Resource Guide: Making Physical AI Practical for Real‑World Industrial Operations
Microsoft’s adaptive cloud approach enables organizations to turn operational technology (OT) data into intelligent actions, autonomously, without requiring everything to live in the cloud by unifying cloud-to-edge management plane, data plane, and intelligence platform. At the center of this approach are key foundational technologies: Key Purpose Offering Direct-to-cloud device management + telemetry ingestion Azure IoT Hub Industrial connectivity + edge data plane Azure IoT Operations Unified analytics + real-time intelligence Microsoft Fabric On-device AI inferencing runtime Foundry Local Microsoft Azure IoT Gartner winner: Microsoft named a Leader in the 2025 Gartner® Magic Quadrant™ for Global Industrial IoT Platforms See it all come together Before diving into each component, watch this end-to-end demo showing how Azure IoT Operations, Azure IoT Hub, Microsoft Fabric, and Foundry Local work as one stack across the edge-to-cloud lifecycle - Making industrial AI practical for real-world operations with adaptive cloud. How these components work together Azure IoT Operations and Azure IoT Hub collect real-time data from operational assets and send semantically-ready, modeled data to Microsoft Fabric, where it's contextualized with enterprise data for downstream analytics. Microsoft Foundry extends to the edge through Foundry Local, so the same tooling used to deploy and manage AI models in the cloud applies to edge use cases. All of it integrates into Azure Resource Manager, bringing OT devices, assets, and edge AI models into the same management and security paradigm as every other Azure-managed resource. This blog walks through where to get started with each product capability: 1. Manage Cloud-Connected Devices and Telemetry with Azure IoT Hub Azure IoT Hub is a fully managed cloud service that enables secure bidirectional communication, device-to-cloud telemetry ingestion, cloud-to-device command execution, per-device authentication, remote management and more. Telemetry from IoT Hub can also be routed downstream into analytics platforms like Microsoft Fabric for visualization or AI modeling. Recommended Usage: Devices that utilize IoT Hub are distributed, stand-alone devices with fixed-functions. These devices typically do not require cloud-managed containerized workloads or cloud-managed proximal industrial protocol connectivity. Examples of appropriate device-to-cloud IoT Hub endpoint devices include water monitoring stations, vehicle telematics, distributed fluid level sensors, etc. Resources Current in-market services overview: IoT Hub: What is Azure IoT Hub? - Azure IoT Hub DPS: Overview of Azure IoT Hub Device Provisioning Service - Azure IoT Hub Device Provisioning Service ADU: Introduction to Device Update for Azure IoT Hub Building scalable solutions with Azure IoT platform: Best practices for large-scale IoT deployments - Azure IoT Hub Device Provisioning Service Scale Out an Azure IoT Hub-based Solution to Support Millions of Devices - Azure Architecture Center Azure IoT Hub scaling Try out our preview of new IoT Hub capabilities (integration with Azure Device Registry and Certificate Management) Learn more about these capabilities on our blog post: Azure IoT Hub + Azure Device Registry (Preview Refresh): Device Trust and Management at Fleet Scale… Integration with Azure Device Registry (preview): Integration with Azure Device Registry (preview) - Azure IoT Hub Microsoft-backed X.509 certificate management (preview): What is Microsoft-backed X.509 Certificate Management (Preview)? - Azure IoT Hub How to start with the preview: Deploy IoT Hub with ADR integration and certificate management (Preview) - Azure IoT Hub 2. Connect Industrial Assets with Azure IoT Operations Azure IoT Operations provides a unified data plane for the edge that runs on Azure Arc–enabled Kubernetes clusters and supports open industrial standards. It allows organizations to connect and capture equipment telemetry, normalize OT data locally, route hot-path signals to real-time analytics, securely manage layered industrial networks, and more. Edge‑processed data can then be sent upstream to Microsoft Fabric for AI‑driven analysis. Recommended Usage: Azure IoT Operations is intended to be the data plane for an adaptive cloud deployment extending the management, data, and AI capabilities of the Microsoft cloud to an on-prem device. This device binds to these cloud planes providing a platform for local data processing and intermittent connectivity. The target for these devices range from a small-gateway-style PC to a full data center. Azure IoT Operations endpoints enable cloud-managed containerized workloads and cloud-managed proximal industrial protocol connectivity. Examples of appropriate adaptive cloud and Azure IoT Operations endpoints include, on-robot computers, industrial machine controllers, retail store sensor/vision processing, and top-of-factory site infrastructure for line of business applications. Resources Azure IoT Operations Overview Azure IoT Operations Documentation Hub Quickstart: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Open-source framework for scaling robotics from simulation to production on Azure + NVIDIA: microsoft/physical-ai-toolchain Demo video showcasing How we built the demo: explore-iot-operations/quickstart at main · Azure-Samples/explore-iot-operations Edge-AI: microsoft/edge-ai: Production-ready Infrastructure as Code, applications, pluggable components, and… Latest Announcements & Blogs Making Physical AI Practical for Real-World Industrial Operations: Part 1 | Microsoft Community Hub Making Physical AI Practical for Real-World Industrial Operations: Part 2 | Microsoft Community Hub Unlock Industrial Intelligence | Microsoft Hannover Messe 2026 From pilots to production: How Microsoft and partners are accelerating intelligent operations 3. Advanced Analytics with Microsoft Fabric Microsoft Fabric delivers a unified, end‑to‑end analytics platform that transforms streaming OT telemetry into real‑time insights and live dashboards. Fabric Operations Agents monitor industrial signals to recommend targeted actions, while Fabric IQ provides a shared semantic foundation that enables AI agents to reason over enterprise data with business context. Together, Fabric turns live industrial data into AI‑powered operational intelligence. Resources Get Started with Microsoft Fabric Learning Path Fabric Real-Time Intelligence documentation - Microsoft Fabric | Microsoft Learn Create and Configure Operations Agents - Microsoft Fabric | Microsoft Learn Fabric IQ documentation - Microsoft Fabric | Microsoft Learn 4.Run AI Models On‑Device with Foundry Local Foundry Local extends on‑device AI to Arc‑enabled Kubernetes edge clusters, providing a Microsoft‑validated inferencing layer for running AI models in industrial, disconnected or sovereign environments. Resources Foundry Local on Azure Local Documentation Participate in Foundry Local on Azure Local preview form Foundry Local on Azure Local: HELM deployment Demo Customer Stories Chevron: Chevron plans facilities of the future with Azure IoT Operations Husqvarna: Husqvarna Group Boosts Operational Efficiency with Azure Adaptive Cloud Ecopetrol: Azure IoT Operations and Azure IoT for energy help Ecopetrol optimize energy distribution while lowering operational costs P&G: Procter & Gamble cuts model deployment time up to 90% with Azure IoT Operations Toyota: Toyota Industries innovates its paint shop processes with Azure industrial AI and Azure IoT HubMaking Physical AI Practical for Real-World Industrial Operations: Part 1
We’ve all read the headlines, but are companies really implementing AI with success, and at scale in manufacturing? The answer is yes - by replicating proven operational practices through simple, but highly practical AI implementations that help improve operational efficiency. For many years I helped industrial customers complete manual data analysis of root causes, lean black-belt style, using pareto charts and continuous improvement cycles to get results. Doing so required practical expertise, manual scrubbing to get good quality data (garbage in, garbage out), and a bit of charting to get to a simple set of recommendations for areas of focus. With modern advances, it’s not hard to see how easily that task can be accomplished with more range, more accuracy, and more speed using agentic AI running over AI-curated data sets. What once required weeks of expert-led analysis can now be surfaced continuously, turning root cause analysis from a retrospective exercise into a real-time operational capability. AI innovation is accelerating rapidly, and industrial organizations are eager to translate that momentum into real business outcomes. As the companies we partner with advance their AI initiatives, they are increasingly focused on designing and deploying scalable, responsible AI systems that can access and contextualize diverse data sources to proactively identify issues, assess business impact, and coordinate appropriate responses. Microsoft has been working closely with these organizations to build the right architecture to support their modernization goals while maintaining the control and stability required in industrial environments. At Hannover Messe 2026, we’re excited to share our vision for running AI‑powered factories, highlight the partners helping bring this vision to life, and announce our latest innovations in this space. Operationalizing Continuous Improvement with Agentic AI Industrial organizations are at varying stages of AI maturity, but many are exploring how AI can enhance established methodologies such as Lean and Six Sigma that utilize continuous improvement loops like identify–observe–analyze–decide–act. By enabling the ongoing collection and analysis of operational data, AI agents can fundamentally change the speed and accuracy of these data driven processes. In the early stages, organizations often start by using AI to generate chat-style insights and recommendations based on observed patterns; but as model performance improves and trust increases, they’re often motivated to include agents as part of a mixed human/AI team for supervised decision‑making and closed loop actions. Even simple agents can be used deliver ongoing analytics including cross‑deployment comparisons, failure clustering, and variance analysis across plants, regions, or device fleets on the fly by continuously monitoring telemetry, reading free-text fields on incident reports & support summaries, and by adding context from deployment metadata. This enables earlier intervention, helping reduce waste, improve sustainability, enhance quality, and increase operational efficiency. To support the effective use of AI agents across industrial operations, organizations need an architecture that captures real‑time operational data from machines and processes, transforms it into cloud‑ready formats, and contextualizes it with enterprise systems such as ERP and CRM to understand both operational and fiscal impact. AI and analytics must be developed and deployed across both edge and cloud environments - using cloud capabilities for scalable analytics, enterprise integration, and complex workloads, while leveraging edge execution for low‑latency insights, resilience to connectivity loss, and support for sovereign on‑site data. Leveraging cloud and edge-based data, agents can help enable coordinated, proactive responses across systems and teams. In addition, organizations need the ability to scale and govern these capabilities consistently across sites, while maintaining the control, security, and reliability required in industrial environments. You can learn more about Microsoft’s Industrial AI stack in Part 2 of this blog, which provides the foundation for scalable industrial AI deployments. However, I would first like to shine a light on the role of our ecosystem, who play a critical role in enabling physical AI in complex operational environments. How our partners help accelerate and scale Physical AI deployments We recently had a partner, Sight Machine, accelerate a customer outcome by reducing their deployment timeline by about 90% using a validated, repeatable solution built on adaptive cloud architectural patterns. This is not an uncommon outcome. Our partners work to extend Azure IoT's capabilities with end-to-end solutions that give manufacturers the confidence to standardize once and deploy everywhere with security, governance, and repeatability built in. Advantech At HMI, Advantech is showcasing its LoRaWAN industrial sensing portfolio, including sensors connected through the WISE‑6610 LoRaWAN gateway. The gateway aggregates telemetry from distributed sensors and publishes it via MQTT, enabling seamless integration with Azure IoT Operations and downstream Azure cloud services. Learn more here and drop by our booth to see it in action as part of the hero demo. Celebal Tech At HMI, Celebal Tech is showcasing how its UniPlant platform integrates with Azure IoT Hub and Azure IoT Operations to harmonize fragmented OT data from machines, SCADA, PLCs, and MES systems into ISA 95 aligned, enterprise ready models. By transforming real time industrial telemetry into contextualized KPIs and operational insights, the joint solution enables unified plant visibility, predictive maintenance workflows, and AI ready data foundations across production environments. Stop by the Microsoft booth or CT’s booth A02 in Hall 16 to see how Celebal Tech and Microsoft are helping manufacturers scale intelligent operations with trusted, decision grade industrial data powered by Azure IoT. Helin Helin has built its edge‑to‑cloud industrial operations platform on Azure IoT Hub, orchestrating secure device connectivity and streaming large‑scale industrial telemetry into Microsoft Fabric and Azure Databricks to power real‑time operational intelligence for energy and maritime customers. As an early validation partner for Azure Device Registry (ADR) and Microsoft-backed certificate management, Helin is helping shape the future of unified asset identity and fleet‑scale device lifecycle management across distributed industrial environments. Meet Helin at Hannover Messe to learn how they're advancing unified asset identity across industrial fleets, learn more here. Litmus Automation At Hannover Messe, Litmus is announcing Litmus Edge Bridge for Azure IoT Operations, enabling automated, real-time discovery, cataloging, and onboarding of industrial assets. When Litmus Edge detects a new PLC, sensor, or controller, it becomes visible in Azure IoT Operations and can be onboarded with a single click – no manual mapping, scripting, or custom pipelines required. Built on the open-source Akri framework, this integration provides Azure-native representation of devices and their data models, enabling OT and IT teams to bring industrial data into Azure in a consistent, governed way. Learn more here and stop by the Litmus booth (Hall 16, booth A09) to see a live demo. Mesh Systems Mesh Systems is showcasing its Akri‑based industrial connectivity architecture at Hannover Messe 2026, designed to enable flexible integration across new and existing OT assets. Built to align with Azure IoT Operations and Kubernetes‑based edge architectures, Mesh’s framework provides a scalable way to onboard industrial protocols at the customer site and route operational data across edge‑to‑cloud environments, accelerating time to value for downstream AI‑driven workflows. As a trusted Azure IoT partner, Mesh delivers end‑to‑end implementation to help customers move from connectivity to production‑ready, data‑driven operations. Read their announcement press release here. NVIDIA Microsoft and NVIDIA are partnering to accelerate customers’ adoption of Physical AI through two new collaborative efforts unveiled at GTC. A public Azure Physical AI Toolchain GitHub repository - integrated with the NVIDIA Physical AI Data Factory and core Azure services - enables developers to build, train, and operate robotics and physical AI workflows that connect physical assets, simulation environments, and cloud‑based training into repeatable, enterprise‑grade pipelines. In addition, a deeper integration between Microsoft Fabric and NVIDIA Omniverse libraries connects live operational data with physically accurate digital twins and simulation, allowing organizations to monitor physical systems in real time and use AI‑driven insights to inform next‑best actions. These capabilities will be brought to life at HMI in Microsoft’s Factory of the Future demo. Rockwell Automation Rockwell Automation and Microsoft are advancing an established adaptive cloud pattern for scalable industrial AI. FactoryTalk Optix captures and contextualizes OT data at the edge, while Azure IoT Operations provides consistent governance and lifecycle management across site deployments through Azure’s control plane. Together, Rockwell and Microsoft are co-innovating to do information model discovery and synchronization at the edge to bring agent-ready data to cloud. Stop by the Microsoft booth at HMI to learn how the joint architecture enables scalable industrial AI deployments from factory floor to cloud analytics. Sight Machine In 2025, Sight Machine integrated its industrial AI platform with Azure IoT Operations and Microsoft Fabric to turn fragmented plant data into contextualized, production-ready insights. Its unified marketplace offering for industrial data ingestion, streaming, and AI analysis helps manufacturers standardize OT data at scale and move from pilot to production in weeks. This is proven in the field: a major global bottler moved from setup to full rollout in under two months using the joint solution, and many manufacturers across industries are seeing similar gains while realizing their industrial AI goals. Toyota Industries Corporation (TICO) reports improved paint shop quality with AI-powered defect detection with Sight Machine and Azure IoT Hub. Join Sight Machine’s MSFT booth theatre session at HMI (Mon, April 20, 2:30 PM) to learn more - or explore their solution here. Siemens Siemens Industrial Edge and Azure IoT help manufacturers move from fragmented OT environments to unified, insight‑driven operations across sites. By standardizing how industrial data is captured and governed from edge to cloud through Siemens’ Industrial Edge Management (IEM) and Azure IoT Operations, customers can reuse operational data across analytics, AI models, and digital twins without re‑architecting underlying systems. The joint solution accelerates time to value, reduces integration effort, and enables real‑time optimization, predictive maintenance, and closed‑loop quality across production environments. Join the theatre session on April 22 at 9:45am in Microsoft’s booth to learn how Siemens and Microsoft are enabling scalable industrial AI in production environments. Conclusion As manufacturers scale AI from pilots to production, success depends on a foundation that connects operational data, applies AI where it’s needed, and governs everything consistently from edge to cloud. At Hannover Messe 2026, we’re proud to share how Microsoft and our partners are helping customers run AI powered factories with an adaptive cloud approach - so teams can move faster with confidence, improve uptime, and turn real-time insights into action. To learn more: Visit us at Hannover Messe 2026 in Hall 17, Booth G06 to explore the latest partner innovations across Azure IoT Operations, Azure IoT Hub, Foundry Local, and Azure Local. Not attending in person this year? Reach out to your Microsoft account team to find the right solution for your environment. Read the next blog in this series that details the latest product innovations we will be sharing at Hannover Messe 2026: Making Physical AI Practical for Real-World Industrial Operations: Part 2 See full list of our industrial ecosystem partners here: Microsoft Industrial AI Partner Guide: Choosing the Right Data Expertise for Every StageMaking Physical AI Practical for Real-World Industrial Operations: Part 2
In my previous blog, I talked about how we collaborate with great partners such as Sight Machine, Litmus, Mesh Systems, Siemens, Rockwell, Schneider Electric, and others to deliver end to end value for the practical application of AI in manufacturing environments. These partners, along with forward-thinking customers like Chevron, Husqvarna and Ecopetrol continue to drive our innovation roadmap and push the boundaries of how to use Agentic AI for operational efficiency improvements, visual inspection, and safety enhancements. How Ecopetrol is modernizing its data platform to take advantage of AI As an integrated energy conglomerate, Ecopetrol operates across the entire hydrocarbon chain encompassing exploration, production, transportation, refining, and commercialization, as well as linear infrastructure, including energy transmission and road concessions. The company is dedicated to leading the energy sector into a more sustainable future by adopting technology that enables intelligent operations. Ecopetrol has partnered with Microsoft to build a flexible and secure edge to cloud platform, based on Azure IoT Operations and Fabric Real-Time Intelligence, that can standardize data across diverse sources to enable data contextualization and eventually, AI integration. This improved ability to harness data can help Ecopetrol meet key objectives such as improved operational performance, cost savings, lowered energy consumption, and reduced carbon emissions. New innovations introduced at Hannover Messe 2026: Microsoft’s Industrial AI platform has been purpose-built to support edge and physical AI scenarios. It offers a comprehensive foundation to build, scale, and govern Industrial AI initiatives with a unified intelligence layer powering AI (Fabric IQ, Foundry IQ, Work IQ) and a consistent framework to managing apps, data and infrastructure with the adaptive cloud approach. Looking more closely at how these components come together to enable business outcomes: Azure IoT Operations and Azure IoT Hub collect real-time data from operational assets and devices and send semantically-ready modelled data to Fabric IQ so that it can be contextualized with other enterprise data sources for downstream analytics. Microsoft Foundry has been extended to the edge with Foundry Local so that organizations can use the same tooling that they use to deploy and manage AI models in the cloud for edge use cases as well. Azure IoT Operations, Azure IoT Hub, and Foundry Local integrate into Azure Resource Manager, which brings OT devices, assets, and edge AI models into the same management and security paradigm as all other Azure managed resources. For organizations with sovereignty requirements, Microsoft enables Sovereign Private Cloud with Azure Local, which can run in connected or disconnected mode, depending on customer requirements. At HMI 2026, we are announcing our next set of innovations in this space to help manufacturers and partners initiate and scale industrial AI initiatives on a trusted foundation. Unlocking value from operational data Azure IoT Operations, enabled by Azure Arc, is a set of scalable edge services, built on industry standards to easily capture device and equipment data, process and normalize it at the edge, seamlessly send and receive operational insights to and from the cloud, and contextualize that data so it can be used directly by Physical AI workloads running at the edge. Through Arc and Kubernetes, Azure IoT Operations empowers our customers with a unified technology architecture and data plane that supports repeatable solution deployment, automated updates to apps, cost-effective high availability, and redundancy at the edge. At HMI 2026, we are announcing the next major release of Azure IoT Operations, 2603. This release enables manufacturers to build, manage, and operate industrial data flows across the full edge-cloud lifecycle without custom code. Key new capabilities include edge-to-cloud industrial data management with no-code visual data pipelines, cloud-to-edge device and asset command and control, unified health status and observability, as well as new connector integrations for third-party MQTT brokers and Litmus Edge industrial gateways. Furthering our commitment to open standards, we also recently announced support for WC3 Web of Things (WoT) in Azure IoT Operations, which simplifies the process of integrating industrial assets into intelligent applications. Agentic operations at the edge Foundry Local enabled by Azure Arc extends cloud grade AI inference to on-premises and edge environments. It allows organizations to deploy, run, and manage generative and predictive AI models directly on customer-controlled infrastructure, such as factory floors, remote industrial sites, and disconnected environments, where low latency, data locality, and operational autonomy are critical. The solution builds on Azure Arc-enabled Kubernetes as a unified control plane, enabling AI workloads to be deployed and operated locally using the same paradigms as in the cloud. Today, we are excited to announce Foundry model catalog in Azure Local, which supports both curated open-source models from a managed catalog and customer proprietary bring-your-own-model (BYOM) deployments. Models can run concurrently across available on-premises hardware, on CPU-only systems or with GPU acceleration, so customers can match performance and cost requirements across diverse edge environments. AI models are delivered as containerized services, exposing standard REST endpoints, including OpenAI compatible APIs for generative models and dedicated APIs for predictive workloads. This allows applications to consume local AI inference in the same way they consume cloud AI services, with minimal changes. New capabilities for secure policy and identity management While properly contextualized data and AI hold the key to optimizing business outcomes, organizations cannot risk rolling out these initiatives without the proper management and security foundation in place. Taking an adaptive cloud approach for these capabilities is also crucial, to ensure organizations have the right level of visibility, control, and protection across their entire digital and physical domain. Azure IoT Hub is Microsoft’s key offering to connect and manage IoT devices and assets with direct connections to the cloud. In November, we announced the public preview of Azure IoT Hub integration with Azure Device Registry which brought IoT devices under the purview of the Azure management plane (ARM, Azure resource manager) and allowed them to be secured with best-in-class Microsoft-backed X.509 certificate management capabilities. Today, we are expanding upon that preview with strengthened device identity, security, and fleet‑scale management. This refresh delivers a more complete, end‑to‑end certificate management experience, covering certificate issuance, renewal, and revocation, with support for hybrid trust models, including customer‑managed root CAs from non‑Microsoft PKIs. It also introduces a more cohesive Azure Portal experience for service configuration and device operations, allowing operators to manage devices consistently as ADR resources using standard Azure tooling and workflows. Firmware analysis capabilities Another critical security consideration for customers is the security profile of the device itself. With firmware analysis, enabled by Azure Arc, our goal is to provide deeper visibility into IoT/OT and network devices by analyzing the foundational software (firmware) they run. At HMI, we are releasing a preview of integration of firmware analysis with Azure Device Registry, delivering fleet-level visibility into where firmware vulnerabilities exist across deployed IoT and OT environments. This new integration correlates analyzed firmware images with devices and assets registered in Azure Device Registry, allowing customers with both Azure IoT Operations and IoT Hub to quickly see how many devices are running a given firmware image and which specific assets may be exposed to known security risks, bridging the gap between firmware insights and real-world operational impact. This release also offers new capabilities to help customers prioritize vulnerabilities, work with larger firmware images, and share security analysis results with supply chain partners. Sovereign options for mission-critical workloads While security continues to be a strategic priority for most large, global organizations, digital sovereignty is an increasingly critical requirement for many manufacturers as well. For these scenarios, Microsoft offers capabilities that support customers across connected, intermittently connected and fully disconnected modes. With Azure Local disconnected, organizations can now run mission-critical infrastructure with Azure governance and policy control, with no cloud connectivity, optimizing continuity for sovereign, classified, or isolated environments. With Azure Local disconnected operations, management, policy, and workload execution stay within the customer-operated environments, so services continue running securely even when environments must be isolated or connectivity is not available. Using familiar Azure experiences and consistent policies, organizations can deploy and govern workloads locally without depending on continuous connection to public cloud services. Azure Local is designed to scale with mission-critical needs from smaller deployments to larger footprints that support data-intensive and AI-driven workloads. Conclusion As a leading forum for industrial innovation, Hannover Messe provides manufacturers with an opportunity to explore the latest technologies transforming modern operations. This year’s theme, “Think Tech Forward,” aligns with the industry’s growing emphasis on applying proactive, AI‑driven intelligence across business processes and ecosystems. Join Microsoft in Hall 17, Booth G06 to discover practical approaches for implementing AI to improve operational outcomes. Learn more about Microsoft’s overall presence at the show here. For a deeper dive into how Microsoft is driving industrial outcomes with partners, please refer to the first blog in this series: Making Physical AI Practical for Real-World Industrial Operations: Part 1 holderAzure IoT Operations 2603 is now available: Powering the next era of Physical AI
Industrial AI is entering a new phase. For years, AI innovation has largely lived in dashboards, analytics, and digital decision support. Today, that intelligence is moving into the real world, onto factory floors, oil fields, and production lines, where AI systems don’t just analyze data, but sense, reason, and act in physical environments. This shift is increasingly described as Physical AI: intelligence that operates reliably where safety, latency, and real‑world constraints matter most. With the Azure IoT Operations 2603 (v1.3.38) release, Microsoft is delivering one of its most significant updates to date, strengthening the platform foundation required to build, deploy, and operate Physical AI systems at industrial scale. Why Physical AI needs a new kind of platform Physical AI systems are fundamentally different from digital‑only AI. They require: Real‑time, low‑latency decision‑making at the edge Tight integration across devices, assets, and OT systems End‑to‑end observability, health, and lifecycle management Secure cloud‑to‑edge control planes with governance built in Industry leaders and researchers increasingly agree that success in Physical AI depends less on isolated models, and more on software platforms that orchestrate data, assets, actions, and AI workloads across the physical world. Azure IoT Operations was built for exactly this challenge. What’s new in Azure IoT Operations 2603 The 2603 release delivers major advancements across data pipelines, connectivity, reliability, and operational control, enabling customers to move faster from experimentation to production‑grade Physical AI. Cloud‑to‑edge management actions Cloud‑to‑edge management actions enable teams to securely execute control and configuration operations on on‑premises assets, such as invoking methods, writing values, or adjusting settings, using Azure Resource Manager and Event Grid–based MQTT messaging. This capability extends the Azure control plane beyond the cloud, allowing intent, policy, and actions to be delivered reliably to physical systems while remaining decoupled from protocol and device specifics. For Physical AI, this closes the loop between perception and action: insights and decisions derived from models can be translated into governed, auditable changes in the physical world, even when assets operate in distributed or intermittently connected environments. Built‑in RBAC, managed identity, and activity logs ensure every action is authorized, traceable, and compliant, preserving safety, accountability, and human oversight as intelligence increasingly moves from observation to autonomous execution at the edge. No‑code dataflow graphs Azure IoT Operations makes it easier to build real‑time data pipelines at the edge without writing custom code. No‑code data flow graphs let teams design visual processing pipelines using built‑in transforms, with improved reliability, validation, and observability. Visual Editor – Build multi-stage data processing systems in the Operations Experience canvas. Drag and connect sources, transforms, and destinations visually. Configure map rules, filter conditions, and window durations inline. Deploy directly from the browser or define in Bicep/YAML for GitOps. Composable Transforms, Any Order – Chain map, filter, branch, concatenate, and window transforms in any sequence. Branch splits messages down parallel paths based on conditions. Concatenate merges them back. Route messages to different MQTT topics based on content. No fixed pipeline shape. Expressions, Enrichment, and Aggregation – Unit conversions, math, string operations, regex, conditionals, and last-known-value lookups, all built into the expression language. Enrich messages with external data from a state store. Aggregate high-frequency sensor data over tumbling time windows to compute averages, min/max, and counts. Open and Extensible – Connect to MQTT, Kafka, and OpenTelemetry (OTel) endpoints with built-in security through Azure Key Vault and managed identities. Need logic beyond what no-code covers? Drop a custom Wasm module (even embed and run ONNX AI ML models) into the middle of any graph alongside built-in transforms. You're never locked into declarative configuration. Together, these capabilities allow teams to move from raw telemetry to actionable signals directly at the edge without custom code or fragile glue logic. Expanded, production‑ready connectivity The MQTT connector enables customers to onboard MQTT devices as assets and route data to downstream workloads using familiar MQTT topics, with the flexibility to support unified namespace (UNS) patterns when desired. By leveraging MQTT’s lightweight publish/subscribe model, teams can simplify connectivity and share data across consumers without tight coupling between producers and applications. This is especially important for Physical AI, where intelligent systems must continuously sense state changes in the physical world and react quickly based on a consistent, authoritative operational context rather than fragmented data pipelines. Alongside MQTT, Azure IoT Operations continues to deliver broad, industrial‑grade connectivity across OPC UA, ONVIF, Media, REST/HTTP, and other connectors, with improved asset discovery, payload transformation, and lifecycle stability, providing the dependable connectivity layer Physical AI systems rely on to understand and respond to real‑world conditions. Unified health and observability Physical AI systems must be trustworthy. Azure IoT Operations 2603 introduces unified health status reporting across brokers, dataflows, assets, connectors, and endpoints, using consistent states and surfaced through both Kubernetes and Azure Resource Manager. This enables operators to see—not guess—when systems are ready to act in the physical world. Optional OPC UA connector deployment Azure IoT Operations 2603 introduces optional OPC UA connector deployment, reinforcing a design goal to keep deployments as streamlined as possible for scenarios that don’t require OPC UA from day one. The OPC UA connector is a discrete, native component of Azure IoT Operations that can be included during initial instance creation or added later as needs evolve, allowing teams to avoid unnecessary footprint and complexity in MQTT‑only or non‑OPC deployments. This reflects the broader architectural principle behind Azure IoT Operations: a platform built for composability and decomposability, where capabilities are assembled based on scenario requirements rather than assumed defaults, supporting faster onboarding, lower resource consumption, and cleaner production rollouts without limiting future expansion. Broker reliability and platform hardening The 2603 release significantly improves broker reliability through graceful upgrades, idempotent replication, persistence correctness, and backpressure isolation—capabilities essential for always‑on Physical AI systems operating in production environments. Physical AI in action: What customers are achieving today Azure IoT Operations is already powering real‑world Physical AI across industries, helping customers move beyond pilots to repeatable, scalable execution. Procter & Gamble Consumer goods leader P&G continually looks for ways to drive manufacturing efficiency and improve overall equipment effectiveness—a KPI encompassing availability, performance, and quality that’s tracked in P&G facilities around the world. P&G deployed Azure IoT Operations, enabled by Azure Arc, to capture real-time data from equipment at the edge, analyze it in the cloud, and deploy predictive models that enhance manufacturing efficiency and reduce unplanned downtime. Using Azure IoT Operations and Azure Arc, P&G is extrapolating insights and correlating them across plants to improve efficiency, reduce loss, and continue to drive global manufacturing technology forward. More info. Husqvarna Husqvarna Group faced increasing pressure to modernize its fragmented global infrastructure, gain real-time operational insights, and improve efficiency across its supply chain to stay competitive in a rapidly evolving digital and manufacturing landscape. Husqvarna Group implemented a suite of Microsoft Azure solutions—including Azure Arc, Azure IoT Operations, and Azure OpenAI—to unify cloud and on-premises systems, enable real-time data insights, and drive innovation across global manufacturing operations. With Azure, Husqvarna Group achieved 98% faster data deployment and 50% lower infrastructure imaging costs, while improving productivity, reducing downtime, and enabling real-time insights across a growing network of smart, connected factories. More info. Chevron With its Facilities and Operations of the Future initiative, Chevron is reimagining the monitoring of its physical operations to support remote and autonomous operations through enhanced capabilities and real-time access to data. Chevron adopted Microsoft Azure IoT Operations, enabled by Azure Arc, to manage and analyze data locally at remote facilities at the edge, while still maintaining a centralized, cloud-based management plane. Real-time insights enhance worker safety while lowering operational costs, empowering staff to focus on complex, higher-value tasks rather than routine inspections. More info. A platform purpose‑built for Physical AI Across manufacturing, energy, and infrastructure, the message is clear: the next wave of AI value will be created where digital intelligence meets the physical world. Azure IoT Operations 2603 strengthens Microsoft’s commitment to that future—providing the secure, observable, cloud‑connected edge platform required to build Physical AI systems that are not only intelligent, but dependable. Get started To explore the full Azure IoT Operations 2603 release, review the public documentation and release notes, and start building Physical AI solutions that operate and scale confidently in the real world.Microsoft Industrial AI Partner Guide: Choosing the Right Data Expertise for Every Stage
As organizations scale Industrial AI, the challenge shifts from technology selection to deciding who should lead which part of the journey -- and when. Which partners should establish secure connectivity? Who enables production grade, AI ready industrial data? When do systems integrators step in to scale globally? This Partner Guide helps customers navigate these decisions with clarity and confidence: Identify which partners align to their current digital transformation and Industrial AI scenarios leveraging Azure IoT and Azure IoT Operations Confidently combine partners over time as they evolve from connectivity to intelligence to autonomous operations This guide focuses on the Industrial AI data plane – the partners and capabilities that extract, contextualize, and operationalize industrial data so it can reliably power AI at scale. It does not attempt to catalog or prescribe end‑to‑end Industrial AI applications or cloud‑hosted AI solutions. Instead, it helps customers understand how industrial partners create the trusted, contextualized data foundation upon which AI solutions can be built. Common Customer Journey Steps 1. Modernize Connectivity & Edge Foundations The industrial transformation journey starts with securely accessing operational data without touching deterministic control loops. Customers connect automation systems to a scalable, standards-based data foundation that modernizes operations while preserving safety, uptime and control. Outcomes customers realize Standardized OT data access across plants and sites Faster onboarding of legacy and new assets Clear OT–IT boundaries that protect safety and uptime Partner strengths at this stage Industrial hardware and edge infrastructure providers Protocol translation and OT connectivity Automation and edge platforms aligned with Azure IoT Operations 2. Accelerate Insights with Industrial AI With a consistent edge-to-cloud data plane in place, customers move beyond dashboards to repeatable, production-grade Industrial AI use cases. Customers rely on expert partners to turn standardized operational data into AI‑ready signals that can be consumed by analytics and AI solutions at scale across assets, lines, and sites. Outcomes customers realize Improved Operational efficiency and performance Adaptive facilities and production quality intelligence Energy, safety, and defect detection at scale Partner strengths at this stage Industrial data services that contextualize and standardize OT signals for AI consumption Domain-specific acceleration for common Industrial AI scenarios Data pipelines integrated with Azure IoT Operations and Microsoft Fabric 3. Prepare for Autonomous Operations As organizations advance toward closed‑loop optimization, the focus shifts to safe, scalable autonomy. Customers depend on partners to align data, infrastructure, and operational interfaces, while ensuring ongoing monitoring, governance, and lifecycle management across the full operational estate. Outcomes customers realize Proven reference architectures deployed across plants AI‑ready data foundations that adapt as operations scale Coordinated interaction between OT systems, AI models, and cloud intelligence Partner strengths at this stage Industrial automation leadership and control system expertise Edge infrastructure optimized and ready for Industrial AI scale Systems integrators enabling end‑to‑end implementation and repeatability Data Intelligence Plane of Industrial AI - Partner Matrix This matrix highlights which partners have the deepest expertise in accessing, contextualizing, and operationalizing industrial data so it can reliably power AI at scale. The matrix is not a catalog of end‑to‑end Industrial AI applications; it shows how specialized partners contribute data, infrastructure, and integration capabilities on a shared Azure foundation as organizations progress from connectivity to insight to autonomous operations. How to use this matrix: Start with your scenario → identify primary partner types → layer complementary partners as you scale. Partner Type Adaptive Cloud Primary Solution Example Scenarios Geography Advantech Industrial Hardware, Industrial Connectivity LoRaWAN gateway integration + Azure IoT Operations Industrial edge platforms with built in connectivity, industrial compute, LoRaWAN, sensor networks Global Accenture GSI Industrial AI, Digital Transformation, Modernization OEE, predictive maintenance, real-time defect detection, optimize supply chains, intelligent automation and robotics, energy efficiency Global Avanade GSI Factory Agents and Analytics based on Manufacturing Data Solutions Yield / Quality optimization, OEE, Agentic Root Cause Analysis and process optimization; Unified ISA-95 Manufacturing Data estate on MS Fabric Global Capgemini GSI The new AI imperative in manufacturing OEE, maintenance, defect detection, energy, robotics Global DXC GSI Intelligent Boost AI and IoT Analytics Platform 5G Industrial Connectivity, Defect detection, OEE, safety, energy monitoring Global Innominds SI Intelligent Connected Edge Platform Predictive maintenance, AI on edge, asset tracking North America, EMEA Litmus Automation Industrial Connectivity, Industrial Data Ops Litmus Edge + Azure IoT Operations Edge Data, Smart manufacturing, IIoT deployments at scale Global, North America Mesh Systems GSI & ISV Azure IoT & Azure IoT Operations implementation services and solutions (including Azure IoT Operations-aligned connector patterns) Device connectivity and management, data platforms, visualization, AI agents, and security North America, EMEA Nortal GSI Data-driven Industry Solutions IT/OT Connectivity, Unified Namespace, Digital Twins, Optimization, Edge, Industrial Data, Real‑Time Analytics & AI EMEA, North America & LATAM NVIDIA Technology Partner Accelerated AI Infrastructure; Open libraries, models, frameworks, and blueprints for AI development and deployment. Cross industry digitalization and AI development and deployment: Generative AI, Agentic AI, Physical AI, Robotics Global Oracle ISV Oracle Fusion Cloud SCM + Azure IoT Operations Real-time manufacturing Intelligence, AI powered insights, and automated production workflows Global Rockwell Automation Industrial Automation FactoryTalk Optix + Azure IoT Operations Factory modernization, visualization, edge orchestration, DataOps with connectivity context at scale, AI ops and services, physical equipment, MES Global Schneider Electric Industrial Automation Industrial Edge Physical equipment, Device modernization, energy, grid Global Siemens Industrial Automation & Software Industrial Edge + Azure IoT Operations reference architecture Industrial edge infrastructure at scale, OT/IT convergence, DataOps, Industrial AI suite, virtualized automation. Global Sight Machine ISV Integrated Industrial AI Stack Industrial AI, bottling, process optimization Global Softing Industrial Industrial Connectivity edgeConnector + Azure IoT Operations OT connectivity, multi-vendor PLC- and machine data integration, OPC UA information model deployment EMEA, Global TCS GSI Sensor to cloud intelligence Operations optimization, healthcare digital twin experiences, supply chain monitoring Global This Ecosystem Model enables Industrial AI solutions to scale through clear roles, respected boundaries and composable systems: Control systems continue to be driven by automation leaders Safety‑critical, deterministic control stays with industrial automation partners who manage real‑time operations and plant safety. Customers modernize analytics and AI while preserving uptime, reliability, and operational integrity. Data, AI, and analytics scale independently A consistent edge to cloud data plane supports cloud scale analytics and AI, accelerating insight delivery without entangling control systems or slowing operational change. This separation allows customers and software providers to build AI solutions on top of a stable, industrial‑grade data foundation without redefining control system responsibilities. Specialized partners align solutions across the estate Partners contribute focused expertise across connectivity, analytics, security, and operations, assembling solutions that reduce integration risk, shorten deployment cycles, and speed time to value across the operational estate. From vision to production Industrial AI at scale depends on turning operational data into trusted, contextualized intelligence safely, repeatably, and across the enterprise. This guide shows how industrial partners, aligned on a shared Azure foundation, create the data plane that enables AI solutions to succeed in production. When data is ready, intelligence scales. Call to action: Use this guide to identify the partners and capabilities that best align to your current Industrial AI needs and take the next step toward production‑ready outcomes on Azure.Bridging the Digital and Physical Worlds with Azure IoT Hub and Azure IoT Operations
Operational excellence starts with people. Empowering those people with the most up to date insights and recommendations requires bridging the gap between the physical and digital worlds to generate the best possible outcomes for real time decision making. Creating this bridge transforms data into insights, insights into intelligent actions, and actions into real-world results. Digital Operations, integrated with AI insights, help make this possible by combining data from connected assets across a variety of physical locations and deployment topologies, and transforming that data into insights and decisions that scale using AI and Analytics. At Microsoft Ignite, we’re extending this vision with new Azure IoT Hub and Azure IoT Operations capabilities to manage connected assets at scale, unify digital operations, and realize AI-enabled outcomes across your enterprise. Connected Operations in Action Azure IoT Hub and Azure IoT Operations form the backbone of connected operations, where every asset, sensor, and system contributes to a continuous loop of intelligence by moving data to Microsoft Fabric for real-time analytics, and for use with AI agents. This pattern applies to nearly every sector of the economy. In manufacturing, these capabilities allow production engineers to predict and avoid equipment failures by analyzing vibration and temperature data at the edge before costly downtime occurs. In energy and utilities, distributed sensors can provide data to control points that help balance load, optimize grid efficiency, and ensure safe operations even in remote areas. In transportation and logistics, connected fleets use edge AI models to detect safety risks in real time, while cloud-based analytics optimize routing and fuel efficiency across entire regions. Across industries, this edge-to-cloud collaboration enables the ability for intelligent systems to sense, reason, and act in the physical world with speed, safety, and precision. From Data to Intelligent Action Organizations today must capture and act on data from both geographically dispersed and tightly collocated assets. That data needs to be processed close to where it’s generated, at the edge, to enable real-time decision-making, reduce latency, and enhance security. At the same time, the cloud remains vital for contextualizing operational data with enterprise systems, training AI models, and managing a consistent identity and security framework across all assets. AI models trained in the cloud can then be deployed back to the edge, where they act on events in real time. Operators can work with AI agents to reason over this data whether it’s structured or unstructured, organized in silos, or contained in free-text fields, to provide results to a mixed team of human and AI operational assets. We have a portfolio of products uniquely designed to make this continuum, from edge to cloud, more intelligent, secure, and repeatable. Together with our partners, we help bridge Operational Technology (OT) with Information Technology (IT) to deliver better business outcomes. New at Ignite: Accelerating Digital Operations We’re excited to share our latest set of investments at Ignite across our portfolio of services. A few key announcements: Azure IoT Hub New Features (Preview): Simplifying Secure Connectivity at Scale Azure IoT Hub empowers organizations to securely and reliably manage connected assets across the globe, providing real-time visibility and control over diverse operations. With proven scalability, broad device support, and robust management tools, IoT Hub delivers a unified platform for developing and operating IoT solutions. As customers evolve, Azure IoT Hub continues to advance, deepening its integration with the Azure ecosystem and enabling AI-driven, connected operations for the next generation of applications. The next generation of Azure IoT Hub investments makes it easier and more secure than ever to connect and manage distributed assets. At Ignite, we’re previewing: New certificate management capabilities that simplify device onboarding and lifecycle management. Integration with Azure Device Registry (ADR) that brings all devices into a common control plane, enabling unified identity, security, and policy management. ADR enhancements that make it easier to register, classify, and monitor assets, paving the way for consistent governance and operational insight across millions of devices. This deeper Azure integration with ADR standardizes operations, simplifies oversight of edge portfolios including IoT devices, and brings the full power of Azure’s management ecosystem to IoT and Digital Operations workloads. Azure IoT Operations New Features (GA): The Foundation for AI in the Physical World Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for achieving AI in the physical world, enabling intelligent operational systems that can perceive, reason, and act to drive new operational efficiencies. Built on Arc-enabled Kubernetes, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and providing a repeatable, scalable foundation for autonomous, adaptive operations. By extending familiar Azure management concepts to physical sites, Azure IoT Operations creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. Our latest GA release of Azure IoT Operations introduced major enhancements: Wasm-powered data graphs deliver fast, modular analytics helping businesses make near real-time decisions at the edge. Expanded connectors now include OPC UA, ONVIF, REST/HTTP, Server-Sent Events (SSE), and direct MQTT for richer industrial and IT integrations. OpenTelemetry (OTel) endpoint support enables seamless telemetry pipelines and observability. Asset health monitoring to provide unprecedented visibility and control. These capabilities help bridge Information Technology, Operational Technology, and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. Integration with Fabric IQ and Digital Twin Builder To fully unlock the value of connected data, organizations need to contextualize it, linking operational signals to business meaning. Fabric IQ, a new offering announced at Ignite, and Digital Twin Builder in Fabric make this possible, transforming raw telemetry into AI-ready context. This integration allows companies to model complex systems, run simulations, and create intelligent feedback loops across manufacturing, logistics, and energy environments. Edge AI: Real-Time Intelligence in the Physical World Azure’s AI capabilities for edge environments bring intelligence closer to where it matters most. And, because these services are Arc-enabled, organizations can develop, manage and scale AI workloads across diverse environments using consistent tooling. Today, we are announcing updates to two of our key services that enable AI at the edge: Live Video Analysis features (Public Preview) in Azure AI Video Indexer enabled by Arc: delivers real-time agentic video intelligence to improve safety, quality, and operations. Edge RAG (Retrieval Augmented Generation) Public Preview Refresh enables local generative AI reasoning with contextual awareness - empowering AI agents to act within industrial constraints securely and efficiently. These innovations accelerate time to insight and help organizations deploy AI where milliseconds matter. Partner Innovation: Scaling Real Business Value Last year, we showcased the breadth of Azure IoT Operations’ industrial ecosystem. This year, we’re celebrating how partners are integrating, co-innovating, and scaling real customer outcomes. Our partners are packaging repeatable, scalable solutions that connect operational data to enterprise systems—enabling AI-driven insights and automation across sites, regions, and industries. At this year’s Ignite, we’re highlighting some great new partner innovations: NVIDIA is working with Microsoft to enable factory digital twins using the OpenUSD standard Siemens is enabling adaptive production through AI- and digital-twin-powered solutions supported by the integration of Siemens Industrial Edge with Azure IoT Operations Litmus Edge integrates with Azure IoT Operations via the Akri framework to automatically discover industrial devices, enable secure data flows, and support Arc-enabled deployment. Rockwell Automation is streamlining edge-to-cloud integration with its FactoryTalk Optix platform by delivering contextualized, AI-ready data seamlessly within Microsoft Azure IoT Operations architectures. Sight Machine is driving advanced analytics for quality and efficiency across multi-site operations. Through initiatives like Akri, Co-Innovate, and Co-Sell Readiness, our ecosystem is developing managed applications, packaged solutions, and marketplace offerings that accelerate deployment and unlock new revenue streams. These collaborations show how Azure IoT Operations is not just a platform, but a growth engine for industrial transformation. The Path Forward With these advancements, we’re helping organizations bring AI to the physical world by turning data into intelligence and intelligence into action. Customers like Chevron and Husqvarna are scaling beyond initial pilots, expanding their deployments from single-site to multi-site rollouts, unlocking new use cases from predictive maintenance to worker safety, and proving how adaptive cloud architectures deliver measurable impact across global operations. By connecting assets, empowering partners, and delivering open, scalable platform solutions, Microsoft is helping industries achieve resilient, adaptive operations that drive measurable business value. The digital and physical worlds are coming together with solutions that are secure, observable, AI-ready, and built to scale from a single site to global operations. Together, we’re creating a smarter, more connected future. Learn More Learn more about Azure IoT Hub and Azure IoT Operations here: Azure IoT – Internet of Things Platform | Microsoft Azure Learn more about new IoT Hub public preview features here: Azure IoT Hub documentation Discover Partner Solutions: Learn how Litmus and Sight Machine are advancing industrial analytics and integration with Azure IoT Operations. Explore Rockwell Automation and Siemens for more on adaptive cloud architectures and shop floor intelligence. Going to Ignite? If you’re at Ignite this week, you can learn more about how Microsoft enables Industrial Transformation at the following sessions: The New Industrial Frontier Reshaping Digital Operations with AI from Cloud and Edge Or come visit us on the show floor at the Azure Arc Expert Meet Up Focus Area in the Cloud and AI Platforms neighborhoodAzure IoT Operations 2510 Now Generally Available
Introduction We’re thrilled to announce the general availability of Azure IoT Operations 2510, the latest evolution of the adaptive cloud approach for AI in industrial and large scale commercial IoT. With this release, organizations can unlock new levels of scalability, security, and interoperability, empowering teams to seamlessly connect, manage, and analyze data from edge to cloud. What is Azure IoT Operations? Azure IoT Operations is more than an edge-to-cloud data plane, it’s the foundation for AI in physical environments, enabling intelligent systems to perceive, reason, and act in the real world. Built on Arc-enabled Kubernetes clusters, Azure IoT Operations unifies operational and business data across distributed environments, eliminating silos and delivering repeatability and scalability. By extending familiar Azure management concepts to physical sites, AIO creates an AI-ready infrastructure that supports autonomous, adaptive operations at scale. This approach bridges information technology (IT), operational technology (OT), and data domains, empowering customers to discover, collect, process, and send data using open standards while laying the groundwork for self-optimizing environments where AI agents and human supervisors collaborate seamlessly. We've put together a quick demo video showcasing the key features of this 2510 release. Watch below to discover how Azure IoT Operations' modular and scalable data services empowers IT, OT and developers. What’s New in Azure IoT Operations 2510? Management actions: Powerful management actions put you in control of processes and asset configurations, making operations simpler and smarter. Web Assembly (Wasm) data graphs: Wasm-powered data graphs for advanced edge processing, delivering fast, modular analytics and business logic right where your data lives. New connectors: Expanded connector options now include OPC UA, ONVIF, Media, REST/HTTP, and Server-Sent Events (SSE), opening the door to richer integrations across diverse industrial and IT systems. OpenTelemetry (OTel) endpoints: Data flows now support sending data directly to OpenTelemetry collectors, integrating device and system telemetry into your existing observability infrastructure. Improved observability: Real-time health status for assets gives you unmatched visibility and confidence in your IoT ecosystem. Reusable Connector templates: Streamline connector configuration and deployment across clusters. Device support in Azure Device Registry: Azure Device Registry (ADR) now treats devices as first‑class resources within ADR namespaces, enabling logical isolation and role‑based access control at scale. Automatic device and asset discovery and onboarding: Akri‑powered discovery continuously detects devices and industrial assets on the network, then automatically provisions and onboards them (including creating the right connector instances) so telemetry starts flowing with minimal manual setup. MQTT Data Persistence: Data can now be persisted to disk, ensuring durability across broker restarts. X.509 Auth in MQTT broker: The broker now supports X.509 authentication backed by Azure's Device Registry. Flexible RBAC: Built-in roles and custom role definitions to simplify and secure access management for AIO resources. Customers and partners Chevron, through its Facilities and Operations of the Future initiative, deployed Azure IoT Operations with Azure Arc to manage edge-to-cloud workloads across remote oil and gas sites. With a single management plane, the strategy unifies control over thousands of distributed sensors, cameras, robots, and drones. Real-time monitoring and AI enabled anomaly detection not only to enhance operational efficiency but also significantly improve worker safety by reducing routine inspections and enabling remote issue mitigation. This reuse of a global, AI-ready architecture positions Chevron to deliver more reliable, cleaner energy. [microsoft.com] Husqvarna implemented Azure IoT Operations across its global manufacturing network as part of a comprehensive strategy. This adaptive cloud approach integrates cloud, on-premises, and edge systems, preserves legacy investments, and enables real-time edge analytics. The result: data operationalization is 98% faster, imaging costs were slashed by half, productivity was improved, and downtime was reduced. Additionally, AI-driven capabilities like the Factory Companion powered by Azure AI empower technicians with instant, data-informed troubleshooting, shifting maintenance from reactive to predictive across sites. [microsoft.com] Together, these success stories show how Azure IoT Operations, combined with capabilities like Azure Arc, can empower industrial leaders to advance from siloed operations to unified, intelligent systems that boost efficiency, safety, and innovation. Additionally, this year we are celebrating how our partners are integrating, co-innovating, and scaling real customer outcomes. You can learn more about our partner successes at https://aka.ms/Ignite25/DigitalOperationsBlog. Learn more at our launch event Join us at Microsoft Ignite to dive deeper into the latest innovations in Azure IoT Operations 2510. Our sessions will showcase real-world demos plus expert insights on how new capabilities accelerate industrial transformation. Don’t miss the chance to connect with product engineers, explore solution blueprints, and see how Azure IoT Operations lays the foundation for building and scaling physical AI. Get Started Ready to experience the new capabilities in Azure IoT Operations 2510? Explore the latest documentation and quickstart guides at https://aka.ms/AzureIoTOperations Connect with the Azure IoT Tech Community to share feedback and learn from peers.Solving the Data Challenge for Manufacturers with Sight Machine & Azure IoT Operations
Delivering Industrial AI: From Data to Results As manufacturers accelerate their digital transformation, the ability to unify and leverage operational data is the difference between incremental improvement and competitive advantage. Today, we’re launching a joint solution with Sight Machine, purpose-built to solve the OT data challenge and deliver the full Industrial AI stack in weeks, not months: Sight Machine and Microsoft Integrated Industrial AI Stack on Azure This offering is proven in the field, already driving measurable productivity gains for customers in automotive, food, and other sectors with rapid POC cycles and commercial-scale deployments. By integrating Sight Machine’s industrial AI platform with Azure IoT Operations and Microsoft Fabric, we standardize and contextualize machine data at scale, enabling analytics, automation, and actionable insights across the enterprise. What Sets This Solution Apart Fast Deployment: Get the full Industrial AI stack up and running in weeks, not months. End-to-End Integration: Sight Machine’s industrial AI platform works seamlessly with Azure IoT Operations and Microsoft Fabric, standardizing OT data for enterprise-wide use. Real Results: Customers in automotive, food, and other industries are already seeing measurable productivity gains and faster decision cycles. Scalable & Secure: Built on Azure’s adaptive cloud and zero-trust security, with SI partners ready to support commercial scale. Delivering a unified Industrial AI stack Today marks a pivotal moment for manufacturers: the launch of a fully integrated Industrial AI solution, jointly delivered by Microsoft and Sight Machine. This offering brings together the entire Industrial AI stack spanning cloud, edge, and on-premises, enabling organizations to unlock transformative business value. The integrated solution enables customers to transform data into business value by seamlessly contextualizing and moving data from the Edge using Sight Machine and Azure IoT Operations to Microsoft Fabric. Within Microsoft Fabric, the data can be further contextualized and enriched to support AI agents and can be extended to visualize 3D digital twins using NVIDIA Omniverse. The integrated solution has following key components: Azure IoT Operations Streams secure, real-time telemetry from industrial assets to the cloud, enabling visibility and control across edge and enterprise environments. Microsoft Fabric Provides a single analytics and governance platform, merging IT and OT data for enterprise-wide insights. Sight Machine Industrial AI Platform Refines data into “gold-level” quality, fully contextualized and structured for AI, predictive maintenance, and process optimization. M365 Copilot & Agentic Intelligence Surfaces actionable insights directly in familiar tools like Teams and Excel, empowering operators and managers to make informed decisions instantly. NVIDIA Omniverse Integration Extends capabilities into immersive 3D digital twins and physics-based simulations, enabling manufacturers to visualize live operations and test changes virtually before implementing them. Customer Impact Manufacturing is the world’s largest sector, generating twice as much data as any other industry. Yet, the complexity and fragmentation of OT (Operational Technology) data have long limited the adoption of AI at scale. Sight Machine solves this challenge by integrating with every level of the Azure stack, structuring raw OT data into high-quality, contextualized “gold” data, ready for advanced analytics and AI. This integrated offering removes barriers to AI adoption. Manufacturers can connect assets, contextualize data, and deliver actionable insights directly to teams, whether in Teams, Excel, or immersive 3D digital twins. The result: higher productivity, smarter operations, and continuous improvement. Take the Next Step Ready to accelerate your digital transformation? Explore the Sight Machine + Azure IoT Operations solution in the Marketplace. Start your journey to smarter manufacturing today: Sight Machine on AzureMicrosoft and Rockwell Automation: Transforming Industrial AI Together
Unlocking the Future of Connected Operations In today’s rapidly evolving industrial landscape, manufacturers face mounting pressure to increase agility, optimize operations, and harness data-driven insights across every level of production. The collaboration between Microsoft and Rockwell Automation represents a pivotal step toward achieving these goals. By combining Rockwell’s deep expertise in operational technology (OT) with Microsoft’s adaptive cloud approach, this partnership bridges the gap between OT and IT, creating a unified, intelligent ecosystem that empowers manufacturers to innovate at scale. Together, we enable seamless connectivity, advanced analytics, and AI-driven optimization across the factory floor from edge and cloud environments. Connected Operations powered by Microsoft and Rockwell Rockwell Automation’s FactoryTalk Optix and Microsoft’s Azure IoT Operations together deliver a powerful foundation for industrial transformation. FactoryTalk Optix provides a modern, flexible visualization platform for real-time monitoring and control of OT systems. FactoryTalk Optix supports numerous industrial protocols for secure interoperability and “smart-object” data modeling to provide analytics-ready data. Paired with Azure IoT Operations, a unified, adaptive cloud solution built on open standards and powered by Azure Arc, manufacturers gain seamless connectivity across the factory floor enabling edge to cloud orchestration. With support for protocols like OPC UA and MQTT, camera and third-party integration through Akri and WASM connectors, and Copilot-driven automation for observability and deployment, this partnership bridges OT and IT to unlock advanced analytics, AI-driven optimization, and predictive maintenance at scale. A Partnership That Delivers Scalable Innovation Customers can start utilizing FactoryTalk Optix with Azure IoT Operations as a scalable physical to digital foundation for transforming how they manufacture, design, and operate going forward. In partnership with Rockwell, there is a published GitHub sample that demonstrates how FactoryTalk Optix native IIoT connectivity protocols unlock contextualized data from industrial assets into Azure IoT Operations. With the 2510 Azure IoT Operations release , OPC Write capability is now available as well, creating a true read/write path for richer interoperability. The synergy between these technologies is a game-changer for manufacturers, unlocking advanced analytics, and AI-driven use cases. This collaboration delivers: Improved efficiency and reduced downtime through real-time connectivity and predictive maintenance Scalable edge-to-cloud architecture leveraging OPC UA and MQTT standards for unified OT/IT data Highly replicable, scalable deployments across hybrid and multicloud environments Proactive optimization with AI-driven design and analytics Democratized automation via Copilot capabilities for observability and deployment Unified IT management and centralized monitoring for streamlined operations Robust security and reduced integration complexity for faster time-to-value From the Shop Floor to the Boardroom By combining Rockwell’s industrial expertise with Microsoft’s cloud innovation, manufacturers can break down data silos, unify operations, and drive continuous optimization. AI-powered insights become accessible at every level, helping organizations anticipate change, improve safety and efficiency, and maintain a competitive edge in the digital era. Join Us at Rockwell Automation Fair Visit the Microsoft booth at Automation Fair to experience end-to-end demonstrations, explore customer stories, and see firsthand how the Rockwell–Microsoft ecosystem accelerates your digital transformation journey. Join live sessions at the Discovery Theatre – o Tuesday Nov 18th, 11:15am – 11:45am → The new industrial frontier - Using AI to scale faster, work smarter and unlock new value o Tuesday Nov 18 th 2pm – 3pm, and Thursday Nov 20 th at 10:00am – 11:00am → Bringing AI to the Factory Floor o Wednesday Nov 19 th , 1:45pm – 2:15pm → Start with Secure Solutions From Edge to Cloud Visit us at the Expo at Booth #1931 – For demos and conversations to see what we have to offer. Explore the products Learn more about Azure IoT Operations → https://azure.microsoft.com/en-us/products/iot-operations Explore FactoryTalk Optix → https://www.rockwellautomation.com/en-us/products/software/factorytalk/optix.html Hear more about our integration story at Microsoft Ignite → The new industrial frontierFirmware Analysis now Generally Available
Back in June, we announced the public preview of firmware analysis, a new capability available through Azure Arc to help organizations gain visibility into the security of their Internet of Things (IoT), Operational Technology (OT), and network devices. Today, we are excited to announce that firmware analysis is generally available (GA) for all Azure customers. In modern industrial environments, firmware security is a foundational requirement. IoT sensors and smart devices collect the data fueling AI-driven insights; if those devices aren’t secure, your data and operational continuity are at risk. During the preview, we heard from many customers who used firmware analysis to shine a light into their device software and address hidden vulnerabilities before attackers or downtime could strike. With general availability, firmware analysis is ready to help organizations fortify the “blind spots” in their infrastructure – from factory-floor sensors to branch office routers – by analyzing the software that runs on those devices. What Firmware Analysis Does for You Firmware analysis examines the low-level software (firmware) that powers IoT, OT and network devices, with no agent required on the device. You can upload a firmware image (for example, an extracted embedded Linux image), and the cloud service performs an automated security inspection. Key features include: Software inventory & vulnerability scanning: The service builds a Software Bill of Materials (SBOM) of components within the firmware and checks each component against known CVEs (Common Vulnerabilities and Exposures). This quickly surfaces any known vulnerabilities in your device’s software stack so you can prioritize patching those issues. Security configuration and hardening check: Firmware analysis evaluates how the firmware binaries are built, looking for security hardening measures (e.g. stack protections, ASLR) or dangerous configurations. If certain best practices are missing, the firmware might be easier to exploit – the tool flags this to inform the device manufacturer or your security team. Credential and secrets discovery: The analysis finds any hard-coded credentials (user accounts/password hashes) present in the firmware, as well as embedded cryptographic material like SSL/TLS certificates or keys. These could pose serious risks – for instance, default passwords that attackers could exploit (recall the Mirai botnet using factory-default creds) are identified so you can mitigate them. Any discovered certificates or keys can indicate potentially insecure design if left in production firmware. Comprehensive report: All security findings – from the Software Bill of Materials (SBOM), list of vulnerabilities to hardening recommendations and exposed secrets – are provided in a detailed report for each firmware image analyzed. This gives device makers and operators actionable intelligence to improve their device security posture. In short, firmware analysis provides deep insights into the contents and security quality of device firmware. It turns opaque firmware into transparent data, helping you answer, “What’s really inside my device software?” so you can address weaknesses proactively. What’s New and Licensing We’ve been hard at work making firmware analysis even better as we move to GA. Based on preview feedback, we’ve addressed bugs, implemented usability suggestions and improved the firmware analysis SDKs, CLI and PowerShell extensions. A new Azure resource called “firmware workspace” now stores analyzed firmware images. Firmware analysis workspaces are currently available as a Free Firmware Analysis Workspace SKU with capacity limits. Getting Started If you have IoT, OT and network devices in your environment, use firmware analysis to test just how secure your devices are. Getting started is easy: access firmware analysis by searching “firmware analysis” in the Azure portal, or access using this link. Onboard your subscription and then upload firmware images for analysis. For a step-by-step tutorial, visit our official documentation. The service currently supports embedded Linux-based images up to 1GB in size. We want to thank all the preview participants who tested firmware analysis and provided feedback. You helped us refine the service for GA and we’re thrilled to make this powerful tool broadly available to help secure IoT, OT and network devices around the world. We can’t wait to see how you put it to work. As always, we value your feedback, so please let us know what you think.
