MWC26 Barcelona, the GSMA’s flagship connectivity event, brought the global ecosystem together at scale: GSMA reported over 105,000 attendees from 207 countries and territories. In that backdrop, one theme kept surfacing in nearly every discussion I had: telecoms have moved past debating whether AI creates value and into the harder question of how to scale it across the enterprise with the right security, governance, and operating model.

In other words, the industry is shifting from isolated pilots to enterprise execution, embedding AI into customer engagement, network operations, and day-to-day workflows. This recap shares what we heard, what we showed, and what it signals for the next phase of telecom transformation.

Ahead of the event, we shared our point of view on how telecoms can realize AI ROI with a unified, trusted AI platform in our industry blog: MWC 2026: Microsoft Helps Telecoms Realize AI ROI. We described how Microsoft helps telecoms achieve return on intelligence and trust by scaling AI through a single intelligence platform—Microsoft IQ—with built‑in, carrier‑grade trust and governance so operators can innovate with confidence. During the week, additional customer and partner momentum included:

• AT&T unveiled Connected AI for Manufacturing and Connected Spaces for Enterprise, powered by Azure, enabling real-time data integration across factories, supply chains, buildings, and campuses to improve efficiency, safety, and decision-making.
• Tech Mahindra launched an ontology-driven agentic AI platform built on Microsoft Fabric and Azure AI Foundry to help operators modernize complex data environments and move from AI experimentation to production across use cases such as network optimization, fraud detection, and revenue assurance.
• Orange introduced Sharlie, a next-generation voice assistant for Sosh built with Microsoft AI, expected to handle more than 3 million customer conversations annually at full rollout.

What changed at MWC this year

The most important shift I saw wasn’t a single product announcement, it was a change in posture. Telecom leaders are increasingly treating AI as a core capability to be industrialized, not a set of experiments to be evaluated. The questions sounded less like “What use cases should we try?” and more often pointed to a simple reality: Scaling AI is a systems challenge. It requires bringing data, security, governance, and operational processes together so insights consistently turn into action. That’s the idea behind Microsoft’s Return on Intelligence—measurable business outcomes created when intelligence is embedded end-to-end across the telecom value chain.

At MWC, our goal was to make this practical, showing how AI can be applied across customer experience, operations, and growth, with trust built in from the start. Three themes came up repeatedly in these conversations:

• Data readiness: Connected intelligence that brings network, customer, and operational data together so models and agents can act with context.
  
• Trust at scale: Security, privacy, compliance, and governance that are designed in, not bolted on after pilots.
  
• Operationalization: Integrating AI into workflows, tools, and KPIs so teams can adopt it and leaders can measure outcomes.

That’s why we focused on an end-to-end story: Not just what AI can do, but how it can be delivered responsibly and repeatedly across the business. The show floor is where those ideas get tested quickly, so we designed the booth experience to reflect the real priorities operators are working on now.

What we showed: Turning intelligence into action

In the Microsoft booth, we brought Return on Intelligence to life with hands-on experiences designed around real operator workflows. The intent was simple: show how AI moves from insight to execution when it’s connected to the data people rely on, the tools they already use, and the guardrails organizations need.

Across 14 interactive demo stations, we explored five priorities many operators are investing in right now. Each one reflects a different place AI can create value and a different set of operational requirements to get it into production.

1. Copilots and AI agents for employees to reduce toil and speed decisions across customer care, operations, and field teams.
   
2. Agentic customer experiences that resolve issues faster, personalize interactions, and escalate to humans when needed.
   
3. Intelligent business operations that streamline order-to-cash and service fulfillment with better orchestration.
   
4. Autonomous network operations to detect, predict, and remediate issues—moving from reactive to proactive operations.
   
5. AI-enabled growth and monetization that helps identify opportunities and launch new offers faster.

What connected these scenarios wasn’t a single model, it was the operational pattern behind them: Unified data, secured access, governed AI, and integration into the workflows where work actually happens. That’s what turns a compelling demo into something a team can deploy, adopt, and measure.

The level of engagement reinforced the momentum behind this shift. Over the course of the week, more than 12,000 customers and partners visited the Microsoft booth. More than 3,200 attendees took part in more than 30 demos across 14 stations, and 1,387 people joined more than 38 in-booth theatre sessions with Microsoft and partner speakers. We also held 396 executive meetings with priority customers and partners—many focused on what it will take to move from pilot success to enterprise-scale execution.

Beyond the booth: Keeping the momentum going

MWC is four days on the calendar, but it’s really a milestone in a longer journey. The weeks before and after the show are where teams align on priorities, validate approaches, and translate interest into concrete next steps.

Our announcement blog helped frame the week by sharing Microsoft’s approach to scaling agentic and autonomous AI on a unified, trusted platform—and we continued the dialogue through customer and partner communications, follow-ups with teams exploring next steps, and ongoing industry programs.

Four takeaways from the week:

1. AI is an operating layer, not an add-on. The most consistent message was that AI is being stitched into how telecoms run: across customer experiences, operations, and growth. That shift changes what leaders prioritize, from isolated tools to enterprise foundations.
   
2. The maturity journey is speeding up. Many conversations reflected the same evolution: From pilot projects to targeted productivity improvements, to enterprise-wide transformation and growth. The winners will be the teams that can standardize what works and scale it across functions.
   
3. Agentic experiences raise the bar on trust. As copilots and AI agents take on more autonomous work—from customer interactions to network operations—security, privacy, and governance can’t be optional. Operators want guardrails, monitoring, and controls that work in production, not just in proofs of concept.
   
4. Outcomes depend on integration. AI delivers ROI when it connects to real data, real processes, and real workflows, so it can move from insight to action repeatedly. That’s why unifying data and AI, embedding security, and governing end-to-end matters: It’s what makes execution scalable.

Together, these themes point to the same conclusion: Telecoms that operationalize AI, securely and at scale, will move faster and compete differently.

What comes next: Moving from momentum to measurable outcomes

The post-MWC opportunity is straightforward: take the excitement and turn it into a repeatable operating model. For most operators, that means industrializing AI as a trusted layer, grounded in enterprise data, secured by design, governed end-to-end, and integrated into the workflows where customer experience and operational performance are won.

MWC 2026 made one thing clear: The telecoms that lead in the next cycle won’t just deploy AI, they’ll operationalize it. The organizations that can reliably turn intelligence into action, measure impact, and scale what works will set the pace for the industry’s next wave of transformation.

Continue the conversation

• Consolidated list of MWC announcements and news: Return on Intelligence: Powering the AI-First Telco
• Fierce Network: Microsoft & Nokia; Microsoft & Lumen; Microsoft, PWC & Orange

Explore related content

• YouTube videos: Mobile World Congress 2026
• MWC announcement blog
• Evolving the Network Operations Agent Framework
• Rise of Agentic BSS in the IQ Era
• AI-Powered RAN and the Intelligent Edge
• Accelerating revenue in telecommunications through agentic sales processes

The post MWC 2026 recap: From AI pilots to enterprise execution in telecom appeared first on The Microsoft Cloud Blog.

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, Advantech, 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, Advantech, 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.

As the world navigates a critical phase for energy, the recent CERAWeek 2026 event showcased how leaders are addressing an era defined by convergence and competition—where technology and energy markets are deeply intertwined, and resilience must align with sustainability. The theme of CERAWeek 2026—“Convergence and Competition: Energy, Technology and Geopolitics”—proved to be remarkably relevant as discussions unfolded throughout the week.

It was evident that energy markets have become inseparable from digital infrastructure. AI is rapidly reshaping power demand, while supply chains remain vulnerable to geopolitical pressures. Throughout the conference, energy leaders confronted the simultaneous demands of security, affordability, and decarbonization.

Microsoft’s perspective was clear: we are entering the new Energy Frontier, where data, AI, and cloud computing serve as essential enablers for trust, resilience, and growth throughout the energy value chain.

AI emerged as a central topic in creating a more responsive and robust energy system. In this landscape of convergence and competition, energy executives are being challenged to identify disruptions earlier, pivot strategies rapidly, and make informed decisions amid global uncertainty, market fragmentation, and ongoing climate challenges. AI is no longer just a productivity booster—it’s now foundational to secure, real-time, and adaptive energy operations. The consensus at CERAWeek was that the decade’s leading organizations will be those that leverage AI to transition from static systems to intelligent, adaptive solutions.

From physical infrastructures to smart systems: Lessons from the event

This transformation was highlighted across the energy chain:

• Oil and gas companies are striving to balance investment with emissions reduction, safety, and operational resilience.
• Power and utilities are contending with surging electricity demand from electrification and data centers, while modernizing grids and preparing for extreme weather.
• Natural gas and liquefied natural gas (LNG) are more global and sensitive to geopolitical change than ever before.
• Mining and materials are recognized as essential to clean energy and electrification efforts.
• Policy, permitting, and regulation were frequently cited as critical to the pace of new projects.

At the heart of the discussions was AI—driving higher electricity consumption, transforming asset management, and unlocking new opportunities for analysis, prediction, and system optimization. The lines between energy and technology have all but disappeared.

Rising competition in a fragmented world: What CERAWeek revealed

The conference underscored how competition is intensifying. Geopolitical tensions, trade barriers, and supply chain disruptions are now permanent features of the marketplace. Energy leaders spoke candidly about the ongoing need to operate in environments marked by uncertainty—managing regional conflicts, cyber risks, volatile weather, and price swings.

Resilience, once seen as a defensive posture, is now a source of competitive strength. The organizations best able to anticipate disruptions, respond dynamically, and maintain secure operations in complex conditions will continue to lead. But as participants agreed, this requires more than incremental change—it calls for innovative operating models grounded in trusted data, intelligent platforms, and secure cloud infrastructures.

Microsoft at CERAWeek: Advancing on the Energy Frontier

At CERAWeek 2026, Microsoft reaffirmed its commitment to empowering energy and resource companies to seize the opportunities created by convergence and competition.

The Microsoft Agora House hosted sessions and live demonstrations illustrating how cloud, data, and AI technologies are addressing industry-wide challenges—not in isolation, but across the entire energy ecosystem.

Our collaboration with clients and partners around the world continues to focus on four key priorities:

1. Developing an AI‑ready energy workforce

The complexity of energy systems is rising as workforce dynamics shift. Companies struggle with talent shortages, aging employees, and the urgent need for large-scale reskilling—while keeping operations safe, dependable, and efficient.

AI stands out as a catalyst for equipping the next generation workforce. With Microsoft tools, organizations can:

• Automate tasks, both routine and knowledge-based, across engineering, operations, and business functions.
• Deliver AI-powered training and reskilling tailored to technical and frontline jobs.
• Foster collaboration among IT, operational technology (OT), and field teams using secure, integrated platforms.

Our aim isn’t to replace people but to enhance expertise, streamline processes, and let teams concentrate on high-impact work—whether they’re managing intricate assets, tackling emergencies, or speeding up innovation.

2. Strengthening and optimizing operations amid volatility

Operational reliability is more important than ever in a divided world.

With increasing digitization, energy infrastructures are more exposed to cyber, physical, and geopolitical threats. Meanwhile, profitability relies on maximizing uptime, performance, and safety.

Microsoft assists energy firms to:

• Integrate IT and OT data for comprehensive operational insights.
• Leverage AI and analytics for improved asset reliability and predictive maintenance.
• Bolster cybersecurity across critical and industrial infrastructures.

These advancements help leaders shift from merely reacting to events toward predicting and modeling scenarios—a necessity as disruptions grow more frequent and less predictable.

3. Driving net‑zero progress through data and AI

Net-zero commitments are now about action, not theory.

Tackling methane emissions, scaling carbon capture, updating power grids, and bringing renewables online all require accurate data, transparent tracking, and sophisticated systems operating at scale.

Microsoft enables customers to:

• Track and manage emissions with dependable, verifiable data.
• Utilize AI for methane detection, carbon accounting, and climate risk assessments.
• Modernize grid infrastructure to support electrification and renewable integration.

Success in sustainability now goes hand-in-hand with competitiveness. Organizations that transform climate ambition into actionable results will attract investment, secure approvals, and build public trust.

4. Becoming the Energy Frontier with agentic AI

A transformative development shaping the Energy Frontier is the advent of agentic AI—intelligent systems capable of reasoning, acting, and adapting in complex contexts.

At CERAWeek, we presented agentic AI solutions that:

• Streamline approval and regulatory workflows.
• Refine energy trading, logistics, and supply chain management.
• Support scenario planning involving weather, geopolitics, and changing demand.

These are not isolated fixes—they represent a new class of systems built for navigating uncertainty, helping leaders evaluate trade-offs, anticipate impacts, and accelerate decision-making confidently.

A call for leadership and collaboration

CERAWeek 2026 reaffirmed its role as a crucible for shaping the future of energy. Technological convergence is dissolving traditional industry boundaries, while competition is redefining how value is created and protected. As the event concluded, the message was clear: those who invest early in intelligent platforms, credible AI, and flexible operating models will define the coming era.

Microsoft remains committed to working closely with energy and resource organizations, building secure, sustainable, and adaptive systems that fulfill the promise of the Energy Frontier. We look forward to ongoing collaboration with industry, government, and technology leaders to continue shaping the future together—well beyond CERAWeek 2026.

Thank you to everyone who joined us at the Microsoft Agora House. The conversations, forums, and real-world stories shared have set a new pace for innovation—demonstrating how data, AI, and cloud are truly revolutionizing the global energy system.

The post CERAWeek 2026: Reflections on convergence, competition, and the new Energy Frontier appeared first on The Microsoft Cloud Blog.

Across banking, capital markets, and insurance, some of the fastest-moving institutions are not simply “adopting AI.” They are becoming Frontier Firms, AI-powered organizations built around human-agent collaboration. In a sector where the cost of error is high, the financial services sector is emerging as an early proving ground for the Frontier Firm model.

The Microsoft 2025 Work Trend Index highlights a widening AI divide. While many organizations remain stuck in pilot mode, Frontier Firms are scaling agentic AI across their operations.

Our work with financial services leaders worldwide shows a clear pattern. The winners in the next generation of innovation will be those that combine human judgment with AI and agents, without compromising security, compliance, or customer trust. Critically, these advantages are best enabled through migration to a modern cloud foundation that can scale AI responsibly and reliably.

The crossroad: Modernize or let legacy debt grow?

Legacy systems have powered financial services for decades. Yet the very qualities that once made them indispensable—custom integrations, tightly coupled architectures, and deeply embedded processes—now create friction and fragility. Increasingly, they can be expensive to maintain, slow to change, and difficult to secure end-to-end. Worse, they can inherently constrain data access across the business, which limits advanced analytics and AI from delivering full value in key areas like customer engagement, fraud prevention, credit decisions, underwriting, and financial crime.

In many institutions, this accumulated technical debt is, in effect, an understated balance-sheet liability. It can increase operational overhead, complicate resilience planning, and broaden the cyber-attack surface. At the same time, regulators are demanding that firms prove stronger controls while, competitively, digital-native challengers are showing what’s possible when technology is designed for continuous change.

Modernization can help answer many of these challenges by helping position firms to gain competitive advantages that go well beyond cost efficiency. As workloads become increasingly cloud-native (in other words, designed to be built, updated, and scaled continuously in the cloud rather than tied to legacy infrastructure), organizations can launch new services faster, respond with agility, and use AI as part of everyday operations.

Waiting to migrate can increase risk and cost

A variety of factors are converging to increase the urgency of modernizing.

• Regulatory pressure is growing. Requirements for operational resilience, third-party risk oversight, data governance, and AI accountability are becoming more explicit and more enforceable. In Europe, the Digital Operational Resilience Act (DORA) raises the bar on stress testing, incident reporting, and information and communication technology (ICT) governance. In parallel, the European Union AI Act introduces demanding expectations for high-risk AI, including transparency, explainability, and bias mitigation. Globally, frameworks shaped by Basel guidance and securities regulators continue to push for stronger risk management, auditability, and controls across financial operations.
• Customer expectations are becoming non-negotiable. “Digital-first” now means more than building a polished mobile app. It means enabling instant transactions, proactive service, and personalized guidance—delivered consistently across channels. Doing all this at scale means that data must move securely and quickly, products should evolve continuously, and controls must be embedded rather than bolted on.
• The threat landscape is getting scarier. Threat actors are using automation and AI to increase both scale and sophistication. In a legacy environment, security improvements often arrive as point solutions, unevenly applied, and hard to validate. Cloud architectures, implemented with the right governance, help enable consistent identity controls, continuous monitoring, and policy-based protection that can be audited and improved over time.

Migration as a lever for innovation

Migration is too often framed as a technology initiative. For business and risk leaders, the more useful long-term view is as to regard it as a control and value strategy, a way to embed governance into the operating fabric of the firm.

This is why many transformation leaders manage cloud adoption as a sequence rather than a singular initiative, with a pathway from rehosting (“lift-and-shift”) through optimization and ultimately to AI acceleration. In this framing, modernization is not the finish line; it is the first step of compounding advantage.

Cloud migration, when managed well, can support a compliance‑by‑design approach, by which policy, identity, and data protections are consistently enforced. It can strengthen operational resilience through architectures that are built for redundancy, automated recovery, and continuous validation. And it can create an innovation pathway by making agentic AI practical to deploy and manage.

The AI-first divide: Cloud as operating model

As we see with Frontier Firms in financial services, innovation leaders tend to treat cloud architecture as more than an infrastructure choice. They use it as an operating model to standardize controls, build reusable platforms, and design processes that are increasingly AI-operated but human-led. The payoff can show up in faster deployment cycles, a lower cost per transaction, and predictive insights that make customer experiences more personal and operations more resilient.

Reaching that maturity typically requires progress across four transformation engines:

• Infrastructure modernization
• Legacy systems migration
• Systems modernization (including new business systems)
• Data modernization with AI integration

Financial services firms face stricter scrutiny than most industries, so the differentiator is not speed alone, it’s the ability to sustain speed while continuously demonstrating security, compliance, and control effectiveness.

We see this in practice across the industry. For example, UBS, following its acquisition of Credit Suisse, migrated a mission‑critical records platform from mainframe to a cloud‑native service on Microsoft Azure, reducing total cost of ownership by nearly 60% and improving their ability to meet regulatory demands. After LSEG migrated its high-volume, mission-critical Autex Trade Route (ATR) trading network from on-premises to Azure, the gains in scalability and resilience helped them absorb a sudden 400% surge in trading volumes with zero incidents. And the National Bank of Greece modernized document processing to improve accuracy and enable faster, more digital customer journeys. The common thread is not a single tool or model, it’s a cloud foundation that supports governed data, resilient operations, and repeatable innovation.

Turning migration into long-term value

For many firms, the hardest part of migration is not the technology; it’s making the journey auditable, repeatable, and aligned to risk appetite. That’s why a structured approach matters.

The Microsoft Cloud Adoption Framework, tailored for financial services, is designed to help institutions align cloud modernization to business outcomes while addressing the governance realities of the industry: data sovereignty expectations, operational resilience, and security-by-design. Importantly, cloud migration need not undermine data sovereignty; done right, migration strengthens locality, control, and compliance through governed architectures.

In practice, migration means helping businesses to build a compliant foundation, innovate responsibly, and maintain continuous control visibility as they scale. Microsoft supports this with financial-services-ready architectures, built-in governance and security capabilities, and a broad set of certifications and controls. Just as importantly, we work closely with customers and regulators globally to help ensure that cloud adoption can be evidenced properly in terms of risk reduction, resilience, and measurable operating improvement.

Trustworthy AI starts with the cloud foundation

Boards and regulators are right to focus on AI governance. Generative AI, agentic systems, and intelligent automation can improve productivity and customer outcomes, but only when they operate on governed data, with strong identity controls, clear lineage, and auditable policies. Those prerequisites are difficult to achieve in fragmented legacy environments.

Cloud migration creates the conditions for AI to be adopted responsibly, with modern data platforms and pipelines, elastic compute for experimentation and scale, consistent policy enforcement, and continuous monitoring.

To help institutions navigate migration with confidence, Microsoft combines a financial-services-tailored methodology with practical tooling and built-in governance. The Cloud Adoption Framework for financial services provides a proven, risk-aligned approach to planning and executing secure migrations. Azure Migrate and the Azure cloud migration and modernization programs help accelerate discovery, modernization, and execution with guidance and incentives. And capabilities like Microsoft Purview and Microsoft Defender for Cloud help establish compliance guardrails and security posture management from day one.

Lead the next generation with cloud

Migration is not the end state of digital transformation. It is the foundation for Frontier transformation, one which can enable firms to innovate faster, demonstrate stronger controls, and adapt quickly to new demands and opportunities.

The financial services firms that lead in the next generation of financial services will not be those that move the fastest in a single quarter. They will be the ones who modernize with technology that is durable, designed for operational resilience and evidence-based governance, and that makes innovation repeatable. Cloud migration is the inflection point where these powerful advantages become possible.

Learn more

• To explore a five-phase, risk-aligned approach to cloud migration that helps build the foundation for AI-first financial services, read the e-book: Becoming an AI-First Firm in Financial Services.
• Register for the Migrate and Modernize Summit to learn from industry leaders.
• Explore the Microsoft Cloud Adoption Framework for Financial Services.

The post Why cloud migration is key to realizing AI value in financial services appeared first on The Microsoft Cloud Blog.

Maritime trade accounts for more than 80% of global trade by volume, making ports a foundational pillar of the global economy, according to UN Trade & Development (UNCTAD).¹ As trade volumes grow and supply chains become more interconnected, ports are asked to do more than move goods efficiently. They must coordinate increasingly complex operations, integrate data across fragmented systems, and enable safer, more predictable decision-making across a diverse ecosystem of stakeholders.

Meeting these demands requires a fundamental shift in how ports modernize their operating models to meet these demands, moving from siloed, reactive operations toward integrated, data‑driven, and intelligently orchestrated systems.

From Port 4.0 to Port 5.0: Capability over complexity

Port 4.0—widely used across the industry as shorthand for digitalized, connected port operations—laid the foundations through shared data, connected infrastructure, and more informed decision-making.

In our Ports of the Future framework, Port 5.0 is how we envision the next stage of operational capability—where ports orchestrate flows of goods, data, energy, and trust through integrated platforms and governed intelligence.

At a high level, Port 5.0 is about:

• Moving from visibility to coordinated action
• Embedding intelligence into daily decisions, with people in control
• Designing collaboration, governance, and security from the outset

This evolution is shaped by interconnected building blocks—from AI-supported control towers and connected inland corridors, to energy aware operations, trusted data collaboration, advanced optimization, immersive digital twins, and all hazards infrastructure resilience.

A new wave of enabling technologies

In the Ports of the Future framework, Port 5.0 is defined by a set of core operational capabilities. What has changed in the last 12–18 months is the maturity of technologies that now make these capabilities practical to deploy at scale.

• AI-supported operations
  AI systems can now assist with multistep operational workflows—monitoring conditions, proposing replans, and surfacing high impact exceptions for human decisionmakers—moving control towers from visibility toward orchestration, while remaining governed.
• Confidential computing for sensitive collaboration
  Hardware- based trusted environments enable organizations to process sensitive data while maintaining strong protections, supporting cross agency analytics and collaboration without compromising established data handling policies.
• Advanced optimization approaches
  Quantum-inspired and heuristic optimization methods help ports address complex scheduling and routing challenges—berths, yards, rail paths, labor, and inspections—particularly under disruption, when suboptimal decisions compound quickly.
• Digital twins and simulation
  Immersive digital twins increasingly serve as shared operational environments, integrating real-time data with simulation to support planning, training, and coordinated decision-making. AI-based simulation contributed to improved vessel punctuality and measurable operational gains, according to a case study of Busan Port,² illustrating the potential of these approaches when deployed thoughtfully.
• Security and governance by design
  As ports become data hubs, cybersecurity, identity management, and access controls are increasingly embedded into platform architecture from the outset.

Together, these capabilities help ports move from reactive operations to coordinated, system level performance—while keeping people in control and governance at the center.

Develop core operational capabilities

The Ports of the Future whitepaper explores these building blocks in depth, with real world examples and a pragmatic 24–36 month roadmap that helps ports move from vision to execution.

Explore Microsoft for public finance to help reignite the economy and drive financial accountability with public finance technology solutions.

¹ Shipping data: UNCTAD releases new seaborne trade statistics 

² In August container ship punctuality at 65.3% — World Ports Org 

The post Ports of the future: Building a framework for the modern port 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.

That is where AI comes in. To break the infrastructure bottleneck and shift the industry from ambition to delivery, Microsoft is announcing an AI for nuclear collaboration with NVIDIA, to provide end-to-end tools that streamline permitting, accelerate design, and optimize operations across the industry.

This set of technologies brings disciplined engineering to the entire lifecycle of a nuclear plant—spanning site permitting, design, construction, and continuous operations. By enabling these capabilities within a connected, AI-powered foundation, we are empowering energy developers to make highly complex work repeatable, traceable, secure, and predictable—slashing development timelines and eliminating rework without sacrificing safety.

The digital foundation for nuclear at scale

The only thing that may be more complex than building a nuclear plant is designing and permitting one. Permitting alone can take years, cost hundreds of millions of dollars, and involve an immense amount of data processing and reporting. It’s not a lack of need, knowledge, or even willingness that’s holding development back, but rather the inability to progress efficiently and consistently through rigorous permitting and development processes.

Engineers can spend thousands of hours drafting, cross-referencing, formatting, searching, reviewing, and reworking materials. They have to identify and fix inconsistencies across tens of thousands of pages. It is little wonder that plants have been notorious for construction delays and cost overruns.

To break this infrastructure bottleneck, we need to move away from highly customized engineering towards repeatable, reference-based delivery—while maintaining regulatory standards and engineering accountability.

With AI, we can identify tiny documentation inconsistencies and resolve them quickly. By unifying data and simulation across the lifecycle, we ensure complex work remains:

• Traceable: Every engineering decision is digitally linked to the evidence and regulations that back it up.
• Audit-Ready: The system keeps a perfect “paper trail,” ensuring that regulators can verify safety instantly.
• Secure: High-level intelligence is applied within a governed, protected environment.
• Predictable: High-fidelity simulations map time and cost, catching delays before they happen in the real world.

This isn’t just about speed; it’s about trust. Engineers and regulators are freed to focus on what matters most: building a safe, secure, high-capacity, carbon-free power source that’s on-time and on-budget.

Here is how AI and Digital Twins can carry a project from the initial phases to efficient operations:

• Design and engineering: Digital Twins and high-fidelity simulations enable faster iteration. Engineers can reuse proven patterns and instantly see how a tiny design change impacts the entire model, creating a validated plan before breaking ground.
• Licensing and permitting: Generative AI handles the heavy lifting of document drafting and gap analysis. It unifies all project information, ensuring comprehensive applications aligned with historical permits. This allows expert regulators to focus their time on safety judgments rather than reconciling thousands of pages of text.
• Construction and delivery: While traditional 3D models only map physical space, 4D (time scheduling) and 5D (cost tracking) simulations can virtually construct the plant before shovels hit the dirt. AI and Digital Twins allow developers to track physical progress against the digital plan in real-time, catching potential delays and preventing the schedule collisions that lead to expensive rework.
• Operations and maintenance: AI-powered sensors and operational digital twins detect anomalies early, ensuring higher uptime and predictive maintenance that keeps the grid stable with human operators firmly in control.

By unifying data, traceability, and simulation across phases, AI accelerates design validation with high-fidelity 3D models and Digital Twins, improves licensing consistency through AI-assisted document workflows, and connects design assumptions to operational performance—giving operators, regulators, and stakeholders clearer, continuous visibility.

Accelerating delivery: How Aalo Atomics, Idaho National Labs, and Southern Nuclear are deploying AI for nuclear

The proof is in the progress. Our collaboration is already changing the pace of nuclear delivery.

Aalo Atomics

Aalo Atomics has reduced the time-intensive permitting process by 92% using the Microsoft Generative AI for Permitting solution, saving an estimated $80 million a year. For Aalo, the value of the Microsoft and NVIDIA collaboration isn’t just speed—it’s confidence.

“Two things matter most: enterprise-scale complexity and mission-critical reliability. We’re deploying something complex at a scale only a company like Microsoft really understands. There’s no room for anything less than proven reliability.”

—Yasir Arafat, Chief Technology Officer, Aalo Atomics

Southern Nuclear

Southern Nuclear has developed and deployed agents using Microsoft Copilot across its fleet, including engineering and licensing, to improve consistency, reuse knowledge faster, and support better decision-making in key workstreams.

Idaho National Laboratory

When it comes to the public sector and specifically United States Federal, Idaho National Laboratory (INL) has become an early adopter of AI for nuclear technology. By using the AI capabilities to automate the assembly of complex engineering and safety analysis reports, INL is streamlining the review process and creating standard methodologies for regulators to adopt these tools safely, further speeding deployment.

Expanding the ecosystem: How Everstar and Atomic Canyon are operationalizing AI for nuclear on Microsoft Azure

Microsoft is actively expanding this secure ecosystem. Everstar—an NVIDIA Inception startup—brings domain-specific AI for nuclear to Azure to modernize how the industry manages project workflows and governed data pipelines.

“The nuclear industry has been bottlenecked by documentation burden and regulatory complexity for decades. This partnership means our customers get the secure, scalable cloud deployments they demand. It’s a significant step toward making nuclear power fast, safe, and unstoppable.”

—Kevin Kong, Chief Executive Officer, Everstar

We are also excited to highlight Atomic Canyon, whose Neutron platform is now available in the Microsoft Marketplace, allowing nuclear developers to deploy these capabilities with consistency and control through trusted procurement pathways.

Progress at the pace this moment requires

AI is enabling the energy industry to deliver more power, faster, and safely. This Microsoft and NVIDIA collaboration provides the path to do exactly that for advanced developers, owners, and operators. By turning fragmented, high-variance workflows into governed, auditable systems, we can compress timelines without compromising rigor. By unifying data, simulation, and evidence across design, permitting, construction, and operations, we are accelerating the deployment of firm, carbon-free power while strengthening regulatory confidence and operational resilience.

The AI for nuclear operations collaboration brings together NVIDIA Omniverse, NVIDIA Earth 2, NVIDIA CUDA-X, NVIDIA AI Enterprise, PhysicsNeMo, Isaac Sim, and Metropolis with Microsoft Generative AI for Permitting Solution Accelerator and Microsoft Planetary Computer to create a comprehensive, AI-powered digital ecosystem for nuclear energy on Azure.

Microsoft, NVIDIA, and Aalo Atomics will be presenting this AI-lead industry perspective at CERAWeek 2026 in a session entitled “A Digital Age for Nuclear: Aalo Atomics, NVIDIA, and Microsoft.”

Discover more

Ready to move from ambition to delivery? See how the Microsoft and NVIDIA nuclear for AI collaboration can drive change within your organization.

Contact us to learn more.

The post AI for nuclear energy: Powering an intelligent, resilient future appeared first on The Microsoft Cloud Blog.