Whether you are just starting out or are not in a technical role at all, you likely already have the skills you need to build your own tools. If you have ever thought “I’m not technical enough to build that,” this post is for you. Let me introduce the team. We are lawyers, program managers, and business professionals—not engineers. A large part of our work is often repetitive, like reviewing the same kinds of contracts over and over or answering the same legal questions, and our prior guidance is frequently recycled. These are problems AI could help solve, but we lacked confidence in how to build the right tools. That is where GitHub Copilot CLI came in. We asked for what we wanted in plain language, plugged into our repos, and saw real changes fast. “I could never code” turned into “I just built something,” and that habit spread on its own until every one of us was building something. What follows are two real accounts of people who did exactly that. Plus, watch the videos for two additional stories. Why I built an internal drafting style guide The following is a first-person account from Ngandu Kasuku, Principal Product Counsel. I’m a product attorney, but commercial work remains a sizable part of my practice. Around March or April, I found myself buried in partnership deals involving data, infrastructure, and product integrations. No two deals looked quite alike, so each new matter felt like starting over. I started using Copilot CLI to manage the surge, which helped, but also had some problems. Then, after seeing what others had built with Copilot, I realized I wasn’t thinking big enough. Instead of using AI for one task at a time, I could build something around the way I work. So, I created a contract drafting tool using Copilot CLI. I called it terms-ai, which I admit isn’t the most original name. I started by scaffolding the project and storing key documents in a repository. This gave me one place to organize and version the instructions, drafting resources, and workflows that guide the AI. That structure made the results more consistent and reduced the copying and pasting that had slowed me down when I was using a library of prompts. One of the tool’s main features is an internal drafting style guide. Since my days as a commercial lawyer, I’ve favored plain language. I never understood why contracts needed words like “heretofore” and “therewith.” When I discovered that an entire legal drafting movement shared this view, I used its principles as the foundation for my style guide. I also built a library of agreements I had already completed. Now, when an existing partner sends over an addendum or a new agreement, the tool can draw on that earlier work. These agreements remain in an approved, access-controlled internal environment. The tool and its general workflow are open source. The agreements and other sensitive information aren’t part of the open source repository. Since I began using terms-ai, I’ve cut my review and drafting time roughly in half. My provisions are more consistent across agreements, and the drafts reflect the plain style I prefer. The tool still has a long way to go. But the biggest lesson wasn’t that AI could help me draft faster. It was that I could use AI to build a tool around my own judgment, experience, and way of working. How I built legal workflows without writing traditional code The following is a first-person account from Jesse Geraci, Online Safety Counsel. I started with a narrow problem. We needed to analyze source code quickly and accurately to evaluate DMCA (Digital Millennium Copyright Act) notices. The original project began as a set of GitHub Copilot instructions for recurring tasks like DMCA triage, comparing code, license checks, and circumvention review. We wanted to turn the messy, one-off prompt work that everyone was doing independently into something repeatable that a legal team could trust to gather the right facts and analyze the data consistently. I was surprised at how far I could go without engineering support. The core “programming” was plain-language files consisting of workflow instruction sets, policy reference materials, and templates for writing reports. Instead of writing source code, I was able to use my language crafting skills as a lawyer to build structured legal judgment into the workflow itself. It grew from there. We added different analysis modes for clients and lawyers (with faster outputs and escalation recommendations for clients, and deeper review and both-sides arguments for lawyers) and integrated external data sources. When I handed the workflow off to the team, they started using it right away and asked Copilot to do more. That foundation has since evolved into a full desktop app for running predefined legal workflows in a clean interface. Building the desktop app required writing some code (a lot of code, actually), but the core instructions used to customize workflows are easily edited and customized in the app using plain language. The app we created has now expanded well beyond only code analysis for DMCA notices. It includes instructions for many in-house workflows like contract review, NDA triage, risk assessment, compliance checks, and response drafting. Under the hood, it can route work through reusable skills and agents (intake, playbook alignment, risk scoring, evidence verification, escalation routing, report assembly), but the important part is not technical complexity—it’s that legal teams can still control behavior in readable Markdown. For me, the key lesson was that I don’t need to wait for the perfect software vendor—or become a full-time developer myself—to build serious AI tooling. If you can clearly define your methodology, your standards, and your output format, GitHub Copilot makes it easy to operationalize that knowledge. My legal Copilot is not a replacement for legal judgment, and it shouldn’t be treated that way. It’s a structured decision-support system designed to keep human review central while making legal analysis more consistent, more transparent, and more scalable. Check out the eyeball tool

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

Today, we’re excited to share a long-term commitment to the Department of Energy’s (DOE) Genesis Mission, backed by a $60 million investment designed to accelerate AI for science and the breakthroughs it can deliver for the country. This deepened commitment includes Microsoft’s new Scientific Partnership Advancing Research & Knowledge coordination hub and program office, otherwise known as SPARK, that is focused on facilitating collaboration and scientific discovery for the Genesis Mission. The Genesis Mission represents an ambitious vision, bringing together DOE’s 17 National Laboratories, world-class experimental facilities, decades of irreplaceable scientific data and next-generation computing into a single, unified platform capable of transforming how science gets done. The critical goal of the Genesis Mission is to double the productivity and impact of American research and innovation within a decade by embedding AI directly into the scientific process. It’s a bold step forward and exactly the kind of moonshot that defines American science. At Microsoft, we believe that this goal is not only achievable, but also essential as a national security imperative and an economic engine for generations to come. With this commitment and the launch of SPARK, we’re ready to partner fully in this mission and build on our collaborations across government and with academia. Microsoft investing in AI for science Today, Microsoft’s $60 million investment package will begin to accelerate AI for science in support of DOE and the National Laboratories. As sustained scientific impact requires more than infrastructure alone to enable lasting scientific outcomes, Microsoft’s investment package is structured in two parts: $40 million in Azure compute and AI credits, distributed over three years, to power the large-scale AI and scientific workloads at the heart of the mission. This gives researchers room to train, simulate and iterate at a scale that matches their ambition. $20 million in solution engineering enablement services. Dedicated engineering, architecture, deployment, adoption and acceleration support to turn cloud and AI capacity into operational scientific outcomes. This investment helps ensure that the Genesis Mission advances in practice and in principle. Together, this investment reflects a simple conviction: While infrastructure opens the door, it is people, expertise and disciplined delivery that carry discoveries across the finish line. Microsoft is committing to both. Introducing SPARK: Microsoft’s catalyst for advancing scientific partnerships Great partnerships need more than good intentions and good technology — they need a clear way to work together. So, alongside this commitment, we are standing up a new program office and coordination hub: SPARK, or Scientific Partnership Advancing Research & Knowledge. SPARK is the single front door and coordination hub for Genesis Mission collaboration with Microsoft. SPARK orchestrates the full breadth of what Microsoft brings to bear: program, technical, research, engineering, security, compliance, partner and field teams into one clear, consistent path from first idea to deployed science. SPARK is built around five commitments: A dedicated Genesis Mission Program Management Office. This office handles intake, prioritization, a disciplined sprint-and-checkpoint cadence and a steady operational interface with DOE for alignment, reporting and sustained partnership. An AI for Science Center of Excellence. An integrated delivery team that moves use cases from concept to secure, compliant, scalable implementation, with enablement through office hours, hackathons and training grounded in responsible AI and reproducible research. Optimization of Azure credits. Ensures computing resources are directed toward the projects where they can accelerate science most. Management of technical services. Dedicated support to help labs adopt and operationalize AI-for-science capabilities, delivered by Microsoft teams and select partners. Joint research and development. A focused set of Genesis Mission-aligned challenge problems, co-developed by Microsoft researchers and DOE scientific leadership, with milestones, publications and IP governed by mutually agreed terms. Underneath SPARK sits the full breadth of Microsoft’s secure, FedRAMP-authorized cloud and AI portfolio. This includes Microsoft Azure infrastructure, Microsoft Foundry and Zero Trust security through Microsoft Defender, Sentinel and Entra. Azure capabilities will help extend the DOE’s American Science Cloud capabilities by providing computing, AI, data and collaboration services that complement existing scientific infrastructure. Together, these capabilities will help accelerate discovery, enable secure collaboration across institutions and provide researchers with flexible access to advanced technologies and resources. This will enable DOE and National Laboratory scientists to move faster from hypothesis to discovery, without compromising the security, reproducibility and governance that mission-critical research demands. Accelerating science with Microsoft Discovery and Quantum Microsoft’s new commitments build on capabilities we recently announced through Microsoft Discovery and Microsoft Quantum. They are designed to accelerate scientific research and will support the Genesis Mission’s ambition to bring AI, advanced computing and emerging technologies more deeply into the scientific process. To enable a unified research platform for Genesis Mission work, Microsoft will provide access to Microsoft Discovery, our integrated platform that unites AI models, simulation, data and experimental workflows driven by advanced cognition rooted in scientific method, within a single governed environment. Microsoft Discovery, now generally available, includes support for autonomous lab orchestration, integration of Microsoft Research’s AI models for science, continuous AI learning through the scientific loop and agentic memory, advances in Discovery Bookshelf for data curation and scalable indexing and deeper integration and multi-hop reasoning over enterprise science data estates, including data governance. Together, these capabilities are designed to help researchers move more quickly from data to insight, from simulation to experiment, and from promising idea to scientific breakthrough. We also recently announced the preview of the Microsoft Discovery app, a local desktop experience that helps researchers, students and scientific teams begin working with Microsoft Discovery today. Our support for the Genesis Mission will include access to the Microsoft Discovery app to enable DOE and National Laboratory teams with early access to its capabilities. Microsoft’s recent quantum progress further strengthens the foundation for Genesis Mission work. With Majorana-based quantum advances, including more reliable topological qubits and a roadmap toward scalable quantum computing, Microsoft is helping move quantum from long-range research toward practical scientific capability. For DOE and National Laboratory teams, that progress can open new ways to model complex materials, chemistry, energy systems and national security challenges that are

AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, including expanding its AI fleet with AMD’s most advanced AI and high-performance computing (HPC) solutions. Our approach to AI infrastructure is designed to support the breadth of how AI systems are built and run. We closely work with industry innovators like AMD as well as our own purpose-built silicon and systems to provide customers with a comprehensive, open and heterogenous platform to achieve the best performance, cost and energy efficiency outcomes. Building on our close collaboration with AMD, Microsoft is bringing AMD’s latest Helios AI platform and next-generation EPYC datacenter processors to Azure. These technologies will power three upcoming Azure offerings: HDv2 VMs for data processing, HXv2 VMs for electronic design automation (EDA) and ND MI455X v7 VMs for AI inference workloads. Expanded infrastructure for inference, AI data systems and chip design YouTube Video Click here to load media Built for AI data systems — Azure HDv2 CPU infrastructure is essential to the performance and efficiency of modern AI systems. AI accelerators depend on high-density, power-efficient CPU compute to process data, coordinate workloads and keep pipelines running at scale. Without this, training jobs don’t have enough data to learn from, and agents don’t have enough capacity to perform tasks on behalf of customers. Azure HDv2 virtual machines are one of our latest offerings designed from the ground up to eliminate these bottlenecks and empower massive agentic workload adoption. Co-designed with AMD, HDv2 VMs expand Azure’s portfolio of purpose-built solutions for the most demanding CPU workloads from AI customers, including data preparation, search, reinforcement learning and agent coordination at scale. Featuring nearly 500 physical 6th Gen AMD EPYC CPU cores, 4 terabytes of RAM, 32 terabytes of local NVMe storage and 400 Gb Azure Boost networking, HDv2 VMs are built for the workload needs of our most demanding AI customers. Optimized for silicon design and technical computing — Azure HXv2 The AI era has created tremendous need and opportunity for firms developing the silicon products that power this infrastructure. For this reason, Azure HX virtual machines, launched in partnership with AMD in 2023 and featuring AMD’s unique 3D V-cache technology, have seen significant adoption among silicon design firms working to bring more capable and efficient AI silicon to market. Today, we are announcing the next step in our workload optimized journey for these customers, HXv2. HXv2 virtual machines build on and extend the strengths of HX. They both continue the differentiation Azure offers for RTL simulation workloads by again employing 3D V-cache technology, while offering significant improvements to single threaded performance and memory. HXv2 VMs will feature 176 AMD 6th Gen EPYC CPU cores with a clock frequency of more than 5 GHz, 50% more addressable cache per core and VM sizes with nearly 2 or 4 terabytes of RAM, helping customers optimize their workloads to memory needs. Azure HXv2 is also designed to support a broader range of technical computing workloads including scientific simulation, engineering analysis and other distributed memory applications. The significantly increased per VM and per core performance, and the inclusion of 800 Gb InfiniBand, enable large-scale MPI-based simulations and make HXv2 an ideal fit for a wide variety of HPC customers. AMD, a leading HX-series customer, highlights this impact directly: “Engineering teams are pushing the limits of simulation, chip design and scientific computing. At AMD, we experience those demands firsthand as we design future AMD EPYC CPUs and AMD Instinct GPUs. Azure HX is an important platform for scaling complex EDA workloads, and we’re excited about Azure HXv2, which is designed to deliver even greater performance and scalability. We look forward to continuing our collaboration with Microsoft as we help advance infrastructure for the world’s most demanding engineering and scientific workloads.” — Mark Papermaster, Executive Vice President and CTO, AMD The HXv2 also leverages Microsoft’s long-standing collaboration to optimize Synopsys AI-powered EDA solutions on Azure: “As AI compute continues to push the limits of semiconductor design, our collaboration with Microsoft on the Azure HX-series demonstrates a shared vision for enabling customers to deliver next-generation AI systems with precision and scale in accelerated design cycles. These systems have enabled Synopsys customers to reliably and efficiently leverage cloud-based compute, extending EDA workloads beyond traditional infrastructure constraints so they can meet ambitious development schedules while maximizing design quality and delivering dramatic performance gains.” — Shankar Krishnamoorthy, Chief Product Development Officer, Synopsys Production-scale AI inference — ND MI455X v7 ND MI455X v7 is designed for the reasoning, search and agentic workloads behind modern AI services. Powered by the AMD Helios rackscale solution, it expands Azure’s infrastructure options for large-scale inference and is designed to deliver strong performance and efficiency for demanding AI workloads. Together, these new capabilities expand Azure capabilities while giving customers more flexibility to choose the right compute for each unique AI workflow: from inference, to data systems, to chip design. Customer choice is a core design principle built directly into Microsoft Azure, and we’re excited to bring AMD’s most advanced innovations at production scale. To learn more about Azure’s high-performance computing and AI infrastructure capabilities, visit Azure.com. Scott Guthrie is responsible for a set of hyperscale cloud computing solutions and services including Azure, Microsoft’s cloud computing platform, generative AI solutions, data platforms and information and cybersecurity. These platforms and services help organizations across the globe solve urgent challenges — and transform for the future. The post Microsoft expands Azure AI and HPC infrastructure with AMD appeared first on The Official Microsoft Blog. ​AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, including expanding its AI fleet with AMD’s most advanced AI and high-performance computing… The post Microsoft expands Azure AI and HPC

Amy Coleman, EVP and Chief People Officer, shared the following communication with employees today. When I stepped into this role, I promised to communicate more openly with you and share the “why” behind our decisions. Today we are eliminating around 4,800 roles, about 2.1% of our global workforce, as we focus our people, investments, and energy on the priorities that will keep Microsoft positioned to deliver for customers in a fast-changing industry. The people whose jobs are impacted today are our colleagues and friends. They have made meaningful contributions to Microsoft, and we are deeply grateful for everything they have done. Decisions like these are never easy, and you have my commitment that we are always looking for ways to reduce the need for job eliminations. Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity. Over the past year, we have redeployed more than 4,000 employees into new roles, including another 500 this month. We will also transition four of our gaming studios to operate under new management, with the goal of preserving both their intellectual property and ongoing projects. In addition, more than 30% of eligible employees chose to participate in our recent voluntary retirement program, and we will continue exploring similar approaches in the future. While this doesn’t change the difficulty of today’s news, we will continue to do everything we can to create opportunities for our people, reduce the need for job eliminations where possible, and responsibly support those affected with care and respect. The “why” is this: Our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself – what we do, where we focus, and how we’re organized – has to transform too. Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it. That means we will need to adjust resources and roles and shift how we operate so we can have the greatest impact for our customers. I also want to be direct that the roles eliminated today are not being replaced by AI. At the same time, what is true is that AI is changing how work gets done. Some of the tasks we do every day can now be automated, and that means we all need to keep learning, keep building new skills, and keep adapting as the work evolves. Our customers are navigating this same shift, and they’re counting on us to help them through it. We can’t do that well unless we’re doing it ourselves. This comes down to two commitments: making the decisions needed to drive differentiated customer value, and supporting the people affected by them. First, we will make the hard changes required to build differentiated products and services that deliver differentiated customer value. We are aligning our investment, people, and energy to our business priorities. Today’s changes mostly fall within our Commercial and XBOX organizations. In our Microsoft Commercial Business, they build on last week’s Frontier Company announcement, reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments. In XBOX, we are restructuring to position the business for long-term success. Engineering teams across the company will also evolve their structure and priorities to meet customer needs and innovate for the future. Second, we will do this thoughtfully.  As mentioned above, we are working on alternative solutions to job eliminations, and beyond this, we will continue to invest in equipping employees with new skills, including in AI. For those who are impacted, we provide financial support and resources to help them take their next step. I know many of you want to help those who are leaving but aren’t sure how. Reach out and check in on your colleagues. Use your network to bring people together, share what makes them exceptional, and help create connections to opportunities that might not happen otherwise. We are still early on this journey, and there will be more changes ahead; other parts of our business will need to make similar changes. Each time, you can hold us to the two commitments. During my time at Microsoft, I’ve seen this company reinvent itself again and again. What makes that possible has always been our people – their resilience, creativity, and willingness to keep learning. Thank you for everything you bring to Microsoft. Amy Read more: Resetting XBOX. The post The latest in our company transformation appeared first on The Official Microsoft Blog. ​Amy Coleman, EVP and Chief People Officer, shared the following communication with employees today. When I stepped into this role, I promised to communicate more openly with you and share the “why” behind our decisions. Today we are eliminating around 4,800 roles, about 2.1% of our global workforce, as we focus our people, investments, and… The post The latest in our company transformation appeared first on The Official Microsoft Blog.  Featured, The Official Microsoft Blog The Official Microsoft Blog

The pace of AI adoption is moving incredibly fast. Customers have moved well beyond experimentation and understand the importance of adopting AI to transform their business. They are now concentrating on delivering measurable business outcomes and demonstrating a return on their AI investments, while ensuring their intelligence is amplified and their IP is protected. Today we are introducing Microsoft Frontier Company, a new operating business focused on delivering Frontier Transformation through AI for our customers around the world. It will provide a unique combination of skills inclusive of deep industry knowledge, change management and continuous improvement experience, and enterprise-grade AI engineering expertise. This goes beyond what has been labeled as Forward Deployed Engineering (FDE) and will be the largest, most capable, outcome-driven engineering organization in the industry. We are making a $2.5B investment in Microsoft Frontier Company, embedding 6,000 industry and engineering experts at customers to co-design, co-innovate, deploy and continuously improve AI systems at scale based on measurable business outcomes. I recently wrote more about my conviction that Intelligence + Trust are the two most important components of any AI solution and how our customers can use different levers to manage cost. Companies need to establish an intelligence platform so their unique IQ — their proprietary data, expertise, workflows and decision-making processes — compounds over time from within, using their choice of models to build AI solutions and workflows. They need a trusted platform that allows them to observe, govern, manage and secure AI solutions across every layer of the technology stack, using FinOps to assess their ROI. Enterprise AI engineering expertise with deep industry knowledge is required to build a system that acts as a continuous loop of improvement between the two platforms to fine tune agentic business processes, ensuring that a customer’s intelligence compounds over time and delivers real business outcomes. This is what Microsoft Frontier Company was built to do: focus on end-to-end Frontier Transformation, enabling customers to amplify their IQ with AI while refining their differentiated value in the markets that they serve. Early results demonstrate meaningful impact: Our engineers and industry experts partnered with LSEG (London Stock Exchange Group) to embed AI into LSEG Workspace, helping finance professionals ask complex questions and get quick answers across structured and unstructured financial content. The solution is underpinned by a foundation that is iteratively refined through client feedback and real-time user testing that accelerates each cycle and steadily improves model quality and scope. From LSEG to Land O’Lakes to Unilever to Novo Nordisk, our differentiated approach is already delivering measurable outcomes on our customers’ Frontier Transformation journeys. To achieve scale, we will work closely with our partner ecosystem to extend this unique value to our customers across all markets and segments globally. We have robust FDE partnerships with our Global SI partners, including Accenture, Capgemini, EY, KPMG, PwC and others. Central to this approach is a principle that is non-negotiable: a customer’s IQ is protected. Their data, their IP, their competitive advantage — none of it is used to train models in ways that commoditize what differentiates them in their industry. Satya put it clearly recently: there is no societal permission for an AI future that eats the intelligence of the companies it’s deployed inside. We built Microsoft Frontier Company to make sure that does not happen. We protect that intelligence with a model-diverse, open, heterogeneous AI platform. Customers shouldn’t be locked into a single model any more than they should be locked into a single technology vendor. Microsoft’s platform gives organizations the flexibility to run the right model for each scenario — whether it comes from OpenAI, Anthropic, Microsoft AI, open source or a specialized model tuned for a specific industry — without ceding control to any one of them. To lead this new organization, I have asked Rodrigo Kede Lima to be the President of Microsoft Frontier Company. Rodrigo brings 30 years of industry experience, and for the past six at Microsoft has led enterprise-wide transformations as a sales leader in the Americas and Asia. He has been at the forefront of helping customers and partners translate technology shifts into business outcomes, and understanding how platform innovation, engineering and partner ecosystem collaboration come together to drive growth. I am excited about all the things that Microsoft Frontier Company will do for our customers to realize the gains of Frontier Transformation. At the end of the day, it comes down to Intelligence + Trust and empowering our customers to achieve meaningful outcomes and a return on their investments. Learn more at www.microsoft.com/en-us/frontier-company. Judson Althoff is the chief executive officer of Microsoft Commercial Business. He is responsible for the product strategy, sales, services, support, marketing, operations and revenue growth of the company’s commercial business, which operates in more than 120 regional and national subsidiaries globally. The post Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence appeared first on The Official Microsoft Blog. ​The pace of AI adoption is moving incredibly fast. Customers have moved well beyond experimentation and understand the importance of adopting AI to transform their business. They are now concentrating on delivering measurable business outcomes and demonstrating a return on their AI investments, while ensuring their intelligence is amplified and their IP is protected. Today… The post Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence appeared first on The Official Microsoft Blog.  Featured, The Official Microsoft Blog, AI, Frontier Transformation, Microsoft Frontier Company The Official Microsoft Blog