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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The mobility industry is undergoing a transformative shift, driven by rapid advancements in data and AI. Generative AI and agentic AI are revolutionizing how vehicles are designed, manufactured, sold, and used—becoming crucial to the development of software-defined vehicles (SDV), advanced driver assistance systems (ADAS), autonomous driving (AD), and in-vehicle customer experiences. Generative AI is a game changer, redefining research and development (R&D) and product design. It’s helping automotive manufacturers realize time and cost savings, as well as quality improvements, by adding value at all stages of the R&D process. Agentic AI systems leverage a combination of rules, logic, and machine learning to function autonomously, adapting to real-time data and executing complex workflows. For instance, agentic AI can enhance efficiency and effectiveness across the enterprise, including customer engagement, supply chains, manufacturing, internal training, and overall process optimization.    

Microsoft is committed to supporting the automotive industry with cutting edge technologies, an ecosystem of partners, and industry-specific reference architectures and toolchains—helping automotive companies enable game changing autonomous agents, accelerate their digital transformation, and quickly monetize their innovations. The future of mobility is being shaped by AI, and Microsoft is here to enable the automotive industry to drive that change forward.  

Microsoft for mobility

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Here are some highlights of Microsoft’s solution portfolio for the automotive and mobility industry:

Microsoft reference architectures for mobility

• Design and engineering
  • PLM on Azure
  • Azure Innovation Accelerator ^{new AI agent} 

• Vehicle platform  
  • Software Defined Vehicle ^{new AI agent}
  • Digital Cockpit ^{new AI agent}
  • Autonomous Vehicle Operations ^{new AI agent}  

• Mobility services and customer experience 
  • Mobility Agents ^{new AI agent}
  • Connected Mobility ^{new AI agent}
  • AI for Airlines and Airports ^{new AI agent}
  • Digital Selling
  • Unified View of the Customer  

Microsoft reference architectures are crafted to offer essential structure and guidance for industry engineers and architects, enabling them to achieve agile business value. Solutions leveraging these mobility industry reference architectures are accessible through our network of qualified partners and in-house expert consultants, Microsoft Industry Solutions Delivery. Whether you are looking for an off-the-shelf solution or a hyper-personalized custom delivery, Microsoft industry reference architectures enable cutting edge innovation for all.  

Design and engineering solutions 

Product Lifecycle Management migration reference architecture

Product Lifecycle Management (PLM) solutions are one of the most mission-critical applications for automotive manufacturers, serving as a source of truth for digital product definition and configuration across the manufacturing lifecycle. Moving PLM to the cloud has become imperative as pressure increases for innovation—accelerating software and hardware content, time-to-market, quality, and sustainability. This is a critical foundation where automotive companies can unlock innovation with AI, and in doing so realize real value—reducing design cycle times, improved productivity efficiencies, improved quality, enhancing existing products, and developing new, differentiated, and innovative products—ultimately getting new products to market faster. AI-powered PLM and design and engineering solutions are transforming highly complex automotive engineering today. The Microsoft Azure PLM migration reference architecture provides an overview of common and recommended implementation choices, terminology, technology principles, common configuration environments, and composition of applicable Azure services that unlock this AI-powered innovation as part of your digital thread of multi-disciplinary design and engineering solutions, including PLM, Application Lifecycle Management (ALM), mechanical and electrical computer-aided design (CAD), computer-aided manufacturing (CAM), simulation, and analysis.

PLM on Azure partners

Azure reference architecture

Azure Innovation Accelerator (Azure IX) is an Azure-cloud based transformation toolkit helping teams reduce time-to-value covering technology, process, and culture. This offer can be used as a neutral tenant collaboration platform for multiple teams and partner ecosystems and offers a high security production-grade environment within weeks. Enabled with AI, Azure IX enhances communication, data sharing, and project management, ensuring that every team can work together seamlessly. AI capabilities are already built-in out-of-the-box to utilize existing content. Automotive customers are leveraging Azure IX to create automated driving platforms that integrate a data-driven and agile development architecture to run advanced driver-assistance systems (ADAS) and autonomous driving (AD) pipelines in a safe and neutral environment. Leveraging AI, this platform processes vast amounts of data, enabling rapid development and deployment of ADAS and AD technologies. This collaboration exemplifies the power of AI in driving the future of mobility. 

Vehicle platform solutions  

Software-defined vehicle reference architecture

In the evolving landscape of SDVs, where vehicle functionalities traditionally governed by intertwined hardware and software are now decoupled, virtualized, and steered predominantly by software, several industry challenges emerge—managing software complexity, adapting to evolving technology, and increasing consumer demands. SDVs require a new approach to the design and development of vehicle software and hardware architectures, as well as the tools and processes to manage them.

Microsoft’s software-defined vehicle reference architecture enables a modern cloud-native SDV software development toolchain that uses Microsoft’s strong developer-centric tooling. The reference architecture covers development, testing, and delivering high quality software. This is further supported by open source, community-based in-vehicle software components and stack contributions as part of the Eclipse Foundation SDV Working Group. These contributions define key components and features to facilitate integration, deployment, and management of various vehicle functions and services. 

Software-defined vehicle partners

Digital Cockpit reference architecture  

The Digital Cockpit reference architecture integrates AI services to enhance customer in-vehicle experiences, transforming the way users interact with their vehicles. By incorporating AI, the Digital Cockpit creates a personalized and intuitive user experience, making every journey more enjoyable and efficient. With customers like Mercedes-Benz, TomTom, Audi, SAIC, MG, NIO, XPeng, Sony Honda Mobility and Lynk & Co, the Digital Cockpit is redefining the future of in-car technology and user interaction. 

Digital Cockpit partners

Autonomous Vehicle Operations reference architecture 

Autonomous Vehicle Operations (AvOps) is the digital testbed for autonomous driving development that integrates all development processes into one platform. This revolutionary platform brings together every aspect of autonomous driving development, creating a seamless and efficient environment for innovation. By leveraging AI, AvOps ensures that every stage of development, from initial design to final testing, is optimized for performance and safety. With prominent customers like Wayve, Sony Semiconductor Solutions, Bosch, Claas, Denso, NVIDIA, Ansys and Voxel51, AvOps is setting the standard for the future of autonomous driving.

AvOps partners

Customer experience solutions  

Mobility Agents reference architecture

Mobility Agents are transforming the mobility industry by enabling copilot and AI assistant use cases across the entire mobility value chain, from R&D to Aftersales. These agents, built on industry-specific AI models, seamlessly integrate into user experiences, enhancing both customer and employee interactions. By leveraging AI, Mobility Agents significantly improve efficiency, accuracy, and user satisfaction, making them indispensable tools for automotive companies. Leading companies like CarMax, Porsche, Mercedes-Benz, Audi, Volvo, Bosch, BMW, Nissan, KPIT, and Maruti Suzuki are utilizing Mobility Agents to streamline operations and enhance customer interactions. For instance, Toyota’s O-Baya agents employ AI to provide valuable support throughout the entire lifecycle of a vehicle—from research and development to aftersales service. Mobility Agents are at the forefront of this transformation, empowering partners to build advanced mobility solutions that integrate AI to boost productivity and decision-making, ultimately accelerating the automotive sector’s transformation.  

Mobility Agent partners

Connected Mobility reference architecture

Connected Mobility accelerates value derived from vehicle and asset data combined with analytics, geophysical, and back-office technologies to manage vehicle operations. By harnessing the power of AI, Connected Mobility transforms raw data into actionable insights—optimizing vehicle operations and enhancing overall efficiency. With customers like Volvo, Diamler Trucks, BWT Alpine, Porsche Motorsports, HERE Technologies, Netstar, Navistar, Annata, and BMW, Connected Mobility is at the forefront of the smart transportation revolution, driving innovation and value across the industry.

Connected Mobility partners

AI for Airlines and Airports reference architecture

The AI for Airlines and Airports reference architecture is the first in our portfolio for mobility in the aviation industry. The reference architecture is designed to enhance critical areas of airlines and airports, from passenger experience to operational efficiency. Industry-specific AI models seamlessly integrate into various touchpoints, providing enhanced experiences for both travelers and airport staff. Leveraging AI, this architecture improves efficiency, accuracy, and user satisfaction, making it an indispensable tool for airlines and airports. Companies like American Airlines, Lufthansa, and Miami International Airport are utilizing this architecture to streamline operations and enhance customer interactions. For example, AI-powered solutions are being used to provide real-time updates, personalized travel recommendations, and efficient crew coordination, ensuring a seamless and enjoyable journey for passengers. 

AI for Airlines and Airports partners

Digital Selling reference architecture

The Digital Selling offering is a comprehensive digital retailing offer that empowers mobility service providers and marketers to use AI for promoting hyper-personalization while building customer trust and increasing brand loyalty and profitability. This solution leverages AI to deliver tailored marketing campaigns and personalized customer experiences, driving engagement and loyalty.  

Digital Selling partners

Unified View of the Customer reference architecture 

The Unified View of the Customer provides a blueprint for new revenue generation and customer-centricity. By leveraging AI, this solution offers a comprehensive and holistic view of each customer, enabling personalized and targeted marketing strategies. Leading automotive companies enhance their platforms to improve user experience and connectivity, showcasing the transformative potential of AI in creating customer-centric solutions that drive revenue and loyalty. Additionally, the profile-centric approach delivers significant value and benefits to both the customer and the company. For customers, it unifies data that was once siloed, such as vehicle maintenance schedules, finance, payment, and vehicle features. For companies, this data can inform large-scale trends among customers and enable targeted benefits. Furthermore, the Unified View of the Customer serves as the foundation for generative AI customer-facing chatbots, assistants, and agents. 

Explore Microsoft mobility solutions

Customers can work directly with Microsoft Industry Solutions teams on cutting-edge custom projects that offer a short go-to-market time. Whether you choose ready-to-deploy partner solutions or bespoke projects with Microsoft partners or Microsoft Industry Solutions Delivery, we provide the expertise and support to ensure your success. With Microsoft, you can leverage the value of agentic AI, accelerate your digital transformation, and quickly monetize your innovations. We’re here to help you lead the future of mobility quickly, creatively, and securely. 

Microsoft in mobility and manufacturing industries 

• Learn more about Microsoft for automotive and manufacturing. 
• Read the documentation for Microsoft reference architectures for mobility and Microsoft Cloud for Manufacturing. 

The post Revolutionizing mobility: Solutions for an AI-powered future appeared first on The Microsoft Cloud Blog.

Supporting these objectives, Microsoft has created reference architectures that enable its partner ecosystem to help customers drive the future of mobility across research and development, manufacturing, supply chain and logistics, marketing, sales, and aftersales.

These reference architectures are designed to provide the structure and guidance needed for project managers, enterprise architects, and IT managers to deliver agile business outcomes.

Solutions utilizing the following automotive, mobility, and transportation industry reference architectures are available through qualified partners.

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Microsoft Connected Fleets reference architecture

The Microsoft Connected Fleets reference architecture enables faster, lower cost, higher value fleet management solutions—such as asset management and field service—by simplifying value extraction from connected vehicle data, streamlining integration with business systems, and facilitating specialized analytics.

This reference architecture utilizes a framework based on the Microsoft Common Data Model. Connected Fleets thus enables partners to build value-added solutions with unique capabilities, while also enabling the customer to take advantage of key Microsoft solutions such as Microsoft Azure, Microsoft Dynamics 365, and others. This eliminates the need for fractured solutions built from multiple sources, allows faster development and time to value, and can utilize the Microsoft Cloud to reduce costs. With flexible, Microsoft Azure Internet of Things (IoT) based data ingestion, the architecture supports existing connected vehicle solutions, original equipment manufacturer (OEM) feeds, or data exchanges as appropriate. This flexibility helps to reduce the overall cost of vehicle data acquisition.

With a focus on fleet customers, mobility service providers, and emerging or smaller OEMs, the Microsoft Connected Fleets reference architecture is enabled by the following partners where additional value-added capabilities are provided:

• Accenture
• Cognizant                                      
• Connected Cars              
• DSA
• HCL Technologies                         
• Humanising Autonomy           
• Luxoft                              
• Wejo

Microsoft Autonomous Vehicle Operations (AVOps) reference architecture

The Microsoft AVOps reference architecture provides a comprehensive set of cloud, edge, vehicle, and AI services that enable an integrated, end-to-end workflow for developing, verifying, and improving automated driving (AD) functions. This autonomous development workflow will be supported by partners utilizing the Microsoft Cloud. These capabilities are helping our customers accelerate time-to-market for new autonomy capabilities.

Our first-party and partner solutions create a strong, industry-vetted toolchain that spans the various stages of development, validation, runtime deployments, and feedback loops.

We provide industry-leading Microsoft Azure Cloud services to plug and play in the toolchain while our partners provide tightly integrated capabilities to enable the much-needed, global, secure, and hyper-scale cloud-based development platform for advanced driving assistance systems (ADAS) and autonomous vehicles (AVs).

Regardless of the level of autonomy, developing an autonomy stack and bringing a new car to market presents a series of workflow challenges:

• Fleets of test vehicles generate petabytes (PBs) of data daily. How do you extract it efficiently and inexpensively?
• Every single software and AI update requires validation through simulation with massive amounts of computing.
• PBs of data and petascale computing is expensive—delivering on time and under budget is challenging.
• How do you accurately validate and test performance?

The Microsoft AVOps reference architecture provides a templatized digital testbed that can be spun up on demand, shared across the entire value chain, and shorten time-to-market across any security operation center (SoC) companies to provide systems-level parity. This approach fundamentally helps our automotive customers and partners transform themselves into software and AI companies.

The Microsoft AVOps reference architecture is also focused on optimizing operational end-to-end ADAS and AV workflow coordination and feature development, verification, and validation by integrating and scaling:

• DataOps: From edge to cloud, the orchestration of PBs of data to support parallel workstreams.
• DevOps: Scaling the continuous integration (CI) and continuous delivery (CD) pipeline.
• MLOps: Scaling machine learning pipelines and integrating with CI and CD pipelines.
• ValidationOps: The ability to accurately simulate software and AI updates across all edge cases.

The Microsoft AVOps reference architecture provides a collaborative, open framework built on common processes that helps automate, manage, and closely monitor the validation processes for software and hardware deployment. The following Microsoft partners provide additional value-added capabilities to this framework:

• Ansys   
• Applied Intuition            
• Cognata             
• Linker Networks             
• Akridata

Microsoft Digital Selling reference architecture

The Microsoft Digital Selling reference architecture enables the reinvention of the traditional customer journey by transforming real-life spaces into an immersive and interactive digital environment that reaches a wider range of customers.

The reference architecture enables unique, efficient, and immersive ways to engage with consumers by unlocking the power of the metaverse to improve communications, sales, and operations. Enhanced by partner capabilities, focus areas include digital marketing, eCommerce, high-fidelity 3D rendering, and industry-specific systems. By integrating disparate data from the customer, field service, and dealer management systems, improved visibility, collaboration, and data insights can drive more personalized, multi-channel customer experiences. Through the Digital Selling reference architecture, Microsoft Cloud, and partner capabilities, OEMs, dealers, and mobility service providers now have the flexibility to enhance in-person interactions along with enabling new digital customer engagement models.

This recent announcement by Fiat highlights the transformative impact of digital selling.

Microsoft is curating a group of expert partners that will drive innovation while providing excellence in omnichannel fulfillment, immersive and virtual user experiences, and end-to-end process coverage. The initial partners include:

• Sitecore                           
• Touchcast                        
• Annata

Microsoft at CES 2023

We’re excited to share our vision for innovation at CES 2023 and encourage you to visit our booth #6017 in the Las Vegas Convention Center West Hall.

Learn more

To learn more about how Microsoft is accelerating the future of mobility, visit our website highlighting the automotive, mobility, and transportation industry.

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