Microsoft’s vision for Physical AI connects the full lifecycle, from data and simulation through training, evaluation, deployment, and operational feedback. Our partnership with NVIDIA brings complementary capabilities into that workflow, as Microsoft Azure provides compute, data services, orchestration, and governance, while NVIDIA contributes physical AI technologies for data curation, retrieval, synthetic data generation, and scene reconstruction. Together, we aim to help engineers build a repeatable development process, with customers retaining control of their data, engineering decisions, and validation.

For me, the strategic opportunity is how each development cycle improves the next. A driving scenario captured today can become reusable evidence across vehicle programs and software generations. That is where this work can create lasting value for automakers.

The future of automotive software

The next competitive advantage in automotive software will come from how effectively an organization turns each mile into reusable validation evidence. That evidence should serve the vehicle being developed today and the generations that follow. Collecting road data is a starting point. The strategic question is whether teams can use what they learn to make better engineering decisions, repeatedly and across programs.

This is the promise of autonomous vehicle operations, or AVOps: an engineer-directed development loop that connects continuous road data, curated scenarios, simulation, model improvement, validated software, and real-world feedback. It supports the development of autonomous driving and advanced driver assistance systems, or ADAS. Engineers determine what needs to be learned, choose the evidence that matters, and retain authority over validation and release decisions.

At Microsoft, we are working with NVIDIA and automotive partners to make that loop more connected and practical. Three priorities shape my view of what it takes: purposeful data, repeatable development, and customer control. Together, they create a foundation for learning that can compound across software releases, vehicle programs, and brands.

Turn driving data into engineering evidence

A development team needs to find the scenarios that expose a gap in model behavior, understand their quality, and turn them into useful tests. Search, scenario mining, annotation, and curation give road data a specific engineering purpose. Synthetic generation can then help expand the scenarios available for development, with engineers setting the questions and assessing whether the resulting data is suitable.

The ecosystem brings complementary capabilities to this work. NVIDIA is bringing its technologies to Azure, including NVIDIA Cosmos Data Factory for search and synthetic generation and NVIDIA Omniverse NuRec for scene reconstruction. Voxel51 integrates with Azure and uses these NVIDIA technologies alongside its capabilities for data quality validation, search, scenario mining, annotation, and curation.

Azure provides compute, storage, orchestration, integration, and governance to connect these capabilities into workflows that automotive teams can operate.

For me, the value of this foundation is the connection between a development question and the evidence used to answer it. A scenario becomes more valuable when another team can understand why it was selected, reuse it, and compare results. That is how learning from one program can become an asset for the next.

Connect development around repeatable evidence

Connected workflows allow engineers to carry a scenario from road data into reconstruction, simulation, and model improvement, then bring the results back into validation. Shared workflows also make scenarios reusable across programs, releases, and brands. The opportunity is to build a growing body of evidence so teams can revisit a known condition when software changes and see whether behavior has improved or regressed.

Virtual validation compares reconstructed camera inputs and simulated outputs with real measurements. The aim is to enable more repeatable testing, faster feedback, and fewer repeated drives for tests that can be meaningfully reproduced. Simulation has limits: it does not establish road safety or replace all real-world testing. Engineers must assess how closely the virtual conditions represent the physical ones and decide what further evidence is required.

I would measure speed through the validated release. Generated data volume alone tells us little about engineering progress. The useful questions are whether a workflow helps resolve a development issue, whether its results can be reproduced, and whether the evidence supports the next decision. Azure’s role is to support the compute and operational connections that help teams move through that process.

Maintain OEM control across AV development

Porsche, working with Volkswagen Group brands and CARIAD, is building an OEM-controlled, modular approach to a shared autonomous vehicle data flywheel. Porsche is playing a key role in the development of a shared AV data flywheel across Volkswagen Group brands, using Cosmos Data Factory through Voxel51 on Microsoft Azure. This collaboration is expected to reduce repeated physical test drives by up to 90% and shorten AI regression test cycles by up to 85%. The direction is significant: shared capabilities and reusable scenarios can support collaboration while keeping the automotive manufacturer in control of its development process.

That control is central to trust. Customers need to govern their data, choose how capabilities fit together, and maintain responsibility for their engineering decisions. Microsoft’s intelligent, trusted platform with a broad partner ecosystem gives customers the flexibility to bring specialized tools together around their own business priorities securely and at scale. A modular ecosystem gives customers a way to connect specialized tools around their own priorities.

My view is that the lasting opportunity extends beyond any single tool: each development cycle should leave behind evidence that the next one can use. When engineers can turn road experience into repeatable validation and carry that learning across vehicle generations, the value of every mile can continue to grow.

Check out these resources

• Explore the NVIDIA AVOps on Azure Reference Implementation.
• Learn more about Microsoft mobility solutions.

The post Advancing autonomous vehicle development with Microsoft, NVIDIA, Porsche, and Voxel51 appeared first on The Microsoft Cloud Blog.

Insurers are navigating climate volatility, cyber threats, evolving customer expectations, regulatory pressure, and an overwhelming volume of data. They need trusted AI-powered solutions that help them manage amid these headwinds. AI creates an opportunity to do more than accelerate isolated tasks. It can help insurers bring together data, organizational knowledge, and human expertise to support better, faster, and more trusted decisions across the enterprise.

Managing AI agents across the enterprise

At ITC Vegas 2026, the shift from isolated AI pilots to practical, governed AI that can deliver measurable business outcomes was hard to miss. Across discussions about underwriting, claims, distribution, and risk, the focus has moved beyond experimentation toward solutions that help insurers make better decisions at scale. After 10 years at ITC, we see a clear inflection point: the industry is no longer asking whether AI will reshape insurance, but how to scale it responsibly while keeping people accountable for consequential decisions. Beyond the event, the durable challenge is how insurers orchestrate data, expertise, workflows, and AI agents across the enterprise without compromising security, compliance, and governance standards.

When I first attended ITC a decade ago, the conversation centered on digital experiences and connected ecosystems. In returning this year, I saw how that ambition has expanded to reimagining and optimizing every function across insurance through AI. This new realm of innovation makes orchestration, governance, and security essential to scaling responsibly—and that is where Microsoft’s foundation of trust sets us apart.

Jeffery Williams, Managing Director, Microsoft

Four areas stand out as especially consequential: accelerating underwriting and decisioning, evolving the actuarial function into a real-time risk intelligence hub, modernizing claims operations, and empowering relationship managers. Underpinning all four is a secure data and AI foundation that turns fragmented signals into actionable intelligence, and actionable intelligence into stronger decisions, operations, and customer trust.

1. How AI can accelerate underwriting and decisioning

Underwriting is where the promise of AI becomes tangible: a faster path from fragmented submission data to a decision an underwriter can stand behind. Teams face growing volumes, increasing risk complexity, and pressure to respond with greater speed and accuracy.

AI can synthesize data, surface the signals that matter, and streamline workflows so underwriters spend less time gathering information and more time applying their expertise, evaluating risk, and making informed decisions. This helps insurers modernize underwriting processes while improving speed, consistency, and decision quality. Agents can parse broker emails, forms, and attachments; enrich submissions with internal and third-party data; and present prioritized worklists based on risk, value, and complexity.

For example, Shift Technology uses Azure OpenAI, Azure AI Vision, and Azure AI Document Intelligence to help insurers automate the classification and extraction of information from policies, claims, and supporting documentation, reducing process time from weeks to days. For more complex risks, AI agents can synthesize engineering, financial, and medical documentation into concise, auditable briefs with clear source attribution. The result is faster, more informed decisions, more consistent risk assessment, greater underwriting capacity, and improved experiences for brokers and customers.

Importantly, autonomy should remain bounded by clearly defined roles, authority levels, and escalation paths. AI can summarize risk, propose draft terms, or recommend next actions within defined thresholds, while higher-impact decisions are escalated to human underwriters with the relevant context, source attribution, explainability, and audit trail needed to exercise judgment and act confidently.

2. How AI can evolve actuarial function into a real-time risk intelligence hub

The same shift extends beyond underwriting. For decades, actuarial leaders have talked about flipping the familiar 80/20 equation: too much time spent gathering, reconciling, and preparing data, and too little spent on analysis, interpretation, and strategic decision-making. Cloud, generative AI, and agentic systems are making that ambition more achievable.

AI agents can support model-run orchestration, data validation, experience studies, assumption management, scenario generation, variance commentary, and regulatory documentation. They can ingest and normalize geospatial data, satellite imagery, hazard layers, sensor feeds, and unstructured field reports, then coordinate model execution and flag changes in exposure, climate conditions, or model behavior that warrant actuarial review. Emerging multi-agent approaches can also test models overnight, identify anomalies, document adjustments, and prepare review-ready summaries before the workday begins.

As a first-time attendee at InsureTech Connect, I learned a tremendous amount from the discussions on new data sources and the rapidly expanding applications of agentic AI. These capabilities will help actuaries better understand and model emerging risks, which is increasingly important as societies face evolving global challenges and protection gaps that affect financial, health, and overall community well-being.

Rich Moyer, Chief Product Officer, Milliman Vehicle Telematics Practice

This does not diminish the actuarial role; it elevates it. The actuary of tomorrow will be increasingly AI-enabled and human-led—with actuaries directing agents, validating outputs, challenging assumptions, explaining tradeoffs, and translating risk intelligence into better-informed decisions across pricing, reserving, capital, product, and business strategy. As AI takes on more of the technical execution, human differentiation will increasingly come from creativity, cross-disciplinary judgment, communication, and accountability.

That evolution depends on disciplined governance. Actuarial teams need model lineage, reproducibility, confidence thresholds, bias and drift monitoring, peer review, and clear human ownership of outcomes. AI can inform and strengthen decisions, but it should never obscure how a conclusion was reached or who is accountable for the outcome.

Partners are already helping insurers modernize in this direction. Milliman Integrate brings cloud-scale actuarial modeling, workflow management, collaboration, and reporting to life insurers, while Aon PathWise supports high-performance actuarial and balance-sheet modeling. These platforms point toward a more open, interoperable actuarial ecosystem in which compute, data, models, and AI services can work together without sacrificing auditability.

3. How AI can modernize claims operations

If underwriting is where risk begins, claims are where the insurance promise is tested. After a loss, what matters to a policyholder is not a workflow; what matters is whether their insurer understands what happened and can help. What matters is speed, accuracy, empathy, and the quality of the response.

AI can help insurers modernize claims operations by reducing manual effort, accelerating intake and triage, and equipping adjusters with the information they need to act quickly and confidently. Agents can capture loss details, verify coverage, organize documents and images, identify missing information, surface potential fraud, and recommend next actions.

Organizations such as Gallagher are already applying AI to claims workflows. Gallagher uses claims summarization capabilities that reduce their review times from hours to minutes, all while helping adjusters identify critical information more quickly. Low-complexity claims may move through defined workflows with minimal intervention, while complex or high-impact cases are routed to experienced professionals with the context needed to exercise judgment.

This is especially important as catastrophe frequency, litigation, and repair complexity put pressure on claims organizations. The goal is not automation for its own sake. It is to reduce friction, improve consistency, and create more capacity for the human judgment and empathy that policyholders need in difficult moments. The strongest operating models will pair speed with clear escalation paths, explainability, and human accountability so that every claim decision can be made and communicated with confidence.

4. How AI can increase relationship manager effectiveness

The same expectation for immediacy extends to every customer and broker interaction. Relationship managers are expected to be trusted advisors while navigating growing volumes of account information, open issues, market signals, and internal knowledge.

AI can help employees turn organizational knowledge, customer context, and market signals into actionable items, allowing them to spend more time strengthening relationships, advising customers, and delivering value. Before a meeting, an agent can assemble account history, open issues, renewal timing, relevant products, and recent interactions. During and after the conversation, it can capture notes, draft follow-up, assign actions, and keep commitments from slipping. Generali France, for example, uses AI to help employees access the right information quickly, synthesize customer data, and better understand client intent across millions of interactions. This helps teams deliver more relevant and personalized experiences while spending less time searching for information and managing administrative tasks.

When connected to governed customer and enterprise data, these capabilities can make interactions more timely and personal without losing context across channels. The value is not just faster preparation. It is better engagement, stronger follow-through, and more time for relationship managers to focus on advice, negotiation, and trust.

Building insurance intelligence on a foundation of trust

AI is only as valuable as the intelligence behind it and the trust that supports it. As AI adoption accelerates, insurers need to govern how data, intelligence, and actions move across people, systems, agents, and workflows. Realizing the full potential of AI requires more than adding new technology. Insurers need solutions that securely connect data, knowledge, processes, and people while maintaining the privacy, compliance, and governance standards the industry demands.

That starts with turning organizational data into actionable intelligence and grounding AI in business context and institutional knowledge. It also means bringing AI into the tools and workflows employees already use, rather than creating another disconnected layer of work.

As agents take on greater responsibilities, governance must become part of how work is designed and executed. Insurers should define what an agent can do, on whose behalf it can act, which data it can access, and under what authority. Controls for identity, access, data protection, monitoring, testing, auditability, and human escalation should be designed into the workflow from the beginning. Responsible AI principles—including fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—should translate into practical controls that employees, regulators, and customers can understand.

In a trust business, governance is what makes innovation scalable. The insurers that combine enterprise-grade security and compliance with transparent, well-governed AI will be better positioned to move quickly, improve decisions, and earn confidence at the same time.

In insurance, governance isn’t what slows down AI innovation; done correctly, it’s what gives organizations the confidence to accelerate. The goal isn’t to govern AI at the speed of compliance but to use governance to enable AI at the speed of the business. The maturity question for insurers is no longer simply how many AI use cases they can create. It’s how safely, repeatedly, and quickly they can turn those use cases into enterprise capabilities.

Dan Alecia, Partner, IBM Consulting Americas Microsoft Practice, Financial Services Leader

Frontier Firms deliver in moments that matter

The competitive question is no longer who can launch the most AI pilots. The question is who can scale AI across the enterprise with the governance, security, compliance, and accountability required to turn intelligence into a trusted operating advantage. That advantage will help people make better decisions, serve customers more effectively, and respond with confidence when the stakes are highest.

The next generation of Frontier Firms will not simply automate processes. They will redesign work around outcomes, bringing people and AI agents together across connected data, expertise, and enterprise, workflows within a secure, governed foundation built on intelligence and trust. For actuaries, underwriters, claims professionals, and relationship managers, that means less time spent assembling information and more time exercising judgment, interpreting uncertainty, making informed decisions, and serving customers.

That is the strongest signal we took from this year’s ITC conversations: AI is moving from a collection of experiments to a trusted operating layer for insurance. For insurers, the long-term advantage will come from combining governed data, responsible AI, and human judgment across underwriting, actuarial work, claims, and customer engagement. The insurance organizations that lead will combine human expertise with AI-powered intelligence to make better decisions, move with greater speed, and deliver stronger outcomes while preserving the trust on which the industry is built.

The post The next insurance advantage: Intelligence and trust appeared first on The Microsoft Cloud Blog.

Today, Microsoft marks a milestone with the general availability of Azure Enclave in both Azure and Azure for US Government. Azure Enclave is a managed Azure service designed to help organizations streamline the deployment and management of their isolated cloud environments for sensitive and special access workloads while applying policy governance and monitoring.

For US Government agencies and the Defense Industrial Base (DIB), Azure Enclave builds on Microsoft’s experience supporting sensitive, classified, and special access program missions where agencies, mission partners, contractors, national laboratories, academic institutions, and suppliers need to work together across organizational boundaries. That experience requires a repeatable approach to network isolation, explicit connectivity, and centralized visibility.

Those needs are not unique to a single mission or sector. Governments worldwide and organizations in highly regulated industries also need to collaborate around sensitive information while maintaining strong controls over access, connectivity, monitoring, and operations. Azure Enclave addresses that shared challenge by helping organizations start from a governed foundation rather than assembling custom isolation architectures independently for every program or workload.

A repeatable foundation for mission success

Sensitive government work rarely occurs within a single program office, agency, or organization. Agencies may need to collaborate with mission partners, contractors, suppliers, national laboratories, or academic institutions, and some participants may not have the resources to independently establish and manage a complex isolated environment.

Azure Enclave brings together managed infrastructure and predefined architectural isolation patterns so organizations can begin with a foundation designed for isolation and adapt it to their workload, policy, and operational requirements. The service uses a community-and-enclave model: a community provides shared network controls, while each enclave provides an isolated workload boundary. Policy and routing controls can be applied across explicitly approved connection paths among authorized users, applications, services, and data.

This model helps teams streamline planning, configuration, testing, and ongoing management. Workload resources can span subscriptions while organizations apply shared governance across related enclaves, giving agencies and partners a consistent way to establish compartmentalized, policy-governed environments, potentially reducing the need to create one-off architectures for every workload.

Delivering mission-critical capabilities requires infrastructure that helps us move quickly without compromising security. Azure Enclave helps simplify the deployment of applications and services across secure, isolated environments, allowing us to provision mission workloads and expand capacity more efficiently while maintaining the controls our customers require. This enables teams to spend less time managing infrastructure and more time focused on delivering outcomes for the mission.

—Justin Shirk, Joint & Strategic Defense Lead at Accenture Federal Services

Our customers are looking for ways to modernize sensitive workloads without compromising security, compliance, or operational effectiveness. Azure Enclave helps address that challenge by reducing the complexity of establishing secure cloud environments and enabling trusted collaboration at scale. Azure Enclave is well-aligned to the security standards the US Government and its partners rely on, making compliance reviews and security conversations much easier compared to traditional deployment models. Combined with our expertise in cloud operations, security and compliance, we look forward to helping our customers accelerate their delivery while staying focused on what matters most: the mission.

—Carroll Moon, CEO and co-founder, CloudFit

Layered security, governance, and visibility

Azure Enclave is designed around a defense-in-depth approach. Virtual networking infrastructure is isolated by default, connections are explicitly configured and managed by the customer, and Azure Policy supports policy governance across communities and enclaves. Network segmentation, role-based access, controlled endpoints, defined permissions, and approval workflows provide multiple layers of control and oversight.

Centralized logging and built-in observability help teams monitor access, configuration changes, and data flows. Security alerts and analytics can be established with Microsoft Sentinel or other Security Information and Event Management (SIEM) tools. This will provide additional visibility into activity and system changes, helping mission owners, security teams, platform teams, application teams, and compliance stakeholders maintain a clearer view of an environment’s posture.

Azure Enclave complements, rather than replaces, an organization’s broader security and compliance program; customers remain responsible for configuring their environments, evaluating the service against applicable requirements, and meeting the obligations associated with their workloads. For US Government agencies and their partners, Azure Enclave provides a governed foundation for sensitive mission workloads, with accreditation efforts underway to comply with FedRAMP and DoD Impact Levels 4, 5, and 6, as well as Intelligence Community environments governed by ICD 503 requirements. Its tools are designed to support alignment with the applicable requirements as workloads move across classifications up to the Top Secret level.

Extending the model to worldwide governments and regulated industries

Government organizations worldwide may face similar requirements to control connectivity, govern access, monitor activity, and collaborate with external institutions or partners. Azure Enclave can provide a repeatable model for isolated environments, while each organization remains responsible for evaluating its deployment against the legal, regulatory, security, and operational requirements applicable to its workloads.

Highly regulated industries face similar collaboration and data-protection challenges. Healthcare providers, pharmaceutical companies, and research institutions can establish isolated environments for projects involving protected health information, clinical research, genomic data, and other sensitive datasets. Universities, laboratories, and research organizations can create shared environments for joint studies and multidisciplinary programs while maintaining separation among projects and applying consistent controls. Financial institutions can use isolated environments to support sensitive analytics, risk modeling, regulatory reporting, and controlled collaboration involving confidential financial information.

Azure Enclave provides capabilities for bringing authorized users, applications, services, and data into controlled environments while preserving boundaries between workloads.

Get started with Azure Enclave

Azure Enclave helps support sensitive collaboration by helping reduce the complexity of the underlying infrastructure, allowing teams to focus on mission, research, services, and business outcomes.

Organizations can start with a focused deployment for one workload and a defined group of users, then expand to include additional teams, applications, partners, or data sources. Azure Enclave supports that progression by enabling users to group enclaves with similar requirements into communities, deploy workload resources across subscriptions, and manage access at the application level. This structure gives organizations a path to evolve deployments as mission and operational requirements change while maintaining a consistent approach to isolation, governance, and observability.

Explore Azure Enclave to learn more about its capabilities, use cases, and pricing. Connect with your Microsoft representative to discuss how Azure Enclave can support your organization’s security, governance, operational, and collaboration requirements.

Azure Enclave

Streamlines the deployment and management of isolated cloud environments for sensitive workloads.

Get started

The post Azure Enclave is now generally available: Managed workload isolation for government and regulated industries appeared first on The Microsoft Cloud Blog.

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

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

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

Physical AI closes the loop between intelligence and action

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

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

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

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

Physical AI is creating value across the industrial lifecycle

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

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

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

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

What physical AI changes for operations leaders

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

Physical intelligence changes three enterprise models:

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

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

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

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

AI governance in manufacturing belongs in the operating architecture

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

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

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

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

Start with one bounded operation

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

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

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

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

Step in the Factory of the Future

Turn shop-floor signals into coordinated action across your factory.

Get the guide

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

These questions are especially important in Europe and around the world. Organizations want access to leading AI capabilities while maintaining confidence in how sensitive data, security, infrastructure, and operations are governed. Microsoft’s goal is to enable organizations to adopt AI while helping them maintain the level of control each workload requires to give leaders the confidence to move forward with AI.

Microsoft defines sovereign AI as the design, deployment, and operation of AI workloads under defined controls for data, access, governance, infrastructure, and operations. The right controls depend on the workload, which is why our framework is built around four principles: control, choice, flexibility, and resilience.

This framework is the focus of a new white paper we developed in collaboration with NVIDIA. It helps leaders translate the four principles into practical decisions; from the level of control each workload needs to the operating model that best supports it.

Begin with the workload

We see digital sovereignty as a risk-management principle. Start with the workload and ask: What data will it use or generate, and where can that data be processed? Who needs access to the system, including administrative access? What must keep working if connectivity is interrupted? And how easily must the organization be able to change models or infrastructure as its needs evolve? Consider these questions alongside the workload’s mission, regulatory obligations, and threat profile.

For many workloads, those requirements can be met in the public cloud. Others need greater operational control or infrastructure that remains under the organization’s authority. Some need to operate with limited or no connectivity. Microsoft Sovereign Cloud gives organizations options across the spectrum. Sovereign Public Cloud and Sovereign Private Cloud support different operating models based on the level of control a workload demands.

Sovereignty at every layer

Data location still matters, but an AI workload goes beyond stored data. It can include prompts, models, agents, outputs, and the systems that operate them. Leaders need visibility into which models are being used, the power to switch models if the need arises, where they run, who can administer them, and what happens when connectivity or operating conditions change.

Sovereignty can’t be applied at just one layer. Organizations need control over what an AI system can access and do, which models it can use, and where data and processing can occur. Microsoft provides capabilities across identity, governance, encryption, monitoring, confidential computing, and operational control to help organizations apply and maintain workload-specific controls. Building those requirements into the architecture from the outset, while preserving flexibility in model choice, can make it easier to adapt as workloads, technologies, and regulatory expectations evolve.

Microsoft and NVIDIA bring complementary capabilities across the AI stack. NVIDIA accelerated computing provides the performance required for advanced AI workloads, while NVIDIA Confidential Computing helps protect sensitive data, models, and AI workloads during processing. Together, Microsoft and NVIDIA provide security and control across the AI stack, from the underlying cloud infrastructure to the applications running on top of it.

That shared approach also informed the development of the Microsoft Sovereign AI white paper, created with contributions from NVIDIA to provide organizations with a practical framework for evaluating control, choice, flexibility, and resilience across their AI environments.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

Accelerating innovation with model and infrastructure choice

Model choice should start with the flexibility to use the best models available and adopt new models as capabilities, quality, availability, and access evolve, without having to redesign the underlying AI platform. AI is moving too quickly to assume that one model will remain the right answer for every workload. Decoupling the platform from the model gives organizations the freedom to evaluate and change models as technology advances and requirements evolve.

Microsoft Foundry and Foundry Local support development and deployment across a broad model ecosystem, so organizations can evaluate models against the requirements of the workload while maintaining consistent approaches to governance and operations. NVIDIA extends that choice across models, AI software, and accelerated infrastructure. NVIDIA AI Enterprise provides software, frameworks, and inference capabilities for production AI. NVIDIA’s model ecosystem, including Nemotron and physical AI models, gives organizations additional options for building and deploying AI. NVIDIA RTX PRO infrastructure extends that choice to the compute layer, supporting demanding inference, agentic, and reasoning workloads.

AI workloads will continue to become more capable and more distributed. Organizations need accelerated computing, software, and model choice that can follow those workloads across cloud, data center, and edge environments. Our work with Microsoft is focused on giving them that foundation with security and control built in.

Dave Salvator, Director, Accelerated Computing Product Marketing, NVIDIA

Model choice is only part of the equation. Organizations also need flexibility in the infrastructure, software, and environments that support those models. An organization may decide to adopt a different model, move processing closer to its data, or change where a workload runs as its requirements evolve.

Scaling from cloud to edge

Where AI runs is becoming as important as the models an organization chooses. For many workloads, the public cloud will remain the right environment, with access to scale and advanced AI services. Others need AI closer to sensitive data or physical operations. An industrial system may need low-latency inference on site. A government or critical infrastructure workload may need to continue operating during a network disruption.

Organizations should not have to reinvent their AI operating model for every environment. Sovereign Private Cloud, built on Azure Local and Foundry Local, extends AI capabilities into environments that remain under the organization’s authority, including connected, intermittently connected, and disconnected deployments. NVIDIA accelerated computing and AI software stack provides a common foundation across these deployment models. With NVIDIA infrastructure and AI stack available across Azure and Azure Local, organizations can bring AI training, inference and increasingly demanding agentic, industrial, and physical AI workloads closer to the data and operations they depend on.

The real test of deployment flexibility is when conditions change. Identify which functions must continue during a connectivity disruption, which can pause, and what data or services each depends on. Then test those assumptions under the conditions the workload may actually face. As AI expands into agentic, industrial, and physical systems, more critical decisions will happen close to data, equipment, and users. The infrastructure needs to follow.

Building for inevitable change

No one can predict exactly what AI requirements will look like five years from now. Models will improve, infrastructure will change, and regulations won’t remain static. New workloads will create requirements that are difficult to anticipate today. IT architecture choices should account for this.

Organizations benefit from a consistent foundation that allows workloads to move across sovereign environments while maintaining common approaches to identity, security, governance, and AI development.

Durable AI strategies will be built for change. Organizations need enough control to use AI confidently, paired with enough choice to keep adopting better technology, and an architecture that can adapt when requirements change.

Learn more about digital sovereignty at Microsoft

• Read the Microsoft Sovereign AI white paper to explore a practical framework for building AI around control, choice, flexibility, and resilience.
• Explore Microsoft Sovereign Cloud resources for your region.
• Read the Microsoft Sovereign Cloud in Europe white paper.
• Watch the Sovereignty Sessions webinar.
• Read why sovereign AI starts with sovereign data, and how Sovereign Private Cloud puts AI to work on sensitive data without giving up control from Douglas Phillips.

Learn more about Sovereign AI

Explore how Microsoft helps governments and regulated industries adopt advanced AI ​with NVIDIA technologies.

Download the Sovereign AI white paper

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An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, and AI agents under human oversight so capital markets teams can make faster, better-informed decisions. Rather than adding isolated AI tools, firms can use this platform approach to modernize work across research, due diligence, portfolio analysis, risk, compliance, and client service while preserving accountability and control.

Most AI initiatives in investment management begin with point solutions for research, due diligence, reporting, document review, portfolio analysis, or client service. However, these applications represent only the first stage. The larger opportunity is to redesign the investment lifecycle itself so that information, decisions, controls, and execution can flow continuously across the full ecosystem, built around persistent context.

Nowhere is this truer than in capital markets, where the explosive growth of private markets and the convergence of public and private asset distribution have created a complex, multi-stakeholder ecosystem characterized by intense data dependencies, operational complexity, and regulatory scrutiny.

Major global capital markets participants—from large asset managers to investment banks, sovereign wealth funds, and wealth managers serving high-net-worth clients—all seek integrated technology solutions that can unify data, accelerate AI-powered decision support, and help collapse fragmented workflows while ensuring regulatory compliance. Agentic AI can help orchestrate work across these stages, reducing manual handoffs and enabling more intelligent, responsive business processes.

As policymakers and market participants explore broader access to private-market investments, the industry faces a significant scale challenge. Legacy approaches to capital formation, allocation, and distribution will not support a global alternatives market projected to reach $32 trillion in assets under management (AUM) by 2030 unless firms transition to integrated, scalable operating models.¹

Why capital markets firms need more than isolated AI use cases

As AI becomes embedded across capital markets, value increasingly comes from connecting people, data, and processes across the broader ecosystem. This is why the strategic challenge is not deploying more AI tools, but rather creating an end-to-end, integrated platform that enables firms to leverage shared value while protecting core intellectual property.

The firms that succeed will treat AI as a platform for coordination, rather than isolated intelligence. By enabling secure collaboration across participants while preserving governance and accountability, institutional knowledge compounds, which strengthens each insight and decision over time.

What an AI-enabled investment platform should support

At its core, an AI-enabled investment platform should do three things:

1. Connect governed data and work context across portfolio data, market intelligence, research, operating metrics, client information, risk analysis, legal documentation, and internal knowledge. Bringing this context together in a governed environment can reduce fragmentation and create a more consistent foundation for decision support. It can also help teams understand how a conclusion was reached, what sources informed the work, and where human review remains necessary—an approach LSEG is putting into practice by using Microsoft Fabric to unify 30 systems and 1,200 datasets in a governed platform, improving data quality and reducing new-product development timelines from years to months.
   
2. Support reusable expertise and workflows. Organizations develop advantage through their investment processes, institutional knowledge, relationships, and accumulated experience. Reusable AI-assisted capabilities can help teams scale that expertise more consistently across research, portfolio management, investor engagement, compliance, and distribution activities, while keeping people responsible for material decisions.
   
3. Maintain trust, accountability, and control. As AI becomes more deeply embedded in investment workflows, identity, permissioning, auditability, data protection, lifecycle management, and policy controls need to move alongside information rather than being added later as separate layers. Suitability and explainability of investment decisions and advice must be built in by default.
   

This is how Microsoft’s AI platform approach becomes relevant. Microsoft IQ can provide governed context that agents can readily access within applicable permissions, while Microsoft 365 Copilot brings intelligence into familiar workflows. Microsoft Foundry and Copilot Studio help organizations build and manage reusable AI agents, matching each task with an appropriate model and cost profile. For regulated industries, that flexibility must be matched by strong controls. Microsoft Purview, Microsoft Entra, and Agent 365 support the governance, identity, security, and observability requirements needed to deploy agents responsibly at scale.

How agentic AI can redesign investment operations

1. From origination to investment committee

Investment context is often recreated as opportunities move through sourcing, diligence, risk and legal reviews, and investment committee preparation. An intelligence layer helps maintain a governed, permissioned view of information across that lifecycle, bringing together investment data, research and diligence materials, proprietary firm knowledge, and approved external market data as shared context rather than isolated artifacts.

AI-assisted capabilities surface in the flow of work such as in Microsoft 365 Word or Excel, rather than compelling users to switch applications. Copilot then becomes the orchestration surface: a deal-screening agent can pull private-company signals from PitchBook, while a valuation-review agent can cross-check against prior assessments, identify supporting evidence, and prepare materials for review. Investment decisions remain with people, but teams spend less time assembling information and more time evaluating it.

Genworth demonstrates the potential in investment operations, using Microsoft 365 Copilot to reduce the time required to construct complex portfolio trades from days to hours while strengthening decision support within existing workflows. The result is better decision-readiness, fewer manual handoffs, and a clearer institutional record that can be reused when similar questions, risks, or opportunities arise.

2. Creating a stronger organizational learning loop

The investment lifecycle does not end when a decision is made. Portfolio performance, advisor feedback, investor demand, compliance reviews, operating results, and market developments all generate valuable intelligence, but many firms struggle to make those signals reusable across future decisions. Important insights often remain buried within meeting notes, presentations, email threads, and individual workflows.

With a foundation of intelligence and shared context, decisions that traditionally took weeks to propagate through investment, risk, operations, and distribution functions can become immediately available. Over time, that can make the firm’s experience more durable: patterns from prior investments, recurring client questions, risk signals, and operational lessons can become part of the next workflow rather than remaining isolated in the last one. Morningstar illustrates this model by extending trusted research into reusable, governed agents within advisor workflows, reducing hours of preparation to minutes while keeping intelligence grounded in approved data.

As Martina Cheung, CEO of S&P Global, noted in her recent LinkedIn post, with S&P Global’s data, insights and analytics in Microsoft 365 Copilot workflows, an analyst, for example, can ask Microsoft Copilot for a company’s latest EBITDA trends, market capitalization or peer comparisons, with answers grounded in cited S&P Global intelligence. From there, that data can flow into everyday workflows, whether that’s building a comps table in Excel or preparing a competitive landscape deck in PowerPoint.

3. Collaborating without compromising confidentiality

Investment due diligence relies on coordination across teams. That collaboration requires access to information, but it also requires strong controls around confidentiality, entitlements, licensing requirements, and regulatory obligations. In practice, AI-assisted workflows can help drive scale while respecting organizational policies and access boundaries. That is especially important when workflows span internal teams, external partners, and data from different licensing or confidentiality contexts. As firms scale AI adoption, this balance between collaboration and control becomes increasingly important.

Making collaboration possible without weakening control depends on governance that travels with the information. Identity, permissioning, data protection, auditing, and policy enforcement should ensure that people and AI systems can access only the data they are authorized to use. Nasdaq Boardvantage demonstrates this principle in a particularly sensitive context: it uses AI to support confidential board workflows while preserving established security, access, and governance controls. The approach also delivers measurable efficiency gains, reducing directors’ reading time by up to 60% and overall board preparation time by 25%.

How governed data and human oversight build trust

Capital markets organizations have always competed through expertise, relationships, judgment, and privileged information. In the AI era, those advantages will increasingly depend on how effectively firms connect proprietary data, institutional memory, governance, and human expertise. These characteristics make them distinctive while creating value across the broader ecosystem.

Achieving that requires a platform that connects data, workflows, controls, and institutional knowledge without compromising accountability or trust. The goal is not to replace investment professionals, but to give them better context, stronger governance, and greater leverage across increasingly complex public and private markets. The organizations that lead in the AI era will be those that combine trusted data, human expertise, agentic workflows, and strong governance to improve decision-making across the enterprise.

In this future, the AI-enabled investment platform is more than infrastructure. It is the operating foundation through which investment knowledge is created, governed, and applied at scale.

The next competitive advantage in capital markets

• For a practical roadmap on how to turn isolated AI initiatives into enterprise-wide impact, explore Frontier Transformation in Capital Markets.

• For insights on bringing context into flow of work, read our blog, “AI in financial services: Bringing trusted data into the flow of work.”

• To learn more about scaling agentic AI in asset management with “Trust as infrastructure: How agentic AI is rearchitecting asset management at scale.”

Frequently asked questions

What is an AI-enabled investment platform?

An AI-enabled investment platform connects governed data, institutional knowledge, investment workflows, controls, and AI agents in a unified environment. It gives teams trusted context and reusable capabilities across the investment lifecycle while keeping people accountable for material decisions.

How is agentic AI used in capital markets?

Agentic AI can orchestrate multi-step workflows across research, due diligence, investment committee preparation, risk, compliance, and client service. Agents can gather approved information, analyze evidence, prepare materials, and coordinate handoffs, while investment professionals retain responsibility for review and decisions.

How can capital markets firms use AI while protecting confidential data?

Capital markets firms can protect confidential data by applying identity, permissions, data protection, auditing, and policy controls wherever information and AI agents are used. These safeguards limit access to authorized people and systems, while explainability and human oversight help ensure accountable decisions.

What should firms prioritize when modernizing investment operations with AI?

Firms should prioritize governed data, reusable workflows, clear accountability, and measurable business outcomes rather than deploying disconnected tools. A staged approach can begin with high-value use cases, establish controls and success measures, and then scale proven capabilities across investment operations.

¹ Preqin, “Private Markets in 2030: Data Driving the Future of Alternatives,” October 16, 2025.

The post The AI-enabled investment platform: How capital markets firms can redesign the operating model appeared first on The Microsoft Cloud Blog.

That commitment continues today, in a technological moment unlike any we have seen. AI can personalize learning, reduce administrative burden, accelerate research and expand access to high-quality instruction. It also brings real risk. Educators and parents are asking whether students are still doing the thinking, and whether the tools their children use are safe. Those are the right questions.

At its core, teaching and inspiring students to become their best selves is a deeply human endeavor. Education is built on a meaningful partnership among educators, students and families that helps unlock human potential and shape brighter futures.

No one company understands entirely how AI will reshape education. But uncertainty calls for more care, not less. That is why Microsoft is taking a holistic approach, grounded in five principles.

Safety, privacy, security and transparency by design
Educators remain in control and at the center of AI use in the classroom
AI designed to support students’ learning, not replace their thinking
AI strengthens education systems, from learning to discovery
Every student prepared for an AI powered future

1. Safety, privacy, security and transparency by design

Every AI system used in education should be designed with robust safeguards that protect student privacy, enhance safety, reduce risk – including safeguards that address bias – and provide the transparency that students, educators and parents deserve.

We believe institutions should never have to choose between innovation and trust.

Security, privacy and transparency are foundational to Microsoft’s approach in education.

That is why last week, we signed a landmark agreement with the American Federation of Teachers (AFT) and introduced a new Privacy & Safety Standard for Schools covering Microsoft Education products. The agreement represents the first time a major technology company and one of America’s largest teachers’ unions have come together to define what trusted AI should look like within these products. The Standard limits how Microsoft Education products can use student and educator data, requires human oversight for consequential decisions, provides meaningful transparency for families and holds Microsoft accountable when we fall short. It also affirms that schools retain ownership of the knowledge and ideas they create.

We did not build this Standard to keep it to ourselves. Students and institutions deserve these protections, whichever technology their school chooses. We invite others across our industry to meet this bar with us.

2. Educators remain in control and at the center of AI use in the classroom

Educators should remain at the center of teaching and learning. AI should augment educator expertise, not replace professional judgment. Educators should help shape the design of AI systems and be equipped with the skills and support needed to use AI in ways that support student outcomes.

Technology alone does not transform education. Educators do. At a school in the Bronx, special education teacher Ashley Hernandez is using Copilot to develop differentiated, sensory-aware materials for students with complex communication and medical needs. Technology helps her adapt learning experiences more efficiently and creates more time to work directly with students. Just as importantly, her experience has helped shape the future of the technology itself.

Educators know best what works in the classroom, and their voices are reflected in the products we create. Through our Education Insiders Program, educators and school leaders participate in private product previews and feedback sessions to help us improve tools before they’re released.

That is why we build products like Teach in Microsoft 365 Copilot. Teach is our education-first AI experience for educators. Designed around real instructional workflows, it helps educators adapt materials for different learners, build lessons that match their standards and spend less time planning. Our goal is not to automate teaching; it is to lift the administrative burden that gets in the way.

Customers tell us this works. Brisbane Catholic Education achieved a 275% increase in learner agency among at-risk cohorts. Since deploying Microsoft 365 Copilot, Miami Dade College has seen a 15% increase in student pass rates and a 12% drop in course dropout rates.

Educators need practical training, communities of practice and in-demand credentials. Through Microsoft Elevate for Educators, we are connecting educators with those resources and pathways. Over the last year, more than 17 million people have completed an in-demand AI skills credential. Through the Elevate for Educators program, we help educators build the understanding and confidence they need to teach about AI effectively and responsibly. We also offer practical materials that help educators introduce generative AI safely and responsibly to students.

3. AI designed to support students’ learning, not replace their thinking

AI systems in education should be grounded in learning science and designed to promote healthy student engagement, critical thinking, student well-being and improved learning outcomes.

According to our recent research on learning science, a significant risk of AI in education is cognitive offloading that creates the appearance of learning without the cognitive work that makes learning durable. This is a real problem. Students think they are learning when they are not.

Many commonly used tools were not designed for education. They prioritize speed over understanding, and many institutions have responded by limiting or blocking access entirely, even as students continue to use AI on their own. Without a purpose-built learning experience, trust becomes the barrier to enabling AI for students at all.

Education needs AI designed to support the purposes of education: helping people build knowledge, critical thinking, judgement and confidence.

Some of the most important learning happens when students are stuck. Difficulty builds understanding that lasts. AI that removes that struggle can quietly move the thinking from the learner to the machine. We must protect the work that helps learners grow.

Microsoft’s AI experiences in education are age-appropriate by design — including default-off access to Copilot Chat for K-12 students with administrator controls for age-based enablement as learners progress. In earlier grades, Minecraft Education builds foundational AI literacy while Learning Accelerators personalize support and provide timely feedback. As students advance, the Study and Learn Agent uses scaffolded questions and interactive practice to help learners work through concepts without doing the work for them. For older learners, Microsoft 365 Copilot introduces the same enterprise-grade tools they will use in their careers.

4. AI strengthens education systems, from learning to discovery

Strong education institutions create opportunity by identifying needs early, acting quickly and directing limited resources where they matter most.

AI can help institutions support students sooner, reduce paperwork, use information more effectively, strengthen security and accelerate research and discovery.

Today, important information is often scattered across different systems and teams. Used responsibly, AI can bring it together so schools, colleges and universities can make better decisions and take coordinated action.

Broward County Public Schools confronted a significant budget shortfall requiring operational efficiency across 235 schools. Despite being data-rich, the district lacked real-time insights. They deployed Microsoft 365 Copilot integrated with its Microsoft 365 ecosystem and identified significant operational inefficiencies that helped them achieve projected facilities savings of $40 to $50 million over five years. That savings could be reinvested in supporting their educators and students.

At University of North Carolina, medical researchers are using a secure research environment built on Microsoft Azure to conduct data-driven clinical research while protecting sensitive patient information. It’s now supporting 25 active studies and expanding access to advanced analytics and AI capabilities within a controlled research environment.

Innovative educational systems do more than adopt new technology. They drive coordinated action that results in better outcomes for learners, educators, researchers and communities.

5. Every student prepared for an AI powered future

Every student should have the opportunity to develop the knowledge and judgment needed to use AI responsibly and the skills to prosper from the emerging AI economy.

The most consequential AI divide of the next decade may not be who has access to AI. It may be who has access to AI skills and the pathways that turn those skills into economic opportunity.

The AI Economy Institute, Microsoft’s flagship think tank, recently released a book exploring how AI technologies shape the education experience and found that AI-related skills command wage premiums up to 40%, surpassing returns from advanced degrees.

AI Literacy is now nonnegotiable across every discipline. The importance of understanding AI fundamentals, limitations and ethical implications — including healthy skepticism toward outputs — is universal.

Offerings like Minecraft Education AI Ready Skills help students build that foundation through a game-based pathway culminating in a credential aligned to CSTA and ISTE standards

Our partnerships with the American Federation of Teachers, the National Education Association, National Applied AI Consortium and North America’s Building Trades Unions help to ensure this work is informed by educators, workers and community members alike. Their insights help guide the tools we build, the skills we teach and the pathways we create so that students can succeed in an AI-powered economy.

Expanding our NABTU partnership, we recently introduced the Jobs Machine to help parents, students and high school counselors explore the skilled trades as a valuable education and career pathway.

In higher education, institutions are turning AI literacy into curriculum. Auburn University created a “Teaching with Artificial Intelligence” course now available to nearly 10,000 faculty at more than 90 institutions. Babson College built an interdisciplinary AI lab — The Generator — integrating Azure OpenAI tools and Copilot assistants across every faculty member and student. And the University of South Florida built a student ambassador program that has driven 55 to 60% time savings on some tasks and now demonstrates practical AI use in real department workflows.

The aim is not to train learners for one fixed set of tools. It is to help them develop the knowledge, agency and judgment to keep adapting throughout their lives.

The future of education will not be defined by technology alone. It will be defined by how effectively we bring together innovation, trust and human potential.

Building the future of education, together

Microsoft will continue investing across all five principles and, more importantly, alongside the educators, learners, institutions, governments, labor organizations and communities shaping education’s next chapter.

AI is already changing how people learn and work. The responsibility and the opportunity before all of us is to ensure it strengthens learning, expands opportunity and keeps people at the center.

That future is already taking shape, and we are committed to building it together.

Justin Spelhaug leads Microsoft Elevate, a global initiative helping education, workforce and nonprofit organizations expand opportunity and help prepare people for the AI economy. During his 28 years at Microsoft, he has held leadership roles advancing technology’s impact for people and communities. He began his career serving in the U.S. Marine Corps.

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The next evolution of Microsoft 365 for government brings productivity, AI, agents, security, identity, compliance, and governance together for organizations operating in Government Community Cloud (GCC) environments.

Across government, leaders are being asked to modernize services, address evolving cybersecurity needs, and help their teams meet growing demands simultaneously. AI tools can play an important role, but technology alone is not enough. Agencies need intelligence grounded in how they work, agents that extend that intelligence, and a control plane to help govern AI adoption securely. Innovation and trust must advance together.

Today, we are announcing the launch of Microsoft 365 G7, the Frontier Suite for government, to Government Community Cloud (GCC) environments. G7 extends the Intelligence + Trust vision introduced with Microsoft 365 E7 to government agencies, bringing together productivity, AI, agents, security, identity, compliance, and governance to support government organizations as they help accelerate mission outcomes and further mature their governed approach to adopting AI at organizational scale.

Both Microsoft 365 G7 and Agent 365 will be available for GCC customers to purchase beginning October 1, with additional purchasing options available November 1, and capabilities expanding in phases. Customers can begin licensing, planning, and adopting available experiences at launch, with additional workloads rolling out as they complete required GCC authorization milestones. This phased approach advances innovation with the rigor government missions demand.

Built on the trusted foundation of Microsoft 365 for government

Microsoft 365 G7 extends the Microsoft 365 foundation agencies know, starting with Microsoft 365 G5 solutions that bring together productivity and collaboration with information protection, security, and compliance capabilities. Microsoft Entra Suite builds on this with advanced identity, access governance, and network security capabilities for users and agents.

G7 is designed to support Frontier Transformation across the U.S. public sector by providing capabilities that agencies can use as they move from isolated AI experimentation toward governed adoption at organizational scale. It connects intelligence to the flow of everyday work, gives agencies a foundation for creating and using mission-focused agents, and provides the controls that IT and security leaders need to manage people, agents, data, and access with confidence.

AI embedded in the flow of work

AI should make everyday work simpler, not create another destination. G7 brings Microsoft Copilot and AI-powered Microsoft 365 experiences into the applications government employees already use. Work IQ grounds these experiences in organizational data, context, and tools with governance capabilities designed to operate in accordance with applicable permissions, so Copilot can provide assistance informed by an agency’s work context and help users complete mission-related work.

At general availability, G7 will bring the value of Microsoft Copilot into a unified offering designed for government. Government teams can use Copilot to support research and analysis, find and synthesize organizational knowledge, and draft, refine, and collaborate on content in the Microsoft 365 apps and experiences they use every day. 

In the months following GA, G7 will also benefit from a surge of new capabilities rolling out in Microsoft Copilot across our US Government clouds, including the latest commercially available GPT models, Work IQ memory and personalization, Edit with Copilot across Word, Excel, and PowerPoint, and Copilot Cowork for longer-running, multi-step work. To learn more about how these advances can help public servants navigate complex work and support their organizations’ mission priorities, read our companion announcement on the Microsoft Public Sector blog.

Agents at the mission’s edge

Agents can support specialized government work, from research and policy analysis to case management, grants, inspections, and constituent service. G7 provides an agentic foundation for government, with mission-focused agents grounded in Work IQ, and operating in accredited government clouds with policy-governed access for connecting to approved agency systems and data.

Agencies can start with Microsoft agents available in GCC, including Researcher and Analyst, and use Agent Builder to create experiences closer to the work. For specialized missions, teams can build custom agents with Microsoft Copilot Studio. Microsoft 365 Copilot Connectors bring agency knowledge from other systems within reach, while managed connections provide secure access to approved data and line-of-business applications. Natural-language creation broadens who can build, while organizational controls keep deployment aligned to agency standards.

Agent 365: The control plane for AI adoption

The speed of agent adoption creates enormous opportunity, but without the right controls it can also create new blind spots. Leaders need to know which agents exist, who owns them, what they can access, and whether they are operating as intended. Agent 365 provides the control plane for agents in G7, helping organizations discover, register, and manage agents and take governance actions such as deploying, blocking, or removing them.

At general availability, Agent 365 will give organizations a unified foundation to discover, identify, and govern agents, with core controls to manage how they are deployed and used. Over time, we expect Agent 365 capabilities to evolve across security, risk management, and lifecycle governance. For detailed information about GCC feature availability, customers can refer to the Microsoft Agent 365 service description. As additional services complete required GCC authorization and readiness milestones, their status will be updated there.

Advancing the mission with confidence

AI creates tremendous opportunity for government teams to build capacity, strengthen resilience, and respond more effectively to the people they serve. Realizing that opportunity requires leadership, workforce readiness, responsible governance, and technology that unites intelligence and trust.

Microsoft 365 G7 advances Frontier Transformation for government, giving agencies a path to empower employees, scale agents and adopt AI securely as capabilities expand. By combining AI in the flow of work, mission-focused agents, and a unified control plane, agencies can advance modernization with controls designed to support the stringent trust, security, and governance needs of public service and national missions.

Public sector transformation is about more than technology. It is about helping people serve communities and advance the mission. With G7, Microsoft builds on its longstanding government partnership to help public servants apply AI with the intelligence to support the mission and the controls to move forward with confidence.

The post Introducing Microsoft 365 G7: Intelligence + Trust for the mission ahead appeared first on The Microsoft Cloud Blog.

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

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

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

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

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

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

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

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

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

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

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

The return on intelligence is already being realized

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

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

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

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

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

When intelligence is shared, innovation spreads

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

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

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

Step into the Factory of the Future

Explore how intelligence connects every part of the factory

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

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

These advancements help enable government organizations to accelerate mission outcomes, empower their workforce, and deliver services more effectively, all while meeting the rigorous security, compliance, and operational requirements their missions demand.

Moving from AI interactions to mission-ready agents
Government organizations often need more than a simple prompt-and-response AI experience. They need AI solutions that can understand their mission, use approved information, follow established processes, and keep people involved in important decisions.

The Microsoft Foundry Agent Service helps organizations build these kinds of AI-powered assistants in Azure Government. These agents can be used with trusted data and tools, directed to follow organizational rules and policies, and support work in secure mission environments.

This makes it easier for agencies to create AI solutions that can be reused across different teams and missions, rather than building a new system for every need. For example, agents can help employees find and summarize information, support case management and service delivery, assist with planning and operations, and provide guided support for employees and the public.

AI agents are designed to help people, not replace them. People remain responsible for important decisions and actions, while agents handle time-consuming tasks, help coordinate complex processes, and provide the right information when it’s needed. This allows government employees to spend less time on the leg work and more time focused on serving their mission and constituents.

Bringing advanced models to government missions
The availability of GPT-5.6 Sol, Terra, and Luna in Azure Government gives organizations the flexibility to choose the AI model that best fits their needs, whether they’re tackling complex analysis, supporting everyday productivity, or delivering fast and responsive experiences at scale.

These models can help government organizations:

Review and summarize large amounts of mission-relevant information more quickly.
Build AI-powered applications and agents that assist employees and improve services for the public.
Speed up software development, research, planning, and operational work.
Create experiences that can understand and work with multiple types of information, such as text, images, and other content, while providing data management and oversight capabilities.
Azure Government provides a trusted environment for adopting these capabilities responsibly. Organizations can take advantage of advanced AI and leverage the enterprise capabilities in Azure to help protect sensitive information.

Building a trusted AI foundation
AI adoption in government must be grounded in trust. Microsoft continues to invest in the controls, platforms, and services that help enable customers to innovate responsibly, protecting sensitive data, supporting compliance obligations, and providing the operational visibility needed for mission-critical deployments.

With GPT-5.6 Sol, Terra, and Luna and the Microsoft Foundry Agent Service now available in Azure Government, organizations can use these models directly or through agents to turn AI experimentation into scalable mission impact.

We look forward to working with our government customers and partners as they use these capabilities to build the next generation of trusted AI solutions.

Learn more about GPT-5.6 and Microsoft Foundry Agent Service and get started today!

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