The Copilot app now has three new capabilities: Home is your new starting point, where Chat and Cowork come together — and with Office in Copilot, the full power of Word, Excel and PowerPoint is now built into the experience. Code lets everyone build their own solutions with the tools to run them safely, and it’s powered by the same underlying technology as GitHub Copilot. And Autopilot is a persistent, proactive and personal agent that keeps working even when you’re not. Home and Code will start rolling out in our Frontier program in the coming weeks and Autopilot is expanding to private preview at the end of the month.

We’re also excited to share new FinOps for AI capabilities to help you manage spend and get the most value from Copilot and agents.

A new experience for every mode of work

Home is your new starting point in Copilot, where Chat and Cowork come together in one place. As work moves constantly between meetings, chats, documents, emails, apps and agents, Home is where you get your bearings, keeping you grounded and moving forward. You can review recent activity, get suggestions on what Copilot can help with, and pick up work right where you left off. Chat is instant and conversational — ready for quick questions, lookups, draft-creation and keeping you in the loop on every turn. Cowork is for work you delegate. You define the task and it runs end-to-end to return a completed result: an RFP response, a launch kit, a customer briefing, a financial close package or any other complex work worth iterating on. And coming soon, you won’t need to choose a mode: simply state what you need to accomplish, and Copilot will route the work to Chat, Cowork or Code — whichever capability is right for the job.

We’re also bringing the full power of Word, Excel and PowerPoint to Copilot with Office in Copilot. Now you can get to real work fast: no broken formatting, no stray versions, no rebuilding the context in another app. From Home you can ask Copilot to draft a launch brief, model a budget, build a deck, or pick up a file your team is already working on, and it creates or updates a real, editable document, workbook or presentation — live for your whole team, not just you. Refine it side by side with your conversation, or @mention a teammate and their edits show up as they make them — things stay in sync between Copilot and the Office apps, so progress travels with you wherever you work.

If you haven’t used Copilot in the Office apps lately, take another look. PowerPoint now keeps decks on-brand automatically and enables you to fine-tune slides and images or quickly apply a custom template. When Copilot or another collaborator makes edits in Excel, you can see exactly what changed and why, so you’re up to speed quickly, and Copilot recommends the best chart to visualize your data. And across Microsoft 365, Skills help bring specialized expertise directly into the apps, like financial skills in Excel and legal skills in Word, and enable organizations to scale best practices through reusable, role-specific workflows.

Code moves solution-building outside the realm of developers alone. To date, the unit of knowledge work has been the file: the document, the spreadsheet, the deck. Those aren’t going anywhere, but Code adds a fourth: small, purpose-built solutions anyone can create to get a job done. Learning to build them is becoming as basic a skill as writing a memo or modeling a budget.

Describe an app, tracker, dashboard, automation or workflow in natural language, and Copilot chooses an approach and builds it — from persistent desktop widgets for quick reference and interactive dashboards for exploring data and testing outcomes to cloud-hosted internal apps you can share with your team.

Powered by the same underlying technology as GitHub Copilot, Code runs in a sandboxed environment and can be hosted securely within your tenant. With Microsoft IQ, everything you create is grounded in the context of your work, and plugins sync automatically. Your software developers continue to use GitHub Copilot for their day-to-day work with more connectivity into the Copilot platform. Code is rolling out to Frontier at the end of the month, with broad availability in the coming weeks. It will be in preview for Microsoft 365 Premium and Pro subscribers later this year.

As it gets easier and faster than ever to build in Copilot, we’re introducing Microsoft Copilot Managed Runtime: hosting infrastructure that lets code run safely right inside your company’s Microsoft 365 environment. It’s governed by IT but easy for everyone else: share an app with teammates, connect it to live data and access it from anywhere. This same foundation enables apps built in Cowork, Code and Copilot Studio, and we’re opening it up to third-party and pro-code developers, too. Copilot Managed Runtime is now in preview and will also be accessible inside Code.

Autopilot, previously called Scout, is your digital teammate. And it works for you: Give it a name, a role and a goal, and it goes to work — watching channels, following up on threads, running recurring work and picking a project back up days later, without waiting for a prompt. It can do things like set up and manage a full supplier review process: building the schedule and workback plan, then handling prep, meetings and follow-ups on its own, right down to reaching out to stakeholders for updates. And Autopilot is cloud-hosted, so it keeps working while you sleep or your attention is elsewhere — no constant monitoring required.

Autopilot lives in your tenant with its own identity, memory, computer and workspace, and it’s built on Microsoft IQ so it understands how your organization actually works. It shows up where people already work — Teams, Outlook, chats, channels and documents — so you can @mention it like a colleague, with permissions, audit and governance behind it. You set the objective and boundaries; Autopilot handles the rest while keeping you informed and in control.

Expanding your business context

Copilot is grounded in Microsoft IQ, the unified intelligence platform for enterprise AI across your Microsoft stack, connecting how the business operates with the knowledge it holds into one real-time view for Copilot and your agents, governed and secure.

Today we are expanding the business context available to Copilot. Fabric IQ brings trusted context from your data, including the 20+ million semantic models in Power BI into Copilot. It grounds Chat and Cowork, and is generally available in both today, with Code integration coming through the Frontier program soon. Rolling out over the next month in public preview, Copilot is now grounded in data and workflows from Dynamics 365 and Power Platform. So, when a salesperson is working on a proposal, for instance, Copilot can incorporate data like deal history and support tickets right into the document, with no app-switching or distracting lookups required.

You also need a trusted way to connect Copilot to the actions and data that move your business forward. Plugins extend what Copilot can do, bringing together skills, connectors and other capabilities so users can complete more workflows without leaving the apps they use every day. Rolling out now and generally available across surfaces in the coming weeks, a new plugin registry will bring Microsoft, partner and custom-built plugins together in one unified catalog. IT can approve and manage plugins centrally, while developers and partners can publish once to extend Copilot across supported experiences. The result is a simpler way to introduce trusted capabilities at scale, with the control your organization needs.

Managing AI spend

For everyday AI — quick answers, first drafts, summaries and analysis — you want the best possible quality at a fixed cost. A user subscription license (USL) gives you exactly that: Copilot in Chat and across Word, Excel, PowerPoint, Outlook and Teams; model selection; and new capabilities and models. Auto is at the heart of the USL. Auto weighs accuracy, speed and cost on each request to route to the model best suited for the job. Agentic work requires a different type of spend, one that gives you the best quality possible with the most advanced models. Usage-based billing (UBB) lets you have full control to choose models and match cost to value. Cowork, Code, and Autopilot, new long-running agentic capabilities, and frontier models like Astra and Fable all run on UBB. As AI capabilities and the ways people use them continue to evolve, we’re evolving Copilot pricing alongside them. Read more here.

FinOps for AI is a shared discipline for managing AI spend and optimizing it for business value. We are committed to enabling organizations to make the most of their AI spend, and, today, we are introducing new capabilities across Agent 365, Insights and Microsoft Copilot to help do that. Cost management in Agent 365 is expanding beyond Cowork and Work IQ APIs to include Code and Copilot Managed Runtime, with support for agents built in Microsoft Copilot Studio planned for October. Admins can manage spending policies at scale with API access; route credit requests from end users into existing approval and automation workflows; and set which model families are available to different groups of users — those settings also shape which models Auto can select from. Business leaders and admins can understand which Cowork tasks are generating the strongest outcomes and determine where to sustain, expand or optimize usage. And finally, end users can view credit usage, remaining balances and usage history directly in Microsoft Copilot, so they can better manage their own AI usage within organizational guardrails. We are committed to investing in FinOps for AI to help organizations stay in control of spend, understand value and optimize their AI investments for impact.

A sneak peek at what’s next

These announcements are just the beginning. We’re excited to share future Copilot experiences that make Copilot more personalized and proactive, and help the team move forward together.

Coming soon in Home, Today is a proactive, personalized command center across mail, calendar, Teams threads, meetings and tasks. It shows you what you missed, what needs attention now and what can wait. Today doesn’t just surface the work, it gets work done. The draft is written and ready for you to edit; the schedule change is already proposed; and the forgotten follow-up is waiting for you to send it, complete with context so you can catch up quickly. Today enters private preview in October in Copilot, and will be coming to Outlook and Teams as well.

And now when you invoke @Copilot in Teams, your entire team gets the shared context and permissions of the channel, group chat or meeting. Ask why a product decision was made, and Copilot pulls the background from across conversations — no digging, no re-explaining. When that decision ripples into manufacturing, packaging and launch timing, Copilot goes further: it offers to prepare a cross-functional review with dependencies, owners, open questions and decisions, so everyone can see how their work connects and what still needs resolving. The team stays aligned and the project keeps moving forward. The @Copilot capability will be in private preview in Teams by the end of the month.

Get started

With the new Copilot, you now have one place to ask, delegate, build, automate and more. It’s rolling out now through the Frontier program, with new capabilities arriving continuously. Check back here for the latest.

There is so much more coming to Copilot. Join us at Microsoft Ignite — November 17–20 in San Francisco and online — to learn about the latest Copilot innovation and how it’s empowering people and organizations to reimagine work and get value from AI.

The post Introducing the new Copilot with Home, Code and Autopilot appeared first on The Official Microsoft Blog.

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Our employees have experimented with AI, while leaders have set ambitious goals and challenged teams to reimagine how we work to achieve more than was possible before. We created cross-company councils spanning corporate functions, go-to-market and engineering to share best practices and learn together. We asked everyone to challenge their fixed mindsets and embrace the growth mindset we have cultivated for more than a decade. That work is producing measurable results: for a sales team deal close rates increased by 20%¹; selected supply-chain workflows cut cycle time by up to 75%²; and a nine-person engineering team shipped an initial product release in 35 days.³

As proven approaches emerged, we codified them into case studies so we could accelerate transformation, scale what worked and learn from what did not. Just as importantly, we knew that if we wanted to help customers realize the full value of AI, we had to do the work to transform ourselves first. Our own first-hand experience needed to be a source of learning we could share with others.

We have been sharing Microsoft’s Frontier Playbook with customers as a practical guide to our AI transformation journey, including what we’ve learned, what has worked so far and where we’ve grown from failures. Drawing on hundreds of AI transformation efforts across the company, the playbook captures what we are learning as we redesign work, build new capabilities, measure impact and help people grow alongside AI.

The playbook also reflects important truths: transformation is hard, and learning is the durable superpower. Among the many insights gained from our successes and our failures, five lessons consistently stand out.

1. Start with the business outcome, not the technology

We initially treated AI like a traditional technology rollout: deploy the tools, provide training, drive adoption. We learned that access and usage do not equal transformation: a tool licensed to over 200,000 people does not change how the work gets done.

Early sales usage made this clear. Despite broad deployment, usage plateaued and impact did not materialize. Rather than push adoption harder, the team started from the business goals — deliver more value to customers, win deals and improve employee experience. They mapped how account managers spent their week and identified the best tools for the moments that mattered most: an Analyst agent for pipeline, a Deal agent for deal packages and Researcher for deep customer understanding. Weekly peer-led huddles turned experimentation into habit and scaled best practices to everyone on the team. Within the group, adoption of priority use cases tripled, revenue per account manager rose 9.4% and close rates were 20% higher.⁴

Success still required investment in helping people build new skills, experiment with new ways of working and learn from one another. But when leaders focused on a clear business outcome and what mattered most to the person doing the job, rather than AI adoption itself, conversations shifted from using AI to creating value.

2. Redesign the entire workflow, not just individual tasks

One of our biggest lessons came from reimagining workflows end to end, not applying AI to existing steps. Early efforts helped people complete familiar tasks faster but rarely transformed outcomes. Adding agents to a broken process still leaves a broken process — speeding up one step just creates a longer queue at the next.

Our cloud supply chain team simplified its processes before reimagining them with agents. Supply chain experts and engineers worked side by side, first mapping and simplifying end-to-end workflows, then created a single source of truth so every agent reasoned from the same data. With that foundation in place, they deployed more than 100 purpose-built agents across planning, sourcing, fulfillment and logistics. Those agents investigate shifts in demand and model capacity while comparing transportation options across air, land and sea on cost, timing and carbon impact — complexity few teams could manage alone. Cycle time fell by up to 75% in selected workflows.

The shift isn’t only about speed, it’s about adding new value by improving what the team can see, anticipate and act on. Within defined permissions and approval thresholds, agents have progressed from answering questions to helping planners update or cancel purchase orders directly. Planners who once spent five to seven days tracing why a demand plan changed can now get an answer in hours, and sometimes in less than 20 minutes. That makes it possible to analyze changes as planning cycles unfold, model more scenarios, build better contingency plans and identify risks earlier — helping the team make better decisions and improve the performance of the supply chain.⁵

We are seeing the same shift in software engineering, where the opportunity extends beyond generating code faster to redesigning how teams plan, build, test and evaluate products with agents across the workflow.

We’ve found the largest gains come when teams step back and redesign how work should flow across people, process and technology from start to finish — including what agents can access and do, how their actions are monitored and where people must review, approve or intervene. AI is most powerful when all three advance together.

3. Put employees at the center of transformation

The people who do the work know where processes break down, where judgment matters and where AI could help — insights that no process map can fully capture. Their expertise needs to shape transformation from the start. Leaders are responsible for setting a clear ambition, helping employees build the skills to contribute and giving them a meaningful role in deciding how the work changes.

At Microsoft, we are creating hands-on ways for employees to build those skills. An early in career development program PRAISE pairs emerging engineers with experienced preceptors and AI-assisted learning, helping newer engineers contribute to complex work while developing their craft.

Camp AIR is a multi-week AI transformation accelerator that helps cross-functional teams learn new AI capabilities while redesigning how they work together around a real business challenge. An early pilot taught us that AI transformation is a team sport and that tools and training alone were not enough. While individuals could learn new technologies independently, meaningful and lasting change occurred when teams learned, experimented and adapted together. Teams needed candid conversations about how AI would reshape roles and workflows, along with the freedom to experiment safely and build confidence in new ways of working. We could not future-proof all jobs as they exist today, but we could help employees future-proof their careers by developing the skills, adaptability and mindset needed to succeed as work evolves.

That lesson became a core design principle of Camp AIR and has helped the program scale to more than 3,000 engineers across that organization.

The team behind Copilot Cowork shows what this can look like in practice. The nine-person team of engineers, designers and product managers was given the freedom to rethink how a product gets built, with AI embedded from day one. Working alongside agents, their roles expanded into what they called meta-engineers, meta-designers and meta-PMs. Together, they shipped an initial release in 35 days and documented what they learned so other teams could build on it.⁶

In our experience, leaders set outcomes and accountability, while the people closest to the work see where AI adds value and where human judgment must stay central. Managers connect the two, and their role-modeling and support has been one of the strongest predictors of success. Our research bears this out: when managers actively model AI use, reported value from agentic AI rises 17 points and trust in it rises 30 points — and employees on teams where managers create psychological safety are 1.4 times as likely to be high-frequency users of agentic AI. Where employees have context, capability and agency, they can become the engine of transformation.

4. Use AI to expand what people can do

We started where many companies start: automating tasks to increase efficiency. That value is real, but only the beginning. We realized over time the larger opportunity is “Capability Add”: combining human and AI strengths to achieve outcomes that were previously impractical or impossible. Think of it as an equation: CI + AI = CA. Continuous improvement takes waste out and AI adds capability in. Together, they produce Capability Add — output with higher strategic value. Continuous improvement alone results in a leaner version of the old company; Capability Add creates a different one. This equals transformation.

In our latest Work Trend Index, 58% of AI users said AI helps them do work they could not do before — it was 80% among advanced users. In our case examples, it looks like predicting a quality failure instead of catching it or exploring twenty options where a team had time for three.

This broader view shapes how we measure ROI. We like to say efficiency is the floor; capability is the ceiling. Usage and adoption, along with improvements in speed, quality and cost, are important signals. But the ultimate measure is whether AI improves customer and employee experiences, drives growth and innovation, reduces risk and expands what the organization can accomplish. Because those outcomes can take time to emerge, we also track leading indicators. For account managers, that might mean more time with customers, a stronger pipeline or better win rates before revenue gains fully materialize. For an engineer, it is not how much code AI produces, but whether the team is building better products faster.

We encourage teams to look beyond efficiency and ask, “What could our people accomplish that they couldn’t before?”

5. Combine human and AI capability to create a continuously learning organization

At Microsoft, we have long believed that a growth mindset is a source of competitive advantage. Our willingness to challenge assumptions, experiment, learn and adapt has shaped our culture for more than a decade — and provides an even more important foundation for the AI era.

AI is giving that idea new meaning. As a thought partner, it can help employees explore new ideas, build skills and learn in the flow of work. It can make learning faster, more personalized and more accessible, helping people take on challenges that once felt out of reach.

But we have also discovered that learning moves in both directions. People help AI become more useful by bringing the ambition, context, judgment and feedback that teach it how work gets done and what good looks like. They set the requirements, correct what falls short, determine where human judgement is essential and remain accountable for how AI is applied.

In our People organization, for example, teams are continuously improving agents to support the many cross-company activities involved in onboarding a new employee. Our team guides the process and sets the bar for what good looks like, determines where human judgment is needed, addresses issues and governs the overall system.

Over time, this learning loop turns individual insights into organizational capability. AI helps people learn faster, while people help make AI more useful within their organization. We recognized that the continuous learning loop we were creating was also generating unique institutional knowledge, and that retaining control of it would be critical to our long-term advantage. And that every organization should be able to build its own continuous learning loop without becoming dependent on any one model provider, while retaining control of its knowledge and how it is embedded in the models and systems it uses.

Having access to the best models will not be enough. The organizations that lead will be those that build the capacity to accelerate learning — where people and AI improve together, and where the knowledge they create becomes a source of lasting advantage.

***

These lessons come together in three practical recipes: Persona Acceleration for a role, AI-Powered Process Redesign for a workflow and AI-First Possibility for a greenfield opportunity. Distilled from hundreds of transformation efforts, they are patterns teams can adapt — often in combination — to move faster and avoid reinventing the journey.

Access to AI will not be the differentiator. The advantage will come from an organization’s ability to empower and engage employees, redesign work, govern AI responsibly and operationalize what works. The results we are seeing — from increased revenue and a more efficient supply chain to faster product development and better employee experiences — show what is possible when those pieces come together. But achieving these results requires more than deploying AI. It requires the intentional alignment of people, processes and technology. That is the hard work of becoming a Frontier Firm — and why we created Microsoft Frontier Company: to bring what we are learning from our own transformation to customers and work alongside them as they navigate their transformations.

Read Becoming a Frontier Firm: Our Frontier Playbook for the frameworks, transformation recipes and lessons from Microsoft’s Customer Zero journey.

Kathleen Hogan is Executive Vice President and Chief Strategy and Transformation Officer at Microsoft, where she leads the company’s enterprise-wide strategy and transformation agenda, accelerating Microsoft’s evolution into a Frontier Firm. Previously, she served as Chief Human Resources Officer and Corporate Vice President of Microsoft Services.

NOTES

¹ Internal Microsoft sales team data based on 687 sellers of Microsoft 365 Copilot from Jan. – June 2024, as compared with sellers with low usage of Copilot. Regular usage of Copilot means sellers who use Copilot daily at least 50% of the time during the testing period.

² Based on Microsoft internal analysis of work led by a 150+ person cross-functional team between September 2025 and August 2026. As of September 2026, more than 111 agents had been deployed across cloud supply-chain workflows. Across 5 monthly planning cycles measured between April 2026 and August 2026, average cycle time declined from approximately 10 to less than 2.5 business days. Separately, across 20+ demand-plan investigations each month, average time to produce a human-validated explanation declined from five to seven days to less than a few hours, with some completed in less than 20 minutes. Results are specific to these workflows and measurement periods.

³ Based on internal project records. The 35-day period was measured from the team’s formal project kickoff to delivery of the initial release in Spring 2026. This result reflects one project undertaken by a dedicated cross-functional team and is not a companywide product-development benchmark.

⁴ Internal Microsoft sales team data based on 687 sellers of Microsoft 365 Copilot from Jan. – June 2024, as compared with sellers with low usage of Copilot. Regular usage of Copilot means sellers who use Copilot daily at least 50% of the time during the testing period.

 ⁵ Based on Microsoft internal analysis of work led by a 150+ person cross-functional team between September 2025 and August 2026. As of September 2026, more than 111 agents had been deployed across cloud supply-chain workflows. Across 5 monthly planning cycles measured between April 2026 and August 2026, average cycle time declined from approximately 10 to less than 2.5 business days. Separately, across 20+ demand-plan investigations each month, average time to produce a human-validated explanation declined from five to seven days to less than a few hours, with some completed in less than 20 minutes. Results are specific to these workflows and measurement periods.

 ⁶ Based on internal project records. The 35-day period was measured from the team’s formal project kickoff to delivery of the initial release in Spring 2026. This result reflects one project undertaken by a dedicated cross-functional team and is not a companywide product-development benchmark.

The post What we’ve learned from Microsoft’s own AI transformation appeared first on The Official Microsoft Blog.

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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.

1. Safety, privacy, security and transparency by design
2. Educators remain in control and at the center of AI use in the classroom
3. AI designed to support students’ learning, not replace their thinking
4. AI strengthens education systems, from learning to discovery
5. 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.

The post Microsoft’s commitment for AI in education appeared first on The Official Microsoft Blog.

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Every industry has a word that shapes how it thinks. For pilots, it’s safety. For insurers, it’s risk.

For the semiconductor industry, it’s yield.

Yield does not ask how elegant the solution is, how many years it took or what the roadmap promised. Rather, it asks one simple question: What useful output did we produce?

For more than 60 years, the semiconductor industry has asked that question, relentlessly maximizing the number of usable chips produced from every wafer. Generation after generation, wafer after wafer, it is precisely that discipline that turned the transistor from a laboratory curiosity into the foundation of modern life.

Today, we need to apply the same principle to the unprecedented resources that the world is pouring into AI: capital on a scale once reserved for nations, gigawatts of power and record-breaking fabs and datacenters.

The question that will define this decade is the same one this industry has always asked: What actually comes out? Not just chips and tokens, but as affordable intelligence, as work that matters and as outcomes that improve lives.‌

That is the yield imperative.

The limit of more 

AI has proliferated with remarkable speed, at a rate of adoption faster than the internet, the PC or even the smartphone. Yet, global penetration still stands at just 18% of the working population, and the vast majority of that usage is chat-based. As systems move from answering individual prompts to reasoning, planning, using tools and executing longer agentic workflows, the infrastructure equation changes dramatically. A single agentic task can use more than 3,400 times as many tokens as a typical chat interaction.

We are only in the early innings of agentic adoption, and the infrastructure is already strained. Power is setting the limits on what we can build and when. Packages and racks are growing larger and denser. Memory is becoming an even tighter constraint.

For years, the industry’s rational answer to each new requirement resulted in more: more silicon in the package, more memory beside it, more power to feed it and more fiber to connect it. Each generation delivered meaningful progress. But when each new gain requires more input than the one before it, we are on a treadmill. It moves only as long as we keep adding to it.

I believe we need to pursue two paths forward. The first is evolutionary: we continue improving the architectures we have today, driving incremental efficiency, utilization and economics within each generation. The second is transformational: changing the curve itself with innovation in new architectures, new materials and new approaches to system and model design.

The history of our industry is defined by transformations like these. When increasing CPU clock speeds ran into the power wall, we moved to multicore processors. When planar NAND reached its limits, memory went vertical.

And now, once again, we have an opportunity to challenge our assumptions and rethink the fundamentals. Because the next chapter of AI won’t be defined simply by how much infrastructure we build, it will be defined by how much intelligence we can create from it.

Engineering useful yield 

For decades, the computing industry has optimized yield in the context of manufacturing. Today, that discipline has to extend across layers, from datacenters and silicon through models and the agentic harnesses that orchestrate them. And the work does not stop once the technology is built. We must then deploy and scale it faster, while developing new tools and systems to maximize utilization across our fleet.

From development through execution, each layer has a yield of its own, and losses and gains compound across them. Capacity at any one layer is only a starting point. The real measure is how effectively those layers work together to produce useful output from the system as a whole.

Our experience at Microsoft building and operating AI infrastructure at scale has reinforced two key lessons. First, the biggest constraints are rarely solved in the layer where they appear. Second, when we attack a constraint across the whole stack, tradeoffs that seemed inherent to the problem often turn out to be artifacts of the architecture.

The greatest advances often come when we apply these learnings through co-design, working across layers to turn apparent limits into solvable system constraints. Innovations in memory, networking and power show what this approach looks like in practice.

Memory: More intelligence from every byte 

Today, memory is viewed as a supply problem or a component problem. In reality, it is a system problem.

In AI inference, memory is now setting the limits on system performance. It must hold larger models, preserve longer contexts and deliver data fast enough to keep the compute fed. And agents raise the bar even further. Generation, retrieval, tool use and persistent memory run together in loops that can last minutes or hours. The result is a much longer memory horizon, with far more information kept close to the compute and available across an expanding sequence of turns. Doing that efficiently at scale will define the next generation of AI infrastructure.

Our experience building the Azure Maia platform demonstrates that memory bottlenecks are not resolved by a single layer.

Model architecture, data science and compression can reduce the amount of KV cache, which stores the model’s working context during generation. Software can manage memory hierarchies more effectively, silicon can be optimized for data movement efficiency and compilers can place data closer to compute. No one change removes the constraint. Together, they increase the useful intelligence the system can deliver from the same memory resources.

That is useful yield: not simply adding bytes but getting more useful intelligence from every byte we already have.

Networking: Designing across layers

As we zoom out to the cluster level, we see that intelligence does not come from one chip. It comes from thousands of chips operating as one system. Faster links matter, but the productivity of the system also depends on congestion management, failure recovery, workload placement, programming complexity and the boundaries between silicon, system and software. Together, they determine whether expensive compute is producing intelligence or sitting idle.

When architecting the platform for Maia, we did not begin with an existing networking design. We began with the outcome we wanted to deliver: efficient inference at fleet scale, designing across silicon, networking and system software. Instead of separate scale-up and scale-out fabrics, we built a two-tier scale-up network, integrated the NIC functionality directly into the chip and developed a custom transport layer.

The result is scalable, consistent performance across dense inference clusters, with a unified fabric that simplifies programming, improves workload flexibility and makes better use of available capacity. And with less network hardware needed to deliver this performance, we also lowered the cost of running the entire system.

Our objective is not merely to move data faster. It is to keep more compute productive and deliver more tokens from every watt and every dollar.

Power: Co-designing for efficiency, from grid to chip

Moving from the cluster to the grid, AI has introduced new challenges around power availability, distribution and utilization. Racks have gone from tens of kilowatts to hundreds of kilowatts, and datacenter campuses can operate on the scale of gigawatts. Power used to be something the system simply plugged into. Now, it is something we design around, from the grid to the chip.

That is why the industry is rethinking power across the system. Solid-state transformers and 800-volt direct current power delivery can reduce distribution losses as power moves through infrastructure. Power and cooling are no longer downstream of the design, they are part of the product definition from the start. And increasingly, that co-design is needed all the way into the silicon.

Azure Cobalt 200, our Arm-based server CPU, shows what this looks like in practice. We designed Cobalt so that every core has its own voltage and frequency controls, paired with software-based, per-virtual-machine power capping. This finer-grained control enables targeted power adjustments while protecting the performance of critical workloads, allowing us to run more servers within the same power envelope.

As Cobalt demonstrates, hardware-software co-design enables us to more effectively turn every megawatt into customer value.

The breakthroughs between boundaries 

The pattern we see across memory, networking and power extends throughout the system: start with the useful output, then optimize the whole rather than any one layer. This first requires us to be precise about the output we are optimizing for and which design constraints are truly fixed. Are we optimizing for peak performance or sustained system throughput? Would the workload benefit from significantly more capacity with marginally less redundancy? What creates more value: a broader set of capabilities or significantly earlier customer deployment?

Not every constraint in today’s systems is a law of physics. Some are inherited from decisions made elsewhere in the system and can change only when we work across traditional boundaries. Evolution comes from the steady gains each company drives within its own domain, but transformation comes when we challenge those assumptions together and redesign the system as a whole.

The breakthroughs ahead will emerge from collaboration across the ecosystem, spanning hyperscalers and silicon providers, equipment makers and materials innovators, utilities and datacenter operators, model builders and software developers.

Toward full yield 

But tokens and intelligence are not the finish line. What we produce becomes the input for someone else’s work.

What matters next is how broadly that input translates into productivity across the economy and value in people’s lives, whether it helps a scientist accelerate discovery, a clinician identify a signal earlier, a student get help at the right time or a small business find a new path to growth.

That happens as AI becomes part of everyday work across industries and around the world. People build on it, new uses emerge, the tools improve and the value compounds.

For that cycle to spread, AI must be broadly accessible. And at scale, accessibility depends on efficiency. It is the only way to deploy enough intelligence, and at a cost that allows it to reach everyone.

When every person and every company can access intelligence, build on it and create value of their own, that is full yield.

We will continue to build capacity because the world will need it. But our defining measure of progress must be what comes out: not only chips or tokens, but useful intelligence translated into empowerment, opportunity and human achievement.

That is the yield imperative. And it is work our entire industry must take on together.

Rani Borkar leads the core organizations responsible for planning, architecting, developing and deploying hardware and infrastructure for Microsoft’s leading cloud computing platform — from silicon, to systems, to supply chain.

Learn more about Microsoft’s silicon to systems approach to Azure infrastructure.

The post The yield imperative: Turning AI infrastructure into useful intelligence appeared first on The Official Microsoft Blog.

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Everything we are doing at Microsoft is empowering Frontier Transformation: Copilot enables AI in the flow of human ambition, Microsoft IQ amplifies and protects an organization’s IQ and Agent 365 is the trust platform that enables observability at every layer of the stack.

Businesses are not static, and neither are the AI systems that support them. As organizations evolve, AI systems must continuously learn and improve. Agentic workflows need to be built, observed and tuned against the outcomes organizations seek and the ROI they demand. Microsoft’s open, model-diverse and heterogenous platform powers that improvement loop. We also recently announced Microsoft Frontier Company, bringing our AI engineering approach to customers around the world to help them build these AI systems to accelerate measurable business outcomes.

Throughout the past year, we saw customers put these capabilities to work in powerful ways — embedding AI into core business processes, building agentic systems, strengthening security, accelerating innovation and creating new sources of value. The stories below highlight organizations leading Frontier Transformation, demonstrating how intelligence, trust and human ambition come together across industries.

To advance its journey to become a global AI-powered company, Atos Group deployed Microsoft 365 Copilot to 56,000 employees across 54 countries — from consultants to engineers to frontline workers — and was one of the first organizations globally to adopt Microsoft 365 E7: The Frontier Suite. Using Microsoft Foundry, Microsoft Copilot Studio and Agent 365, Atos is building, operating and governing a growing ecosystem of 19,000 AI agents through a unified operating model that brings together productivity, security, compliance and agent governance. As Atos embeds secure agentic AI across its workforce, the company is creating a repeatable model to continuously improve thousands of agents at scale while applying the same playbook to help customers accelerate adoption across highly regulated industries.

Facing state-sponsored threats and complex global operations, ASM is strengthening cyber resilience with Microsoft Security Copilot, helping protect the intellectual property behind advanced semiconductor manufacturing. By bringing threat investigations into a unified AI-powered experience, ASM enables analysts to investigate incidents faster, apply consistent decision-making across global operations and accelerate the development of cybersecurity talent. The company reduced incident triage time by 68%, cut laptop compromise investigations from 25 minutes to eight and now saves 337 hours each week on investigations while redeploying 20% of its security operations staff to governance, risk and compliance initiatives.

Banco Popular Dominicano, the largest private-sector bank in the Dominican Republic, transformed operational risk management from periodic, sample-based reviews into continuous, AI-powered supervision. Using AURA — an ecosystem of specialized agents built on Microsoft Copilot Studio and Microsoft Power Platform — the bank monitors 100% of its operational risk universe in real time, up from roughly 40% coverage, and can automatically analyze changes, validate controls and surface issues as they occur. The shift has delivered seven times greater analytical capacity, reduced manual operating effort by 70%, achieved 98% methodological accuracy and enabled continuous processing of approximately 80,000 documents per week and more than 300 cases per day. Just as importantly, risk teams have moved from reacting to problems after the fact to anticipating and preventing deviations before they occur. These results demonstrate the power of AI democratization. By enabling business teams to build intelligent solutions themselves through low-code tools, Banco Popular transformed operational risk management while fostering a culture of innovation led by domain experts.

To help reduce the manual burden for employees while meeting the pharmaceutical industry’s strict data security requirements, Cactus Life Sciences modernized scientific workflows with Microsoft 365 Copilot and agents. The company has deployed more than 30 custom automation agents to streamline document review and structure data extraction and information retrieval across scientific writing and project management teams. Supported by a centralized knowledge repository and the Copilot Champions community, the company reports efficiency improvements of approximately 35% to 50% in structured data extraction. By automating labor-intensive tasks and maintaining human review and quality controls, the company is enabling scientific writers to focus on deeper analysis, synthesis and delivering exceptional science to clients.

Chow Tai Fook is redefining luxury retail with Microsoft 365 E5, Microsoft Purview, Microsoft Azure OpenAI Service, Microsoft Fabric and Microsoft Foundry. The company has deployed over 400 customized AI agents supporting more than 24,000 employees, with millions of AI interactions each month and core business-process efficiency gains exceeding 70%. Through its AI Fook super-agent ecosystem, frontline associates can instantly access product expertise, inventory insights and personalized recommendations, helping drive sales conversion improvements of up to 57% while delivering hyper-personalized omnichannel experiences at scale. With hundreds of AI agents operating across the business, Chow Tai Fook is creating a foundation where customer, product and operational intelligence can be applied across every interaction, helping personalize experiences and improve decision-making across its global retail network.

To accelerate AI adoption, EY moved AI from experimentation into enterprise-wide transformation. After deploying Microsoft 365 Copilot to 150,000 employees and realizing a 15% productivity gain, the firm is expanding the Microsoft 365 Frontier Suite across its global workforce of more than 400,000 people, embedding agentic AI capabilities across the enterprise. As Client Zero, EY is applying Microsoft technologies across its own operations, including Microsoft Power Platform, Microsoft Copilot Studio, Microsoft Azure, Microsoft Foundry and Microsoft Fabric. The results include 95% faster lead times, a more than 37% reduction in finance operating costs and up to a 90% reduction in manual workloads across key business processes.

To reimagine the grocery shopping experience, Grandiose Supermarkets created an AI-powered shopping companion — GrandChef — built on Microsoft Foundry and Azure OpenAI Service. By connecting meal inspiration, recipe discovery and product purchasing into a single experience grounded in live product and inventory data, GrandChef helps shoppers move from intent to purchase faster and with greater confidence. This has led to a 31% increase in conversion, a 20% lift in average basket value and shopping journeys that are 40% faster. Together, these gains are helping Grandiose Supermarkets create more personalized customer experiences while driving measurable business growth. By connecting customer intent, product data and purchasing decisions in a single experience, Grandiose is creating a feedback loop that helps continuously improve recommendations and shopping experiences.

To support audits across its global organization, Grupo Bimbo built two agents with Microsoft Copilot Studio and deployed them through Microsoft 365 Copilot and Microsoft Teams: the result was Audit Assist, an idea generated from a Microsoft-supported internal hackathon and Comatrix. By connecting auditors directly to approved guidance, procedures and templates in SharePoint, the company is helping teams work more efficiently across 39 countries while improving consistency and audit quality. The solution/AI agents reduced planning-phase audit time by 20% and accelerated risk and control matrix creation from days to seconds, enabling auditors to spend less time searching for information and more time on analysis and decision-making. By making approved audit knowledge instantly accessible across its global audit organization, Grupo Bimbo is creating a foundation where expertise can scale with the business — improving consistency, reducing rework and enabling auditors to focus on higher-value analysis and decision-making.

To create a unified foundation for data, automation and AI across its operations in 47 countries, Navien built a connected, intelligent operating model with Microsoft Fabric, Microsoft 365 Copilot, Microsoft Foundry and Microsoft Copilot Studio. By connecting fragmented data and processes across procurement, manufacturing, quality and customer service, the company is enabling more consistent, data-driven decision-making across its operations. Navien saved 28,000 hours annually through AI agents and automation, with 32% of employees using self-service analytics to make decisions without IT support. Through its migration to Azure, it also expects more than 1.4 million in total cost-of-ownership savings over five years. By connecting data, insights and workflows across the business, Navien is enabling faster, more consistent decision-making at scale across its global operations.

NHS England is accelerating AI adoption across the healthcare system with the largest implementation of its kind in the healthcare sector. Following a trial involving 30,000 workers across 90 NHS organizations — where users saved an average of 43 minutes of administrative time per day — NHS England is rolling out Microsoft 365 Copilot to over 500,000 clinicians and support staff. Organizations can use Microsoft Copilot Studio to build and deploy AI agents that streamline clinical, operational and administrative workflows. Through Agent 365, NHS England can govern and scale those agents across the healthcare system while enabling individual trusts to build solutions for local needs — creating a secure framework where agentic workflows can be deployed, managed and expanded consistently to improve service delivery, reduce costs and create more time for patient care.

Novo Nordisk is using AI to help researchers make faster, more quantitative decisions in pharmaceutical R&D. Working with Microsoft’s AI Acceleration Studio within the Forward Deployed Engineering team, the company built a governed reasoning agent on Microsoft Azure with its proprietary dataset, including more than 200,000 patient-years of harmonized clinical trial data, while maintaining rigor, oversight and compliance. The system has expanded the team’s capacity to evaluate potential opportunities from 5 to 10 strong ideas per quarter to more than 50, and the company expects it to reduce time to insight for exploratory analyses from weeks to minutes. By grounding AI in its datasets and governed workflows, Novo Nordisk is helping scientists turn decades of institutional expertise into a reusable intelligence layer that can help accelerate discovery across the organization.

As AI becomes part of investment decision-making, SimCorp is helping financial institutions bring AI into investment workflows without compromising governance, auditability or control. By unifying SimCorp One on Microsoft Azure and leveraging Microsoft Foundry, the company is helping portfolio managers, risk analysts and operations teams access insights faster, automate manual processes and spend more time on higher-value decisions. By standardizing how AI is deployed and governed across global investment operations, SimCorp is creating a foundation for trusted AI at scale, helping organizations embed AI into investment workflows while maintaining the controls required in highly regulated markets. One study found SimCorp One customers realized 134% ROI over three years, improved operational efficiency by 45 percent, saved 10 hours per person per week and accelerated time to market by 50 to 60 days.

Stellantis is accelerating AI-led strategy and digital transformation across its global business, co-developing more than 100 AI initiatives across sales, customer care, product development and operations. By applying AI-powered insights across the business, the automaker is streamlining product development and validation, advancing predictive maintenance and bringing new digital features and services to market faster for customers. Stellantis is also deploying an AI-driven global cyberdefense center to help protect vehicles, customers and operations worldwide while modernizing its infrastructure on Microsoft Azure with a targeted 60% reduction in its datacenter footprint by 2029, powering a more scalable and interconnected digital ecosystem for future digital and connected services and resilient operations.

Facing a growing volume of cyberthreats across a complex healthcare environment, St. Luke’s University Health Network is using Microsoft Security Copilot to help protect the systems clinicians and patients depend on every day. Across 15 campuses, 300 outpatient sites and more than 2.5 petabytes of data and patient records, the organization needed a unified view of threats across a complex environment. Security Copilot connects Microsoft Defender, Microsoft Sentinel, Microsoft Entra, Microsoft Purview and other security tools, helping analysts correlate threats faster, eliminate silos and respond with greater precision. St. Luke’s is saving nearly 200 hours each month in phishing alert triage and creating incident reports in minutes instead of hours, helping security teams focus more time on protecting patient care.

University of Kentucky unified more than 150 AI initiatives across classrooms, research labs, healthcare settings and administrative offices through its CATS AI governance framework. Standardizing on Microsoft’s AI portfolio — including Microsoft 365 Copilot, Microsoft Dragon Copilot, GitHub Copilot and Microsoft Azure — the university achieved campus-wide deployment, providing more than 70,000 students and employees with access to AI capabilities. Clinicians are using Dragon Copilot to reduce documentation burdens and spend more time with patients, and students are using GitHub Copilot to become active builders of digital solutions, such as the Socratic Tutor: an AI-powered learning platform aimed at helping medical students master complex curriculum.

Looking back on FY26, I am inspired not only by the continued pace of AI innovation, but by what our customers are achieving. Across every industry and segment, organizations are turning their unique IQ into strategic advantage with continuously improving agentic workflows. The companies leading this next phase are building, observing and tuning agentic workflows against business outcomes. Intelligence compounds, trust scales and AI can work in the flow of human ambition. As Frontier Firms redefine what is possible with AI, we remain focused on helping our customers amplify and protect their intelligence so they can transform how they operate, compete and grow.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Looking back on Microsoft’s FY26: From AI experimentation to Frontier Transformation appeared first on The Official Microsoft Blog.

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The physics of cybersecurity are changing. Autonomous systems can now reason, adapt and operate continuously. At the same time, the cost of offense is falling, while the volume, velocity and complexity of what must be secured continues to grow. Attackers can generate exploits faster, scale campaigns further and operate with unprecedented efficiency. The approaches built for a world of human actors cannot keep pace with a world of AI, agents and machine-speed attacks.

Security needs a new Cyber Stack. A new Cyber Stack must continuously perceive risk across the entire digital estate, reason across vast amounts of context and take action at machine speed. It must learn and adapt as environments evolve, helping organizations stay ahead of threats. And because security is ultimately a human mission, it must amplify defenders with better insights and more powerful ways to act. The defining characteristic of the next generation of security systems will not be their ability to generate more alerts. It will be their ability to continuously perceive, reason and act.

That vision led us to build Project Perception. A new agentic security system designed for the realities of AI. It turns signals into real-time protections using AI to defend against AI.

Project Perception brings together signals, context, models and specialized agents into a continuously learning system of defense. It can reason, prioritize and act at machine speed while keeping humans firmly in control and empowering them with powerful new workflows.

Project Perception is based on a simple idea: effective defense requires continuous understanding of how an attacker sees the world, how a defender evaluates risk and how protections are improved over time. To accomplish this, Perception coordinates three classes of specialized agents. Red team agents identify potential paths to compromise before an attacker can exploit them. Blue team agents investigate, reason over context and determine what represents meaningful risk. Green team agents take corrective actions and strengthen defenses across the environment. Working together, these agents form a closed-loop system that continuously discovers, evaluates and improves an organization’s security posture.

A system like Project Perception is only as effective as the visibility it has, the actions it can take, the experience of the teams building it and the models it can use. Microsoft brings together all four.

We see across identities, endpoints, applications, data, clouds and AI systems, providing broad visibility across the digital estate. Equally important, we can help customers take action across those environments. Combined with decades of security research, threat intelligence and real-world operational experience defending organizations, these capabilities shape how Project Perception reasons, prioritizes and responds.

Security is a 24/7 mission. Organizations need protection that is highly effective, continuously available and affordable at scale. That requires more than access to the most capable model. It requires applying the right model to the right task. Project Perception adopts a multi-model architecture that combines frontier and specialized cyber models, optimizing for both quality and cost.

As part of this multi-model strategy, we are committed to bringing customers the best models for each security task, including innovating with our own specialized models. The first scenario is software vulnerability management, bringing MAI-Cyber-1-Flash inside MDASH, our software vulnerability multi-model team of agents. MDASH with MAI-Cyber-1-Flash delivers 96% on CyberGym, an industry leading benchmark, +12 points above Mythos. And this same configuration delivers almost 50% of cost savings vs. the current MDASH configuration in market today. That’s the power of a well-tuned, multi-model system with access to uniquely rich historical training data. Next, Project Perception will take advantage of MAI-Cyber-1-Flash for many more security workflows, beyond the software vulnerability scenario.

We are bringing this vision to customers around the world through Project Perception, which enters public preview on August 3.

A Cyber Stack built for agentic security

Delivering agentic security requires more than adding agents to existing workflows. It requires a new Cyber Stack, designed from the ground up.

The stack begins with signals and sensors that provide awareness across the digital estate. Security context transforms those signals into token-efficient understanding that agents can use. Models provide intelligence and reasoning. A harness coordinates models and agents across security workflows. Agents apply that intelligence across security workflows and actuators translate decisions into protection. Together, these layers create a continuous learning system that can understand risk, adapt to changing conditions and improve security outcomes over time.

While each layer provides important capabilities, the power of Project Perception comes from how they work together.

Security context built for AI

Effective reasoning requires more than raw signals. Agents need context.

Microsoft transforms its breadth of visibility, threat intelligence and security expertise into a security context that connects security data, knowledge and semantics across the digital estate. The result is a continuously updated representation of an organization’s assets, identities, relationships, risks and activities that gives agents a shared, near real-time, understanding of the environment they are helping to defend.

This shared understanding is foundational to how Project Perception operates. Rather than forcing agents to continuously gather, correlate and reconstruct context from raw signals, it provides them with immediate and token-efficient access to the information they need to reason over risk, prioritize actions and make decisions. By grounding every interaction in this rich security context, Project Perception improves the accuracy and consistency of reasoning while reducing the time, compute and cost required to operate at scale.

A multi-model architecture built for security

No single model will be optimal for every security task. Effective cyber defense requires applying the right model to the right problem at the right time.

For Project Perception, the right model is determined by the combination of quality, reliability, latency and cost. Rather than relying on a single model, Project Perception adopts a multi-model architecture that continuously selects the capabilities best suited to the task, optimizing for both effectiveness and economics. Because security is an always-on mission, sustainable economics are essential to operating protection at scale.

This approach is shaped by ongoing research, benchmarking and evaluation across frontier and specialized models. Our security researchers continuously assess models against real-world security workflows, enabling us to match each task with the model that delivers the best outcome. This allows customers to benefit from advances in AI without being tied to any single model.

Actuators — insights to actions

Security teams do not need more information. They need better outcomes.

That is why actuators are a critical part of the Cyber Stack. Project Perception is deeply integrated across Microsoft Security products, enabling agents to connect insights to actions. Organizations can continuously reduce risk rather than simply identify it, helping defenders strengthen security while remaining in control.

Built with safety first

Underpinning every layer of the Cyber Stack is a foundation of trust. Project Perception is built in alignment with Microsoft’s Responsible AI principles and inherits the security, compliance, governance and operational controls our customers already rely on. This ensures these capabilities are delivered with the same rigor, accountability and enterprise readiness that customers expect.

The future of security

Security has always been a race between attackers and defenders. AI changes the speed, scale and economics of that race. Defenders need systems that can continuously perceive, reason and act alongside them.

 Project Perception is how we begin to build that future.

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

Other resources:

• Enhancing AI Security Through Global AI Red Teaming 
• From research to reality: An interview with Microsoft VP of Security Research Taesoo Kim

Hayete Gallot leads Microsoft’s work to help organizations operate securely in an AI-driven world. Her scope includes identity, threat protection, compliance and data security at global scale.

The post Rethinking security for the age of AI appeared first on The Official Microsoft Blog.

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The Genesis Mission represents an ambitious vision, bringing together DOE’s 17 National Laboratories, world-class experimental facilities, decades of irreplaceable scientific data and next-generation computing into a single, unified platform capable of transforming how science gets done. The critical goal of the Genesis Mission is to double the productivity and impact of American research and innovation within a decade by embedding AI directly into the scientific process. It’s a bold step forward and exactly the kind of moonshot that defines American science.

At Microsoft, we believe that this goal is not only achievable, but also essential as a national security imperative and an economic engine for generations to come. With this commitment and the launch of SPARK, we’re ready to partner fully in this mission and build on our collaborations across government and with academia.

Microsoft investing in AI for science

Today, Microsoft’s $60 million investment package will begin to accelerate AI for science in support of DOE and the National Laboratories. As sustained scientific impact requires more than infrastructure alone to enable lasting scientific outcomes, Microsoft’s investment package is structured in two parts:

• $40 million in Azure compute and AI credits, distributed over three years, to power the large-scale AI and scientific workloads at the heart of the mission. This gives researchers room to train, simulate and iterate at a scale that matches their ambition.
• $20 million in solution engineering enablement services. Dedicated engineering, architecture, deployment, adoption and acceleration support to turn cloud and AI capacity into operational scientific outcomes. This investment helps ensure that the Genesis Mission advances in practice and in principle.

Together, this investment reflects a simple conviction: While infrastructure opens the door, it is people, expertise and disciplined delivery that carry discoveries across the finish line. Microsoft is committing to both.

Introducing SPARK: Microsoft’s catalyst for advancing scientific partnerships

Great partnerships need more than good intentions and good technology — they need a clear way to work together. So, alongside this commitment, we are standing up a new program office and coordination hub: SPARK, or Scientific Partnership Advancing Research & Knowledge.

SPARK is the single front door and coordination hub for Genesis Mission collaboration with Microsoft. SPARK orchestrates the full breadth of what Microsoft brings to bear: program, technical, research, engineering, security, compliance, partner and field teams into one clear, consistent path from first idea to deployed science.

SPARK is built around five commitments:

• A dedicated Genesis Mission Program Management Office. This office handles intake, prioritization, a disciplined sprint-and-checkpoint cadence and a steady operational interface with DOE for alignment, reporting and sustained partnership.
• An AI for Science Center of Excellence. An integrated delivery team that moves use cases from concept to secure, compliant, scalable implementation, with enablement through office hours, hackathons and training grounded in responsible AI and reproducible research.
• Optimization of Azure credits. Ensures computing resources are directed toward the projects where they can accelerate science most.
• Management of technical services. Dedicated support to help labs adopt and operationalize AI-for-science capabilities, delivered by Microsoft teams and select partners.
• Joint research and development. A focused set of Genesis Mission-aligned challenge problems, co-developed by Microsoft researchers and DOE scientific leadership, with milestones, publications and IP governed by mutually agreed terms.

Underneath SPARK sits the full breadth of Microsoft’s secure, FedRAMP-authorized cloud and AI portfolio. This includes Microsoft Azure infrastructure, Microsoft Foundry and Zero Trust security through Microsoft Defender, Sentinel and Entra. Azure capabilities will help extend the DOE’s American Science Cloud capabilities by providing computing, AI, data and collaboration services that complement existing scientific infrastructure. Together, these capabilities will help accelerate discovery, enable secure collaboration across institutions and provide researchers with flexible access to advanced technologies and resources. This will enable DOE and National Laboratory scientists to move faster from hypothesis to discovery, without compromising the security, reproducibility and governance that mission-critical research demands.

Accelerating science with Microsoft Discovery and Quantum

Microsoft’s new commitments build on capabilities we recently announced through Microsoft Discovery and Microsoft Quantum. They are designed to accelerate scientific research and will support the Genesis Mission’s ambition to bring AI, advanced computing and emerging technologies more deeply into the scientific process.

To enable a unified research platform for Genesis Mission work, Microsoft will provide access to Microsoft Discovery, our integrated platform that unites AI models, simulation, data and experimental workflows driven by advanced cognition rooted in scientific method, within a single governed environment. Microsoft Discovery, now generally available, includes support for autonomous lab orchestration, integration of Microsoft Research’s AI models for science, continuous AI learning through the scientific loop and agentic memory, advances in Discovery Bookshelf for data curation and scalable indexing and deeper integration and multi-hop reasoning over enterprise science data estates, including data governance. Together, these capabilities are designed to help researchers move more quickly from data to insight, from simulation to experiment, and from promising idea to scientific breakthrough.

We also recently announced the preview of the Microsoft Discovery app, a local desktop experience that helps researchers, students and scientific teams begin working with Microsoft Discovery today. Our support for the Genesis Mission will include access to the Microsoft Discovery app to enable DOE and National Laboratory teams with early access to its capabilities.

Microsoft’s recent quantum progress further strengthens the foundation for Genesis Mission work. With Majorana-based quantum advances, including more reliable topological qubits and a roadmap toward scalable quantum computing, Microsoft is helping move quantum from long-range research toward practical scientific capability. For DOE and National Laboratory teams, that progress can open new ways to model complex materials, chemistry, energy systems and national security challenges that are difficult or impossible to solve with classical computing alone. Just as important, the same agentic AI for science approach behind Microsoft Discovery is helping accelerate quantum research itself, creating a reinforcing cycle in which AI speeds quantum breakthroughs and quantum computing expands what scientists can ultimately discover.

The work is already underway

Working alongside and supporting federal researchers and universities, we’re already seeing real-world impact, from faster materials screening to advanced biosurveillance modeling. Four of the first projects taking shape under this partnership show what that looks like in practice:

Discovering critical energy storage materials and biosystems design (Pacific Northwest National Laboratory)
Using AI to dramatically accelerate the discovery of new energy storage materials through novel experimentation design and shrinking analysis of massive volumes of scientific data from years to weeks. In biosystems design, Microsoft Discovery is plugging directly into PNNL’s laboratory automation infrastructure to launch self-driving scientific workflows that autonomously design, run and fine-tune biological experiments in real time.

Strengthening biosecurity (Lawrence Livermore National Laboratory)
Pairing advanced AI with modern bioinformatics to identify emerging biological threats sooner and develop responses faster, strengthening the nation’s ability to stay ahead of the next outbreak and developing proactive defenses against evolving biosecurity risks. By applying advances in AI, scientific computing and biotechnology, Microsoft and the Lawrence Livermore National Laboratory are helping strengthen the nation’s ability to prevent, detect and respond to biological threats, advancing US biosecurity and biodefense priorities while supporting the long-term resilience and competitiveness of the US bioeconomy.

Autonomous labs for accelerated materials discovery (Johns Hopkins University Applied Physics Laboratory)
Supporting the development of AI-enabled autonomous (“self-driving”) laboratories through the integration of advanced foundation models and simulation tools — including MatterGen and MatterSim — to rapidly design, execute and optimize experiments for structural materials and superconductors. By combining autonomous experimentation with accelerated computational materials discovery, this approach dramatically shortens the path from scientific hypothesis to validated, manufacturable, mission-enabling materials, while exploring a vastly larger design space than is possible through traditional methods.

Accelerating nuclear energy permitting and autonomous energy operations (Idaho National Laboratory)
Applying AI to streamline the licensing, permitting and review of new nuclear projects. AI helps automate the development of complex engineering and safety analysis materials, a historically time-consuming and expensive process, to get this critical documentation into the hands of regulators and safety reviewers sooner. The lab also demonstrated that secure, distributed, hyperscale cloud can enable autonomous remote operations for nuclear power generators. Together, these efforts are helping bring safe and reliable power online faster and ensure scalable, efficient and reliable operations to meet the nation’s surging demand for energy.

Why this matters

We are entering an era where AI and quantum don’t just support the scientific process — they are essential to it. Through the Genesis Mission, Microsoft is committing to work as part of America’s research enterprise by providing hyperscale compute, advanced models, emerging quantum capabilities and dedicated expertise that run alongside the labs’ own world-leading systems. We are helping to ensure that the nation’s scientific strength is built on American technology. This commitment builds and expands upon the decades of collaboration through Microsoft Research and partnerships across the US government and our national labs. Now is the time to scale these breakthroughs across the entire R&D ecosystem.

The mission ahead is significant, and it should be. Whether it’s advancing the frontiers of energy, chemistry and materials science, hardening our national security or simply giving the country’s best scientists better tools to ask bigger questions, this is work worth doing and worth doing together. With a $60 million investment and SPARK, Microsoft is ready to help ensure America’s scientific leadership continues to accelerate.

Fueling scientific discovery is one of the most important things these technologies can do. AI and quantum are transforming how we tackle the toughest challenges facing the country, from sustainable materials and clean energy to biosecurity and drug discovery. The Genesis Mission is a pivotal step toward advancing that progress. We commend the DOE’s vision in building a unified, holistic approach to these critical national missions. For America to seize this opportunity and set the global standard, we must put these breakthrough technologies to work advancing the nation’s research and development.

We’re ready to deepen this partnership across government and academia. We move faster together. I’ve never been more optimistic about what’s possible when the nation’s scientific talent meets the best of modern AI and quantum. The Genesis Mission is that moment. Let’s get to work.

To get started, reach out to your Microsoft account team or email genesismission@microsoft.com.

Chris Barry leads Microsoft’s US Public Sector business. He partners with federal, state and local government and higher education agencies and organizations to accelerate digital transformation, bringing Microsoft’s secure cloud and AI capabilities to mission-critical work that serves communities across the nation.

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Our approach to AI infrastructure is designed to support the breadth of how AI systems are built and run. We closely work with industry innovators like AMD as well as our own purpose-built silicon and systems to provide customers with a comprehensive, open and heterogenous platform to achieve the best performance, cost and energy efficiency outcomes.

Building on our close collaboration with AMD, Microsoft is bringing AMD’s latest Helios AI platform and next-generation EPYC datacenter processors to Azure. These technologies will power three upcoming Azure offerings: HDv2 VMs for data processing, HXv2 VMs for electronic design automation (EDA) and ND MI455X v7 VMs for AI inference workloads.

Expanded infrastructure for inference, AI data systems and chip design

Built for AI data systems — Azure HDv2

CPU infrastructure is essential to the performance and efficiency of modern AI systems. AI accelerators depend on high-density, power-efficient CPU compute to process data, coordinate workloads and keep pipelines running at scale. Without this, training jobs don’t have enough data to learn from, and agents don’t have enough capacity to perform tasks on behalf of customers. Azure HDv2 virtual machines are one of our latest offerings designed from the ground up to eliminate these bottlenecks and empower massive agentic workload adoption.

Co-designed with AMD, HDv2 VMs expand Azure’s portfolio of purpose-built solutions for the most demanding CPU workloads from AI customers, including data preparation, search, reinforcement learning and agent coordination at scale. Featuring nearly 500 physical 6^th Gen AMD EPYC CPU cores, 4 terabytes of RAM, 32 terabytes of local NVMe storage and 400 Gbs Azure Boost networking, HDv2 VMs are built for the workload needs of our most demanding AI customers.

Optimized for silicon design and technical computing — Azure HXv2

The AI era has created tremendous need and opportunity for firms developing the silicon products that power this infrastructure. For this reason, Azure HX virtual machines, launched in partnership with AMD in 2023 and featuring AMD’s unique 3D V-cache technology, have seen significant adoption among silicon design firms working to bring more capable and efficient AI silicon to market. Today, we are announcing the next step in our workload optimized journey for these customers, HXv2.

HXv2 virtual machines build on and extend the strengths of HX. They both continue the differentiation Azure offers for RTL simulation workloads by again employing 3D V-cache technology, while offering significant improvements to single threaded performance and memory. HXv2 VMs will feature 176 AMD 6^th Gen EPYC CPU cores with a clock frequency of more than 5 GHz, 50% more addressable cache per core and VM sizes with nearly 2 or 4 terabytes of RAM, helping customers optimize their workloads to memory needs.

Azure HXv2 is also designed to support a broader range of technical computing workloads including scientific simulation, engineering analysis and other distributed memory applications. The significantly increased per VM and per core performance, and the inclusion of 800 Gb InfiniBand, enable large-scale MPI-based simulations and make HXv2 an ideal fit for a wide variety of HPC customers.

AMD, a leading HX-series customer, highlights this impact directly:

“Engineering teams are pushing the limits of simulation, chip design and scientific computing. At AMD, we experience those demands firsthand as we design future AMD EPYC CPUs and AMD Instinct GPUs. Azure HX is an important platform for scaling complex EDA workloads, and we’re excited about Azure HXv2, which is designed to deliver even greater performance and scalability. We look forward to continuing our collaboration with Microsoft as we help advance infrastructure for the world’s most demanding engineering and scientific workloads.”

— Mark Papermaster, Executive Vice President and CTO, AMD

The HXv2 also leverages Microsoft’s long-standing collaboration to optimize Synopsys AI-powered EDA solutions on Azure:

“As AI compute continues to push the limits of semiconductor design, our collaboration with Microsoft on the Azure HX-series demonstrates a shared vision for enabling customers to deliver next-generation AI systems with precision and scale in accelerated design cycles. These systems have enabled Synopsys customers to reliably and efficiently leverage cloud-based compute, extending EDA workloads beyond traditional infrastructure constraints so they can meet ambitious development schedules while maximizing design quality and delivering dramatic performance gains.”

— Shankar Krishnamoorthy, Chief Product Development Officer, Synopsys

Production-scale AI inference — ND MI455X v7

ND MI455X v7 is designed for the reasoning, search and agentic workloads behind modern AI services. Powered by the AMD Helios rackscale solution, it expands Azure’s infrastructure options for large-scale inference and is designed to deliver strong performance and efficiency for demanding AI workloads.

Together, these new capabilities expand Azure capabilities while giving customers more flexibility to choose the right compute for each unique AI workflow: from inference, to data systems, to chip design. Customer choice is a core design principle built directly into Microsoft Azure, and we’re excited to bring AMD’s most advanced innovations at production scale.

To learn more about Azure’s high-performance computing and AI infrastructure capabilities, visit Azure.com.

Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future.

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When I stepped into this role, I promised to communicate more openly with you and share the “why” behind our decisions.

Today we are eliminating around 4,800 roles, about 2.1% of our global workforce, as we focus our people, investments, and energy on the priorities that will keep Microsoft positioned to deliver for customers in a fast-changing industry. The people whose jobs are impacted today are our colleagues and friends. They have made meaningful contributions to Microsoft, and we are deeply grateful for everything they have done.

Decisions like these are never easy, and you have my commitment that we are always looking for ways to reduce the need for job eliminations. Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity. Over the past year, we have redeployed more than 4,000 employees into new roles, including another 500 this month. We will also transition four of our gaming studios to operate under new management, with the goal of preserving both their intellectual property and ongoing projects. In addition, more than 30% of eligible employees chose to participate in our recent voluntary retirement program, and we will continue exploring similar approaches in the future. While this doesn’t change the difficulty of today’s news, we will continue to do everything we can to create opportunities for our people, reduce the need for job eliminations where possible, and responsibly support those affected with care and respect.

The “why” is this: Our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself – what we do, where we focus, and how we’re organized – has to transform too. Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it. That means we will need to adjust resources and roles and shift how we operate so we can have the greatest impact for our customers.

I also want to be direct that the roles eliminated today are not being replaced by AI. At the same time, what is true is that AI is changing how work gets done. Some of the tasks we do every day can now be automated, and that means we all need to keep learning, keep building new skills, and keep adapting as the work evolves. Our customers are navigating this same shift, and they’re counting on us to help them through it. We can’t do that well unless we’re doing it ourselves. This comes down to two commitments: making the decisions needed to drive differentiated customer value, and supporting the people affected by them.

First, we will make the hard changes required to build differentiated products and services that deliver differentiated customer value. We are aligning our investment, people, and energy to our business priorities. Today’s changes mostly fall within our Commercial and XBOX organizations. In our Microsoft Commercial Business, they build on last week’s Frontier Company announcement, reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments. In XBOX, we are restructuring to position the business for long-term success. Engineering teams across the company will also evolve their structure and priorities to meet customer needs and innovate for the future.

Second, we will do this thoughtfully.  As mentioned above, we are working on alternative solutions to job eliminations, and beyond this, we will continue to invest in equipping employees with new skills, including in AI. For those who are impacted, we provide financial support and resources to help them take their next step.

I know many of you want to help those who are leaving but aren’t sure how. Reach out and check in on your colleagues. Use your network to bring people together, share what makes them exceptional, and help create connections to opportunities that might not happen otherwise.

We are still early on this journey, and there will be more changes ahead; other parts of our business will need to make similar changes. Each time, you can hold us to the two commitments.

During my time at Microsoft, I’ve seen this company reinvent itself again and again. What makes that possible has always been our people – their resilience, creativity, and willingness to keep learning.

Thank you for everything you bring to Microsoft.

Amy

Read more: Resetting XBOX.

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Today we are introducing Microsoft Frontier Company, a new operating business focused on delivering Frontier Transformation through AI for our customers around the world. It will provide a unique combination of skills inclusive of deep industry knowledge, change management and continuous improvement experience, and enterprise-grade AI engineering expertise. This goes beyond what has been labeled as Forward Deployed Engineering (FDE) and will be the largest, most capable, outcome-driven engineering organization in the industry. We are making a $2.5B investment in Microsoft Frontier Company, embedding 6,000 industry and engineering experts at customers to co-design, co-innovate, deploy and continuously improve AI systems at scale based on measurable business outcomes.

I recently wrote more about my conviction that Intelligence + Trust are the two most important components of any AI solution and how our customers can use different levers to manage cost.

Companies need to establish an intelligence platform so their unique IQ — their proprietary data, expertise, workflows and decision-making processes — compounds over time from within, using their choice of models to build AI solutions and workflows.

They need a trusted platform that allows them to observe, govern, manage and secure AI solutions across every layer of the technology stack, using FinOps to assess their ROI.

Enterprise AI engineering expertise with deep industry knowledge is required to build a system that acts as a continuous loop of improvement between the two platforms to fine tune agentic business processes, ensuring that a customer’s intelligence compounds over time and delivers real business outcomes.

This is what Microsoft Frontier Company was built to do: focus on end-to-end Frontier Transformation, enabling customers to amplify their IQ with AI while refining their differentiated value in the markets that they serve.

Early results demonstrate meaningful impact: Our engineers and industry experts partnered with LSEG (London Stock Exchange Group) to embed AI into LSEG Workspace, helping finance professionals ask complex questions and get quick answers across structured and unstructured financial content. The solution is underpinned by a foundation that is iteratively refined through client feedback and real-time user testing that accelerates each cycle and steadily improves model quality and scope. From LSEG to Land O’Lakes to Unilever to Novo Nordisk, our differentiated approach is already delivering measurable outcomes on our customers’ Frontier Transformation journeys.

To achieve scale, we will work closely with our partner ecosystem to extend this unique value to our customers across all markets and segments globally. We have robust FDE partnerships with our Global SI partners, including Accenture, Capgemini, EY, KPMG, PwC and others.

Central to this approach is a principle that is non-negotiable: a customer’s IQ is protected. Their data, their IP, their competitive advantage — none of it is used to train models in ways that commoditize what differentiates them in their industry. Satya put it clearly recently: there is no societal permission for an AI future that eats the intelligence of the companies it’s deployed inside. We built Microsoft Frontier Company to make sure that does not happen.

We protect that intelligence with a model-diverse, open, heterogeneous AI platform. Customers shouldn’t be locked into a single model any more than they should be locked into a single technology vendor. Microsoft’s platform gives organizations the flexibility to run the right model for each scenario — whether it comes from OpenAI, Anthropic, Microsoft AI, open source or a specialized model tuned for a specific industry — without ceding control to any one of them.

To lead this new organization, I have asked Rodrigo Kede Lima to be the President of Microsoft Frontier Company. Rodrigo brings 30 years of industry experience, and for the past six at Microsoft has led enterprise-wide transformations as a sales leader in the Americas and Asia. He has been at the forefront of helping customers and partners translate technology shifts into business outcomes, and understanding how platform innovation, engineering and partner ecosystem collaboration come together to drive growth.

I am excited about all the things that Microsoft Frontier Company will do for our customers to realize the gains of Frontier Transformation. At the end of the day, it comes down to Intelligence + Trust and empowering our customers to achieve meaningful outcomes and a return on their investments.

Learn more at www.microsoft.com/en-us/frontier-company.

Judson Althoff is the chief executive officer of Microsoft Commercial Business. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

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Through continuous innovation and advancements in cooling technologies, we have improved our water use effectiveness (WUE), measured in liters per kilowatt-hour (L/kWh), by nearly 90% since our first generation of datacenters in the early 2000s. Our average WUE has decreased from 2.3 L/kWh to 0.27 L/kWh in 2025, reflecting decades of innovation and our ongoing commitment to reducing the water intensity of our datacenters while meeting the growing demand for cloud and AI services.

Across our entire owned fleet of datacenters, we are committed as a company to a 40 percent improvement in datacenter water-use intensity by 2030. As of 2025, we have reduced our water-use intensity by 25 percent, putting us well over halfway toward our goal. This strong progress reflects the impact of our continued investments in water-efficient cooling technologies, operational improvements and responsible water management practices.*

In FY25, Microsoft reached an important milestone toward its 2030 water positive commitment, replenishing more water than it withdrew across its global operations for the year. We have made this progress by decoupling datacenter growth from water use through resilient, responsible water stewardship practices and the deployment of increasingly efficient cooling technologies — demonstrating that digital growth and sustainable water management can advance together. We are committed to building on this progress and working toward sustaining water positive performance over time as we continue advancing toward our 2030 goal.

Early water stewardship by design

Beginning with some of our earliest datacenter designs, we prioritized water efficiency while minimizing impacts on energy use through the deployment of high-efficiency economizing chillers operating at elevated water temperatures. As early as 2008, we adopted direct air cooling with evaporative assist as the primary cooling approach across our datacenter fleet. This design uses significantly less electricity and up to 90% less water than traditional water-based cooling systems by relying on water only when outside temperatures exceed 85°F (29.4°C). In parts of Northern Europe, no water is required for cooling throughout the year, while in other cooler regions like Dublin and Amsterdam, water is used less than 5% of the time. In more temperate climates like Virginia, water is typically required only 10% of the year, while in the hottest climates like Phoenix, water use may increase to as much as 40% of the year. As a result, approximately 90% of our 2025 owned fleet operates using highly efficient, low- to zero-water cooling systems.

While the majority of our existing datacenters are already highly water efficient, we did not stop there. In 2024, Microsoft introduced a new datacenter design optimized for AI workloads that consumes zero water for cooling during operations, further reinforcing our commitment to water stewardship by design. This chip-level cooling solution delivers precise zonal temperature control without water evaporation by recirculating water through a closed-loop, direct-to-chip cooling system. As our datacenter fleet continues to expand, the addition of these zero-water designs will further reduce Microsoft’s water use intensity over time.

Datacenter cooling methods, explained:

• Cooling towers: Traditional systems that remove heat by evaporating water year-round.
• Hybrid fluid coolers: Evaporates water for cooling during hot summer conditions and switches to dry mode when ambient temperatures cool down.
• Direct air: Uses outside air for cooling, with little to no water use. Water is used only when outside air is above 85°F.
• Air cooled chillers: Uses mechanical refrigeration and outside air to remove heat from closed coolant loops with zero water evaporation.
• Liquid-cooled AI DCs: Uses closed-loop, direct-to-chip cooling to provide precise chip-level temperature control, removing heat efficiently with zero water evaporation.

Modernizing cooling in existing datacenters with smarter controls

Design innovation is only part of the story. We are also improving the efficiency of existing facilities that use water through a continuous focus on optimizing temperature and humidity setpoints, enabling more precise environmental control and eliminating overcooling. In addition, we regularly audit water use and compare actuals against design expectations using real-time weather data and operational analytics. This helps ensure our datacenters are performing as intended and enables us to quickly identify and address any unexpected water use. These efforts, combined with ongoing hardware and operational improvements, are all aimed at using as little water as possible.

Specifically in our Phoenix, Arizona, datacenters, implementation of these advancements led to a 23% year-over-year improvement in WUE in FY25 alone. We are now deploying these advancements across our direct-evaporatively cooled datacenters globally.

Operational improvements like these are one reason Microsoft has been able to report significant long-term reductions in water intensity across datacenter generations. They also point to the next phase of our work: expanding the use of recycled and alternative water sources wherever possible.

Leveraging recycled, reused and non-potable water

In addition to driving efficiency, we also prioritize using recycled, reused or non-potable water wherever possible in our operations. We have expanded the use of these non-potable water sources in some of our most water-intensive regions, helping reduce demand on freshwater supplies. For example, in Quincy, Washington, Singapore and San Antonio, Texas, three of our key locations for advancing water stewardship, we leverage 74%, 99% and 79% recycled, reused or non-potable water sources, respectively.

Rainwater harvesting systems are now operational at select datacenters in the Netherlands, Sweden and Ireland, with additional installations planned in Canada, the United Kingdom, Finland, Italy, South Africa and Austria. To illustrate the potential impact, Microsoft’s new datacenters in Quebec are expected to collect up to 1.5 million liters of rainwater annually, depending on local precipitation levels. This water can be used to further offset the already low water withdrawal at these sites. Expanding the use of alternative water sources in this way helps reduce pressure on municipal water supplies while supporting efficient datacenter operations.

As needed, we implement on-site water treatment systems that enable facilities to recycle water multiple times for cooling operations. These systems produce purified water suitable for reuse within cooling systems, reducing overall dependence on utility water supplies. Together, these efforts demonstrate how engineering innovation and operational excellence can work in concert to meaningfully reduce water use at the facility level.

Advancing water stewardship through investment and community partnership

We work closely with local utilities to reduce strain on community resources, and plan ahead for the sourcing and infrastructure needs associated with our operations. Beyond our operations, Microsoft’s Datacenter Community Pledge commits us to protecting local watersheds, engaging stakeholders and investing in projects that strengthen regional water resilience — helping ensure datacenter growth supports both environmental sustainability and long-term community well-being. Where system improvements are required, Microsoft funds those upgrades in full so communities do not have to shoulder the cost of supporting our operations.

Beyond our own footprint, we invest directly in community water infrastructure by modernizing water systems, expanding access, increasing reliability and helping utilities maintain stable rates and pressure. These investments create shared value for both Microsoft and the local communities we work closely with by strengthening critical infrastructure and supporting long-term water resilience. For example, near our datacenter in Leesburg, Virginia, Microsoft is funding more than $25 million in water and sewer improvements to help ensure that the cost of serving our facilities does not fall on local ratepayers. Since 2020, we have invested more than $500 million in more than 75 water and wastewater infrastructure projects that deliver meaningful community co-benefits.

Replenishment

Finally, we pair all of this work with our broader water positive commitment, our goal to replenish more water than we withdraw by 2030, while advancing our Community-First AI Infrastructure approach, which prioritizes delivering measurable benefits to the communities where we operate. This approach extends across our entire datacenter footprint, including leased facilities. In FY25, we replenished more water than we withdrew globally, marking a significant milestone in our water stewardship journey.**

We prioritize and pursue projects designed to deliver meaningful water contribution to each local community. For example, in the greater Phoenix area and nearby Nevada communities, we partner with FIDO Tech and local utilities to deploy AI-enabled leak-detection that identifies and repairs hidden breaks in aging water systems. By preventing water loss before it occurs, these efforts help keep more water in circulation, improving reliability for residents and effectively increasing the amount of usable water available across the system.

Across the Midwest, we work with The Nature Conservancy to restore historic oxbow wetlands — crescent-shaped water bodies that naturally recharge groundwater, reduce flood risk and enhance habitat for native species. These wetlands act as natural reservoirs, capturing and slowly returning water to local aquifers over time. The goal is to create more stable water availability for agriculture, healthier ecosystems and increased resilience for nearby communities throughout the year.

Looking ahead

As water challenges become increasingly complex around the world, Microsoft remains deeply committed to protecting water as a vital natural resource. We continue to advance datacenter innovations that reduce water use intensity while supporting the growing performance demands of cloud and AI services. Through zero-water cooling designs optimized for AI workloads, water reuse initiatives both on and off our campuses and community-focused stewardship programs, we are working toward a future where digital growth and responsible water management go hand in hand.

We are also exploring zonal cooling architectures that more precisely align cooling approaches with the needs of different hardware types, improving efficiency while supporting a diverse mix of AI and traditional workloads.

Datacenters are essential infrastructure for the digital economy, and we believe they should be built and operated in ways that benefit the communities they serve. Over the last decade, we have demonstrated that technological advancement and environmental stewardship can progress together, and we remain committed to continuing that journey as we build the datacenters of the future.

Top image caption: Aerial view of Microsoft datacenter campus in Wisconsin.

Judy Priest leads technology strategy, innovation and research for Microsoft’s global cloud and AI infrastructure, driving advances in datacenter architecture, sustainability, power, cooling, energy and emerging technologies that enable reliable, scalable services.

Steve Solomon is a professional engineer leading the engineering strategy behind the company’s global cloud and AI infrastructure. He specializes in datacenter design, sustainability, power and cooling innovation, helping advance reliable, efficient and community-focused infrastructure at hyperscale.

*Footnote 1: Measured as water withdrawals per megawatt (MW); based on a 2022 baseline.

**Footnote 2: To understand how Microsoft calculates and tracks water replenishment data, please refer to our FY 2024 Environmental Data Fact Sheet.

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As applications, models, APIs and infrastructure become increasingly interconnected, their behavior is harder to understand end to end. Systems no longer fail in isolation. They fail through interactions across dependencies, services and environments that are constantly changing in real time.

To help organizations operate effectively in these increasingly dynamic environments, today we’re announcing the general availability of the Azure Copilot Observability Agent. Built on Microsoft Azure Monitor, it correlates signals across agents, applications, infrastructure and services to provide the context needed to operate confidently in this new environment.

Observability becomes foundational in an agentic world

In a recent survey of 250 IT decision-makers, Microsoft and Material found that 84% of organizations report increased cloud complexity, with 69% saying it is outpacing their current operating model. The impact is most acute across security, cost management and performance, and it extends across the entire operations lifecycle.

As the pace and scale of change accelerate, no individual or team can realistically maintain the full context required to diagnose and resolve issues quickly enough. This is driving a shift toward agentic operations, where intelligence augments how systems are understood and managed.

Observability is foundational to this shift. It provides the real-time understanding of system behavior that agents depend on to reason, adapt and act. Without a connected view across signals, even the most advanced agents lack the context required to operate reliably.

From signals to resolution with the Observability Agent

We designed the Observability Agent to help operators move more quickly from detection to understanding. It connects logs, metrics, traces, topology and operational context across environments, reducing the time it takes to identify the root cause of an issue.

As telemetry spreads across systems, operators are often forced to piece together context across multiple tools. The Observability Agent addresses this fragmentation by reasoning across signals in real time and unifying that context into a single operational view. These agentic capabilities are integrated directly into existing workflows, helping teams move from investigation to resolution faster with clear, actionable insight.

We’re already seeing customers use the Observability Agent to reduce manual effort, accelerate incident resolution and improve operational clarity:

“The biggest value is speed! The [Azure Copilot] Observability Agent helps us resolve incidents faster and reduce operational overhead by turning logs, metrics and traces into plain English insights. These agents run deep investigations and provide remediation recommendations almost immediately, compared to hours or even days previously.

Since adopting these capabilities, we’ve reclaimed an estimated 250 engineering hours monthly that are now redirected toward supporting new applications and features. We can use natural language to detect, diagnose and remediate issues faster than ever before.”

— Narmada Krishnaswamy, Head of KPMG Audit Application Support and Operations

“Azure Copilot Observability Agent helped us move from manual incident hunting to faster, AI-guided investigations. For PolicyVault, it pulls together the telemetry from our service, correlates it with Azure resource health and gives us actionable next steps based on the investigation. That means we’re not just seeing what broke; we’re getting a much clearer idea of why it happened and what to do about it, which saves us a lot of time during incidents.”
— Vladimir Gusarov, Founder & CEO, PolicyVault

“Azure Copilot’s Observability Agent helps us move faster from signal to insight. By bringing together our telemetry and guiding us toward likely root causes, it reduces the time and effort needed to investigate incidents and keeps our teams focused on what matters most.”
— Theus Hossmann, Chief Technology Officer at Ontinue

Beyond improving incident response, this shift reflects a new approach to cloud operations, where systems can continuously reason across signals and act on that understanding.

Check out our Tech Community blog post to learn more about the Azure Copilot Observability Agent.

From observability to agentic operations across the cloud lifecycle

Observability is part of a broader shift to agentic operations. As systems become more autonomous, operations expand from understanding what is happening in production to continuously improving how those systems behave over time.

In an agentic model, this forms a lifecycle. Systems generate signals, agents interpret those signals, take action and learn from outcomes. Over time, this creates a feedback loop where each operational cycle improves the next, increasing system resilience and efficiency.

This shift requires more than better visibility. It requires a coordinated approach across the lifecycle, from observability and diagnosis to optimization and remediation where insight and action are tightly connected.

As agents take on a greater role in that lifecycle, governance becomes central to how systems are trusted and controlled. Policy, auditability and guardrails ensure that actions taken by agents align with organizational intent and operate within defined boundaries. Human oversight remains essential, not as a bottleneck, but as a mechanism for building confidence and ensuring reliability as automation scales.

This is where Azure is uniquely positioned. By bringing together observability, automation and governance within a connected platform, Azure enables organizations to move from isolated tools to an integrated operational model that spans the full lifecycle.

Azure Copilot Observability Agent plays a key role in this model by grounding agentic systems in real-time operational context. As organizations build and deploy more agents, this foundation becomes critical for ensuring those systems operate effectively and responsibly.

Cloud operations are shifting from reactive management to a continuous, agent-driven lifecycle of learning, adaptation and control. This vision of agentic cloud operations is already taking shape across Azure. Read our companion Azure Blog post for more details.

Brendan Burns is a co-founder of the Kubernetes open source project and corporate vice president for Azure cloud-native open source and the Azure management platform including Azure Arc. He is also the author and co-author of several books on Kubernetes and distributed systems.

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This multibillion-dollar datacenter campus investment over the next five to seven years reflects both the immediate needs we are seeing today and the future trajectory of AI and advanced compute, where reliable infrastructure at scale is essential to unlocking the next generation of innovation. This expansion is grounded in a simple principle: we build where our customers need us, and we build for the long term. We have a track record of doing exactly that in Texas. In the San Antonio region, where we have operated datacenters for nearly a decade, our investment has generated billions of dollars in local economic activity and supported thousands of local jobs. We are committed to delivering the same lasting value in Pecos.

Meeting customer demand with reliable infrastructure

Customer demand for AI and cloud services continues to grow rapidly, from startups building new applications to governments, healthcare providers and educational institutions modernizing critical systems. Meeting this demand requires not only more datacenter capacity, but capacity that is predictable, resilient and able to scale quickly.

The datacenter campus in Pecos enables us to deliver on that need. By pairing new datacenter infrastructure with dedicated energy supply located onsite, we can bring capacity online at the pace our customers require while maintaining operational reliability. Critically, the energy infrastructure required to power this datacenter is being funded by Microsoft. We are paying for the new generation and supporting infrastructure needed to serve our own operations. The capacity we bring online in Pecos is built to meet our demand, ensuring that our growth strengthens, rather than strains, the energy resources the community relies on.

Putting Community First in West Texas

While meeting customer demand is critical, how we grow is equally important. At Microsoft, our Community First approach guides us where we build, own and operate our datacenters, including our new datacenter campus in Pecos.

This work begins with a simple commitment: we show up as a lasting partner, not just a builder of infrastructure.

As shared in our letter to the community in Pecos and Reeves County, we are approaching this project as a new neighbor, with a focus on partnership, transparency and listening. We recognize that earning trust takes time, and we are committed to ongoing engagement with local residents, leaders and organizations as this project moves forward. The region’s elected leadership has welcomed the investment. Reeves County Judge Leo Hung, the county’s top elected official, said:

“We are excited to welcome Microsoft to Pecos. This investment reflects the strength of our region and its ability to support innovation at a global scale. It will create new opportunities for local businesses, support workforce development and reinforce Pecos as a place where forward-looking companies can grow and thrive.”

Our Community First approach in this region focuses on three priorities:

1. Listening and engaging early
We engage early and often through community meetings, local partnerships and ongoing communication across the life of the project, which gives residents multiple ways to ask questions and share feedback, just as we have in other Texas communities.

2. Creating local economic opportunity
This project is built to drive lasting regional growth. As well as supporting thousands of construction jobs, the hundreds of permanent operational roles will add a new industry to the local economy. We will also invest in workforce development and small-business support. We are focused on ensuring that local residents are prepared to take advantage of the opportunities created by the AI economy. This is part of a sustained commitment to the region, building on more than a decade of experience in Texas, including our operations in San Antonio:

Near San Antonio, where we have operated for nearly a decade, our Datacenter Academy partners with local colleges to prepare students for datacenter careers, including a $545,000 investment that has already reached more than 450 students. Statewide, workforce programs like TechSpark have helped create more than 1,100 jobs and engaged 20,000 Texans in digital skilling. We will bring the same model of local hiring, training and small-business support to West Texas.

3. Partnering for lasting community impact
Our investment reaches well beyond the datacenter, into education, digital inclusion and nonprofit partnerships. In fiscal year 2024, Microsoft and its employees contributed $11 million in cash and $103.3 million in donated software and cloud technology to more than 10,000 Texas nonprofits, alongside 42,000+ employee volunteer hours. In Pecos, we will direct that same commitment toward the priorities that matter most to West Texas residents.

Advancing sustainability through innovation

As we expand our datacenter footprint, we remain equally committed to building and operating our infrastructure in ways that reduce environmental impact.

Energy and emissions
This includes improving energy efficiency across our infrastructure, from compute to hardware, and building on the 4.7 GW of renewable electricity we have already contracted for our electricity use in Texas, advancing carbon-free electricity through renewable generation and other technologies. This investment is intentionally designed with flexibility in mind, allowing Microsoft to adjust capacity over time as demand evolves.

At launch, the datacenter campus will operate with a co-located natural gas power facility, an arrangement known as “behind the meter.” This serves the campus directly and independently of the public grid, so this demand does not take from the current grid. The plant’s design will integrate state-of-the-art air emissions controls, such as Selective Catalytic Reduction systems to lower nitrogen oxide emissions. Over time, we anticipate connecting the power facility and the datacenter to the broader grid and becoming part of the regional energy system, working in close coordination with utilities and local authorities. We will continue to drive additional improvements in environmental performance in line with our corporate commitments. This evolution reflects our long-term mindset in the region: as we grow, we intend to contribute to a more resilient and reliable grid that delivers value not only to our operations, but to the wider West Texas community.

Water stewardship
We plan to deploy closed loop cooling systems, which significantly reduce water requirements. This approach is expected to limit water usage by requiring only an initial charge of the cooling system at the start of operations, with no additional water consumption during steady-state operation. As a result, the total lifecycle water use of this datacenter is only a fraction of that consumed annually by a typical fast-food restaurant.

We are also designing our operations to minimize reliance on freshwater sources by utilizing nonpotable water where possible, helping to reduce pressure on shared community resources.

This builds on the way we approach water stewardship across Texas. Near San Antonio, Microsoft has helped fund the permanent protection of more than 1,500 acres in the Edwards Aquifer recharge zone — safeguarding a critical water source for over two million Texans — as part of our broader commitment to be water positive by 2030. In Pecos, we will continue to prioritize responsible water use, efficient design and close coordination with local authorities as our operations grow, and we will share our progress with the community over time.

Building for the future, responsibly

The datacenter in Pecos represents an important step forward in how we build infrastructure for the AI era by combining capacity at scale, energy and a commitment to responsible growth.

But just as importantly, it reflects how we do this work: in partnership with communities, with an enduring mindset and with a focus on creating shared value.

As we move forward, we will continue to engage closely with the community in West Texas, provide updates on our progress and ensure that this investment delivers lasting benefits for both our customers and our neighbors. Community members can learn more at Open letter to Pecos and Reeves County – Microsoft Local.

We look forward to building that future together.

Noelle Walsh leads the organization that powers the global Microsoft Cloud. She oversees the company’s physical cloud infrastructure and operations, with a charter focused on safety, security, availability, sustainability and competitive infrastructure growth — bringing decades of global operational leadership.

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1. Will AI amplify the intelligence of my organization and the attributes that make my company unique within its industry to grow my business; or will it use my intelligence for its own benefit, learning from my most important business flows and leveraging my intellectual property?
2. Can I trust that the outcomes are providing durable return on investment and that these solutions are running within the confines of my governance and security standards?
3. How do I get the visibility, control, flexibility and business model innovation needed to manage the costs associated with AI and maximize value?

I consistently advise customers that they need to build their own IQ on a platform of intelligence that is model-diverse, open and heterogeneous at every layer of the stack. Models are commoditizing. No company should be dependent upon any one model or any one model’s harness. Over the weekend, Satya warned of a world where every company across every sector is ceding value to a few models that eat everything they see. AI that is intended for growth should amplify the intelligence of an organization so that it compounds from within.

Companies also need an observability platform that provides governance, management, security and Financial Operations (FinOps) to ensure the ROI with AI. This enables AI to be trusted within the environment over which it reasons and puts the business in control of the outcomes.

Intelligence + Trust is embedded across Microsoft 365 Copilot, GitHub Copilot and Copilot Studio, where model diversity aligns cost and performance to each task. Microsoft IQ optimizes workflows, so context is routed efficiently and reduces unnecessary compute. Agent 365 is the control plane to observe, govern, manage and secure agents. We have built a system to manage AI spend as a core enterprise capability, not an afterthought. It is delivered across clouds and model providers without locking customers into a single approach.

Managing costs at scale

As agent usage scales, organizations need a clear set of levers to manage cost:

Model diversity. Any given inferencing model, model harness or agentic loop on its own does not help build out an organization’s IQ in ways that compound its intelligence. Both Microsoft 365 Copilot and GitHub Copilot are model-diverse by design without locking customers into a single provider. Different models — like GPT-5.5 or Claude Opus 4.8 — serve distinct roles with different economics. Matching the right intelligence to each task optimizes performance and cost.

Your IQ. Agents struggle with raw data. Significant compute is spent interpreting structure and context before useful work begins. The Microsoft IQ platform empowers your IQ by turning raw data into usable intelligence, continuously building a semantic understanding of how your organization operates across Microsoft 365 and line-of-business systems. It provides agents with the context they need upfront rather than requiring them to reconstruct it. The result is measurable: faster execution, higher accuracy and lower token usage. This is how intelligence compounds within your organization.

Financial operations. FinOps became critical when companies moved to the cloud and requires even greater attention as AI shifts from fixed pricing to usage-driven models. With Foundry and Agent 365, we are providing tools to help our customers optimize their AI costs today.

Frontier business models

Business models are evolving as we use AI to drive business outcomes. The User Subscription License (USL) has become the foundation, providing a package of capabilities for a predictable per-user-per-month fee. Usage-based licensing has emerged for long-running, multi-tasking agents, where cost aligns directly to the work performed.

Microsoft gives customers a unique combination of business model flexibility and integrated product experiences that is unmatched in the market. Microsoft 365 Copilot and GitHub Copilot use both models — a USL offering with not only value and capabilities, but flexible consumption on top. Today we’re announcing the general availability of Copilot Cowork worldwide, which requires the Microsoft 365 Copilot USL and is then usage-based.

Our model-diverse strategy allows customers to purchase capacity with the flexibility to use the right model for the job based on model strengths, economics and the latest innovations. Microsoft Agent Factory provides a single consumption model spanning Microsoft 365 Copilot (including Cowork), GitHub Copilot and agents built in Fabric, Foundry and Copilot Studio.

Our integrated product experiences put AI in the flow of work for both knowledge workers and software developers and manage capacity fluidly across the two. Historically these personas have been distinct, but increasingly the line between them is blurring. Coding is becoming a mainstream knowledge worker skill and chat and Cowork are becoming modalities important for software development. With Microsoft 365 and GitHub, we offer market-leading tools for both roles and make it easy to seamlessly manage capacity based on availability and need.

Agent 365: The control plane

As organizations adopt agents from Microsoft, another provider or build their own, a control plane is essential. Agent 365 gives IT and security leaders a single place to observe, govern, manage and secure agents across the organization. It builds on the Microsoft stack that enterprises trust: Entra for identity, Defender for threat protection, Purview for data governance and Intune for endpoint management. We are extending Agent 365 to include cost management, so organizations can monitor and manage agent spend alongside security and compliance. As the Frontier Firm operating model takes hold, leaders will manage human and agentic work as a single system, with visibility into both performance and cost.

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The two most essential elements in any AI solution are Intelligence + Trust. At Microsoft, this conviction shapes how we design every layer of our AI platform. Microsoft IQ enables organizations to harness their own unique IQ, bringing context to data and embedding AI directly into the flow of work to deliver faster, more accurate and more trusted outcomes while safeguarding assets and protecting intellectual property. Agent 365 provides that trust layer, ensuring every agent and AI artifact is observed across the environment so organizations can move decisively from experimentation to enterprise impact with confidence. As Jay Parikh put it at Build, AI alone will not change your business. The system running it will.

We have built this system for our customers and partners, where intelligence compounds from within and every agent operates with control, visibility and trust. Together, we can scale human ambition and define how AI delivers measurable business impact across every role, organization and industry.

Judson Althoff is the chief executive officer of the commercial business at Microsoft. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally.

The post Achieving success with AI appeared first on The Official Microsoft Blog.

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This isn’t just about chatbots, either. Those experiences are useful, but they don’t transform how large organizations operate. The real opportunity is teams of agents executing long running work across functions like software delivery, support, finance, HR, and operations — with the identity, context, policy, and human oversight required to trust them in production.

To make this possible, enterprises need more than access to a powerful AI model or scalable compute. What determines success is the system around the AI: how agents are built and deployed by engineering teams, how they’re contextualized in the enterprise, how they’re governed and observed in production, and how they improve safely over time. Without that system, AI remains fragmented, fragile, and difficult to trust at scale.

We’re taking a fundamentally different approach. We are building a comprehensive agent platform: one that supports many models, is open, and gives you choice and flexibility at every layer of the stack. And we are purposefully designing it with developers at the center. Today, the next pieces of that platform are clicking into place.

Building a system for the agentic enterprise

To succeed in this new era, an agent platform must meet a higher bar. It must run real production workloads, map real organizational complexity, and manage real business responsibility.

We’re building around three key principles:

First, it must be a single, integrated system, with support for a wide range of models.
Enterprises can’t afford to assemble their agent strategy one piece at a time. Disconnected tools stitched together after the fact can slow teams down and introduce unnecessary risk. Building, contextualizing, running, governing, and improving agents should happen within one coherent system. That’s why we’re bringing together Azure, GitHub, Microsoft IQ, Fabric, Foundry, Windows, Microsoft Security, and Microsoft 365 to operate as a single system you can use to deploy agents at enterprise scale. Enterprises also need the flexibility to choose the right model for the task, balancing quality, speed, and cost — including Microsoft models, partner models, and open models.

Second, it must be secured and governed by design.
Governance is easy to claim and much harder to deliver. Making it real means starting with a single stack that spans development through production, built on the identity, access, compliance, and security foundations enterprises already trust. By extending Entra, Purview, Defender, Agent 365, and the broader Microsoft Security stack, governance becomes native to the system rather than bolted on later, supporting the ambitions of an AI first enterprise without compromising control.

Third, it must improve continuously.
Enterprise AI systems can’t be static. Agent behavior, outcomes, and human feedback must flow back into the system, so it can improve safely over time under human oversight. As the system runs, models, workflows, and agents become more capable and more specific to an enterprise’s unique business processes. The result is a system that compounds in value the longer it’s in use.

These properties are becoming must-haves, and enterprises that align their AI ambitions with these three principles will pull ahead in quarters, not years.

So how does a system like this actually take shape inside a real enterprise? It starts where work begins, with how agents are built. Let’s walk through what that looks like on the platform we’ve built.

1. Build in GitHub

GitHub is where your developers already work. It’s where your dependencies live, where your application and code context is kept, where you collaborate with the open source community you depend on, and where you drive innovation. Building agents anywhere else means leaving all that behind.

Agents should be built the same way production software is built. You write code with GitHub Copilot to move faster. You bring together the assets that matter most: codebases, work items, agent skills, and tools. And because agents aren’t just code, you bring your evals and observability assets alongside them, all versioned the way any production system should be.

Agents must follow a lifecycle: source, test, deploy, observe, and improve. GitHub sets up that lifecycle and provides the necessary controls from day one. The result is a workflow designed for building agents with the right guardrails from the start. And you can do all this in one place, in a new app built for this system.

2. Contextualize with Microsoft IQ

Code is only part of an agent. To be useful, an agent also has to understand your business: your customers, your products, your contracts, your processes. Without enterprise context and intelligence you can trust, even the most capable model is guessing.

Enterprises require a wide variety of models and the ability to match the right model to the right job, but model choice alone is not enough. Microsoft IQ grounds agents in enterprise context by connecting to your business data wherever it lives, across Microsoft 365, your core business systems (such as customer and revenue data), and other systems your enterprise already relies on, like knowledge bases and your website. With Web IQ, the latest addition to the IQ platform, agents can also incorporate relevant information from the web when appropriate.

Contextualizing agents in enterprise data isn’t just about access. Pointing AI at raw information is inefficient and brittle. Microsoft IQ organizes, secures, and surfaces the right information in forms agents can actually use, so they can reach accurate insight without drowning in noise or hallucinating answers.

Once agents are grounded in the right context, enterprises can go further. With Frontier Tuning, you don’t just call AI models. You improve how they behave using your data and real-world workflows.

That includes Microsoft’s seven new MAI models, spanning image, voice, transcription, coding, and reasoning. Together, this model family is designed to work across the kinds of tasks that matter in the real world, and critically, these models are not static endpoints. They’re built to learn from how work actually gets done in your business.

Our reinforcement learning environments allow our models to be reinforced through actual outcomes in your environment. Think of them as training gyms for AI. Here the agent learns your very specific processes, standards, and way of working. It becomes specialized and adapted to you, delivering a measurable and better ROI.

Moreover, your custom or post-trained models all stay in your environment. Your intellectual property, your proprietary data, and the way work actually gets done become part of how your agents reason and act. The resulting intelligence runs in your environment, under your control, and the learning stays yours.

Without context and Frontier Tuning, agents are capable generalists. With it, they become a customized partner that understands the business they’re operating in.

3. Run in Foundry

Once agents are built and contextualized, they need a place to run. Not as an experiment. In production.

Agents and teams of agents place very different demands on a runtime than traditional applications do. They need to reason, act, call tools, coordinate with other agents, and adapt over time, all while operating under enterprise controls. Foundry is the runtime designed for that reality.

• The largest collection of models: Different agents need to be good at different things at different price points. Whatever the task, whatever the cost profile, Foundry provides access to the right model, and an optimized model router helps you balance quality, speed, and cost for each agent.
• Optimized performance for open models: With Fireworks AI on Foundry, enterprises get faster, more efficient inference directly into the platform.
• Support for any agent, including those not built on our stack: Bring in agents built on the Microsoft Agent Framework, LangGraph, GitHub Copilot SDK, Claude Agent SDK, or a custom harness.
• Tools and actions: Agents act on enterprise systems through MCP, connectors, APIs, and workflows, with safe execution by default.
• Evals and traces: Observability and traces make agent behavior measurable. If you can’t measure it, you can’t improve it.
• Continuous optimization: Foundry enables tuning of models, harnesses, IQs, tools, and actions over time, improving performance as agents operate in your world.

A trust, security, and policy rail wraps the entire runtime. Policy applies consistently across context access, tool calls, optimization updates, traces, and response delivery. The agent doesn’t just work. It works the way your enterprise requires.

This is where your agent stops being a project and starts becoming a production system.

4. Govern with Agent 365

Now multiply that agent by hundreds. Then thousands. That’s what happens as different teams build agents across an enterprise. Some are well designed. Some aren’t. Some have access they shouldn’t. Others are doing valuable work that no one else in the organization benefits from.

Enterprise governance isn’t optional. Enterprises need a way to see what’s running, understand what it can access, monitor task adherence, and enforce policies across their entire agent estate.

Agent 365, along with Entra, Purview, Defender, and the broader Microsoft Security stack, come together to do just this. And if you’re interested in AI for security in addition to securing your AI, there’s “MDASH.”

Every agent in your organization shows up in a single catalog, whether it was built in Foundry or elsewhere. IT sees who deployed an agent, what data and tools it can access, how it’s behaving, and what it costs. They can enforce policy or take action when required.

One place. Full visibility. Real control over what your agents do and don’t do.

5. Improve continuously

Agents can’t be static. Every agent action generates signal: trajectories, outcomes, feedback. The system captures it, refines it, and feeds it back. Observe. Evaluate. Improve. Roll out safely. Repeat.

This learning loop runs continuously, in production.

Most gains start with eval-driven improvements to the agent itself: prompts, context, skills, and tools. As clear patterns emerge, learning can extend into model routing across multiple models, fine-tuning, or reinforcement learning. But it all stays anchored in evaluation, improving agent quality and ROI to the level the business requires.

The loop is governed, not closed. Enterprises need to audit it, correct it, and control how to roll out changes. The system becomes more capable over time, guided by human oversight and increasingly autonomous, but never beyond your reach.

This is the hill-climbing model in action: system-level improvement, happening continuously while the system runs.

6. Surface where people work, and scale on Azure

Of course, none of this matters if it doesn’t reach the people doing the work.

Agents surface directly in the flow of work, in Teams, across Microsoft 365, and inside your own applications and experiences. Identity, security, and compliance are built in from the start, so the agents that your teams rely on day to day inherit the same trust model as the rest of your environment.

We support multiple platforms, but your agents can be developed and run in an optimized and secure way on Windows. You can run models both in the cloud and locally on your machine, and best-in-class sandboxing lets you run always-on agents safely.

When you need compute optimized for AI, global and sovereign infrastructure, or a route to market, the system scales on Azure, the same enterprise foundation customers have trusted for decades.

The system compounds

Every leading enterprise will converge on this model: a central AI platform that orchestrates work across the business, bringing together data, models, agents, and human judgment into a continuously improving and secure system.

As that system runs, its value compounds. Velocity increases and the bottleneck shifts from effort to human creativity and coordination. People are able to do more work independently, guided by shared context and fewer handoffs, while the business moves faster without adding friction.

We’re in a time of profound disruption. The enterprises that lead in this moment will be those that adapt as conditions change, simplify how work is coordinated across the business, and consistently turn intelligence into real outcomes. Microsoft’s agent platform is designed to do exactly that: it unlocks the ability to build, contextualize, run, govern, and improve agents as a single, integrated system.

At that point, the platform becomes more than a build layer. It becomes the operating system for enterprise AI at scale, where intelligence and trust are built in by design.

The post AI alone won’t change your business. The system running it will. appeared first on The Official Microsoft Blog.

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This platform shift comes with more information than ever, ready at your fingertips. This shift, it’s about building fast AND THEN: it’s about building, operating, optimizing and observing. Securing your infrastructure, applications and agents in a seamless way that doesn’t slow you down from the moment you open your laptop to the moment you ship to production.

But there’s a duality in being a developer – you’re a tinkerer, choosing your own tools and models, and you’re an enterprise builder, shipping systems that demand governance, security and trust from day one.

Developers don’t need another way to just build and run an agent or app. They need trust. They need native context and knowledge. Most of all, they need choice to access the right model for the right problem.

This duality is where Microsoft thrives. We ask: what does it mean to be a modern developer today? And at Microsoft Build, we shared how we empower developers to build in this era of ubiquitous intelligence with the controls and security you expect at scale – on a platform that’s model diverse, open and heterogeneous at every layer of the stack. Bringing together what you know with what the world knows natively.

There’s a lot of news today, but there are three themes to anchor on.

First, intelligence that’s truly yours. With the Microsoft Agent Platform powered by your context and intelligence from Microsoft IQ, you can build your agent in GitHub, deploy it to Microsoft Foundry and optimize it automatically with models best suited for the job. Ground it in your intelligence and the world’s knowledge, then access it via Microsoft Teams, M365 or anywhere your team works. Designed to reduce the need to make tradeoffs between context and governance, security and speed, or models and tools.

Second, the full stack built your way. You should be able to build the way you want to build, with the tools, models and workflows you choose, and make it real. This expands beyond the agent platform to across the stack. Silicon to OS to developer tools to cloud – and that starts with Windows. Not Windows for “Windows developers.” Windows for developers, period. We’re bringing a new developer configuration that gives you more flexibility, a frictionless intelligent shell and terminal experience, local sandboxing for agents, new Windows Subsystem for Linux capabilities and powerful options to do it on your local machine.

Third is what comes next, where agentic systems move from code to human progress, amplifying what scientists and researchers can achieve. New frontiers in science and computing that start with the same developer platform underneath.

Together, developers get a multi-model ecosystem, from your laptop to the cloud, so you can build the frontier without giving up the control and craft that truly makes the work yours.

And as always, it starts with the developer. Let’s dive in.

Agents that know you, your business, and the world

As models become more capable and more available, the differentiator for any organization is no longer access to intelligence, but ownership. How does your expertise, data and way of working become a system that continuously learns and drives better outcomes? The goal is an ecosystem that gives companies their own agency, not one that funnels value back to a consultant or the model maker.

Your agents should reflect how you think and operate, from your business logic and institutional knowledge, down to your workflows.

That starts with context. Microsoft IQ, generally available today across GitHub Copilot, Microsoft Foundry and Copilot Studio, is a new context layer that grounds agents in both world knowledge and enterprise knowledge. Work IQ is the workplace intelligence layer for agents, capturing how work actually happens across Microsoft 365, organizational systems and external sources: people, emails, documents, meetings and how they connect. The Work IQ APIs, generally available on June 16, provide programmatic access to this intelligence layer and give agents the context they need to work effectively in your organization. Fabric IQ provides a shared semantic foundation over structured business data. Foundry IQ ties it together and enables retrieval planning across both enterprise knowledge and the live web.

New to the family is Web IQ, announced today: the fastest real-world grounding you can give your agents. An AI-first web search stack that’s model-agnostic and MCP-native, returning relevant passages at nearly 2.5x the speed of the next best alternative.

We’re also looking at how this context applies to new form factors, specifically always-on autonomous agents. Microsoft Scout is a new personal agent for work that we are bringing to Frontier customers today. Built on OpenClaw and WorkIQ, Microsoft Scout understands how you work, uses the tools you already live in, like Teams and Outlook, and proactively handles things like meeting prep, scheduling conflicts and routine tasks without asking. We’re excited to share more soon as we expand what Microsoft Scout can do and roll it out more broadly.

On the model layer, the Microsoft AI Superintelligence Team released a family of seven new in-house models, starting with MAI-Thinking-1 – Microsoft AI’s first reasoning model. Trained from scratch with zero distillation on enterprise grade, clean and commercially licensed data you can build on with confidence.

It’s a mid-sized, 35 billion active parameter model with a 256K context window built for high efficiency and performance, but importantly, at a low-token cost. On a blind test, independent raters prefer it to Sonnet 4.6 [1], and it matches Opus 4.6 on coding abilities on SWE Bench Pro [2]. MAI-Thinking-1 was designed to be good at complex multi-step instructions, long-context reasoning and code generation, and it’s open now on Foundry in private preview.

But that isn’t the only new model. MAI-Image-2.5 and its flash variant are Microsoft’s first models to serve both text-to-image (#3 on the Arena AI leaderboard) and enabling image-to-image workloads (#2 on the Arena AI leaderboard, surpassing Nano Banana 2). These are especially useful in creative workflows, when you want some assistance taking a concept into reality or enhancing existing image work. These models are live in PowerPoint, rolling out on OneDrive, and today, they’re landing on Foundry with market-leading quality per dollar.

There are other new members of the MAI family too: MAI Transcribe 1.5 combines state-of-the-art accuracy across 43 languages, with streaming coming soon. MAI-Voice-2 and its flash variant are now available in more than 15 additional languages with new voice options. And MAI-Code-1, our inference efficient coding model tuned for GitHub, is now available in Copilot and VS Code.

Developer choice doesn’t stop at our catalog. MAI models will also be available on Fireworks AI, Baseten and Open Router. And Fireworks AI is now generally available on Foundry, giving developers a single platform experience with enterprise governance and Azure data residency, regardless of the model they choose.

For organizations ready to make intelligence truly their own, Frontier Tuning applies reinforcement learning within your compliance boundary so agents can learn how the business actually works. Using your own data, domain knowledge and workflows, the result is a loop that sharpens as agents work. Available in private preview today.

And security and governance wraps the entire system. Agent 365 for local agents extends Entra, Defender and Purview into a single control plane to observe, govern and secure agents across your estate, regardless of where they’re hosted or what framework they’re built on. This is how you build at speed while maintaining control.

Alongside it is an open, end-to-end trust stack for AI agents on any framework anchored by two open-source projects: Adaptive Spec-driven Scoring for Evaluation and Regression Testing (ASSERT) for policy-driven safety evaluation, and the Agent Control Specification to standardize where and how to apply controls in the agent loop.

Also strengthening our defense is Codename MDASH. Our new multi-model agentic security system deploys 100+ agents to find exploitable bugs by reasoning about data flow, business logic and exploit chains with context-aware fixes delivered directly in the Defender Portal.

The full stack, your way

When we think about work in the agentic age, it requires a ubiquitous intelligence platform that spans cloud and edge. But as a developer, how do you build these rich, agentic systems while staying firmly in control? That means staying in flow instead of waiting on tools and running experiments in minutes rather than hours.

It starts at the silicon, and that’s where Surface RTX Spark Dev Box comes in – it’s designed for sustained workloads: long-running training jobs, agentic AI pipelines and local model fine-tuning.

Powered by NVIDIA RTX Spark, it delivers up to one petaflop of AI compute and 128 GB of unified memory, capable of running up to 120B parameter LLMs with up to 1 million tokens context using agents locally without cloud GPU instances [3]. Windows Services for Linux (WSL) 2 with native GPU passthrough and full CUDA support comes pre-configured for developers, with Visual Studio Code, GitHub Copilot and many more of your favorite tools pre-installed. Surface RTX Spark Dev Box will be available later this year in the US via Microsoft.com.

In the OS layer, Microsoft is making Windows an agent-native runtime. Microsoft Execution Containers (MXC), now in preview, gives developers and IT administrators a simpler way to create enterprise-grade sandboxed environments for agents, with containment enforced by the operating system itself. Describe your requirements once, and Windows enforces them everywhere your agents run.

This technology is now being used by OpenClaw on Windows, enabling execution of multi-step workflows inside these OS-enforced boundaries. NVIDIA’s OpenShell secure runtime for autonomous agents uses MXC and adds policy management, inference routing and PII obfuscation. Together, these capabilities give developers a safe environment for agent development and deployment and provide IT teams with the governance tools they need across local devices and cloud environments.

And when agents move to the cloud, hosted agents in Foundry Agent Service, in preview, provide the same model at scale: instant-on sandboxes per session, isolated execution, persistent memory and elastic scale. Think of it as the primitive for agents the way containers were for cloud-native apps.

Agentic development flows, whether in the IDE or in the command line, helps us write code faster than ever before, but that’s only one part of building software.

The GitHub Copilot app, now in preview, brings agentic development to a native desktop experience – and a much wider audience. Start from an idea, an existing issue or PR, orchestrate multiple agent sessions in parallel, and keep changes moving through review, CI and merge. Each session uses git worktrees, so work stays separated. Copilot handles execution, while developers say in control.

Developers can generate applications in seconds, but getting those apps into production still requires stitching together databases, APIs, authentication and infrastructure.

At the platform layer, Rayfin, now in preview, solves that. It brings a managed, backend-as-a-service to Microsoft Fabric, defined through GitHub-based workflows, so developers can move from prototype to production without managing infrastructure. Integration with Replit creates a fast path from prototype to enterprise-grade deployment with governance from day one. And as agentic applications scale, Azure HorizonDB delivers performance and reliability to meet your most demanding database requirements. It’s a fully managed PostgreSQL service on Azure that delivers more than 3x the throughput of comparable self-managed setups in internal testing.

The future belongs to builders

In the same way long-running agents have helped redefine software development and the role of the developer, new agents will help change research and development and what scientists can achieve.

Microsoft Discovery is generally available today. Built on Azure, it gives researchers an enterprise-grade agentic AI platform for the full science workflow. BHP is using it to find copper-leaching solutions in months instead of years. Syensqo is accelerating semiconductor R&D. GSK is iterating on drug discovery. Additionally, a free Discovery local app was announced for the broader scientific community. It is available in preview and only requires a GitHub Copilot account.

Finally, our next generation quantum computing chip Majorana 2 represents a giant step toward scale: an average qubit lifetime of 20 seconds with instances up to a minute, 1,000x higher reliability than our previous generation, and a path to one million qubits on a chip that fits in the palm of your hand. With the help of agentic AI, we will achieve a scalable quantum machine by 2029.

***

Platforms don’t shift on their own; developers build them forward. Today is about giving you more to build with.

These are just some of the announcements at Build. We’re excited to connect with those of you joining virtually and in person for keynotes, code deep dives, hack sessions and more. Many sessions will also be available on demand.

For the full set of news, visit the Microsoft Build Live blog.

Now, let’s build.

***

Footnotes:
1: measured via Surge our independent human rating partner
2: Based on the SWE Bench Pro Benchmark
3: Source: NVIDIA. Based on 1 Theoretical FP4 TOPS using the sparsity feature.

Related:
Check out our live blog, Microsoft Build Live

Connect your local project to the Microsoft Build session catalog with this GitHub Copilot CLI skill: microsoft/Build-CLI: Experience Microsoft Build, directly from your terminal, with GitHub Copilot C…

Read more about all the updates for developers

The post Microsoft Build 2026: Be yourself at work appeared first on The Official Microsoft Blog.

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But many are encountering the same reality: the challenge is no longer deciding whether to invest in AI — it’s scaling adoption and delivering consistent, enterprise-wide impact.

Over the past year, one thing has become clear. Organizations aren’t asking if AI matters. They’re asking how to make it real — how to embed it into the way work gets done and ensure it drives meaningful results.

That’s where many are getting stuck.

Because the barrier is no longer experimentation. It’s execution.

Intelligence and trust as the foundation

At Microsoft, we believe successful AI Transformation depends on two foundational elements: intelligence and trust.

Organizations need to harness their own work intelligence — the data, workflows and expertise that make their business unique — and apply it through AI in ways that are flexible, secure and governed. That requires a platform that supports model diversity and continuous innovation, without compromising enterprise-grade security, compliance and reliability.

Just as importantly, AI must be embedded into the flow of work — how people collaborate, make decisions and operate day to day. For that to scale, systems must be transparent, secure and accountable.

This is where real enterprise value is created — and where many organizations need a clearer path forward.

Achieving impact at scale requires more than deploying new tools. It requires a trusted foundation — integrating data, security, privacy and governance — and a new model for delivering AI into the business.

That’s why Microsoft and EY are deepening our alliance — to help organizations move faster from AI ambition to measurable business outcomes.

From pilots to production

There is no shortage of AI pilots in today’s market. But pilots don’t transform businesses. What organizations need now is the ability to scale AI across the enterprise, integrate it into core workflows and deliver sustained, repeatable impact.

EY brings that experience.

As one of the first global organizations to deploy Microsoft 365 Copilot at scale, EY began with an initial rollout to 150,000 of its people, quickly demonstrating what’s possible when AI is embedded into everyday work.

The results were significant and measurable:

• A 15% productivity gain, reinvested into client delivery and continuous learning
• 94% monthly adoption and 85% weekly usage
• 63% of enabled employees using Copilot three or more days per week
• 81% of employees reporting time savings, with 84% redirecting that time to higher-value work and 73% improving quality of output

The impact goes beyond individual productivity into agentic AI in core business operations:

• Finance operations modernized with intelligent agents, driving 95% faster lead times and more than 37% reduction in operational costs
• A multi-agent AI framework was deployed across 130,000 Assurance professionals and 160,000 audit engagements
• Tax workflows were transformed through document automation, reducing manual effort by up to 90%

With these results, EY is now expanding Copilot through Microsoft 365 E7 to more than 400,000 of its people worldwide, moving from early success to true enterprise scale.

This is what enterprise-scale transformation looks like — not isolated wins, but sustained impact across the organization.

It’s also why EY serves as Customer Zero — applying Microsoft AI technologies internally to prove what works before bringing those solutions to clients.

Investing in what actually drives outcomes

Building on this foundation, Microsoft and EY are jointly investing more than $1 billion in a new initiative designed to help organizations move from isolated AI use cases to enterprise-scale transformation.

This effort brings together Microsoft’s AI platforms, including Azure, Microsoft 365 Copilot, Foundry, Fabric and security — and EY’s deep industry capabilities and transformation leadership.

But what differentiates this initiative isn’t just what we bring. It’s how we deliver it.

At its core is a shared focus on helping organizations become Frontier Firms — where AI is embedded across the enterprise, not layered on top.

In a Frontier Firm, data, workflows and decision-making are connected end to end. AI becomes part of how work happens, and human expertise is amplified by intelligent systems.

Reaching this level requires more than investment. It requires execution.

A new model for execution at scale

This is where our approach is fundamentally different.

Microsoft and EY are cooperating as an integrated transformation engine — co-developing, co-engineering and co-delivering solutions aligned to real business priorities.

A key part of this model is Microsoft’s Forward Deployed Engineers (FDEs), who work side by side with EY transformation teams directly within customer environments.

Together, these teams:

• Co-create solutions grounded in business needs
• Accelerate deployment across complex systems
• Stay engaged from initial use case through full-scale adoption

This integrated model closes the gap between strategy and execution. It reduces friction across the technology stack and creates a direct path from pilot to production — at enterprise scale.
It also ensures that intelligence and trust advance together, embedding AI across data, applications and infrastructure in ways that can be governed, secured and continuously optimized.

Importantly, it establishes a repeatable blueprint — one that organizations can use to scale AI adoption across functions, industries and geographies.

Why this matters now

Organizations are under pressure to move faster — to go beyond experimentation and deliver AI across the enterprise.

What they need is not just technology, but a clear path to execution — grounded in both intelligence and trust.

AI is not simply about doing work faster. It’s about enabling people and organizations to do more — focusing on insight, creativity and higher-value decision-making.

Our work with EY demonstrates what’s possible when AI is deployed with purpose at scale. Together, we are bringing those learnings to customers around the world — helping them accelerate transformation, unlock efficiencies and create new opportunities for growth.

Microsoft and EY are committed to helping organizations turn AI ambition into enterprise impact.

Learn more in the official announcement.

The post From AI pilots to enterprise impact: Why execution is the new differentiator appeared first on The Official Microsoft Blog.

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Spend time with any software engineering team right now and you’ll see something worth paying attention to. Over the last few years, the way software gets built has moved through four distinct patterns of human-agent collaboration — and the same patterns are beginning to show up across other functions of the firm.

• Author: You’re producing the work, calling on AI to help as needed — a line of code, a sentence, a chart.
• Editor: You set the intent and AI creates the first draft for you to edit and approve.
• Director: You create a spec and hand off entire tasks for AI to execute in the background.
• Orchestrator: You design a system where multiple agents run in parallel across a workflow, flagging exceptions and escalations to you.

Every business leader knows the world is changing, but far fewer have a clear picture of what to do about it. These four patterns are the place to start. The real work ahead for leaders is redesigning their firm’s operating model around the collaboration patterns.

As agent use increases, human involvement doesn’t disappear — it changes shape. What declines is the amount of tactical, step-by-step execution work humans do themselves. And what rises is the need for humans to set direction, define standards and evaluate outcomes.

Ultimately, the goal is not to move every task and business process to the fourth pattern. Instead, it’s up to leaders to help their organizations develop clarity around matching workstreams to the right collaboration pattern. That’s the shape of the Frontier Firm: defined by how deliberately leaders design work across functions, matching the level of human involvement to the outcome.

What the data shows

Our 2026 Work Trend Index research reinforces this shift across roles and industries. We analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. We also spoke with leading experts in AI, work and organizational psychology to help us unpack the insights from the data and understand where all this is going. The conclusion is consistent: the constraint is no longer what people can do, it is how work is structured around them.

• AI lifts individual potential. A privacy-preserving analysis of more than 100,000 chats in Microsoft 365 Copilot shows that 49% of all conversations support cognitive work — helping workers analyze information, solve problems, evaluate and think creatively. This shift is already visible in output, with 58% of AI users saying they’re producing work they couldn’t have a year ago, rising to 80% among Frontier Professionals, the most advanced AI users in our research. Additionally, when AI users were asked which human skills are most important as AI takes on more work, they said two topped the list: quality control of AI output (50%) and critical thinking — that is, analyzing information objectively and making a reasoned judgment (46%).
• The Transformation Paradox. We are seeing a pressure point emerge within the organization where the pull to perform collides with the push to transform. 65% of AI users surveyed fear falling behind if they don’t use AI to adapt quickly, yet 45% say it feels safer to focus on current goals than to redesign work with AI. And only 13% of workers say they’re rewarded for reinvention of work with AI even if results aren’t met. The same forces accelerating AI adoption are holding it back.
• Every organization is a learning system. Our results show that organizational factors like culture, manager support and talent practices account for more than 2X the AI impact of individual factors like mindset and behavior (67% vs. 32%). Specifically, the findings underscore the importance of an AI-ready environment: a culture that treats AI as a strategic advantage and encourages experimentation, managers who model and incentivize AI use and talent practices that build skills and create space to apply them. The real question isn’t whether people have the right skills, it’s whether the organization is built to unlock them.

The firms that build a new operating model today won’t just move faster in the short term. They’ll build something more durable, setting themselves up to create value in ways that we can’t yet conceive of: an organization that learns faster than its competitors, compounds its own intelligence and gets harder to catch with every cycle.

For deeper analysis, see the 2026 Work Trend Index Report.

Enabling the Frontier Firm with Copilot Cowork — now mobile, extensible and enterprise-ready

None of an organization’s system scales without infrastructure that brings people and agents into the same flow of work with connected data and the ability to manage and govern it all. Microsoft 365 Copilot is built for exactly that.

Today, we’re expanding Copilot Cowork with new capabilities for Frontier customers to help organizations move from isolated AI tasks to coordinated, multistep work. Cowork enables people to define outcomes and delegate work across apps, business systems and data, with execution that stays directed and controlled throughout.

This update introduces Copilot Cowork Mobile for iOS and Android, along with a growing plugin ecosystem for Cowork, bringing more of an organization’s tools and data into these experiences. This includes native plugins across Microsoft services like Dynamics 365 and Fabric, and partner integrations available in the coming weeks like LSEG (London Stock Exchange Group), Miro, monday.com, S&P Global Energy and more. Organizations can also build custom plugins to turn their own workflows and expertise into reusable, scalable processes. Additionally, a first wave of federated Copilot connectors in Researcher and Microsoft 365 Copilot Chat is generally available today from partners like HubSpot, LSEG (London Stock Exchange Group), Moody’s, Notion and more.

Together, these updates extend Copilot Cowork from a task-based assistant into an extensible platform that helps orchestrate work across Microsoft and third-party systems. With management and governance through Microsoft Agent 365, organizations can deploy and scale agents across core business functions like sales, service and operations.

For more on these product innovations: Microsoft 365 blog.

AI is no longer an experiment. It is an execution challenge. Employees are already working across all four patterns. The open question for every leadership team is whether they can catch up. Access to AI won’t be the advantage for much longer. How the work is designed around it will be.

Jared Spataro, CMO, AI at Work at Microsoft, shapes how every organization applies AI and agents to reduce costs, create new value and define the future of work. He leads research, strategy and product across Copilot, Copilot Studio, Microsoft 365, Dynamics 365 and Power Platform.

The post How Frontier Firms are rebuilding the operating model for the age of AI appeared first on The Official Microsoft Blog.

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