Microsoft News

Most business leaders I meet no longer need to be convinced that AI works. They’ve seen the pilots, the demos and the copilots. The question I hear in boardrooms now is different: How do we move from AI that impresses to AI that changes how the business actually runs?

That is the question Microsoft Frontier Company was built to answer, and to date, we have engaged on nearly 500 projects with over 200 customers across 28 countries.

Customers including 3M, Alaska Airlines, BMW, Core42, KT, Land O’Lakes, London Stock Exchange Group, Mastercard, McKesson, Northern Trust, Novo Nordisk, Publicis Groupe, Saint-Gobain, Siemens and Unilever are seeing real value from working side by side with our Forward Deployed Engineering teams.

More importantly, most customers where we have completed initial projects have returned for additional engagements, generating hundreds of follow-on projects. Customers don’t come back for engineers. They come back because the first engagement delivered something they could measure and they can see the next workflow worth redesigning.

The technology is ready. What slows AI down is everything around it. That means identifying where AI can deliver the greatest value, building in trust and governance, redesigning the operating models and helping people work in new ways. Technology and people have to move forward together.

Unilever is collaborating with Microsoft engineers to bring AI into its factories. On the factory floor, intelligent technology helps teams boost efficiency and deliver superior products to customers. In Pondicherry factory — Unilever’s largest Home Care Liquids factory in South Asia and a 2026 World Economic Forum Lighthouse site — a digital twin built on NVIDIA Omniverse and hosted on Azure simulates a real factory environment with integrated live data. This helps anticipate issues before they occur, monitors performance in real time and guides operators through the fixes and recommendations.

3M is working with our engineers to help its Global Business Services team automate customer order management. The companies are building an AI agent-driven workflow to assist with credit checks, delinquency assessments and system updates, with human-in-the-loop controls and a custom monitoring dashboard for real-time visibility and approvals. The solution is expected to significantly reduce manual effort, improve speed and consistency and accelerate cash flow — freeing staff for higher-value work while enabling scalable, auditable operations.

Publicis Groupe is co-building with our engineers to bring AI into the heart of how it works and delivers for clients. Microsoft engineers work side by side with Publicis teams to design and build agentic processes grounded in Microsoft IQ, so agents work with the business context, data and knowledge behind Publicis’ work. These processes use reinforcement learning, drawing on real-world outcomes and human feedback to keep improving how the agents perform over time. The approach combines very fast engineering with hands-on knowledge transfer, helping Publicis build lasting AI capability. The work builds on Publicis’ rollout of Microsoft 365 Copilot to all 114,000 of its employees.

Northern Trust needed to accelerate onboarding to its Asset Servicing Front Office Solutions platform, a process that took about two and a half weeks per client. Our engineers worked alongside Northern Trust’s experts to build an AI-powered onboarding agent within the Excel and SharePoint tools analysts already use. The result: data preparation time fell 83%, from two and a half weeks to three days, with 96.5% first-pass accuracy in testing. Analysts remain in control while gaining more time for higher-value client service.

KT, one of South Korea’s largest telecommunications providers, wanted to help network operators identify and respond to problems faster. Our engineers worked from KT’s offices to co-build AIO-Net, a continuously learning AI agent platform informed by KT’s operating procedures and veteran network experts. It now detects network issues in two to three minutes, down from more than ten, while keeping people in control of decisions and actions.

Saint-Gobain, the 360-year-old construction materials company, is using AI to help reduce the carbon footprint of cement. Working alongside its researchers, Microsoft engineers used Microsoft Discovery to gather 62,000 patents and 44,000 research abstracts into a knowledge base in hours, work Saint-Gobain estimates would have taken researchers a year. In about six weeks, the team identified new hypotheses for cement additives that scientists are now testing in the lab.

Across all of these customers, the pattern is the same:

1. Begin with the value, not the technology.
2. Redesign the process, not just the task.
3. Build in fast cycles with the customer’s teams.
4. Put security, governance and cost management in place from day one, so scaling doesn’t stall.

This is how organizations move from isolated AI projects to sustainable transformation: by focusing on measurable outcomes, redesigning work and building the capabilities to keep improving over time.

New research from IDC underscores why this approach matters. While 66% of organizations report substantial or transformative results from AI, only 48% are scaling or seeing consistent value across their organizations. At the same time, 71% expect AI spending to increase by at least 10% in the next year, putting even greater pressure on businesses to demonstrate measurable returns. The challenge is no longer simply adopting AI. It is reimagining how work gets done and turning that transformation into lasting business impact.

If we’re asking customers to redesign how they work around AI, we have to do the same. Microsoft Frontier Company was deliberately designed to be flat and fast, with one front door for customers, and as a reference for what an AI-native organization can look like. We are learning, adjusting and moving fast, the same way we ask our customers to.

The gap between AI that impresses and AI that delivers won’t close on its own. It closes workflow by workflow, with people who understand the business sitting next to people who can build.

Our success will be measured by more than what we deliver. The real test is what our customers can do after we’ve worked together: run, govern and improve their own AI systems, on their terms, with their intelligence protected. Their success is the clearest measure of ours.

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

Rodrigo Kede Lima is the President of Microsoft Frontier Company and leads the operating business of 6,000 industry and engineering experts focused on delivering Frontier Transformation through AI for customers around the world.

NOTE

IDC Infographic, sponsored by Microsoft, The Real Business Value of AI, IDC #US54985026-IG, November 2026

The post From AI ambition to business impact appeared first on The Official Microsoft Blog.

Results are widespread. Value at scale is not, and that gap is what this year’s research from IDC, commissioned by Microsoft, set out to explain.

In our fourth year commissioning IDC, more than 5,200 senior IT and business leaders across 28 industries shared what it takes to maximize value as AI scales. The clearest lessons come from Frontier Firms, the group last year’s research introduced: the top 10% of organizations on IDC’s AI maturity scale.

• Frontier Firms lead on return. They report an average 4x return on AI versus 2.7x for slow adopters (the bottom 30% on IDC’s scale), and 39% see returns of 5x or more versus 4% of slow adopters.
• Measurement is the next test. Only 61% of organizations are highly confident they can measure AI’s business value and ROI, versus 93% of Frontier Firms.
• Governance is in place, but confidence lags. 65% of organizations have an established process to govern agents, yet only 60% have high confidence in addressing AI security and governance risks.
• Workforce readiness is uneven. 67% say their workforce reports high trust in AI, yet only 20% have a comprehensive, organization-wide skilling strategy.

“Organizations that scale AI value aren’t simply spending more. They manage AI investments, models, agents, governance and skills as one discipline, and that is what turns early results into value they can repeat across the business,” says Kathy Lange, Research Director, AI, Data and Automation Software, IDC.

Organizations now use AI in 6.5 business areas on average, led by IT infrastructure, cybersecurity and customer service, with the fastest growth in industry-specific uses.

Employee experience, revenue growth, customer experience, innovation and risk reduction now rank nearly even as the places AI strengthens the business. Cost is an important consideration, but it is evaluated alongside accuracy, security, compliance and scalability.

66% of leaders say AI has delivered substantial or transformative results, but only 48% are scaling that value across multiple business units. Agents follow the same pattern: 36% of organizations are still piloting them.

The widest gap is people: workforce confidence is running well ahead of the plans to support it. The organizations pulling ahead are closing these gaps by design.

IDC’s maturity scale weighs spending discipline, breadth of use, agent adoption, skills, confidence in managing AI risk and outcomes. On agents specifically, Frontier Firms report a 3.8x return versus 3.0x for slow adopters. These returns are leader-reported, and the research shows association rather than cause.

What they do differently comes down to five behaviors:

• They invest ahead of plan. 90% of Frontier Firms exceeded their planned AI spend.
• They deploy AI broadly, across most of the business, not just a few teams.
• They put agents to work. 96% are rolling out agents at scale, and 53% orchestrate them across workflows.
• They govern so they can scale. 87% have a process to govern and oversee agents, versus 51% of slow adopters.
• They build skills everywhere. 80% have an organization-wide AI skilling strategy, versus 3% of slow adopters.

At Bristol Myers Squibb, the RABIT agent “connects the dots across meetings, helping teams make better decisions while reducing administrative follow-up work,” says Lakshman Yerramalla, Director of Portfolio Governance and Delivery for Global Manufacturing Operations. Project managers who use it save up to an estimated 20% of their time.

The lesson from Frontier Firms is one I believe every leader can act on: scaling AI value means managing models, agents, governance and measurement as one accountable system. Value and control aren’t a trade-off.

I see the same pattern with customers: the organizations scaling fastest treat AI as a change in how the business runs, not a technology rollout. “The skilling that a lot of companies miss is the business skilling,” says Eric Tabor, Chief Digital Officer, KOHLER. “We also focus on how to dissect processes and convert them into AI workflows.”

Trust and security, leading-edge technology and model performance tie as the top criteria for choosing an AI platform (44% each). Match the right model to each job, balancing cost with protection of intellectual property and data.

Agents create the most value when work is redesigned around them. The bigger opportunity is what they free people to do more of: leaders point to creativity, innovation and business reinvention (41%), customer-facing work and relationships (39%) and analytical work and data interpretation (39%). Connect agents across processes, with human oversight where it matters.

Trust is building faster than confidence. Most leaders report high trust in their AI systems, yet fewer are highly confident addressing security and governance risks. As agents take on more of the work, trust has to be earned continuously, and governance has to reach beyond models to keep people in control of what agents decide and do.

Conagra Brands made governance its starting point. “Laying the foundation of proper governance, observability and responsible AI was our first step to engaging our employees with AI,” says Tracy Schaefer, Chief Information Officer, Conagra Brands. “We built a SteerCo to review risk factors, access and identity management.”

Connect investment and performance data to business outcomes, compare how processes perform before and after AI and direct spending where it delivers the most value. With 71% of organizations expecting AI budgets to rise 10% or more in the next 12 months, this discipline will separate leaders from the rest.

Underneath all four disciplines are people. None of it scales without an organization-wide plan to build AI skills. That’s where Frontier Firms are furthest ahead, and it’s where I’d urge every leader to start.

Black & Veatch reached 98% adoption of Microsoft 365 Copilot through deliberate change management, including early-adopter cohorts that surfaced real business use cases. “Our goal is to be a Frontier Firm. The intention is not to stop at pure adoption,” says Mike Adams, Chief Digital Technology Officer, Black & Veatch.

IDC Executive Analyst Shari Lava will lead three sessions at Microsoft Ignite on Nov. 18, including The AI ROI Divide: Why Some Organizations Scale and Others Stall.

The gap that matters now isn’t between companies that use AI and those that don’t. It’s between those getting results in pockets and those turning AI into repeatable value across the business. There are three key ways leaders can step into this kind of transformation.

First, treat AI as business transformation, not a technology rollout. Frontier Firms connect AI to the outcomes that matter most, from growth to customer experience, and manage it as one accountable system. That is the heart of Frontier Transformation, and it’s the work we’re doing alongside our customers every day.

Second, start with your own function. In marketing, we’re applying these same disciplines to our own work: redesigning workflows around agents, setting clear guardrails and measuring what AI actually delivers. Every function can become its own proof point for the rest of the business.

Third, invest in people as much as in technology. Skills are the widest gap in this year’s research, and it’s the one I care about most. The organizations that pull ahead will be the ones where everyone, at every level, has the support to learn, experiment and grow with AI.

The next phase of AI won’t be defined by who uses it. It will be defined by who can scale its value and stay in control. This research gives every leader a benchmark for where they stand today and a clear view of what to do next.

Download the global infographic, The Real Business Value of AI, to see a summary of findings and benchmark your organization against Frontier Firms before your next AI investment review. Regional infographics are also available for Europe, Asia Pacific and the Americas. The full InfoBrief will follow.

Alysa Taylor is the Chief Marketing Officer for Commercial Cloud and AI at Microsoft, leading teams that enable digital and AI transformation for organizations of all sizes across the globe.

NOTE

IDC Infographic, sponsored by Microsoft, The Real Business Value of AI, IDC #US54985026-IG, November 2026

IDC Resource Map Document, Sponsored by Microsoft, Business Opportunity of AI Study, IDC Survey, July 2026, N=5204, Doc. #US54951426, November 2026

The post Scaling AI value without losing control: Lessons from Frontier Firms appeared first on The Official Microsoft Blog.

If you want to compete, you need to be multi-model. And if you want to take advantage of everything that different AI models have to offer, you need a great harness. But building one that works seamlessly across multiple models and surfaces is incredibly hard. You need to solve for performance, quality, and cost. And then you have to maintain it, which is even harder.

Luckily, the GitHub engineering team built a best-in-class, truly multi-model harness, which we’ve adopted company-wide. The Microsoft Copilot harness powers Copilot Code, Copilot Studio, the GitHub Copilot app, the CLI, VS Code, and more. And today, we’re making that harness available through Microsoft Foundry. That gives developers and enterprise customers a fully managed way to build agents that reason, use tools, and complete complex, long-running tasks. You get the right model for the job without switching the harness or the rest of your tech stack. That frees up your time, energy, and resources to focus on your bottom line.

If the model supplies the intelligence, the harness is everything around the model. It’s the orchestration layer that gives an agent the right instructions, context, execution environment, and controls to accomplish meaningful work. And a harness worth its salt has support for open standards like MCP and agent skills, as well as agent plugins.

This is a fast-moving space. A frontier model today might fall behind tomorrow. And building your own harness or model router to keep up can be painful and slow. Now, you can build on the same harness Microsoft and GitHub ship in our own products, and you don’t have to maintain it.

We invest in science and engineering work to continually improve and evolve the harness and ensure high-quality, efficient, and low-cost performance across a range of models and workloads. The benefits compound for us and our customers who use the harness to build and run their own agents. Improve the harness, and every surface it touches benefits.

The Copilot harness was built by the same GitHub engineers behind new technologies like our multi-model orchestration. And those technologies are only possible because we built our own harness. Designed to be fast, token-efficient, and predictable, it was our original, high-quality harness. And it had the highest satisfaction among our most demanding audience: developers. That’s why we’re standardizing all of our Copilot experiences on the strongest foundation possible.

The harness is built to perform across model families, with 20+ frontier models plus bring your own key, so you can use the right model for the right job. Instead of doing the work to keep up with and eval new models, lean on us to intelligently route work to the right model or models based on the task.

Our harness is flexible and configurable to any type of app, agent, or service. We recently ported the harness runtime to Rust, which has resulted in industry-leading performance and reductions in both session overhead and time-to-first token. And because the Copilot harness powers surfaces across Microsoft, it’s battle-tested and proven performant at the scale of millions of users and hundreds of real-world use cases.

A unified harness lets us ship faster, learn faster, and improve Copilot over time. And because the agents you build in Foundry can run on the same harness we’ve adopted across Microsoft, our continuous harness improvements will benefit your agents, too.

The SDK is available to download, or you can run the harness in the cloud, all managed by Foundry. Our platforms remain open and interoperable, so you can continue to bring in any harness and framework you want, too.

With the Copilot harness, Microsoft runs the agent loop and sandbox, with governance and optimization built in from day one. You bring the instructions, skills, tools, and model choice.

This is an important step towards our vision of a stronger system around the model that preserves choice. It represents the best of GitHub, brought to the Microsoft platform and beyond.

We’ll keep pushing the harness forward, so you can focus on what only you can build.

The post Introducing the Microsoft Copilot harness: Unlocking the potential of truly multi-model agentic work appeared first on The Official Microsoft Blog.

Last year at Ignite, we introduced the concept of Frontier Transformation and how it is different from any technology-led pursuit, because it requires us to focus on business outcomes first. It starts with a simple idea: technology was invented to serve humanity, not the other way around. There is no path where this notion of infinite cognitive compute replaces human purpose. Frontier Transformation is an opportunity for a wholesale reinvention of how you think about your unique ideas coming to life and how your organization can thrive in your industry.

Over the course of this past year, we have been leading our customers to focus on the three durable traits that are required for successful transformation: human ambition, intelligence and trust. We are taking a platform approach toward enabling the same: Copilot as the place to call home for interacting with AI to empower human ambition; the Microsoft IQ platform to compound and amplify your intelligence from within while protecting your unique differentiation; and Agent 365 to enable Trust and Observability at every layer of the stack so that you can govern, manage, secure and get business value and return out of the capabilities you deliver.

Our latest earnings are another important marker on that journey. Behind the numbers are thousands of customers, partners, developers and employees putting AI to work in meaningful ways. We have so many inspiring stories from our customers that highlight these outcomes, and below are some of our favorites.

Volkswagen has scaled Microsoft Copilot to more than 330,000 employees as it expands the role of AI in decision-making and knowledge-based processes across the enterprise. Building on Copilot Chat adoption that reached more than 300,000 monthly active users, employees are now using Copilot with Work IQ for more advanced scenarios, bringing greater organizational context to decision-making and knowledge-based work. Volkswagen is also working with Microsoft to further scale its Intelligent Data Foundation on Microsoft Fabric to make data easier to find, understand and use across the entire company. With this investment, Volkswagen is demonstrating how companies have evolved from using Copilot as a productivity tool to an operating system to empower human ambition.

Following a successful pilot, Fujitsu is scaling Microsoft Copilot across its global workforce of approximately 100,000. Copilot usage has surpassed 90%, and the company is using the tool to connect employees with specialized agents and business systems. AI agents are already automating roughly 75% of initial responses and triage for a service desk that receives nearly 300,000 inquiries annually, while new agents are supporting work across sales, systems engineering, procurement, finance and accounting. As customer zero, Fujitsu is using Copilot as the front door to a multi-agent work environment and using insights from its own AI adoption to better serve its customers.

KOHLER is using GitHub Copilot to transform software development, with one organization improving code releases by 25% and reducing error defects by 75%. These engineering breakthroughs are part of a broader effort to embed AI processes across the company, spanning IT, finance, marketing, supply chain and operations. Microsoft Copilot has reached 98% adoption, saving an average of 2 to 5 hours per month as employees are making informed decisions and getting answers faster in the flow of their work. Employees across the company have also created more than 3,000 agents to date, with hundreds active daily.

At Krafton, Microsoft Security Copilot and Microsoft Defender XDR are helping the security team respond to a growing volume and complexity of threats. The global gaming company automated key parts of security operations, from alerting and investigation to reporting, combining Microsoft Security solutions with its own systems and data to quickly gather context that helps distinguish real threats from false positives. This approach is having immediate results: security alert recognition time has fallen by approximately 95% to less than one minute, investigation work that previously took tens of minutes now takes about three minutes and roughly 95% of report creation has been automated. By taking on repetitive work, AI is giving Krafton’s security professionals more time to focus on responding to real threats and making critical security decisions.

To accelerate discovery across decades of scientific data, NASA is making complex information easier to explore through Microsoft Copilot. NASA employees are using Copilot to sift through massive amounts of academic literature and streamline daily tasks, and specialized assistants are helping researchers navigate complex Earth science data through natural language, extending access to information that once required significant technical expertise. Hydrology Copilot, for example, can analyze variables like precipitation and soil moisture to help communities better understand water conditions, prepare for droughts and anticipate flood risks, while automatically performing calculations and analyses.

To transform frontline work, Williams, a US natural gas company, is using Microsoft Copilot to help employees spend less time searching documents and completing manual processes, and more time on critical field operations. Copilot helps teams generate plans, interpret procedures, assess regulatory requirements, troubleshoot issues and complete safety reporting, while employees can create agents tailored to their own workflows. Demonstrating that the person closest to a problem is often the best one to solve it, one operations technician built an anomaly detector using Copilot that compares equipment data against baseline statistics and alerts teams to potential issues before they become costly or dangerous. Since deploying Copilot, AI adoption at Williams has grown from single digits to more than 90%, helping employees accelerate operations, improve reporting and compliance and identify equipment issues earlier.

Bristol Myers Squibb has rolled out Microsoft Copilot to 25,000 employees, reaching 96% adoption, and is now putting AI agents to work across everyday use cases such as document review and project management in support of the company’s mission to bring more medicines to more patients faster. One agent, called RABIT, analyzes meeting transcripts, surfaces action items and delivers insights directly within employees’ workflows.

Facing rising patient demand and financial pressures, Brown University Health is using Dragon Copilot to give clinicians more time to focus on patients. The health system adopted Dragon Copilot, and an early pilot across 420 clinicians found users spent less time on after-hours documentation and reported lower cognitive burden. Building on that experience, Brown University Health deployed Microsoft Copilot and Copilot Studio across departments, which led to the creation of more than 24 AI agents to support clinical workflows, hospital policies, patient access and operations. These AI solutions are not only supporting clinicians in the flow of their work but also helping to improve the patient experience and access to care.

BNY has made AI part of how its employees work through Eliza, its in-house AI platform built with Microsoft Azure, Microsoft Foundry, Microsoft Copilot and GitHub Copilot – with plans to add Work IQ. More than 300 solutions now run on Eliza, ranging from inquiry management to client onboarding, and 100% of employees have been trained on the platform. Across the bank, that scale is translating into meaningful results: client transaction inquiries are being resolved 80% faster and AI-assisted client onboarding is 20% faster on average. Through the Microsoft CEO Co-Innovation program, BNY also worked with Microsoft to develop an AI research capability with Foundry that investigates complex data issues, analyzes context across multiple sources, identifies likely root causes and recommends next steps, bringing AI-assisted reasoning into processes that have traditionally relied on human investigation alone.

Docusign is helping 1.9 million customers across 180 countries turn agreement data into actionable business insights. The company built an Intelligent Agreement Management platform on Microsoft Azure and used Microsoft Foundry to shift from general-purpose models to a system of task-specific models that better understand context. This change allows Docusign to process more than a million agreement documents daily with up to eightfold greater throughput and 90% lower AI processing costs. Foundry also supports a model lifecycle designed for continuous improvement, where processes are fine-tuned before deployment and monitored in production, while also providing the governance and traceability needed to meet enterprise requirements for compliance, visibility and control. Now agreement content is structured data that compounds from within, so organizations can use it to make decisions and automate workflows, knowing their IP is protected.

To help marketers create more effective content, Kantar turned decades of proprietary advertising intelligence into action with Microsoft Azure and Microsoft Foundry. Working with Microsoft Frontier Company, Kantar connected its LINK AI platform to its proprietary intelligence, AI models and creative workflows, enabling marketers to evaluate, optimize and generate content before it goes to market. The new approach can process and score large volumes of videos 20x faster while reducing platform operating costs by 4x, helping customers move from insight to action in minutes rather than hours. By bringing its unique intelligence into a continuous loop of observation and improvement, Kantar is creating new ways to help marketers turn insight into better creative and business outcomes.

Kinaxis, a Canadian supply chain software company, runs Maestro, its AI-driven supply chain orchestration platform, on Microsoft Azure. Maestro helps businesses make better decisions faster amid constant disruption. It brings together traditionally disparate business data like sales orders, inventory and production capacity with external signals like weather, market shifts and breaking news to give planners a near real-time view of their supply chains. With this unified data foundation, planners and analysts can use predictive AI and optimization models to anticipate demand shifts, evaluate constraints and recommend action, while generative AI agents act as assistants to ask questions, explore scenarios and automate parts of the planning process. With this intelligence in one platform, businesses can understand how disruptions ripple across their interconnected operations, weigh trade-offs and adjust plans as conditions change.

Moody’s is making more than a century of credit and risk intelligence directly accessible through Microsoft Copilot, giving customers trusted, protected context for complex decisions. Through a federated connector built using Model Context Protocol, customers can access live, decision-grade Moody’s intelligence through Researcher in Copilot and within Word, Excel and Teams, without copying or exporting the underlying data. A single question can synthesize information across credit ratings, company financials, ownership information and physical risk models, helping customers reach insights in minutes instead of spending hours or days on research and analysis. This brings trusted, connected intelligence directly into workflows where customers make complex decisions.

To turn conversations into actionable intelligence at global scale, Plaud, an AI hardware and software company, is using Microsoft Azure OpenAI in Foundry Models and Azure Speech in Foundry Tools to power transcription, summarization and AI inference across long-form audio and multilingual scenarios. Serving more than 2 million users worldwide, Plaud increased transcription accuracy and processing speed by 35% and improved inference performance by 30%, helping professionals turn spoken content into structured outputs and insights. Even as its user base doubled, the company used Foundry’s elastic scaling and flexible deployment models to reduce overall compute costs by 35%, while strengthening its ability to meet security, privacy and compliance requirements across global markets.

Rockwell Automation built a Maintenance Copilot using Microsoft Foundry to put decades of manufacturing expertise in the hands of employees. The AI assistant brings together expert knowledge from veteran engineers, manufacturing data and machine manuals, allowing technicians to quickly identify likely causes of equipment issues, see how colleagues have solved similar problems and access step-by-step troubleshooting guidance. At its Singapore factory, the approach has helped reduce machine downtime by 33% and cut servicing and spare-part costs by about 25%. Rockwell estimates it has also reduced the time it takes new workers to learn to troubleshoot hundreds of machines from nine months to three months.

With 1.5 billion passengers riding its elevators and escalators every day, TK Elevator knows that every minute of downtime affects how people move through airports, hospitals and offices. Building on a decade of connected-equipment data on Microsoft Azure, the company created AI agents with Microsoft Foundry that brief service technicians before they enter a building, telling them which elevator, which floor, what the issue is and which tools to bring. After each job, a voice agent records the technician’s account of the work in their own words, with no typing, and adds that expertise to a shared record. With 25,000 technicians caring for 1.4 million units worldwide, TK Elevator is turning each technician’s know-how into compounded intelligence every colleague can draw on.

Telstra partnered with Microsoft Frontier Company to deploy an AI-powered Migration Payment Agent that helps simplify one of the most complex aspects of customer migration: reconciling billing and payments across legacy and new platforms. The agent uses intelligence across these systems to proactively identify inconsistencies, recommend corrective actions and create enriched cases for human review, combining AI-driven insights with human-in-the-loop validation to maintain operational trust and governance. Already supporting more than 272,000 service migrations and delivering recommendations at 95%+ accuracy in production, the solution demonstrates how Telstra is applying agentic AI at enterprise scale to transform complex operations, improve customer outcomes and build reusable AI capabilities while compounding and protecting Telstra’s operational intelligence.

To advance its transformation into an AI-powered organization, Manulife is adopting Microsoft 365 E7: The Frontier Suite, expanding Microsoft Copilot to more than 30,000 employees and leveraging Microsoft Agent 365 to govern, manage and secure AI agents at enterprise scale. With Microsoft Azure and Microsoft Foundry supporting its global enterprise AI platform, Manulife is building a foundation for teams to develop and deploy AI and agentic solutions while maintaining standards for quality, security and cost efficiency. AI is already delivering impact across the business, from generative AI solutions supporting more than 110 million customer calls annually to developers increasing productivity by 30% with assisted and autonomous AI capabilities. With agentic solutions and AI-powered workflows already in production, Manulife is creating the foundation to scale AI responsibly across its global operations and accelerate its next phase of enterprise transformation.

Insight Enterprises, a leading solutions integrator and top Microsoft partner, is using Microsoft Agent 365 to govern AI agents at scale. As a Microsoft customer zero organization for Agent 365, Insight Enterprises is quick to adopt new capabilities internally and pass those learnings on to clients. With Microsoft engineering, it tested how Microsoft and third-party agents could be observed, managed, governed and secured through a unified control plane. To date, Insight Enterprises has discovered 9,734 agents with visibility into which are active, what data they can access and how to monitor their activity over time. The experience has helped Insight Enterprises build practical AI governance expertise it can bring to customers, including organizations in highly regulated industries.

KPMG is adopting Microsoft Agent 365 to manage and secure AI agents across its global organization, providing centralized observability and control as it scales agentic AI. Agent 365 will also strengthen KPMG Workbench, its multi-agent platform built on Microsoft Foundry that coordinates agents across client service delivery platforms. At the same time, KPMG is expanding Microsoft Copilot to more than 276,000 professionals worldwide, helping embed AI more deeply across audit, tax and advisory services. Together, these capabilities are giving KPMG the confidence and security to move agents from experimentation into enterprise-wide deployment, while helping clients do the same.

Operating in one of the most highly regulated industries, where protecting sensitive customer information is essential, Truist Financial Corporation is using Agent 365 to manage, govern and observe more than 13,000 AI declarative agents across its organization. Truist manages its agent ecosystem with discipline: every agent has an accountable owner and agents are automatically disabled after 45 days of inactivity. That rigor is driving strong retention and adoption, with more than 8,000 agents active each month. Building on this trusted foundation, Truist has advanced its broader AI strategy through their investment of more than 50,000 Microsoft 365 E7: The Frontier Suite seats.

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Every quarter gives us an opportunity to step back and look at the progress we are making with our customers and partners around the world. What continues to strike me is not simply the pace of change, but the ambition we see from organizations using technology to achieve aspirations that simply weren’t possible before. We look forward to working alongside you on the path to becoming Frontier, and we hope to see many of you at Microsoft Ignite this year in November to share the latest innovations that will enable you to reach that destination.

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 The platform approach to becoming Frontier appeared first on The Official Microsoft Blog.

Amy Coleman, Executive Vice President and Chief People Officer, shared the below communication with Microsoft employees today.

Today, we’re introducing a new Voluntary Exit Offer (VEO) to eligible employees within participating organizations. This offer gives eligible employees who may be considering a future outside the company a new choice to make that transition now with support. It also gives participating organizations another way to adapt their workforce to changing needs.

The Voluntary Exit Offer reflects an important part of how we’re evolving our approach to change: creating greater flexibility and, where possible, more ways for employees to shape their next chapter. This builds on what we learned earlier this year through the Voluntary Retirement Program.

This offer will be available to eligible employees in participating teams in Canada, India, the United Kingdom and the United States at level 70 and below. This is a time-limited, voluntary program that gives those who are eligible the opportunity to leave Microsoft with financial support and career planning resources. Some organizations are providing the Voluntary Exit Offer, and some are not. Participation varies based on each business’s priorities, customer commitments and workforce needs.

Eligible employees will receive an email with a link to their Voluntary Exit Offer details within the next few hours.

The Voluntary Exit Offer is new for us. We’ll learn from it. At the same time, we’ll keep helping people focus on the work that matters most, build new skills and, where it makes sense, find new opportunities across Microsoft. There is no one-size-fits-all approach for every team or every employee. These are a few of the ways we’re trying to meet different business needs while continuing to create opportunities for our own people to grow and contribute.

My promise is to continue to provide clarity, listen and learn and keep improving.

Thank you for everything you continue to do for our customers, Microsoft and for one another.

Amy

This Viva Engage post has been edited slightly for external use.

The post Introducing a Voluntary Exit Offer appeared first on The Official Microsoft Blog.

Across my conversations with leaders in government, critical infrastructure and regulated industries, one priority consistently rises to the top: putting technology to work on their most important systems while maintaining the control those systems require.

This challenge is becoming more important as advanced cloud and AI capabilities move into public services, regulated processes and essential operations. Organizations are considering more than where data resides. They also seek to understand who can access and operate their systems, how sensitive digital assets remain protected, which technology dependencies matter most and whether critical services can continue as regulatory, geopolitical, security or network conditions change. Digital sovereignty should give organizations greater control and security over the technology they depend on, while continuing to innovate.

Last year, we introduced Microsoft Sovereign Cloud to give customers more choice in how they address these requirements. Since then, we have continued to turn our commitments into practical capabilities across public cloud and customer-operated environments, while expanding how we enable partners to address sovereignty requirements in their local markets. Today, we are sharing the next stage of that work.

Our approach to digital sovereignty starts with the workload. Each workload carries different requirements for data, operations, technology dependencies, continuity and risk. Microsoft Sovereign Cloud is designed to make it easier for organizations to apply the appropriate level of control to each workload without forcing every application into the same deployment model.

Those needs can vary widely across organizations. A public-facing digital service may benefit from the scale and pace of innovation in the public cloud. A system supporting essential operations may need to continue running locally if connectivity is disrupted. An AI workload handling highly sensitive data may require models, data and processing to remain within a customer-controlled environment.

Microsoft Sovereign Cloud is designed around this workload-based approach, providing a continuum of management, control and options across Sovereign Public Cloud, Sovereign Private Cloud and the Sovereign Partner Ecosystem. Organizations can apply the level of control appropriate to each workload while maintaining a consistent approach to governance and operations across their digital estate. Sovereign Public Cloud adds capabilities for workloads that require greater control and transparency while continuing to benefit from the scale and innovation of Azure.

Sovereign Private Cloud extends cloud capabilities into customer-operated environments, including connected and disconnected operations. The Sovereign Partner Ecosystem broadens the continuum through independently operated environments, specialized partners, locally relevant expertise and technologies that support customers’ sovereignty requirements.

These are not isolated destinations. Customers can combine public cloud, private cloud and partner capabilities across their digital estate, applying different levels of control to different workloads as their operating needs evolve.

For many workloads, the public cloud provides a combination of scale, innovation, integrated security and compliance. Organizations can benefit from these capabilities while applying additional sovereignty controls for residency, confidentiality, operator access, policy and continuity. Our work in Sovereign Public Cloud makes these controls easier to understand, apply and demonstrate.

Sovereign Control Panel, entering public preview at Microsoft Ignite, brings these signals together in a unified Azure portal experience. It helps organizations understand their sovereignty posture, apply and manage controls, identify gaps and risks and take action to address them. It also supports business continuity readiness by helping customers understand workload dependencies and assess portability across operating environments when required.

This visibility complements controls across the operator-access lifecycle. Just-in-time Access and Customer Lockbox give customers additional control over access to their environments. For customers in the European Union (EU) and the European Free Trade Association (EFTA), we are rolling out Data Guardian, which adds European-resident oversight by a trusted EU partner for qualifying Microsoft engineer remote access to in-scope production systems. Production Touch Ledger, also launched with phased rollout in the EU and EFTA, creates tamper-evident records of that access and the actions performed.

Azure Key Vault Managed Hardware Security Module (HSM) already gives customers dedicated, single-tenant protection and control over the encryption keys used by supported Azure services. Last year, we committed to extending that control beyond Microsoft infrastructure. External key management with Azure Managed HSM is now generally available, allowing customers to keep the root encryption key in hardware they own and operate on-premises or through a trusted third party. This gives organizations another option for workloads that require physical control of key hardware outside Microsoft infrastructure.

Azure confidential computing helps protect supported data while it is processed in a hardware-based and attested trusted execution environment. We are also increasing transparency into the technologies that underpin that trust. Azure Confidential Clean Rooms, now in limited preview, applies confidential computing to approved multiparty collaboration, allowing participants to analyze shared sensitive data while limiting broader exposure of the underlying information.

Together, these capabilities provide additional technical and operational controls that can support a broader digital sovereignty strategy aligned to an organization’s legal, policy, security, resilience and business requirements.

For some workloads, digital sovereignty also means maintaining critical operations as connectivity, geopolitical and operating conditions change. Resilience must be designed from the start so essential workloads can continue operating through disruption.

To help customers prepare for these scenarios, we are publishing new sovereign business continuity guidance and architecture patterns. These resources help organizations translate resilience requirements into architectures they can plan, implement and test before they are needed.

For many workloads, the scale and geographic reach of the public cloud provide a strong foundation for resilience across locations and operating conditions. For workloads that require greater operational independence, Sovereign Private Cloud extends that continuum into customer-operated environments. It supports connected and disconnected operating models. The ability to transition workloads from connected to disconnected operations is entering public preview, giving organizations greater flexibility to sustain critical services as connectivity and operating requirements change.

Operational independence requires more than infrastructure. Increasingly, organizations need data, development tools, model capabilities and productivity services their applications depend on to operate within the same customer-controlled environment.

Sovereign Private Cloud, built on Azure Local, extends Microsoft cloud capabilities into environments that customers operate and control. SQL Server on Azure Local is already generally available for connected and disconnected deployments. DocumentDB and Blob Storage on Azure Local are both entering public preview, giving organizations more choice in how they run and manage critical data within customer-controlled environments.

GitHub Enterprise Local will become generally available at Microsoft Ignite as a validated GitHub Enterprise Server appliance on Azure Local, helping organizations keep repositories, code and development artifacts under local control. Developers can also use GitHub Copilot with locally hosted models for inference, bringing AI-assisted development closer to their code and data within the environments they control.

The local application environment is expanding with Foundry Local. At Ignite, Foundry Local will become generally available for connected and disconnected operations on Azure Local, with an expanded model catalog that includes Microsoft, partner, open-weight and open-source models. Local inference and agentic retrieval will also enable users to interact with local enterprise data using natural language and receive responses grounded in that data. Integration with Microsoft 365 Local, available in preview for approved customers, extends this functionality to locally operated Microsoft SharePoint and Exchange servers.

Confidential virtual machines on Azure Local are generally available, extending confidential computing capabilities to customer-controlled infrastructure. Hardware-encrypted memory and hardware-based isolation help protect sensitive data while it is being processed. Workload attestation adds assurance before sensitive data or keys are released. For regulated, sensitive and mission-critical workloads, these safeguards provide another layer of protection while giving organizations greater control over where their data and applications run.

Azure Local will support multitenancy, allowing organizations to separate resources, governance and management across multiple tenants while sharing the underlying infrastructure. This significantly expands what customers with distinct business units or security boundaries can achieve with Sovereign Private Cloud. Multitenancy also creates new opportunities for local operators and service providers to deliver differentiated sovereign cloud services on Azure Local infrastructure.

These additions bring more of the application lifecycle under local control, spanning data, development, productivity, AI and operations. Organizations can determine what runs locally based on the requirements of each workload.

Navigating sovereignty is increasingly complex. Regulatory, security, operational and business requirements vary across countries, industries and workloads. Organizations must translate these needs into technology and operating models that work in their local context. Meeting these needs often requires a combination of technology, local operating authority, sector expertise and specialized solutions.

The Sovereign Partner Ecosystem broadens the ways customers can address these requirements. National Partner Clouds, including Bleu in France, provide independently operated environments for customers that require a different ownership and operating model. Software partners can enhance Azure Local environments with capabilities across security, identity, observability, backup and recovery, migration, data, networking and applications. Digital sovereignty specialized partners combine Microsoft technologies with local knowledge and implementation expertise. Platform co-innovation partners expand infrastructure choice from datacenter to the edge.

This ecosystem gives customers the flexibility to combine technology, operating models and trusted expertise based on the requirements of each workload.

AI is amplifying both the opportunity and the impact of technology choices. As AI systems and agents interact with sensitive data, business processes and essential infrastructure, they also expand the security considerations organizations need to manage. Organizations need clear control over where these systems run, who can operate them, what actions they can take and how critical services continue when conditions change.

Microsoft Sovereign Cloud brings these choices together as a continuum of control across the public cloud, customer-operated environments and our partner ecosystem. It gives organizations the flexibility to apply the appropriate level of control to each workload while continuing to benefit from cloud capabilities and ongoing innovation.

We will continue to expand this continuum so organizations can innovate with cloud and AI while maintaining the control, confidentiality, resilience and operational authority their most important workloads require.

Douglas Phillips leads global engineering efforts for Microsoft’s specialized, sovereign and private clouds. He is responsible for Microsoft’s global strategy, products and operations that bring Microsoft’s industry-leading solutions, including Azure, our adaptive cloud portfolio and Microsoft 365 collaboration suite, to customers with additional sovereignty, security, edge and compliance requirements.

The post Advancing digital sovereignty with greater control and choice appeared first on The Official Microsoft Blog.

In the last few months, concerns have arisen about AI safety as a growing number of incidents were publicly disclosed. These incidents exposed how powerful models have become and how they represent a significant threat if not handled well. And with the global race on innovation, the challenge will compound as models become even more powerful.

While most have been worried about threat actors using powerful models to attack, the focus has been less on agents within our own walls. Since we were the ones who deployed agents internally for our companies, we implicitly trusted our agents.

Unfortunately, frontier models, regardless of their origin, cannot be trusted. This is not because models are inherently malicious. Models have been trained to attain results, which often leads to reward hacking, and models are not optimized around organizational norms or judgment. Agents are unpredictable. They will adapt as the context changes, may not behave as intended and will collaborate and collude with other agents to reach their objectives, without needing to be compromised.

Agents have been unleashed in the enterprise, at scale, and their numbers are growing rapidly. These agents are frequently overprivileged, have access to data, tools and systems and can take actions and make decisions in critical processes. They have become active participants in the enterprise.

They are agentic insiders.

Monitoring and securing those agents must become continuous. We need to understand in real time what the agents are doing and what risks those actions create, and we need to adjust their ability to act accordingly.

To manage and control these insiders, we will need Guardian Agents — an established industry term — that watch and moderate every action of every agent, before it happens, 24/7.

An agent brings together a model, a harness that orchestrates its work, an action space of tools and resources and a runtime in which it executes. Securing agents requires protection across multiple layers: identity, permissions, data, endpoints, network, cloud and runtime.

In July, we announced Project Perception, an agentic security system that uses our new cyber stack and teams of red, blue and green agents to help defenders act with speed, while defending against AI driven attacks from outside of the enterprise.

The same cyber stack is also directly applicable to managing and governing the agentic insiders that run in the enterprise.

Project Perception can be leveraged to contain agentic insiders, implementing deterministic controls on non-deterministic intelligence:

• Sensors/Signals/Actuators: bring together signals across the various sensors (identity, data, cloud, endpoint, network, runtime) to discover agents in the enterprise and bring them under management by assigning an identity, container and set of policy and permissions.
• Context: use those signals to compute, dynamically, the risk associated with what the agents are attempting to do.
• Harness/agents/models: based on dynamic agent risk, a team of Guardian Agents, with green and blue agents, actively and continually supervise every action of every agent and surgically respond as the risk profile increases, in real time.

This is where agentic security changes the equation for security practitioners.

Project Perception can supervise in real time while providing traceability for practitioners to examine what happened, understand why the risk emerged and apply the judgment needed to update their security system accordingly. Agentic security provides speed. Practitioners provide judgment. That is how defenders begin operating at the same speed as the systems they are protecting against.

Today, security systems can reliably observe the actions of the agents. Those signals are essential and tell us what we need to contain. Our security controls should increasingly be able to ask not only “What is happening?” but also “Why is it happening?” Is this consistent with the intent of the task?” This will require greater transparency into the model itself, not only the input and output tokens, but the thinking and chain of thought tokens. Only then can we truly understand whether the model’s intent is aligned with the purpose of the agent. Model transparency will not replace containment. It will make containment smarter and stronger. And it will allow us to better inform accountability and traceability. This needs to be the next step in securing agentic insiders.

With Project Perception, you can get started today on the journey to secure your agentic insiders. There should not be a choice between innovation and safety.

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 Securing the agentic insider appeared first on The Official Microsoft Blog.

As we move into the era of Super Intelligence, Microsoft is entering a new phase of infrastructure. We will scale our Azure Maia family to multi-gigawatt deployments starting in 2027, making Maia the primary inference system for Microsoft products and services.

The multigenerational Azure Maia program has become a foundational element of Microsoft’s AI infrastructure strategy. Maia 200 is now serving Microsoft production workloads, including Microsoft Copilot, and supporting the MAI team as they develop next-generation models, with GitHub Copilot and Microsoft Foundry to follow by end of year. Across these workloads, Maia 200 runs OpenAI models, Microsoft’s own MAI models and leading open-source model families. Insights from the scale of Microsoft’s AI services allow for unique co-design between hardware systems and software applications to optimize real-world use cases across enterprise applications and industry scenarios.

Maia 300 is a complete intelligent system designed for the new types of AI workloads emerging today: Long-context reasoning, latency-sensitive conversational experiences and sustained, multi-step agentic tasks. By improving efficiency across the system, we expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems. And in a full 96-accelerator configuration, it delivers up to 7x the rack-scale token throughput of Maia 200.

YouTube Video

Click here to load media Maia 200: Driving the workloads that Microsoft runs at scale

The Azure Maia system is designed around the real-world workloads Microsoft runs at scale and the models we expect to build in the years ahead. Because silicon development takes years, our software and hardware teams collaborate early to anticipate architectures, scale and capabilities, translating those requirements into greater memory capacity, higher bandwidth and more accelerators operating as a single system.

That co-engineering is already delivering measurable efficiency gains. Maia 200 is deploying across the United States, Europe and Australia and delivering up to 1.7x more tokens per watt for built-in AI capabilities in Microsoft Teams, Copilot and Cowork experiences compared with leading GPU infrastructure.

Experience running our AI models and services on Maia 200 directly informed the design of Maia 300. Together, the two generations are designed to support a broad set of additional Microsoft’s AI services: multi-step agentic work that will help GitHub deliver coding experiences more efficiently; cloud-scale model serving that will help Microsoft Foundry support diverse architectures and deployment environments; inference and data generation for the MAI team; and latency-sensitive interactions that will help Dragon Copilot scale workflows and reduce clinicians’ administrative burden.

AI is becoming a foundational layer of every application, workflow and business process. As enterprises move beyond chat experiences to systems of agents that can reason, act and collaborate, AI infrastructure must deliver intelligence securely and efficiently at scale while continuously adapting as models and workloads evolve.

As the third generation of our Azure Maia program, Maia 300 is designed with an inference-first architecture optimized for long-context, memory-intensive and decode-heavy AI workloads. This involves balancing compute, memory bandwidth, networking and power efficiency at cloud scale.

Key capabilities of the Maia 300 system include:

Less data movement, more useful compute: Moving data efficiently, rather than raw compute power, is increasingly the bottleneck in large inference systems. Maia 300’s software-defined dataflow architecture and AI-assisted kernel optimization work together to maximize locality, reduce data movement and improve utilization across compute, memory and networking resources. By treating dataflow as a first-class design principle, Maia 300 delivers a significant performance-per-watt advantage over traditional GPU architectures.

Higher memory bandwidth within every watt: As AI workloads become increasingly context and memory-bound, Maia 300 delivers industry-leading HBM bandwidth per watt. Eight HBM4 stacks provide 18.0 TB/s of peak memory bandwidth within a 950-watt TDP, enabling efficient execution of large models and long-context workloads. Specialized kernel support uses on-die SRAM as a software-managed scratchpad, reducing pressure on HBM.

Chiplet architecture for enhanced efficiency: A new multi-chiplet architecture separates compute, memory-controller and I/O functions, improving efficiency, scalability and cost structure. Compute chiplets are built on TSMC’s N2P process node to maximize performance-per-watt.

Larger context, richer intelligence: By designing for advanced low-precision formats, including enhanced MXFP4, Maia 300 reduces memory and bandwidth requirements for both computation and KV-cache storage. The result is larger context windows, higher throughput and improved model efficiency within the same power envelope.

Scale-up networking at datacenter scale: Maia 300 treats networking as a seamless extension of the software-defined dataflow architecture. Rather than moving data through separate communication layers, dataflow operations span compute, memory and network resources as a single system. High-bandwidth interconnects between Maia accelerators are optimized for tensor parallelism, while a custom AI transport delivers performant and reliable scale-up communication across accelerators and racks. The AI-optimized transport leverages Ethernet-based networking infrastructure to enable a unified topology that scales efficiently from a single rack to datacenter-scale deployments.

Higher AI throughput from every rack: Maia 300 rack systems bring together 96 accelerators within a 167-kilowatt power envelope, delivering 27.6 TB of HBM capacity and 1.7 PB/s of aggregate HBM bandwidth across a high-speed scale-up domain. That density allows Microsoft to serve more simultaneous AI interactions and larger workloads from each rack, improving datacenter capacity and the economics of expanding AI services.

Maia 300 will begin production deployments in 2027. For more information on Maia 300 architectural details, read our technical paper.

The future will not be defined by a single model or a single piece of silicon or a single innovation. Model and silicon choice are inherent in our vision for Frontier Transformation. Our focus is on developing responsible, affordable and scalable platforms that will help everyone amplify their intelligence to drive breakthrough outcomes that matter for them.

Microsoft is unique because we are able to optimize from silicon to systems to agent on an open, heterogenous and model diverse platform our customers can count on for their AI transformation, today and in the years ahead. This platform is at the heart of Frontier Transformation, and Azure Maia is a foundational component of our approach to expand access, accelerate innovation and help customers capture more value from AI.

Next month at Microsoft Ignite 2026 in San Francisco and online we’re looking forward to sharing more information on our strategy, technology adoption trends and success in Frontier Transformation.

For more details, photos and videos, visit our Maia 300 site.

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.

The post Microsoft to expand Azure Maia system to multi-gigawatt deployments appeared first on The Official Microsoft Blog.

Satya Nadella, Chairman and CEO, and Ryan Roslansky, Executive Vice President of LinkedIn and Microsoft 365, shared the below communications with Microsoft and LinkedIn employees this morning.

After nearly 18 years at LinkedIn and Microsoft, Ryan Roslansky has decided to leave Microsoft and embark on his next chapter.

As Ryan and I have been discussing our roadmap and work ahead, we felt this was the right moment for this change. Ryan will stay through the end of the calendar year as an advisor to me and will support on the transition.

Ryan leaves the organization in a strong position. With Copilot, we are executing rapidly against our vision to build a new OS for work that spans every model, every form factor, and every task, with Office fully embedded in Copilot and vice versa! Over the past year, Ryan’s team has done critical work bringing together the product, engineering, and design foundations that have made this next phase possible. Last week’s launch was an important milestone in that journey, and the strong response from customers, partners, and the broader ecosystem reinforced both how far we’ve come and how much opportunity remains ahead.

At LinkedIn, we remain focused on our core vision of creating economic opportunity for every member of the global workforce, helping our 1.3 billion members access the knowledge, insights, and opportunities they need to navigate rapidly changing professional and labor markets.

Effective today we will make the following changes:

• Sumit Chauhan and Lan Ye will report to Charles Lamanna.

• Jon Friedman will report to Jacob Andreou.

• Dan Shapero will continue as CEO of LinkedIn and Mohak Shroff will continue in his role, both reporting to me.

Over his nearly two decades at Microsoft, Ryan has helped shape how people around the world find opportunity, build their careers, and get their work done. I’ve always appreciated his clarity of thinking and I’m deeply grateful for everything Ryan has built, for the leaders and teams he has developed, and for his friendship and counsel.

Please join me in thanking Ryan for his extraordinary contributions to LinkedIn and Microsoft, and in wishing him the very best in what comes next.

Satya

Team,

In 1996, as a college freshman, I taught myself to code so a few friends and I could help other students find a place to live around campus. I didn’t know much. I did know the internet was about to change how people found things, and I wanted to be as close to that as possible.

In 2009, Jeff Weiner made me his first hire at LinkedIn. I’ve spent the nearly 18 years since working on a much bigger version of that same idea: helping people find opportunity, and then helping them do great work once they have it.

This has (obviously) been the best professional experience of my life.

As a product manager, I got to build dozens of features that are still around today (some good, some not so good!). When I became CEO in 2020, the world was months into a pandemic that changed what work even meant. Nobody hands you a playbook for that. This team wrote one anyway. Since then, LinkedIn has passed a billion members and nearly tripled its business, and every bit of that belongs to the people who built it.

Last year, Satya asked me to help build the future of M365. Once again, the old rules didn’t apply. AI was rapidly changing how professionals work, and the world’s largest productivity suite needed to lead the way. That meant figuring out what Copilot, and the future of work, should look like as one ecosystem.

We still have a ways to go, but the progress over the past year has been inspiring to watch. A lot of talented people set aside their own roadmaps to make that happen. Thank you. I know it wasn’t easy.

Satya, thank you. When LinkedIn joined Microsoft, you gave us room to build, and then trusted many of us with a lot more. I’ve learned more from you than I can fit in an email, and I’m grateful for the friendship that came with it.

All of which made this a hard decision. I know what it would take for me to lead the next phase of this work well: being in Redmond full time, every day. But my life and my family’s life are here in the Bay Area. My wife Leigh gives her time to more local organizations than I can keep track of. Our youngest daughter just started high school, and our dog Buddy remains deeply committed to the trails on the Peninsula!

I thought about this for a long time. It’s the right call. The work now goes to leaders who are set up to take it further. I can’t wait to see what they do with it.

Over the years, people have asked me what’s kept me so energized and motivated in this work. I think the answer is that I got to spend my career working where doing good and doing well are the same thing. In technology, that’s the best place to operate.

Most companies start by focusing on doing well, so doing good becomes a compromise. It’s the last slide of the PowerPoint, or a separate .org. But when doing good is built into the product itself, it becomes a real competitive advantage. When we build valuable products, people find jobs, they hire, they learn skills, they start companies, they run companies. That creates value for members, customers and the world. And for our business too.

Not many companies get to work this way, and not many people get the chance to build something like it. We do.

That matters more now than it ever has. Technology will continue to touch every job on the planet, and the people building it will decide what kind of world it helps create. I can’t think of a better group to figure that out than the people reading this note.

Thank you for the best 18 years I could have asked for.

RR

The post Microsoft 365 and LinkedIn leadership update appeared first on The Official Microsoft Blog.

Every organization has knowledge that is uniquely its own: the way its business operates, the data it has built over time, the expertise of its people and the context behind thousands of everyday decisions.

As powerful AI models become more widely available, that unique business intelligence matters more than ever. The difference will come from how effectively organizations combine those models with the data, expertise and business context that make them unique.

Turning that intelligence into lasting advantage requires three elements to work together: human ambition to set the direction, intelligence grounded in the organization’s unique knowledge and trust to provide the foundation for operating at scale. Microsoft’s approach is designed to bring those elements together so organizations can build on what they know and create greater value over time.

That value can compound through a continuous learning loop, where AI draws on an organization’s data and expertise, improves the systems and experiences it creates and generates better outcomes over time without making the organization dependent on any single model provider.

Today at the European Microsoft Fabric + SQL Community Conference in Barcelona, we are introducing advances across Microsoft Fabric and Azure Databases that help organizations put their data estate at the heart of a successful AI strategy.

As Microsoft Copilot evolves across Home, Code and Autopilot, access to trusted business context becomes essential. Fabric IQ brings governed business context into Copilot, grounding experiences in the Power BI semantic models, metrics, relationships and business definitions organizations already trust.

With Fabric IQ in Microsoft Copilot Chat and Cowork now generally available, users can ask business questions and receive answers grounded in governed Power BI data, while Cowork can use those insights as part of broader multistep work. Integration with the new Code experience is coming through the Frontier program.

That means when someone asks Copilot for the latest sales forecast or another critical business metric, the answer can be grounded in the same semantic model that powers the organization’s reports, helping avoid confusion about which number is correct. For Power BI customers, this extends the semantic foundation they already have into Microsoft Copilot while preserving existing access controls and governance.

I experienced this myself recently as I was preparing for a sales review and asked Copilot, “What’s our latest sales forecast?” With Fabric IQ, that answer was grounded in the same governed Power BI semantic model that powers the team’s sales reporting, using the same business definitions and metrics my team already trusts. So, instead of spending the meeting uncovering and debating whose number was right, the team was able to get straight to what the numbers mean and focus on what we do next. That’s exactly the kind of experience we want to create.

For Fabric and Power BI customers, this capability is on by default in Copilot Chat and Cowork and extends the semantic foundation they already have into Microsoft Copilot while preserving existing access controls and governance.

Business knowledge does not stop at the boundary of a team or business unit. Organizations increasingly need to collaborate across customers, partners and broader ecosystems while maintaining control over the information they share.

IQ Sharing in Fabric, now in preview as part of Fabric IQ, breaks down company boundaries by enabling organizations to securely share data and the business context that gives it meaning, while maintaining governance over the underlying assets. Starting with key Fabric IQ artifacts and context, IQ Share is designed to evolve beyond preview to help customers discover, share and activate trusted context wherever it’s needed.

Together with OneLake mirroring and shortcuts, these investments create a more open foundation for sharing governed data and context across organizations, platforms and ecosystems.

As organizations put more workloads on their unified data platform, they need to understand system health and address issues before they affect business and AI outcomes.

Observability in Fabric helps teams move from reactive troubleshooting to proactive operations. Cross-workspace monitoring and an enhanced Monitor Hub provide a unified view across jobs, capacities, workspaces and workloads, while Operations Agents and Activator help teams investigate issues, identify root causes with AI and automate responses.

Together, these capabilities help organizations maintain oversight as they scale data and AI.

AI is transforming how applications are created, but enterprises still need a secure, governed path from idea to production. Fabric Apps brings application development directly to governed data in Microsoft Fabric, helping organizations build and scale AI-powered business applications faster.

With new capabilities for data connectivity, transactional workloads, backend logic, storage and policy-controlled access, developers can move from prototype to production on a unified data foundation. These experiences bring application creation closer to where enterprise data and insights already live, helping more people turn data into action.

A continuous learning loop is only as good as the data behind it, which must be prepared, refined and improved without pause. As those environments become more complex, organizations need automation that can take on more of that work without pushing people or controls out of the loop.

Earlier this year, we shared our vision for autonomous data engineering. Now, new agentic data engineering features in Fabric bring that vision to life, bringing autonomous execution to complex data engineering initiatives while keeping organizations in control. Engineers define the outcome and set the boundaries. Inside those limits, the agent plans and executes the work, validates the results and refines the approach.

Maintaining control over business knowledge also means having flexibility over where critical data and workloads run.

Now generally available, SQL Server on Azure Local brings SQL Server and Azure services into local environments, giving organizations more choice to run workloads closer to their applications and operations. SQL Server on Azure Local with disconnected operations, now in preview, extends that flexibility to environments where cloud connectivity is interrupted, restricted or unavailable.

These options give organizations operating across regulated, local, edge and disconnected environments greater choice over where their critical data and workloads reside and operate.

Intelligent applications depend on operational data. Teams need a clear view of the databases that power those applications and practical tools to manage them.

Building on its introduction at FabCon Atlanta, Database Hub in Fabric is now in public preview. It provides a central control plane for managing SQL Server, Azure SQL, PostgreSQL, Cosmos DB and SQL databases in Fabric, helping administrators monitor and govern their database estate.

Complementing this management experience, specialist database agents for SQL and PostgreSQL in Database Hub in Fabric and Visual Studio Code, coming soon to public preview, help teams investigate issues, understand performance bottlenecks and identify optimization opportunities within their development environment.

Together, these capabilities help teams understand their database estate and make more informed operational decisions.

Our goal is straightforward: help organizations use the best AI models and tools available while turning their proprietary data, expertise and workflows into an advantage that grows more valuable over time. That direction is reflected across today’s announcements as the data foundation comes into focus.

At Ignite, we’ll broaden this story across the full Microsoft Frontier ecosystem, connecting data, applications, agents, governance and infrastructure into one system that turns AI ambition into business outcomes.

Explore the complete set of announcements across Microsoft Fabric and Azure Databases in today’s blog from Arun Ulagaratchagan, Executive Vice President of Azure Data.

The post New Microsoft data innovations unlock what only your business knows appeared first on The Official Microsoft Blog.

We’re reimagining Microsoft Copilot to enable work as it evolves and to help expand what every individual and every organization can accomplish in the flow of human ambition. Today we’re introducing the new Copilot to connect the tools people rely on with the next generation of capabilities they’ll need to build, customize and scale AI across work.

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.

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Click here to load media 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.

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.

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.

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.

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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.

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.

AI is reshaping work faster than any organization has fully mastered. Across industries, the conversation has shifted from what AI can do to how companies can use AI to create business value and expand what people are able to achieve. At Microsoft, we believe the organizations that succeed will be what we call Frontier Firms: human-led, but increasingly AI-enabled. That responsibility begins with how AI is built and continues through how it is put to work: AI should expand human capability while people retain meaningful control, judgment and accountability. We committed to being Customer Zero, learning through our own transformation so we could help others navigate their own.

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.

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.

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.

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.

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?”

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.

Microsoft has been in the business of education for five decades. Since the earliest days of this company, we have worked alongside educators, students, families and educational leaders through each new wave of technology — and we have learned what helps learning and what gets in the way.

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.

As we enter the next era, what will be the defining measure of our progress?

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.

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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.

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.

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.

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.

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 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.

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.

Throughout this past fiscal year, customers across every industry and segment moved from AI experimentation to deploying AI for real-world business outcomes. They unlocked innovation and created new opportunities for growth. We saw the emergence of Frontier Firms as they moved beyond efficiency gains to focus on human ambition and embed AI at the core of how they operate. Successful customers are building an intelligence platform so their unique IQ — their knowledge, data, workflows, applications and expertise — can continuously compound, ensuring the value of AI accrues to the customer, not the model. They have a trust platform that is pervasive, with the ability to manage, govern, secure and measure AI across every business process.

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.

Editor’s note: Updates with additional details on the model’s crash score.

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% (any-crash score*) on CyberGym, an industry benchmark. It also outperforms competitors like Mythos on the CyberGym leaderboard. 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.

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Click here to load media 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.

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.

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.

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.

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.

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.

Note: *Any-crash measures the ability of the agent to identify vulnerabilities that can crash the code under evaluation with an input that triggers any existing or 0-day vulnerability. Our 96% score is an any-crash score.

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