Data Security Index, only 47% of organizations across industries report implementing specific generative AI security controls, underscoring a need for clearer security visibility to support safe AI adoption. A multinational survey of more than 1,700 data security professionals commissioned by Microsoft from Hypothesis Group found that 29% of employees have already turned to unsanctioned AI agents for work tasks.¹ 

Together, these trends are creating new challenges around data handling, security visibility, and compliance, especially as AI tools interact with sensitive or unstructured data. As AI moves into autonomous agents embedded in workflows, these gaps in governance and visibility become exponentially harder to manage.

At the same time, healthcare leaders are responding. Healthcare organizations are accelerating investment in technical and operational safeguards and implementing more specialized controls to govern AI responsibly. The message is clear: governance and security foundations play an important role in responsible AI adoption.

Operating security at a global scale gives Microsoft a unique perspective on how threats evolve and how defenses must adapt. Microsoft processes more than 100 trillion security signals every day,² applying insights from a global network of security engineers and partners to develop protections that support the unique regulatory requirements of environments like healthcare.

What real-world impact looks like in healthcare security

Across healthcare, organizations are facing expanding digital environments, rising threat volumes, and teams under constant pressure to protect patient data. The following examples illustrate how some organizations are approaching these challenges as they modernize their security operations.

St. Luke’s University Health Network: Scaling security operations without slowing care delivery

With 15 campuses, 300 outpatient sites, and more than 2.5 petabytes of data in motion, St. Luke’s University Health Network manages a highly complex digital environment. Protecting that environment while maintaining operational continuity requires security operations that can scale efficiently and respond quickly to potential threats.

Like many large health systems, St. Luke’s faced fragmented visibility across multiple security platforms. Analysts were overwhelmed by user‑reported suspicious emails and false positives, slowing response times and increasing the risk that real threats could be missed.

To modernize its Security Operations Center, St. Luke’s adopted Microsoft Security Copilot, giving analysts unified, real‑time visibility and AI‑assisted investigation. By consolidating information across security tools and using AI‑assisted analysis, the organization reduced manual effort for analysts and improved consistency in how potential threats are reviewed and prioritized.

The impact:

• Nearly 200 hours saved per month.
• Thousands of false positives automatically resolved.
• Faster, more consistent threat response at scale.

Providence Care: Unifying security to improve visibility and response

Serving more than 15,000 patients across over 14 sites, Providence Care faced a challenge around complexity. A patchwork of disconnected security tools created visibility gaps and operational strain for a small IT team responsible for thousands of users and devices.

This fragmented approach made it harder to detect issues early and respond quickly, keeping the team stuck in reactive mode. Providence Care needed to simplify its environment while strengthening protection across identities, devices, and data.

By consolidating on Microsoft 365 E5 and unified Microsoft security capabilities, including Microsoft Defender and Microsoft Purview, Providence Care established a modern, cloud‑native security foundation. Consolidation reduced complexity and gave the IT team time back to focus on higher‑value work.

The impact:

• Reduced tool sprawl and improved visibility.
• Faster detection and response.
• IT teams shifted from reactive work to analytics, automation, and AI readiness.

Mitsubishi Tanabe Pharma: Modernizing security to scale innovation

As life sciences organizations expand digital transformation efforts, the volume and value of sensitive research and clinical data continue to grow, along with the cyber threats targeting it. Advancing its long‑term vision for data‑driven innovation and precision medicine, Mitsubishi Tanabe Pharma faced increasing security alert volumes across cloud environments and rising pressure on specialized teams responsible for protecting critical systems and data.

Fragmented security visibility limited context for rapid analysis, slowing response times and making it harder to securely scale digital initiatives across the organization. To address these challenges, Mitsubishi Tanabe Pharma modernized its security operations by unifying cloud visibility and security monitoring, strengthening threat detection and incident analysis, and improving security literacy across teams. This approach established a more resilient, cloud‑ready security foundation aligned to its broader digital strategy.

The impact:

• Reduced manual effort through automation and consolidation.
• Improved focus for security and IT teams.
• A shift from reactive investigation to proactive risk management.

Across providers and life sciences, the same fundamentals show up again and again: simplify, unify visibility, and reduce the noise that slows response. AI-powered, end-to-end security helps healthcare organizations run security operations across complex IT environments.

Building secure AI foundations with a phased approach

Strengthening healthcare security is a journey. A phased approach helps organizations address the most critical risks first while building long-term resilience. Microsoft’s Cloud Adoption Framework outlines three phases: Govern AI, Manage AI, and Secure AI. This approach helps healthcare organizations establish responsible AI practices and reduce risk as innovations like AI agents reshape how data is accessed and used. Grounding this work in Zero Trust principles, “never trust, always verify,” helps ensure interactions are authenticated, authorized, and continuously monitored as part of a broader security strategy.

Healthcare leaders are navigating AI adoption in one of the most regulated and trust‑sensitive industries in the world. Microsoft brings a distinct advantage to this moment: decades of experience supporting healthcare organizations, combined with security operations at global scale.

Through its Secure Future Initiative, Microsoft applies lessons learned from operating one of the world’s largest security platforms and translates them into practical patterns and practices designed for highly regulated environments like healthcare. When security is embedded as a foundation, not an afterthought, organizations are better positioned to govern AI responsibly, protect patient trust, and move forward with confidence.

From real‑world impact to practical next steps

Across these examples, the common thread is not technology alone, but disciplined progress, building security foundations that can support increasingly autonomous AI scenarios over time. For healthcare leaders navigating similar pressures, progress often starts with a phased, intentional approach rather than a single, all-at-once transformation.

As healthcare organizations introduce new AI innovations like agents, establishing a strong security foundation rooted in Zero Trust principles helps leaders move forward with confidence and control. While achieving Zero Trust takes time, adopting a phased strategy allows for steady progress and builds confidence in securely integrating AI. 

Extending the conversation

Security is a shared responsibility, and progress depends on collaboration across the healthcare ecosystem—including customers, technologists, and partners. Through open dialogue and shared learning, healthcare leaders can continue strengthening resilience as technologies and threats evolve.

Explore guidance on building a more resilient healthcare security posture, covering cloud security, compliance, and governance in an AI‑enabled world.

• Stay ahead with our Healthcare Security Brief podcast, offering practical insights from Microsoft security partners on AI‑era security, privacy, and compliance. Listen to the podcast on Spotify, Amazon Music, and Apple Podcasts.
• Read the e-book: Strengthen cybersecurity and compliance in the era of AI.
• Download the infographic: How AI can help fortify security and compliance.
• Deep dive into the Microsoft Data Governance and Security Guide.

¹ July 2025 multi-national survey of more than 1,700 data security professionals commissioned by Microsoft from Hypothesis Group.

² Microsoft Digital Defense Report 2025: Safeguarding Trust in the AI Era, Microsoft Security, 2025.

The post Building secure foundations for responsible AI in healthcare with Microsoft appeared first on The Microsoft Cloud Blog.

• Watch global leaders discuss the future of industrial intelligence: Register now 
• Get the latest insights on industrial transformation: Get the e-book 
• New: The Procurement Agent in Dynamics 365 Supply Chain Management 

Three global industrial leaders—ABB, Krones, and TK Elevator (TKE)—are redefining their industries by using advanced AI and trusted cloud platforms to become Frontier industrial organizations. With Microsoft, they’re turning data, processes, and context into intelligence that drives efficiency, agility, and innovation. Every Frontier organization gets two fundamental things right: intelligence and trust. They amplify what’s unique in their people and operations with AI that is governed securely on their own terms. Trust isn’t added at the end; it ensures intelligence is used responsibly and outcomes happen as intended. Hannover Messe 2026 is where these transformations take center stage, showing how Frontier organizations are shaping the next era of manufacturing.  

• ABB—AI as a Real-Time Industrial Co-Pilot: ABB, a global technology leader in electrification and automation, will showcase its cloud-powered Genix Industrial AI platform running on Microsoft Azure. Genix acts as a real-time industrial co-pilot on the factory floor by analyzing streaming data from equipment and sensors and delivering actionable insights and recommendations to operators and managers in real-time. At Hannover, ABB’s demos illustrate how Genix enables closed-loop, AI-driven optimization of production processes on the fly, for example, by automatically adjusting parameters to improve energy efficiency, asset performance, and reduce unplanned downtime. Designed with a modular, scalable architecture, Genix integrates seamlessly with existing industrial systems, eliminating the need for large‑scale platform replacements and enabling rapid time to value. With the integration of generative and agentic AI, the platform not only provides insights, but also automates actions, while keeping humans “in the loop” for all critical decisions to ensure safety and operational decisions. This approach demonstrates how ABB is moving toward autonomous, self-optimizing operations without sacrificing human oversight, a hallmark of a Frontier organization. 

• Krones—From Bottling Machines to “Bottle-as-a-Service”: Krones, one of the world’s largest bottling equipment manufacturers, is using AI to reinvent its business model and engineering process. With Microsoft’s help and the ecosystem of trusted partners Ansys (part of Synopsys), NVIDIA, Softserve, and CADFEM, Krones integrated advanced AI-based fluid simulation into its digital twin of a filling line, packaged with a multi-agent experience, for their engineers to create these complex simulations with natural language queries. This innovation has slashed simulation times from four hours to under five minutes (a 95% reduction), allowing engineers to optimize machine parameters virtually and dramatically shorten commissioning time. The payoff is huge—Krones can now rapidly tailor designs for each customer and ensure optimal throughput. At Hannover Messe 2026, Krones will demonstrate how these AI-powered digital twins let them forecast and fine-tune production faster and more flexibly than ever, turning a traditional machinery business into a Frontier digital services company. 

• TK Elevator—Digital-Native Elevators and Agentic AI: TK Elevator (TKE) is revolutionizing mobility for 1.5 billion users by combining digital-native products, secure cloud and data platforms, and agentic AI, all in partnership with Microsoft. At Hannover Messe, TKE highlights its EOX and HELIX elevators, which are eco-efficient, AI-ready, and IoT-enabled as part of the MAX on Azure platform. Azure Databricks supports their unified analytics, ensuring data governance and enabling scalable AI workflows. TKE’s specialized AI agents, alongside the Digital Operations Center, streamline service by assembling contextual briefings before technician visits and capturing insights afterward, turning technician knowledge into organization-wide intelligence. 

These three examples are among many Microsoft customers and partners joining us at Hannover Messe 2026, with live demos showing how industrial intelligence turns data into faster decisions, safer operations, and more resilient manufacturing. 

Industrial Intelligence Unlocked, Microsoft’s overarching theme for Hannover Messe 2026, reflects the belief that manufacturing’s next era will be driven by human ingenuity and AI—grounded in trust. Microsoft provides a unified intelligence layer for the tools your employees use; Work IQ understands how people collaborate and decide. Fabric IQ delivers real-time visibility across assets, production, and supply chains. And Foundry IQ combines institutional knowledge like procedures, standards, and history with AI. Together, they help manufacturers connect teams, processes, and technology across the value chain. 

1. Redefine product lifecycle intelligence

This neighborhood focuses on uniting engineering and operations through data-driven intelligence, so manufacturers can design and deliver better products in less time. Here you’ll see how Microsoft is helping companies create a closed-loop product lifecycle —connecting every stage from design and simulation to production feedback. For example, Microsoft and NVIDIA are collaborating to power the next generation of physical AI by integrating NVIDIA Omniverse libraries with Microsoft Fabric.  

By blending real-time data, AI, and virtual simulation in one environment, companies can iterate designs faster with greater confidence. Imagine optimizing a new machine design virtually (with accurate physics and live data) before anything is built—reducing costly physical prototypes and accelerating time-to-market.  

In short, the product lifecycle intelligence zone shows how integrating data + simulation + AI yields smarter product decisions and faster innovation. 

Microsoft ecosystem partners showcased in this area: Aras, Brembo Solutions, Celebal Technology, NVIDIA, PTC, Tata Consultancy Services (TCS) 

2. Run AI-powered factories 

In our AI-Powered Factories area, we demonstrate how to coordinate machines, materials, and people with AI, turning traditional facilities into adaptive, self-optimizing operations. Microsoft supports the ability to scale these operations with a unified intelligence layer powering AI insights and a consistent framework for managing agents, models, data and infrastructure with the adaptive cloud approach. 

A highlight here is Microsoft’s approach to industrial edge AI. Foundry Local on Azure Local enables manufacturers to deploy and run AI models, including those from the Foundry model catalog —directly on factory equipment or on-premises servers for scenarios that require ultra-low latency, data locality, or offline operation. This capability supports high-speed vision inference for quality inspection, anomaly detection, and predictive maintenance, all in real time without relying on constant cloud connectivity. Manufacturers can choose curated open-source models from the managed catalog or deploy custom OCI/Docker models on CPU or GPU systems. 

Discover how the latest Azure IoT Operations release simplifies OT data management—now with no-code pipelines, seamless device control from cloud to edge, and direct support for third-party MQTT brokers and Litmus Edge gateways. In addition, upgrades to Azure IoT Hub and firmware analysis, enabled by Azure Arc make it easier for industrial organizations to securely manage and update large device fleets with unified Azure security and certificate management via Azure Device Registry integration. Learn more about how Microsoft and our partners are providing the foundation to initiate and scale industrial AI projects in our two-part blog series: Making Physical AI Practical for Real-World Industrial Operations: Part 1 and Part 2. 

Together, these capabilities come to life in Microsoft’s Factory of the Future demo—showing how adaptive cloud, edge intelligence, and Physical AI work together in a real manufacturing environment. The Factory of the Future demo shows how Physical AI comes to life when design, simulation, and execution are connected into a single, adaptive manufacturing system. In collaboration with Hexagon, Siemens, NVIDIA, KUKA, and others, Microsoft demonstrates an end-to-end scenario where AI-assisted product design is validated in simulation and then executed in a live manufacturing cell. Real-time telemetry flows from the factory floor through Azure IoT Operations at the edge and into Microsoft Fabric, where AI agents’ reason across operational signals to proactively detect issues and support action.  

Microsoft ecosystem partners showcased in this area: Accenture Avanade, AVEVA, Hexagon, Kuka, NVIDIA, Schneider Electric, Siemens, Sight Machine, Rockwell Automation 

3. Build trust across human–agentic teams

As AI agents move from pilots to daily operations, trust becomes the factor that separates insight from impact. In manufacturing environments, AI only delivers value when people are confident enough to act on its recommendations. For frontline workers, trust means clarity at the moment of action. AI agents assemble contextual briefings that bring together equipment performance, recent alerts, maintenance history, and safety guidance, so technicians arrive informed and prepared. Recommendations are visible, explainable, and designed to support human judgment, not replace it. For engineers, planners, and operational leaders, trust means confidence at scale. As AI agents operate across factories, service networks, and supply chains, organizations need visibility into how decisions are made, what data is used, and when human approval is required. Governance, auditability, and clear accountability ensure AI actions align with operational priorities and policies. 

Manufacturers can now use Researcher in Microsoft 365 Copilot in Dynamics 365 Field Service. Powered by WorkIQ, teams can bring together signals from work orders, service history, parts availability, and Microsoft 365 context to investigate issues faster and take informed action, improving first-time fix rates, reducing downtime, and maintaining governance. 

This human–agent operating model reflects Microsoft’s approach to industrial AI. Intelligence proposes that. People decide. Trust is built into the system so AI can move beyond insights and support real operational action across the enterprise. 

Microsoft ecosystem partners showcased in this area: Bosch Connected Industry, Cognite, Kongsberg Digital, SymphonyAI. 

4. Orchestrate supply chains with AI agents

The fourth booth zone looks beyond the factory floor to the end-to-end value chain, where volatility, constraints, and customer expectations converge. Here we show how manufacturers can go from reactive coordination to agentic supply chains. From networks of suppliers, plants, and logistics partners connected by AI agents that continuously scan for change, reason across data, and support action in real time. These systems go beyond visibility, helping leaders anticipate disruption and respond with speed and confidence. 

Procurement is often first to feel disruption, where speed, context, and control matter most. The Procurement Agent in Dynamics 365 Supply Chain Management helps teams handle supplier communications and exceptions, assess downstream impact, and keep people in review.  

AI-assisted agents reduce manual effort while keeping our people in control…strengthening collaboration and improving outcomes.

—Andre Scheepers, Chief Digital Officer, Farmlands Cooperative

AI agents help organizations move from delayed reaction to proactive control. By detecting demand volatility, supplier risk, or inventory imbalances earlier, teams can evaluate tradeoffs, align cross functional responses, and act before issues escalate into revenue loss or excess cost. Embedding these insights directly into operational workflows shortens decision cycles, reduces manual intervention, and improves outcomes such as on-time, in full delivery, inventory turns, and working capital efficiency. 

This approach reflects a shift in how supply chains create value. AI strengthens human decision‑making by improving speed, consistency, and coordination across the value chain. Thus, enabling supply chains to operate with greater predictability, control, and customer confidence, even in volatile environments. 

Microsoft ecosystem partners showcased in this area: Resilinc, Fractal, C3.ai 

Join us—onsite or online—Live from Hannover Messe 2026

Microsoft is hosting a series of executive conversations at our Hannover Messe booth, where top manufacturing leaders will share how they’re navigating the journey to an AI-powered, data-driven future. The conversations feature voices from companies like Siemens, Accenture, Schneider Electric, TK Elevator, Bosch Connected Industries, and more. Register here to watch for strategic insights into how global manufacturers are using AI to connect data, systems, and workflows.  

We’re also thrilled to invite everyone to the Hannover Messe Center Stage keynote by Deb Cupp, Microsoft’s President and Chief Revenue Officer. Deb will present “Return on Intelligence: The Next Frontier of Manufacturing,” exploring how organizations can move beyond incremental efficiency gains to achieve transformative growth with AI. This keynote takes place on April 20 at 2:00 PM CET (opening Monday) on the main stage.  

For a quick recap of Microsoft’s Hannover Messe 2025 presence and to see what to expect in 2026, check out the 2025 recap video: 

Learn more about how Microsoft helps Frontier organizations prioritize efficiency, agility, and innovation 

• The Industrial Frontier: Four ways manufacturers can unlock intelligence across the value chain. Get the e-book

The post Industrial intelligence unlocked: Microsoft at Hannover Messe 2026 appeared first on The Microsoft Cloud Blog.

It is a task that is not taken lightly by the Jets’ coaches, front office and scouting department, who will be featured in the team’s draft room as they make their final decisions.

But that gathering is just the tip of the iceberg when it comes to player evaluation, and the Jets are careful to explore every avenue available when assessing the thousands of players who are draft-eligible each season. Utilizing the latest technology to help them make more informed decisions is now a critical part of the team’s overall strategy.

“The draft is one of the primary ways in which we’re able to acquire talent, and it’s an extremely important event for us,” says Dan Zbojovsky, senior director, football operations for the Jets. “It’s a year-long process and oftentimes multi-year process for us to evaluate these players coming out of college and then get the opportunity to select them.”

A ‘Titan’ off the field

In the traditional draft scenario, teams receive dispatches from scouts around the country who file reports on players that range from height and weight measurements to 40-yard dash times to vertical leaps. Colleges will conduct Pro Days, in which scouts are invited to see a team’s top performers run drills and catch passes. And independent scouting services provide roundups of prospects major and minor, with their own evaluation systems.

In short, there’s a lot of information on a lot of potential draftees, and that doesn’t even include each NFL team’s own preferences based on organizational philosophy, coaching schemes and roster needs. While some teams prefer to keep things more analog, the Jets have been at the forefront of embracing technology to help them prepare not only for the draft but also the fast-paced nature of an NFL season.

The team’s proprietary Titan app (winkingly named after the team’s original moniker, The Titans of New York) is the team’s “mothership” for football operations – a custom-built web application that contains essential tools for draft preparation, scouting and personnel strategy.

“Titan is really the hub behind everything we do on the football side,” says Paul Marsh, senior director of application development. “It’s a legacy application of 15 years now through many, many different iterations, but it’s always remained Titan. It is where all of our scouting and football data is housed. It is the view into that data and it enables the powers that be to help make their decisions and come up with their plans to help make the team win.”

Titan is built on Microsoft technology, including Microsoft Azure, GitHub Copilot and GitHub Actions. Marsh’s team relies on GitHub Copilot to speed up coding, prototyping and iteration, helping them gain greater efficiency when time is tight leading up to the draft. GitHub Actions are used to automate, build and deploy pipelines, enabling frequent updates and continuous integration across Titan’s modules.

Another key element of Titan is the team’s draft/trade calculator, a points-based tool the Jets use to evaluate draft-day trade scenarios. In real time, New York’s football brain trust can plug in picks, compare values and determine whether a proposed trade would result in a net gain or loss for the team.

“This is a UI that was designed really by our [general manager] and his close advisors to work the way that they want to work,” says Marsh, who has been with the Jets for 24 seasons. “And it simply allows them to kind of dig into the information and game plan on what they’re going to do going forward into the draft.”

The Jets’ process has paid off with several important contributors being acquired via the draft, including wide receiver and 2022 Offensive Rookie of the Year Garrett Wilson, 2025 No. 7 selection Armand Membou, defensive end Will McDonald IV, running backs Breece Hall and Braelon Allen, and tight end Mason Taylor.

Old school, meet new school

For Zbojovsky, who is entering his 19th season with the franchise, the draft successes reflect the balance the team uses when combining the old-school scouting mentality and the technology and analytics of the new school of player evaluation.

“[Titan] is an extremely important internal website for us, and we’ve made a lot of really cool advancements over the years on it,” he says.

“I think everything has its piece of the puzzle. On certain players, some parts might be a bigger piece of that puzzle. We like to, of course, rely on our film work as the foundation of our reports and our scouting evaluations, and then we can utilize all these other cool tools or data points to help inform those evaluations and really help us, whether it be our stacking of our players or comparing players to each other. And really it adds a little bit of an objective piece into what can be a largely subjective evaluation off film.”

Zbojovsky and Marsh work closely with each other to ensure that any late-bloomers, fast-risers or strategic adjustments are reflected quickly in Titan so that everyone is on the same page.

“We always joke that if the GM wanted to come down to our office and say, ‘I need a button here, here and here, and I need it to do these things,’ we’re working on that right out of the gate as soon as he leaves that office,” Marsh says.

“We’re able to turn around very, very quickly because we’re able to push those changes right into our Microsoft stack and get them in front of him before he hits the end of the hall. It’s the trust that we can get things done very quickly because these guys have deadlines that don’t move. We can’t push back the draft. We can’t push back free agency.”

The fastest agent at the Combine

The Jets recently wrapped their time at the NFL Combine in Indianapolis, where all 32 teams convene to scout draft prospects as they go through a whirlwind of testing and drills. Numbers and measurements are flying fast and furious, so the Jets, along with the league’s other squads, use the NFL Combine App to help surface the official Combine data to coaches and scouts.

A custom Copilot AI agent is built into the NFL Combine App to allow coaches and scouts to surface fast insights and prospect comparisons with natural language questions that allow teams to get information on, for example, the average, highest and lowest linebacker results for each drill since 2015.

“The Copilot feature not only allows us to ask questions and filter through the information that’s present at the time, but also compare that back to previous years,” Zbojovsky says.

“So you start to really be able to stack how this player not only performed against this cohort here, but also against players that are currently in the NFL. And that helps you start to really understand where that player’s performance metrics on the field might fit within the players that he’s going to be joining in the league.”

Let the countdown begin

In a league where every decision matters and every potential advantage could swing the final score, both Marsh and Zbojovsky are thankful that the Jets continue to see technology as an integral part of scouting and preparation.

“We really are in a great spot for what we need to do. It keeps us nimble,” Marsh says. “Talking to other organizations and other technology companies, they are impressed with how quickly we’re able to iterate and move to get those solutions. We’re not bogged down. We’re given a lot of flexibility and the trust to do what we need to do.”

When the Jets turn in their pick, it will be so much more than writing a name on a card to hand to the commissioner. It will be the final product of research, data, scouting and technology all coming together to welcome the next potential superstar to the NFL.

“A lot of work goes in from a lot of people throughout the organization,” Zbojovsky says.

“We incorporate a lot of different data points and different types of evaluations, whether it be analytics or our scouts. We put a lot of work behind that to make sure we get our board right in advance and then we see how things fall on draft day. We look forward to success in April.”

Top photo courtesy of the Jets.

Learn how the NFL is using AI on and off the field to enhance operations and read how technology could help the Minnesota Vikings build next year’s winning edge. 

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn.

The post The New York Jets are happy to have a ‘Titan’ in their corner at the NFL Draft appeared first on The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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.

The post Manufacturing at the 2026 inflection point: How Frontier companies are entering the agentic era appeared first on The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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 The Microsoft Cloud Blog.

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.

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