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.

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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

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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

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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.

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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 Microsoft Industry Blogs.

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

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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 Microsoft Industry Blogs.

Manufacturers are rapidly modernizing by using Microsoft cloud and AI solutions to transform key operations—streamlining product testing with cloud-based analytics, accelerating R&D through generative AI, and optimizing factory operations with intelligent automation.

For instance, BMW has enhanced the driving experience for over 13 million active users by integrating digital services within its vehicles. The MyBMW app, modernized with Cloud services, connects drivers to a range of digital features designed to enhance convenience and engagement. For BMW Group, this approach not only streamlines repetitive processes but also provides an open-source platform to support future scalability.

The time is now for manufacturers to modernize with AI in mind

BMW Group showcases its ongoing digital transformation in manufacturing. What began with a few sensor clusters has evolved into the Industry 4.0 revolution—the coming together of IT and operational technology to solve perennial challenges in manufacturing and heavy industry. Frontier leaders have achieved outcomes like Emirates Global Aluminum subjecting 97.5% more products to quality inspection, DEXIS reducing on-site service needs by 30%, and Fischerwerke construction enhancing the service life of structures. 

Rockwell Automation describes today’s opportunity:

“Our customers are looking to us for faster delivery, new functionality, reduced time-to-value, and new ways of working[…] It’s a perfect time to bring the power of modern IT—including the cloud—to the factory floor.”

— Brian Shepherd, Senior Vice President for Software and Control at Rockwell Automation  

The next wave of transformation—the AI and automation wave—has arrived. We’re seeing early adopters achieve significant business outcomes by integrating these technologies into their daily lives and work. The following section will highlight some powerful manufacturing success stories—each in key impact areas where leaders are modernizing with AI in mind.

Transforming the product testing lifecycle

Let’s talk again about BMW, this time overviewing how it has digitally transformed their product lifecycle through Internet of Things (IoT) cloud modernization and AI. 

Cloud modernization: BMW was experiencing slowdowns due to the enormous amounts of data sent by its 3,500-car test fleet, To address this, it developed an IoT data recorder connected to Microsoft Azure cloud platform, using Azure AI services, Azure App Service, Azure Kubernetes Service (AKS), and Azure Data Explorer. This combination was able to handle the massive amount of IoT data while modernizing the apps that monitor, manage, and analyze it. The upshot? 10 times faster data delivery and analysis handling twice the volume.

Adapting and infusing AI: BMW wanted to democratize and scale the impact of this test data, so it connected with Microsoft to see how it could adopt a generative AI chat experience. By using Azure AI services, BMW made its data available through natural language queries while Microsoft Power BI provided data visualizations to empower decision-makers across all business roles. “We can put very complex raw data into an understandable and comprehensive web interface so many BMW employees who aren’t engineers are also able to access it,” explained Heinz Gebhart, co-creator of BMW’s IoT data recorder. “Azure is the turbocharger for delivering the right data to the right person on a large scale.” 

Augmenting R&D innovation

For cutting-edge products to deliver real ROI, they need to address in-demand customer use cases. That’s why Denso, a leader in automotive parts manufacturing, embraced modernization to prepare the apps powering its advanced robotics for real-world applications. 

Cloud modernization with AI: Denso, Japan’s largest automotive parts manufacturer, is exploring new markets for expansion (like advanced automotive safety features and connected driving to factory automation and agriculture). To transform its own operations and enhance customer satisfaction, it turned to autonomous robots powered by generative AI. However, this approach would have to look different than the apps and functions that ran its traditional robotics.

“Conventional robots are inflexible machines that act based on the movements and instructions they are given. In contrast, we are developing control technology in order to realize a human-like robot that acts according to human language and can also easily correct its errors in judgment when a human points them out.”

—Keitaro Minami, Project Assistant Manager of Automation Innovation Section, Business Innovation Department, Cloud Services R&D Division, Denso Corporation

Denso rearchitected its control program to interface with generative AI using Azure OpenAI, Github Copilot, and Azure App Service. This let them significantly streamline development and allowed a container approach to address future customer use cases where robots cannot always be connected directly to the cloud. 

Optimizing factory operations

After a major company shift, powertrain solution provider Aurobay needed to rebuild their digital environment, including apps and infrastructure that ran mission—critical operational devices on the factory floor.

Cloud modernization: Aurobay collaborated with Microsoft to implement a hybrid cloud architecture powered by Microsoft Entra ID and Azure Arc. This initial migration prepared it to better manage its operational applications both on-premises and in the remote cloud using Azure Virtual Machines and Azure Kubernetes Service. 

“A specialist team from Microsoft helped us set up the full Azure tenant with all the landing zones, subscriptions, and group policies, which was critical in getting this right from the start.”

—Carol Wittgren, Head of Digital Acceleration at Aurobay

Adapting and infusing AI: With new software and architecture leadership in place, Aurobay is continuously embracing innovative methods of data modeling and management to maximize its data, taking its capabilities to the next level with Azure AI services, Azure Machine Learning, and Azure high-performance computing (HPC).

Modernize your manufacturing

Microsoft cloud and AI offerings provide end-to-end solutions for manufacturing, supporting interoperability, scalability, and modernization from backend to frontline. Azure enables manufacturers to modernize efficiently while minimizing risks and maximizing the benefits of AI-powered tools. 

We invite you to learn more about partnering with Microsoft to unlock real-world manufacturing modernization, including how to innovate by accelerating cloud and AI adoption. 

Modernize for AI innovation

Accelerate app and data estate readiness for AI innovation with Microsoft Azure

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¹Cloud Radar: Manufacturing Industry Report, Infosys, April, 10 2024.

The post Unlocking the potential of manufacturing with cloud modernization appeared first on Microsoft Industry Blogs.

Leading this shift are what Microsoft calls “Frontier Firms.” These companies change how they work to get the most out of AI. They run on on-demand intelligence and rely on hybrid teams where people and AI agents work side by side. They move fast, scale quickly, and deliver value sooner. In these companies, AI tools grow from simple assistants into trusted digital colleagues. These firms are ready to reinvent workflows and entire business models for the AI era. They are not just using technology; they are transforming how they operate, make decisions, and innovate.

A clearer path forward

Many manufacturers are still working through basics like old equipment, scattered data, manual processes, and not enough skilled workers. That is where a clear roadmap can make all the difference.

This is why we worked with IDC to create The Maturity Path for Manufacturers to Embrace AI Enabled Operations. It is a short assessment that gives you a custom report and benchmark, showing where you are today and what steps to take next. 

Real stories, real results

We are seeing transformational efforts from customers evolving with AI:

• Sandvik is rethinking how it makes products with Microsoft AI.
• Textron Aviation is using Microsoft Azure AI solutions to make aircraft maintenance faster and smarter.
• Husqvarna Group is boosting efficiency with Microsoft Azure Adaptive Cloud and is already putting generative AI on the factory floor.

Our partners are helping too. Siemens is working with Microsoft to help manufacturers automate and scale AI across industries and Sight Machine is putting agentic AI on the plant floor—giving operators real time help to solve problems and make better decisions.

What gets in the way

For most manufacturers, a few challenges keep coming up:

• Data stuck in silos or old systems. 
• Manual workflows that cannot keep up with demand. 
• Security gaps as factories connect more machines to IT systems and the cloud. 
• A shortage of workers with digital skills. 
• Growing complexity as product lines expand and change faster than ever. 

These challenges do not solve themselves, Frontier Firms face them head on. They break down data silos, modernize infrastructure, strengthen security, upskill people, and make data easy to use every day.

How the maturity model works

IDC’s five stage MaturityScape for Data Driven Operations shows how to move forward step by step. Progress happens across three areas at the same time: data, technology, and people.

1. Data: Good AI starts with clean, reliable, and well-organized data. Many manufacturers still need to break down silos and build a single source of truth. A cloud platform helps keep everything connected, secure, and ready for AI.
2. Technology: Once the data is in place, it is time to digitize, connect, and automate. Smart companies pick use cases that make sense—like predictive maintenance or quality checks—and build from there. Security must be part of every step.
3. People and process: The best technology does not matter without people ready to use it well. Leaders need to champion the shift, connect IT and operations, and build teams that mix data scientists with factory experts. Workers need the skills and tools to make faster, better decisions with AI at their side.

Getting from pilot to full scale

A lot of manufacturers run pilots but get stuck. Frontier Firms keep moving. They have a clear roadmap, celebrate quick wins, and stay focused on the bigger picture. They share what they learn, expand infrastructure, and build skills in house. Many set up a center of excellence to keep things on track and executive support and clear measures keep the momentum going.

IDC predicts that by next year nearly half of manufacturers will be using AI at scale, unlocking up to a 5% boost in profit or revenue. For those reaching the highest maturity levels, AI agents will not just assist, they will work alongside people as trusted digital colleagues—changing how factories run every day.¹

Where to start

Get your data in shape. Take stock of what you have, fix the gaps, and build data skills across teams. Pick a simple pilot with clear value and secure leadership support early.

Build on it. Expand how you collect and centralize data. Put governance in place, add more analytics tools, set up teams focused on digital transformation, and share early wins.

Scale and standardize. Use the cloud when it makes sense. Standardize tools and platforms, and make sure teams are truly cross functional and that decision making is faster and backed by real data.

Keep going. Even at higher stages of maturity, keep sharing data with partners when appropriate, test new technology, and keep AI models governed responsibly.

Where we are headed

The future of manufacturing is smart, connected, and powered by AI. The companies that lead will be those that work like Frontier Firms. They will treat AI as part of how they operate, not just a bolt on. They will pair people with AI agents that become trusted digital teammates and they will keep looking for new ways to reinvent what is possible.

At Microsoft, we are here to help. Our work with leaders like Sandvik, Textron, Husqvarna, Siemens, and Sight Machine shows what happens when manufacturers commit to the journey. You can start today, no matter where you are now.

Each step moves you closer to what Frontier Firms already see, including faster decisions, stronger resilience, and a business ready for what comes next.

Take the assessment and see where your journey begins. 

Find your organization’s path to advancing its industrial AI transformation

Benchmark your digital maturity across key dimensions of success

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¹ IDC FutureScape: Worldwide Manufacturing 2023 Predictions.

The post Embracing AI-powered operations: A maturity path for manufacturers appeared first on Microsoft Industry Blogs.

We see tremendous AI adoption across process manufacturing industries. The focus is shifting from experimenting with pilots to implementing AI in a way that delivers real business value. Leaders are now focused on how to get started and how to ensure a clear return on investment. Artificial Intelligence in Process Manufacturing: Preparing for an AI Future, a new manufacturing signals industry report published by Microsoft with research by IoT Analytics, presents insights into how manufacturers in process industries prioritize technology today and where AI fits into the picture. The report provides valuable insights for navigating the implementation of AI.

AI adoption is accelerating and entering a new phase

AI is gaining real traction in process manufacturing. Building on investments in Internet of Things (IoT), automation, and advanced process controls, manufacturers are focused on how AI can drive enterprise-wide decision-making and long-term value. This shift is no longer about if AI is worth pursuing—it’s about how to start effectively and drive measurable impact. As manufacturers move from pilot programs to broader deployment, the opportunity extends beyond task-level automation. AI is enabling predictive, real-time decision making across operations, research and development (R&D), and the supply chain—unlocking value that legacy systems can’t deliver alone. From my conversations with customers, the biggest barrier to generative AI isn’t the technology, it’s getting the data right.

This next phase of AI adoption depends on strong data foundations, grounded in enterprise data and context, with clear business alignment, and an organization-wide readiness to operationalize insights. Manufacturers that get this right are already seeing the results.

AI is supporting real business priorities

AI is helping manufacturers tackle two of their top business priorities: improving operational efficiency and driving revenue growth. By reducing waste, minimizing downtime, and optimizing output, AI-powered insights enable targeted operational improvements. The same data intelligence also fuels research and development (R&D), accelerates time-to-market, and uncovers opportunities for market expansion and business differentiation. One global chemical company reported that AI helped reduce the time-to-market for molecular enhancements from six months to just six to eight weeks¹—a powerful example of how operational innovation translates into business acceleration. 

The signals report also explores how industrial AI drives benefits beyond cost and throughput, from better data integration to improved customer satisfaction—ultimately enabling smarter, faster decisions across the value chain.

AI use cases with measurable business impact

The signals report surfaces real-world use cases where AI is delivering measurable results—not just technical improvements, but business transformation. From reducing downtime to accelerating product development, industrial leaders are applying AI in areas such as: 

• Process optimization
• Sustainability, energy efficiency, and waste reduction
• Research and development
• Predictive maintenance and analytics

Adoption is scaling fast: 80% of manufacturers surveyed are either using or planning to adopt generative AI. These solutions are driving change across every level of the organization—from frontline operations to management decision-making. 

A rubber and plastics manufacturer reported significant improvements to plastic design for more efficient production. A chemical company achieved a 90% reduction in demand forecasting costs and dramatically accelerated knowledge retrieval—enabling users to access answers in seconds instead of days.¹ And in the words of one life sciences organization: “Our employees have more power to support farmers, help cure diseases and see consumers healthier.”¹

These examples offer a compelling view into how industrial AI is already reshaping core operations, creating value well beyond the pilot stage.

Addressing security and complexity head-on

As more manufacturers embrace AI, leading organizations are not just navigating challenges—they’re building the strategies to overcome them. The signals report highlights two areas that require thoughtful planning: security and system complexity. 

Security remains a key consideration. Nearly half of respondents say concerns around data protection—from IP theft to regulatory compliance—impact their AI adoption decisions. In industries where uptime, safety, and proprietary processes are critical, protecting sensitive data is non-negotiable. 

Fortunately, security and AI aren’t mutually exclusive. Companies are investing in responsible AI practices, secure architectures, and governance models that enable innovation without compromising protection. 

Complexity is the other major hurdle. Legacy systems often lack interoperability, and introducing AI may require adapting long-standing workflows. But many manufacturers are proving that modernization is possible—and that the payoff is worth it. 

The signals report offers guidance on how to approach these challenges with the right foundation, so AI becomes a source of advantage, not friction.

Laying the foundation

Successful AI adoption requires a strong governance framework—it’s not about experimenting endlessly with every possible AI use case but rather focusing on the most strategic use cases that will deliver business value. Building this framework requires the right foundation to scale impact over time. Leading manufacturers are taking a structured approach: aligning AI investments to business goals, modernizing infrastructure, and investing in the skills needed to sustain innovation. 

The signals report outlines four practical steps manufacturers are taking to move from isolated pilots to enterprise-wide transformation: 

• Identify business needs
• Embrace structural flexibility
• Get the data in order
• Use AI to develop workforce capabilities 

These are more than recommendations—they reflect what real manufacturers are doing to turn AI into a competitive advantage. And for many, AI is no longer optional, but essential to unlocking the next wave of efficiency, innovation, and competitiveness. The signals report brings each step to life with examples from the field. 

Download the full report on Artificial Intelligence in Process Manufacturing to explore the research, benchmark your readiness, and take your next step toward AI-powered transformation. 

Preparing for an AI future

Artificial Intelligence in Process Manufacturing

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¹ Artificial Intelligence in Process Manufacturing

The post AI in process manufacturing: From operational gains to strategic advantage appeared first on Microsoft Industry Blogs.

Optimizing supply chains: Visibility, risk, and technology

Supply chains have been thrust into the spotlight today more than ever, so it’s no surprise that conversations around how to optimize, improve, and reduce complexity in supply chains are now a top boardroom conversation. Supply chain leaders face both opportunities and challenges, and are primarily focused on key levers to optimize such as: 

• Gain more visibility across value chains to identify potential risks and develop mitigating strategies.
• Drive cost optimization and efficiency improvements to address rising transportation, warehousing, and material costs.
• Improve worker experience and utilize automation to compensate for talent shortages.
• Become more proactive through data-driven decision making and get in front of potential supply chain issues.
• Identify patterns in customer demand to ensure optimal service levels, cost savings, and profitability.  

Supply chain disruptions, triggered by events like natural disasters, geopolitical tensions, and economic volatility, however exposed vulnerabilities in global networks, highlighting the critical need for enhanced supply chain visibility. Businesses must track and monitor materials and products in real-time to identify and mitigate potential risks effectively. The adoption of advanced technologies, such as AI and near-time sensor data, has enabled comprehensive visibility into supply chain operations. 

However, visibility alone is insufficient without accurate risk quantification. By assessing the likelihood and impact of disruptive events, companies can prioritize mitigation strategies, allocate resources efficiently, and make informed decisions to enhance resilience. Predictive analytics, scenario planning, simulation, and risk assessment models play crucial roles in evaluating financial, operational, and strategic implications, and can aid in a proactive posture to reduce impact and shocks to the system. Consequently, the drive for greater supply chain visibility has underscored the importance of risk quantification, enabling businesses to build robust and adaptable supply chains capable of withstanding unforeseen challenges. 

Supply chain depends on data chains 

The supply chain heavily relies on the data chain because data is central to coordinating, optimizing, and managing all processes within the supply chain. Accurate and real-time data allows for effective demand forecasting, ensuring that production meets customer needs without excess inventory. Data enables real-time tracking of goods and materials, enhancing visibility and transparency throughout the supply chain. This visibility helps identify and resolve bottlenecks and disruptions promptly. 

Data also supports supplier management by providing insights into supplier performance and reliability, enabling informed decision-making. Additionally, data analytics can optimize logistics and transportation, reducing costs and improving delivery times. Quality control processes benefit from data-driven insights, ensuring that products meet standards and reduce waste. 

To be successful, organizations must adopt a comprehensive approach, combining intelligent solutions to break down data silos to create a robust and well sorted out data estate. This unlocks numerous opportunities by leveraging comprehensive and well-managed data to drive strategic initiatives and innovation and is a critical step in moving forward with AI. Several key components need to be in place for this to happen: 

• Business sponsorship: Strong leadership and business sponsorship are crucial for prioritizing data-driven initiatives, securing necessary resources, and aligning data strategies with organizational goals. This ensures that data projects receive the support needed for successful implementation and integration across all business units.
• Customer focus: A robust data estate enables deep customer insights through advanced analytics, helping businesses understand customer behaviors, preferences, and needs. This knowledge allows for personalized marketing, improved customer service, and the development of products and services that better meet customer demands—enhancing customer satisfaction and loyalty.
• Process maturity: Mature processes ensure consistent data quality, governance, and security, which are essential for reliable data analytics. Process maturity also facilitates efficient data integration from various sources, enabling comprehensive analysis and more informed decision-making.
• Organizational change management: Successfully leveraging a robust data estate requires effective organizational change management. This includes training employees, fostering a data-driven culture, and managing resistance to change. By ensuring that staff are skilled in data use and understand its value, organizations can maximize the benefits of their data initiatives.
• Value proposition: A well-managed data estate provides a clear value proposition by driving efficiency, reducing costs, and uncovering new revenue opportunities. It supports innovation, enhances competitive advantage, and improves strategic planning—ultimately contributing to improved profitability and shareholder value. 

AI can further enhance this process by providing insights from a multitude of data sources and variables to aid decision making for supply chain planners and operations teams. AI can also optimize inventory management, warehouse operations, route planning, and resource allocation to improve efficiency and reduce costs. AI evaluates supplier performance, supports decision-making with actionable insights, and simulates scenarios for robust contingency planning. IDC predicts that by 2027, 50% of global organizations will deploy a GenAI-powered platform that combines these disparate data sources.¹ By integrating, AI ensures transparency and traceability, detecting anomalies and ensuring compliance. Enhanced collaboration platforms improve communication among stakeholders, while AI-powered demand forecasting aligns supply chain strategies with market trends, ultimately improving adaptability and efficiency in the face of disruptions.  

In summary, data is the backbone of the supply chain, driving efficiency, reducing risks, and enabling informed decision-making across all stages, from procurement and production to logistics and customer delivery. This central role of data ensures a responsive, agile, and resilient supply chain and is a critical step to prepare for a successful rollout AI. 

Supply chain focus area 

A comprehensive resilient supply chain strategy must include visibility and risk management, forecasting and planning, and warehousing and fulfilment as key focus areas. 

• Visibility and risk management: Supply chain visibility is crucial for tracking goods in real-time, ensuring timely delivery, and maintaining customer trust. It also aids in risk management by identifying bottlenecks and potential disruptions, enabling proactive measures to mitigate losses.
• Forecasting and planning: Accurate forecasting and planning can optimize inventory levels, reduce holding costs, and prevent stockouts or overstocking. It involves analyzing historical data and market trends to predict future demand, facilitating efficient resource allocation.
• Warehousing and fulfilment: Efficient warehousing and fulfilment processes ensure that goods are stored properly, and orders are fulfilled accurately and promptly. This enhances customer satisfaction and loyalty. 

While these areas are critical, it is important not to “boil the ocean” by trying to do everything at once. Instead, businesses should prioritize based on their specific needs and capabilities. Implementing changes incrementally can lead to sustainable improvements without overwhelming the organization. In this way, a balanced and focused approach can significantly enhance supply chain performance and competitiveness. 

Within these key focus areas, Microsoft’s suite of services and capabilities play a pivotal role in driving a resilient supply chain: 

• Platform services: Microsoft Azure provides a robust and scalable platform for deploying supply chain applications. It offers flexibility, reliability, and global reach, enabling businesses to operate and innovate at scale.
• Data platforms: Microsoft data platforms like Azure SQL Database and Azure Cosmos DB provide the backbone for storing and managing supply chain data. They offer real-time analytics, enabling businesses to make data-driven decisions.
• Security: Microsoft security solutions protect sensitive supply chain data from threats. Tools like Microsoft Sentinel and Microsoft Defender for Cloud provide advanced threat insight and protection, ensuring the integrity and confidentiality of data.
• Business applications: Microsoft Dynamics 365 Supply Chain Management integrates and streamlines all aspects of the supply chain. It offers modules for planning, production, inventory management, and logistics—driving efficiency and effectiveness.
• Analytics: Microsoft Fabric is an end-to-end data and analytics platform that includes real-time analytics capabilities. OneLake is a unified logical data lake that centralizes and simplifies data management, with multiple analytical engines and workspaces. Fabric enables organizations to process and analyze data for timely insights and decision-making. Supply Chain and logistics are data intensive processes, therefore, it is important to integrate data from other ecosystems such as customers’ existing enterprise systems, connected assets, external sources, partner data, and so forth. It is important to integrate existing data systems, such as connected assets as well as existing systems.
• AI capabilities: Microsoft AI capabilities can transform supply chain operations. AI can enhance demand forecasting, automate warehouse operations, and provide predictive maintenance for logistics. Azure AI Foundry provides critical functionality to design, customize, and manage AI apps and agents at scale. Microsoft Copilot Studio facilitates the creation of custom AI agents to support their work.
• Partner ecosystem: The Microsoft partner ecosystem continues to play a critical role in enabling customer supply chain resiliency and agility. A rich supply chain partner ecosystem includes advisors and implementers and you can find your partner at our Partner center. 

By integrating these elements, Microsoft empowers businesses to build a more resilient, efficient, and intelligent supply chain. It enables customers like C.H. Robinsons to anticipate and respond to disruptions, optimize operations like ABB, and deliver superior customer service through AI, thereby gaining a competitive edge in the market like Dow.

Microsoft products, platforms, and services are designed to integrate seamlessly with existing technology landscapes. They offer interoperability and compatibility, allowing businesses to use their current investments while benefiting from Microsoft’s advanced capabilities. This approach avoids the need for costly and disruptive “rip and replace” strategies. 

Furthermore, Microsoft’s commitment to open standards and cross-platform compatibility means its solutions can work alongside competitor’s products. This flexibility allows businesses to build a best-of-breed technology ecosystem that aligns with their unique needs and objectives. Thus, Microsoft enables businesses to evolve their technology landscapes in a gradual, sustainable manner, maximizing ROI and minimizing disruption. 

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¹ IDC FutureScape: Worldwide Supply Chain 2025 Predictions, doc # US52640524, October 2024.

The post Creating a resilient supply chain using connected data chains appeared first on Microsoft Industry Blogs.

Industrial AI in Action 

Through 31 demos, 53 theater sessions, and three ancillary events, Microsoft highlighted how AI agents are helping manufacturers unlock new levels of productivity, resiliency, and growth. As the new interface to industrial data and operations, generative AI tools allow every worker—from the factory floor to the boardroom—to surface timely, relevant insights that drive decision-making. Test agents built with the power of Microsoft Copilot Studio for yourself. 

In the booth, Microsoft focused on the entire manufacturing value chain: advancing innovation in digital engineering with generative AI, preparing the factory edge for AI, AI agents supporting the development of frontline workers, and finally making intelligent digital threads a reality. Microsoft brought these four opportunities to life through four distinct neighborhoods filled with demos, partners, and customer stories. Highlights included collaborations with Rolls-Royce, Siemens, PTC, Sandvik, Husqvarna, Sight Machine, Sanctuary AI, SymphonyAI, Bridgestone, and Databricks. Microsoft’s Hannover presence garnered incredible media attention, notably several news channel interviews with Anges Heftberger, CEO, Microsoft Germany, and a visit from Roland Busch, CEO, Siemens AG. 

This year, Microsoft’s centerpiece displayed the Rolls-Royce transformation journey from design engineering through the factory to maintenance operations. For over a century, Rolls-Royce has been a force for progress; powering, protecting, and connecting people everywhere. Today, with digital transformation at the forefront, the company is redefining how its world-class products are designed, built, and maintained. With help from Siemens and Microsoft, Rolls-Royce is now using AI to streamline production, boost engine efficiency, and predict maintenance needs before issues arise.

Making intelligent digital threads a reality 

Grounded in unified operational (OT), enterprise information (IT), and engineering (ET) data, digital threads connect every phase of manufacturing—delivering timely, actionable insights to every team, from design and production to maintenance and customer support. This continuous, connected flow of data enriches every stage of the manufacturing value chain. 

Without a strong data foundation, manufacturers will struggle to tap into the full potential of AI. Data quality, standardization, and integration are often inconsistent, making insights hard to access and trust. Microsoft Fabric is helping manufacturers overcome these barriers—turning fragmented data into intelligent digital threads that power better decisions, faster innovation, and operational excellence. Alongside Fabric and Microsoft Dynamics 365 demos, Microsoft partners AVEVA, Databricks, Kongsberg, and Parsec displayed how AI is influencing real-time production monitoring and predictive maintenance to fuel resilient, efficient, and sustainable manufacturing. 

Engineering with generative AI 

AI is disrupting design and engineering, unlocking new levels of innovation, speed, and creativity. With generative AI, manufacturers can now rapidly explore a wide range of possibilities, optimizing products for performance, manufacturability, and cost. Microsoft partners PTC, Sandvik, Schneider Electric, Eplan, Rescale, and NTT DATA demonstrated real-world applications of AI reshaping product development and lifecycle—from accelerated design iterations to predictive simulations. The result is higher-performing, more customer-centric products brought to market faster and more efficiently. 

Preparing the factory edge for AI 

AI is redefining factory operations. Manufacturers must integrate industrial edge solutions with the cloud to fully capitalize on their shop floor investments. The Microsoft Azure adaptive cloud approach captures data from industrial equipment assets and devices, normalizing it at the edge, sending insights to the cloud and back. Along with partners Accenture Avanade, Cognite, Litmus, Schneider Electric, Sight Machine, Rockwell, and Tulip, Microsoft showcased how AI at the edge is transforming real-time factory visibility and performance monitoring.  

Supporting frontline workers with AI agents 

AI transformation is reshaping every aspect of manufacturing operations. As the industry grapples with high turnover, upskilling the workforce has become a critical challenge. AI agents are now giving frontline workers real-time guidance to help them make faster, better-informed decisions. AI-powered agents are streamlining industrial environments, allowing operators, production teams, and facility managers to access insights and optimize processes through natural language interactions. By accelerating issue resolution and root cause analysis, the agent improves day-to-day productivity and operational resilience. In addition to Microsoft 365 Copilot and Microsoft Dynamics 365 Field Service demos, partners Sanctuary AI and SymphonyAI highlighted how AI and automation are redefining the future of frontline work. 

Driving AI leadership and industry innovation 

The Microsoft theater was busy this year. Moved in the booth, this space connected business leaders, innovators, and customers to the experts, creating a forum to discuss the unique challenges facing manufacturing and how AI and cloud technologies are helping address them. Here are a few highlights from the theater: 

• “Celebrating women in manufacturing” brought together influential female voices in manufacturing to explore their career journeys, achievements, challenges, and advice to inspire the next generation of talent. Thank you to panelists Elise Hersko, Sandra Anderstedt, and Monica Ugwi.  

• An Industrial AI leadership conversation between Roland Busch, Siemens CEO, and Uli Homann, Microsoft CVP of Cloud and AI, who shared their learnings on leading in AI. Both agreed that success depends on a trusted data ecosystem, responsible AI practices, and a commitment to scaling AI initiatives that start with the customer.  

• Microsoft Intelligent Manufacturing Award (MIMA) showcase, in partnership with Roland Berger, celebrated the winners of the MIMA, recognizing innovation in smart manufacturing across Europe, Middle East, and Africa. The 2025 winners included Continental, Diehl Metering, Philip Morris Manufacturing & Technology, ZEISS Digital Innovation, plus Cereal Docks and MIPU.  

Unlock new possibilities with Microsoft 

Thank you to the customers, partners, and the thousands of attendees who engaged with the Microsoft booth throughout the week. We’re looking forward to HANNOVER MESSE 2026. 

The post Hannover Messe 2025: Microsoft puts industrial AI to work  appeared first on Microsoft Industry Blogs.