The challenge is no longer creating more engineering data. It is transforming trusted product and lifecycle context into engineering intelligence that helps teams make better decisions, move faster, and manage complexity at scale.

From the digital thread to AI-powered engineering intelligence

Agents are only as effective as the engineering context they can access. For manufacturers, that context resides within the digital thread connecting requirements, product structures, design decisions, validation evidence, engineering changes, manufacturing information, and lifecycle decisions. When that thread is governed and accessible, agents help teams understand how information relates to the product and the decision at hand.

This is what distinguishes engineering agents from general productivity copilots or traditional workflow automation. Engineering agents must operate within structured product data, configuration rules, lifecycle states, requirements traceability, and governance controls. They can assemble context across these sources, identify dependencies, and support decisions while engineers remain accountable for the outcome.

Consider an engineer evaluating a design change. The decision may affect product structures, requirements, manufacturing constraints, prior designs, and downstream processes. An engineering agent can bring relevant evidence together and expose relationships that otherwise require manual investigation. Engineering leaders routinely balance cost, quality, performance, risk, and schedule. Agents can surface tradeoffs, enabling faster, better-informed decisions without removing expert judgment.

The opportunity is not simply task automation. Engineering intelligence can expand capacity by reducing time spent assembling context and allowing experts to focus on tradeoffs and innovation. It can preserve lessons from previous programs, make knowledge reusable across teams, and strengthen impact analysis as changes move through the lifecycle.

A governed foundation for engineering agents

Delivering this value requires more than connecting a LLM or SLM to engineering files. Agents need trusted product context, lifecycle governance, and a framework for operating within engineering processes.

Built on Microsoft Azure, Aras combines PLM with engineering AI through InnovatorEdge AI, a governed framework for embedding agents into engineering workflows. Manufacturers can manage agents across the digital thread, allowing them to work with structures, requirements, configurations, and engineering knowledge in context. This creates a foundation for engineering intelligence while extending investments.

The strongest value emerges in engineering decision support. Agents can help teams retrieve knowledge, understand product dependencies, evaluate options, and apply previous decisions to new programs. They can also support engineering change management by improving impact visibility, traceability, and lifecycle control, where incomplete context increases downstream cost and risk.

Turning variant knowledge into decision support

SICK AG, a provider of sensor-based solutions for factory, logistics, and process automation, sought to improve transparency across a product portfolio, identify reuse and harmonization, and make product knowledge easier for teams to apply across programs. Achieving those goals required making variant logic across product bills of material accessible and actionable.

SICK partnered with Aras and Microsoft to connect that knowledge through a governed digital thread. Aras InnovatorEdge AI, powered by Microsoft Foundry and Azure, brings agentic capabilities to trusted PLM data while preserving the context and controls required for industrial engineering.

By analyzing existing 100% BOMs, the Variant BOM Agent can surface product features, options, dependencies, and configuration rules, then help engineers reconstruct variant logic and derive a consolidated 150% BOM. This gives teams a clearer foundation for evaluating commonality, reuse, harmonization, and product decisions.

The agent makes embedded product knowledge more accessible rather than treating BOM analysis as an isolated automation task. Engineers can review relationships and recommendations within governed workflows, applying their expertise to the resulting decisions. The approach supports transparency and decision speed while maintaining lifecycle context, traceability, and human accountability.

Specific productivity and return-on-investment figures are still being validated. SICK demonstrates how trusted product context can enable engineering analyses difficult to perform at scale. Its contribution is engineering decision support, with relevance to change impact analysis and portfolio decisions. It does not need to represent every engineering scenario to show value.

Scaling engineering intelligence across the enterprise

Successful agentic engineering begins with trusted context, but the long-term opportunity extends beyond automating individual tasks. Manufacturers can create an environment where engineering knowledge becomes continuously available, reusable, and scalable across the enterprise.

In that environment, agents do not replace engineering expertise. They amplify it, helping organizations manage complexity, accelerate development, improve reuse, preserve knowledge, and reduce the cost and risk of change. By combining Microsoft’s agentic technology, Aras product intelligence, and SICK engineering expertise, the collaboration shows a path toward engineering intelligence, enabling faster innovation while maintaining the governance, traceability, and control for industrial product development.

The post From digital thread to engineering intelligence appeared first on The Microsoft Cloud Blog.

Physical AI is moving intelligence into the work of manufacturing itself. Robots, vehicles, production systems, and connected infrastructure can now perceive changing conditions, reason over context, and act within defined boundaries.

Automation delivered precision, productivity, and safety by executing known instructions. Physical AI extends that foundation by helping systems respond to more variation in the conditions around them.

At the International Manufacturing Technology Show (IMTS) 2026, standing on the main stage in Chicago, Illinois, I framed the opportunity around a practical question: how do manufacturers turn intelligence that works in one machine or process into a capability they can deploy, govern, and improve across the enterprise?

Physical AI closes the loop between intelligence and action

Physical AI is intelligence operating in the physical world through machines, systems, and people. It works through a continuous loop:

1. Perceive: Read machines, environments, and operational signals.
2. Reason: Interpret that context and determine the next best action.
3. Act: Execute through machines, systems, and people, then use the result as feedback for the next cycle.

Connected operations gave people visibility. Industrial AI added reasoning over patterns and outcomes. Physical AI closes the loop by acting in the physical world, measuring the result, and improving the next cycle.

When AI moves from recommending on a screen to acting in the physical world, value and risk both become physical. Operating limits, identity and security, observability, validated fallback, and human approval and override must be designed into the system.

Physical AI is creating value across the industrial lifecycle

Manufacturers are already applying these capabilities from engineering through live operations.

At the engineering end, KUKA is making industrial robots easier to program and deploy. With iiQWorks.Copilot, users can describe a task, generate code, simulate the workflow, and deploy it. For simple tasks, programming can be up to 80% faster. Krones is using AI agents and physically accurate digital twins to speed filling simulations. Runs that took three or four hours can now take five minutes or less.

In live operations, ARUM is bringing the same shift into the machining center. TTMC Brain turns a conversation into a machining program and reduces numerical control programming from 177 steps to two. ABB is applying live industrial data to predictive maintenance and real-time optimization. Across its customer base, ABB reports up to 20% higher critical-asset reliability and up to 60% less unplanned downtime.

These examples address two constraints on scaling automation: the engineering effort needed to deploy it and the reliability needed to keep it running.

What physical AI changes for operations leaders

In my conversations with manufacturers, the challenge is increasingly orchestration. A successful machine or workcell is only the first step. Leaders have to connect that capability to the way the enterprise operates, decides, and scales.

Physical intelligence changes three enterprise models:

• The operating model moves from site automation to network coordination. Lines, plants, fleets, and field environments can operate as one connected system.
• The decision model moves from manual planning to agent-assisted operations. People set intent, policy, approval, and override while agents interpret context and coordinate work within those boundaries.
• The technology model moves from disconnected tools to one governed environment. Data, controls, platforms, and cross-vendor systems must work together across cloud and edge.

Those shifts depend on two connected loops. One accelerates physical systems innovation: teams simulate conditions, train and adapt models, validate behavior and failure modes, and iterate before deployment. The other operationalizes physical intelligence: teams orchestrate work, execute across machines and people, govern what happens, and optimize performance across systems and fleets.

Agentic orchestration connects the two. Operational feedback returns real-world conditions, interventions, failures, and outcomes to the next simulation and validation cycle.

Repeatability matters more than any single deployment. A successful machine or workcell becomes an enterprise capability when its data, integration, governance, and operating model can be reused across lines, plants, and fleets. Otherwise, the organization scales exceptions instead of capability.

AI governance in manufacturing belongs in the operating architecture

Identity, policy, monitoring, security, and accountability must travel with the capability from one machine or site to the next. Operational, engineering, and business context must inform the same decisions across cloud and edge.

For global manufacturers, that architecture must also support sovereignty, data residency, and operational technology security across sites and regions. Leaders need visibility into what the system can do, why it acted, and when a person must approve, intervene, or override.

A consistent trust model lets teams deploy, manage, observe, and improve capabilities across mixed environments without rebuilding the controls at every site.

For more on Microsoft’s industrial AI platform, read Microsoft named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms.

Start with one bounded operation

Manufacturers do not need to automate an entire plant to begin. A practical starting point is one operational problem where the outcome matters and the boundaries are clear.

Choose a workflow with measurable value, such as quality inspection, maintenance triage, machine programming, or process optimization. Map the data and operating context required to make a sound decision. Define what the system may recommend, what it may execute, and where a person must approve or intervene. Then design the deployment so that the successful pattern can be reused across another line, site, or fleet.

For examples of how manufacturers can scale high-value use cases, read Step into the Factory of the Future.

Physical AI will be measured in the operation: faster engineering, safer work, more reliable assets, and systems that improve under clear human authority. Start with one consequential workflow. Define its boundaries. Build so success can repeat.

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The post Physical AI in manufacturing: From intelligent machines to coordinated operations appeared first on The Microsoft Cloud Blog.

Intelligence lives in the engineer who can hear a spindle drifting out of tolerance before a gauge detects it. It lives in the systems teams open every morning, and in the data that keeps production moving shift after shift. It is the accumulation of knowledge, experience, and context that empowers people to make the right decisions at the right time. For most manufacturers, however, that intelligence is fragmented across people, processes, data, and systems that were never designed to work together.

It is the accumulation of knowledge, experience, and context that empowers people to make the right decisions at the right time.

Ben Grimes, corporate vice president, US Manufacturing & Mobility, Microsoft

Unlocking industrial intelligence means connecting that knowledge and putting AI into the flow of work, so it can understand how the business operates, surface what matters, and help people act with greater speed and confidence. That is the difference between companies experimenting with AI through isolated pilots and those turning intelligence into measurable, competitive advantage across the value chain.

And this is the key conversation taking shape this week at IMTS 2026, the largest manufacturing technology show in North America.

Intelligence is half the equation. Trust is the other half.

In my keynote at IMTS, “Industrial Intelligence Unlocked,” I took these observations one step further: Frontier Firms do not simply buy more AI tools. They connect intelligence that understands how work gets done, how business impact is measured, and how enterprise knowledge fits together. At scale, trust is what sets the pace.

Leaders design trust in from the beginning, on a secure, compliant, and observable foundation beneath every agent. Agents carry identity, operate inside clear boundaries, and are accountable for outcomes. When agents are set in motion, innovation moves quickly, more employees are empowered in the flow of work, and IT keeps control without becoming the bottleneck.

If intelligence and trust are the foundation, the practical question is how they show up in daily operations. What I’ve seen across the industry is that three forms of intelligence must come together:

1. Work intelligence. It starts with understanding work: how people get things done, who they collaborate with, what was discussed and agreed. Present across conversations, meetings, and documents, AI can recognize patterns, carry context forward, and turn individual experience into a shared understanding.
   
2. Data intelligence. Every decision on a factory floor runs on data. To move from insight to action, intelligence has to understand not only the data, but what it means: which metrics matter, how performance is measured, where the tradeoffs are made. Data intelligence means both people and agents can make informed decisions from the same source of truth.
   
3. Operational intelligence. Intelligence is also a holistic understanding of how the business is meant to run: the policies, the standards, the operating procedures. Grounding organizational intelligence in context is what lets AI apply the right guidance, at the right moment, for the right person.

When brought together, these facets reveal the most impactful shift in the industry: intelligence stops being something you consult and becomes something that runs alongside the business, reasoning consistently and acting within context as it moves across teams and systems.

The return on intelligence is already being realized

This is not a forecast. Manufacturers are reporting results now, and that is coming through clearly at IMTS, where conversations with our customers and ecosystem partners are focused on practical AI deployments with measurable operational impact.

Riddell put trusted ERP data directly into the flow of its frontline sellers’ work. Its AI assistant, RIA, connects to SAP and answers natural language questions about pricing, delivery dates, and order status inside Microsoft Teams. By reducing time spent searching across disconnected systems, Riddell has returned more than 21,000 hours to the team each year.

AGCO is taking a governed approach to scaling employee-built AI agents with Microsoft Copilot Studio and Microsoft 365 Copilot. Its maker community has grown to roughly 2,000 people, with several hundred enterprise agents now in production. In some quality workflows, reviews that once took weeks can now be completed in about an hour.

Siemens, Rockwell Automation, PTC, RSM, MCA Connect, and Insight featured Microsoft in demonstrations of their latest AI innovations—spanning the intelligent digital thread and product lifecycle, AI-powered engineering and factory operations, and more adaptive supply chains.

Together, these experiences showed how Microsoft technology and deep industry expertise come together to move AI from promising use cases into practical solutions built for the realities of manufacturing.

When intelligence is shared, innovation spreads

Across IMTS, the conversations on the digital thread, physical AI, and agents at scale pointed to the same shift: industrial intelligence creates the most value when it moves beyond isolated teams and becomes part of how the entire organization works.

Frontier Firms do not centralize innovation in a small group of experts. They put the power to create in every room of the house—closer to the people who know the work, see the opportunity, and can imagine a better way forward. With trusted intelligence in the flow of work, more people can solve problems, improve processes, and turn ideas into measurable outcomes.

Because when intelligence is connected, trusted, and shared, innovation no longer belongs to a few. It spreads, and human ingenuity scales with it.

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Some visuals in this article were created using AI-assisted tools.

The post Industrial intelligence unlocked: The next competitive advantage in manufacturing appeared first on The Microsoft Cloud Blog.

Safety, quality, energy, maintenance, production targets, and supply volatility now collide in real time. In many plants, the response has been more dashboards, more alerts, and more analysis. But what manufacturers need next is intelligence embedded in daily work—systems that help teams understand what matters, decide faster, and act with confidence while the plant is running. 

That is the promise of agentic AI for plant operations: AI that works alongside people, grounded in industrial context and governed by operational guardrails. At its best, it does more than explain what happened—it helps teams determine the next best step and move from insight to action in the flow of work. 

What agentic AI for plant operations means in practice

In process manufacturing, agentic AI cannot mean black-box autonomy. Plants run on physics, safety standards, and regulatory requirements that do not bend. In practice, agentic AI means human-agent teams: systems that observe, reason, and recommend—and in some cases initiate workflow steps—with the right approvals and guardrails in place. 

• Bring operational and engineering context together: Connect OT data, engineering documents, maintenance history, alarms, work orders, and shift logs so teams can see what is happening in the right plant context. 
• Guide decisions within real plant constraints: Recommendations need to reflect safety, quality, process limits, and operating conditions so they can be trusted in production. 
• Help the right teams act faster: The value is not just insight—it is helping operations, maintenance, reliability, and quality teams move on the next step with less delay and less guesswork. 
• Capture what the best operators already know: Over time, agentic systems should help turn tribal knowledge into repeatable practices that scale across shifts and sites. 

This matters most in high-pressure moments—such as troubleshooting, abnormal situation management, reliability planning, outage recovery, energy optimization, and quality response—where minutes matter and context is often scattered across systems. In plant operations, AI only matters when it helps teams act faster, safer, and with more confidence. 

What manufacturing leaders are prioritizing in agentic AI

That is why the conversation with manufacturers is changing. At Hannover Messe and ManuChem, leaders were no longer asking for definitions of generative AI. They were asking a harder question: where can this create measurable value in a live operating environment? Across those discussions, six market signals came through consistently. 

1. Focus on outcomes and guardrails. The strongest conversations were not about abstract AI potential. They were about measurable operational value and a governance model where humans remain accountable for judgment, escalation, and approvals. 
2. Start with operational friction, not architecture. The best use cases begin with real pain points—lost time finding the right drawing, inconsistent troubleshooting across shifts, slow outage recovery, or low trust in legacy system data. Technology matters, but the entry point should be a business problem that teams already feel. 
3. Brownfield reality is the default. Most process manufacturers are not starting from scratch. Winning approaches respect legacy systems, edge constraints, and the need to modernize without stopping the plant—making a flexible operating model across edge, cloud, and AI critical. 
4. Trust is the adoption lever. In industrial environments, trust goes beyond compliance—teams need identity, access control, auditability, lineage, and explainability. If a recommendation cannot be traced and bounded, it will not be used in production. 
5. Design for scale across shifts and sites. The most valuable scenarios are often not the flashiest demos—they are the ones that reduce variability, capture institutional knowledge, and help teams work more consistently from one shift, line, or site to the next. 
6. Sovereignty and ontologies are rising priorities. Customers increasingly care where data resides, who can access it, and how policy applies across environments. At the same time, semantic models and ontologies are becoming critical for connecting operational, engineering, and business context—enabling AI to reason more effectively. 

One story that captures these themes well is the work Yara and Kongsberg Digital have done together. 

Real-world agentic AI: How Yara and Kongsberg Digital built an operational intelligence layer

Yara’s Porsgrunn fertilizer plant offers a good example of what this looks like in practice. The challenge was familiar: critical plant data existed, but it was spread across systems and difficult to use in real time. Engineering documents, 3D models, maintenance history, and live operational information were not coming together fast enough to support troubleshooting and day-to-day decision-making. The result was slower response times and more field work than necessary. 

Working with Kongsberg Digital, Yara built an Azure-based operational digital twin experience that brought these sources together into a usable operating environment. That matters because, in process manufacturing, better decisions rarely come from more data alone—they come from putting the right context in front of the right people at the right moment. The goal is not more intelligence in the system—it is better decisions on the plant floor. Once that foundation is in place, copilots and agents can do something practical: help teams understand situations faster, guide the next action, and reduce the time lost moving between disconnected tools. 

The business impact is what matters—and Yara’s results are a strong signal of what becomes possible when you remove operational friction: 

• Up to 70% faster shutdown recovery by consolidating operations onto a unified, actionable platform. 
• 60% fewer field trips through improved remote visibility and diagnostics. 
• 50% efficiency gains in targeted engineering tasks—helping teams resolve issues faster and operate with greater precision. 

Access to the right data at the right time is how we define success, and our digital twin gets us there. We’re building something that can scale safely and deliver value over time.

Roar Nilsen, Program Manager, Digital Engineering, Yara 

The broader lesson is important. Before AI can guide work in a plant, teams need a trusted operational foundation that connects data, documents, assets, and process knowledge in the flow of work. Yara’s results show what happens when that foundation is in place: operational knowledge becomes easier to access, decisions become faster, and expertise scales more effectively across the organization. For manufacturers asking what to do next, the implication is straightforward. 

A pragmatic playbook for agentic plant operations 

For teams asking where to begin, the next step is not to copy every emerging use case—it is to build the conditions that allow a few high-value use cases to succeed, earn trust, and scale. In other words, the opportunity is not to experiment more—it is to operationalize what works. In my view, that starts with six practical moves. 

1. Build the context layer. Unify operational, engineering, and maintenance knowledge so systems can answer basic but essential questions about asset state, process context, constraints, and history. 
2. Use ontologies where they add business meaning. Unified data alone is not enough. Manufacturers increasingly need semantic models that connect systems, assets, documents, and work patterns in ways AI can understand. 
3. Prioritize a high-friction use case. Start where value is clearly being lost—such as troubleshooting, shutdown planning, work preparation, reliability triage, or quality response—and pilot with the expectation that successful patterns will scale. 
4. Design the guardrails early. Be explicit about permissions, approvals, explainability, and which actions an agent can recommend versus execute. 
5. Embed intelligence in the flow of work. The goal is not to force people into another portal—it is to support productivity and decision-making where teams already operate. 
6. Scale through repeatable playbooks. Capture what the best teams do, standardize it across shifts and sites, and improve it through feedback over time. 

The north star is not how many models are deployed—it is fewer hours lost to data hunting, faster recovery after disruptions, shorter resolution times, fewer unnecessary site visits, lower variability across shifts, and a safer environment where teams can focus on judgment instead of searching for information. 

What comes next for manufacturers

Across customer conversations this season, one point has become clear: manufacturers will not realize the full value of AI through pilots alone. The real opportunity is to build an operating model where intelligence is trusted, governed, and embedded in the workflows that keep plants safe, efficient, and resilient. 

If you are exploring agentic AI for plant operations, start where value is being lost. Choose a workflow, unify the operational context around it, establish the right guardrails in place, and scale what works. That is how process manufacturers move from experimentation to execution—and from dashboards to decision-making. 

The post Agentic AI for plant operations: From dashboards to decisions appeared first on The Microsoft Cloud Blog.

• Watch global leaders discuss the future of industrial intelligence: Register now 
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• 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

From insight to action: A 2026 checklist for manufacturing leaders

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

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

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

Advancing intelligent manufacturing with Microsoft

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

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

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

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

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

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

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

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

Healthcare: Modernizing securely while powering next-generation clinical experiences

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

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

What healthcare organizations need, according to the IDC study: 

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

Customer spotlight: Franciscan Health

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

The results included: 

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

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

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

Financial services: Enabling real-time intelligence and automated compliance

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

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

Key challenges the IDC study identifies: 

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

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

Customer spotlight: Crediclub

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

The impact:

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

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

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

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

Unique modernization challenges according to the IDC study:

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

Opportunities unlocked by cloud:

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

Customer spotlight: ASTEC Industries

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

The results:

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

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

Microsoft’s approach: Continuous, intelligent, collaborative modernization 

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

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

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

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

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

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

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

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

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

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

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

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

The post Modernizing regulated industries with cloud and agentic AI appeared first on The Microsoft Cloud Blog.

Manufacturing’s moment: Why AI, why now?

Today, AI isn’t just a buzzword or a distant promise. It’s a practical lever for manufacturers to unlock new value, drive efficiency, and build resilience for the future.

At Microsoft Ignite 2025, our team explored how industrial AI is reshaping the economics of manufacturing. Drawing from real-world customer stories and the latest research, we’ll unpack where the ROI is real, what’s driving it, and how you can take the next step on your own AI journey.

Drive measurable outcomes with AI

Return on Intelligence: Scaling Business Value with Industrial AI

Get the e-book

From siloed data to intelligence on tap

Every manufacturer knows the pain points: unplanned downtime, inefficiencies that eat into margins, and supply chain blind spots that disrupt delivery. Too often, these issues are compounded by fragmented systems and a lack of real-time data visibility. The result? Slow decision-making and missed opportunities.

But the landscape is changing. According to a 2025 commissioned  Forrester Consulting Total Economic Impact™ study on the economic impact of industrial transformation with Microsoft AI, manufacturers that invest in unified data platform and bring together data across IT and Operations Technology (OT) systems could see the following benefits¹:

• Up to 50% reduction in defects
• Up to 50% fewer inventory shortages
• Up to 40% decrease in frequency of equipment failures
• Up to 457% projected ROI over three years

Take KUKA, a global automation leader. Facing fragmented systems and a growing robotics skills gap, KUKA turned to Microsoft Azure AI and Microsoft Foundry Models. The result? Programming time cut by up to 80%, democratizing robotics and accelerating workflow deployment. With predictive insights and real-time analytics, KUKA broke down data silos and empowered teams to innovate faster.

Sustainability: A greener, more profitable path

Manufacturers today are under intensifying pressure from regulators, customers, and their own boards to reduce emissions, increase energy efficiency, and eliminate waste. Yet many sustainability challenges stem from the same root problem: disconnected systems that make it difficult to measure, optimize, and scale improvements across facilities.

But the momentum is shifting. According to the Forrester study on the economic impact of Microsoft’s industrial AI capabilities, manufacturers see AI as a critical lever to drive measurable environmental and financial gains by optimizing energy usage, refining processes, and reducing carbon emissions. With Microsoft AI solutions, surveyed manufacturers who are Microsoft Azure customers expect to achieve:

• 78% expect to reduce energy consumption
• 88% expect to improve energy efficiency
• 53% expect to reduce CO₂ emissions

Take Schneider Electric, a global leader in energy management with ambitious sustainability goals to reduce environmental impact and improve efficiency across its operations. By integrating Azure OpenAI and Azure Machine Learning into its EcoStruxure platform, Schneider gained real‑time insight into energy usage, carbon‑related performance, and optimization opportunities. The impact? AI‑powered models that surface efficiency recommendations, accelerate sustainability decision‑making, and help facilities cut waste at scale. And because EcoStruxure underpins thousands of customer deployments, these AI‑powered insights also lets its customers pursue their own sustainability goals with greater speed, accuracy, and measurable operational improvements.

Empowering people: AI as a workforce multiplier

Labor shortages, rising workload complexity, and persistent training bottlenecks continue to stretch manufacturing teams thin. Many frontline and knowledge workers spend too much time searching for information, navigating outdated systems, or performing repetitive tasks that slow productivity and sap morale.

But AI is shifting this dynamic. Manufacturers are adopting intelligent assistants, predictive tools, and automated workflows that free employees to focus on higher‑value work. According to recent industry data, organizations are already seeing material gains:

• 66% of repetitive tasks automated
• 70% of organizations report productivity gains
• 75% reduction in onboarding time

Take Audi AG, a global automotive leader navigating rising internal demand for support and process guidance. To alleviate mounting human resources and IT strain, Audi launched its first AI-powered self-service assistant using Foundry that was deployed in just two weeks. The impact? Faster access to information, fewer routine queries, and more time for teams to focus on meaningful, high-value work. Audi’s example shows how AI doesn’t replace people but amplifies them.

The agentic era: What’s next?

Manufacturers are moving beyond task‑level automation toward a new operating model where AI works alongside teams to coordinate decisions, optimize workflows, and adapt to changing conditions in real time. This next era isn’t about experimenting on the margins, but about treating AI as a core capability that strengthens every part of the enterprise.

Platforms like Azure OpenAI, Microsoft Fabric, Foundry Models, and Microsoft 365 Copilot are already helping organizations make that shift. And the economic signal is strong. Forrester’s Total Economic Impact study attributes the financial upside of the broader Industrial AI value stack to improvements across operations, productivity, and supply chain performance:

• Up to 457% projected ROI over three years

These gains compound as AI becomes embedded across the business, accelerating impact as intelligent systems take on more routine work, surface insights faster, and support teams in making better decisions at every stage of production. Manufacturers who fail to operationalize AI risk falling behind peers who are building intelligence directly into their processes, products, and customer experiences.

A practical path forward: How to get started

To fully realize this next chapter, manufacturers need a clear, actionable roadmap grounded in governed data, scalable AI systems, and measurable business outcomes.

Here’s a practical roadmap from aspiration to action:

• Identify high-impact use cases: Focus on areas like predictive maintenance, supply chain optimization, and quality control where AI can deliver quick, measurable wins.
• Define success metrics: Set clear key performance indicators (KPIs) to track ROI and impact across teams and facilities. What gets measured gets managed.
• Leverage proven platforms and partners: Don’t reinvent the wheel. Use established AI platforms like Azure and work with partners who understand both technology and manufacturing.
• Start small, scale fast: Begin with urgent, actionable business challenges. Build on proven frameworks and architect for scale. As Audi AG showed, operational AI can be deployed at enterprise scale in weeks, not years.
• Invest in data foundations: Migrating legacy systems to the cloud and breaking down data silos are essential. Unified, AI-ready data is the backbone of successful industrial AI initiatives.

Learn how industrial AI can transform your business

Industrial AI is no longer a vision for the future, but a proven source of measurable value today. The manufacturers pulling ahead are the ones building AI into how they operate, scale, and compete. Whether you’re laying the groundwork or accelerating existing initiatives, now is the moment to turn momentum into impact.

Return on Intelligence: Scaling Business Value with Industrial AI

Deeper insights, real-world case studies, and a practical roadmap

Download the e-book

Start your journey toward smarter, more resilient, and more competitive manufacturing.

¹ New Tech: The Projected Total Economic Impact™ Of Microsoft Artificial Intelligence Solutions For Industrial Transformation. Results are over three years for a composite organization based on interviewed and surveyed customers.

The post The ROI of AI in manufacturing: Where adoption becomes advantage appeared first on The Microsoft Cloud Blog.

Events like Industrial Transformation Asia Pacific (ITAP) 2025 and Microsoft Ignite 2025 are critical moments to meet customers where they are—bringing together diverse business and technical decision makers to explore what’s possible today, while introducing new technological breakthroughs that redefine tomorrow’s potential. At ITAP 2025, in Singapore, the spotlight is on how a vibrant ecosystem of partners is working with Microsoft to accelerate AI adoption and drive manufacturing excellence across Asia—proving that the future of industrial innovation is built on collaboration. Microsoft Ignite 2025, in San Francisco, California, extended this momentum to a global stage, showcasing cutting-edge AI, data, and cloud innovations that help organizations reimagine their operations end-to-end. Together, ITAP 2025 and Microsoft Ignite 2025 don’t just reflect where the industry is headed—they actively propel thinking forward, shaping the next industrial era.

Partner-led innovation on display

This year, the ITAP 2025 show floor was energized by partner-led demos that are reshaping the manufacturing value chain. Siemens, Sight Machine, and SymphonyAI each brought forward compelling demonstrations of how their technologies—integrated with Microsoft platforms—are delivering real-world impact:

• Siemens: Showcased “Realize AI-powered digital threads for manufacturing,” featuring NX X and Teamcenter X demos. These AI manufacturing solutions, centered around Rolls Royce engine engineering, highlighted how generative AI and digital thread technologies are driving innovation and collaboration in design, engineering, and product lifecycle management.
• SymphonyAI: Demonstrated “Improve operational efficiency and performance with actionable AI,” integrating SymphonyAI IRIS Foundry with Microsoft 365 Copilot and Microsoft Teams. Operators can now query production performance or asset anomalies in natural language and receive instant, contextual responses—highlighting how AI-powered insights trigger maintenance actions and optimize throughput.
• Sight Machine: Delivered “Unlock rapid AI transformation with a complete Industrial AI stack,” illustrating how manufacturers can break down silos and accelerate their journey to AI-powered enterprise operations.

The Industry Hub at Microsoft Ignite 2025 was alive with partner solutions that displayed what becomes possible when AI, data, and cloud come together at scale. Hexagon, Krones, and PTC showcased compelling demonstrations of how their AI-powered solutions, alongside Microsoft, are transforming industry processes:

• Hexagon: Demonstrated “Scaling intelligent robotics with simulation-driven AI,” featuring Hexagon’s AEON humanoid powered by Microsoft Azure and NVIDIA. Scalable AI training, governed deployment, and rapid simulation-backed adaptation for new stock keeping units (SKUs) help manufacturers boost speed, quality, and safety while reducing risk.
• Krones: With SoftServe and Ansys, delivered “Reinvent beverage production with AI-powered digital twins,” using Azure and advanced graphics processing unit (GPU) technologies to cut simulation times from hours to minutes. This enables real-time optimization, autonomous decision-making, and more sustainable, efficient beverage lines.
• PTC: Highlighted “Unifying the digital thread across engineering, manufacturing, and service,” with AI-powered PTC solutions on Microsoft’s cloud. By aggregating data into a connected digital thread, customers can quickly surface issues, analyze real-time performance, and accelerate resolution across the product lifecycle.

Together, these solutions—built on data, AI, and cloud platforms—enable manufacturers to modernize legacy systems, unify their data, and scale AI beyond pilots to production with greater speed and flexibility than ever before.

Driving thought leadership—with partners

Across these events, the Microsoft Theater became a hub for knowledge sharing, hosting 26 sessions that brought together subject matter experts, partners, and industry leaders. Sessions explored practical use cases—from AI-powered Enterprise Resource Planning (ERP) transformation and real-time data insights to frontline worker empowerment—demonstrating how technology, when combined with deep domain expertise from partners, is delivering real-world outcomes. This joint leadership validates emerging best practices, while also aligning Microsoft and its partner ecosystem around a shared vision for industrial AI, giving customers clearer roadmaps, greater confidence in their technology investments, and a stronger sense of what’s possible when we innovate together.

Notable speakers from ABB, Amadeus, AVEVA, Hexagon, Krones, LS Electric, Neudesic, NVIDIA, OpsMate, PTC, S&P Global, Siemens, Sight Machine, SymphonyAI, and Wandlebots shared their perspectives, reinforcing the message that industrial AI transformation is a team sport.

Putting an industrial frontier vision into practice

The ITAP Tech Summit 2025, themed “Human-led, AI-powered: Building agentic factory operations,” attracted more than 125 industry decision-makers, going beyond a traditional session—giving attendees a first-hand experience of building and interacting with agentic solutions. Participants learned how to design agentic operations using Microsoft AI, Microsoft Copilot Studio, and partner solutions connected to internal manufacturing execution system (MES), ERP, and other critical OT systems. The session combined reference architectures, live walkthroughs, and best practices, empowering organizations to start their own frontier journeys with the support of a robust partner ecosystem.

The latest AI innovations from Microsoft are helping manufacturers modernize operations and unlock new value. These AI manufacturing solutions are not theoretical—they are solving the most crucial industrial challenges.

Advancements

• Adaptive cloud for manufacturing: Companies like Husqvarna are modernizing legacy infrastructure with scalable, secure, and flexible cloud-based operations. This approach integrates AI and Internet of Things (IoT) to enable future-ready production environments.
• Agentic ERP transformation: Microsoft Dynamics 365 is streamlining supply chain management and resource allocation, giving manufacturers the agility to respond to market shifts with speed and precision.
• Copilot for Frontline Workers: Intelligent agent orchestration in Teams is empowering frontline employees with real-time data, proactive alerts, and seamless collaboration.
• Generative AI for Field Service: Intelligent scheduling, predictive maintenance, and real-time technician support improve service outcomes and operational efficiency.

Announcements

• Microsoft Agent 365: Announced at Microsoft Ignite 2025, Microsoft’s new control plane for AI agents helps organizations securely deploy, govern, and manage agents at scale—streamlining industrial workflows by integrating AI into existing systems to improve efficiency and reduce downtime.
• Microsoft Azure Copilot enhancements: An upgraded, more agentic Azure Copilot that assists with designing, deploying, and operating cloud environments and applications. It can generate architectures, scripts, and configurations, and help troubleshoot workloads running on Azure.
• Microsoft Work IQ, Fabric IQ, and Foundry IQ: Together, these new IQ capabilities create intelligence, data, and knowledge layers across Microsoft 365, Fabric, and Foundry—so copilots and agents can act with organizational context, a consistent view of data, and governed access to content. For manufacturers, this means clearer visibility into how workflows, a unified backbone for data and knowledge, and faster decisions in workforce planning, production, maintenance, and quality.

Looking ahead: Empowering the future together

Across the globe, manufacturers are seeing the benefits of a collaborative approach. In Southeast Asia, DMG Mori’s adoption of Azure and AI-powered solutions from partner Tulip has resulted in a 66.6% improvement in product quality, increased reliability, and measurable gains in data efficiency and consistency. These outcomes are a testament to the power of partnership in driving operational digitization, transformation, and intelligent automation.

At Microsoft Ignite 2025, manufacturers saw similar real-world impact as partners showcased AI-powered use cases across the value chain—from Hexagon’s AEON humanoid on Azure and NVIDIA accelerating safer, more autonomous industrial robotics, to Krones’ Azure-based digital twin with SoftServe and Ansys cutting simulation times from hours to minutes, to PTC’s Azure- and AI-enabled digital thread improving engineering speed and quality.

Whether in Southeast Asia, Germany, or California, Microsoft’s commitment to customers is rooted in partnership. By combining visionary thought leadership, deep partner collaboration, and immersive technology experiences, we are empowering the future of smart, sustainable manufacturing. The momentum from ITAP 2025 and Microsoft Ignite 2025 is just the beginning—together with our partners, we look forward to accelerating AI adoption and frontier innovation across every region.

Start your industrial AI journey

Explore how our partners are leading the way in industrial AI transformation. Connect with them to discover solutions, best practices, and opportunities to accelerate your own AI journey.

Industrial AI is delivering real-world impact. Learn how with the Return on Intelligence e-book.

The post Partners powering AI transformation globally: Agentic solutions in action at ITAP 2025 and Microsoft Ignite 2025 appeared first on The Microsoft Cloud Blog.

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

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

Reenergizing change management with AI

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

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

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

Powering AI change management end-to-end

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

Some key capabilities set it apart:

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

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

Transforming change management at Coca-Cola Beverages Africa

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

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

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

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

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

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

—Joshua Motsuenyane, Chief Information Officer, CCBA

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

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

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

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

Ready to simplify change and accelerate execution?

Product Change Management agent template

Join the public preview

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

Shaping the manufacturing Frontier

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

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

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