New data from Microsoft 365 Copilot telemetry signals, along with examples from our customers around the world and teams inside Microsoft, demonstrate this transformation in action. It shows up in three ways: in how deeply and broadly people now use AI, in what AI makes possible\u2014from faster work to net-new capabilities\u2014and in how organizations tailor it to specific roles. Let\u2019s take a closer look, starting with the technology itself.<\/p>\n
A step change in innovation <\/p>\n
In the past few months, a new kind of AI product has emerged: a model wrapped in an agentic harness that can close its own feedback loop\u2014planning, executing, testing, and correcting its work to deliver a finished result. This approach enabled Copilot Cowork<\/a>, which became generally available in June. Cowork can take on multi-step work: you define the task, and it runs it end to end and returns a completed result. Its multi-model design lets you run the models a task needs, so capability scales with the work as more models become available. And testing shows that Cowork was 30% to 40% cheaper2 compared to a single model option. We also introduced our first autopilot agent, Microsoft Scout<\/a>, which stays active in the background, with its own identity and permissions, to keep work moving even when your attention is elsewhere. All of the outputs are grounded in Work IQ to understand the context that shapes work inside your organization, including people\u2019s roles, projects, priorities, and institutional knowledge\u2014so they are uniquely relevant to your business. <\/p>\n We\u2019ve seen a step change not just in the products we deliver to customers, but also in the way our engineering teams build them. While many employees have contributed to Cowork, the core team remains intentionally small\u2014growing from three engineers at the start to just nine when the product became generally available. And yet the speed-to-market was remarkable, with Cowork moving from inception to preview to being used by half the Fortune 500<\/a> all within the space of six months. The breakthrough came from rebuilding the software development process around agents, allowing a tiny team to achieve levels of speed, scale, and automation that would have been impossible with traditional engineering workflows.<\/p>\n Microsoft Scout also started with a small group of people working alongside autonomous agents with shared context, evaluations, and clear specifications, and again moved from an initial concept to a working product in a matter of weeks rather than months. \u201cIn the past, I spent a lot of time helping teams scale: aligning large groups, coordinating work, and making sure everything moved forward together,\u201d says Omar Shahine, Corporate Vice President of Microsoft Scout. \u201cNow it\u2019s more about creating the conditions for expert teams to move quickly, experiment safely, and continuously capture what they learn so every project starts further ahead than the last.\u201d Frontier teams like these\u2014nimble, focused, and defined by human-agent collaboration\u2014provide a strong signal for how knowledge work will transform in the months and years to come.<\/p>\n Depth and breadth of use<\/p>\n As AI products and the approach to building them mature, Copilot\u00a0usage\u00a0across organizations is also deepening and broadening.\u00a0In the\u00a0past\u00a0year\u00a0alone, the number of\u00a0conversations per user\u00a0has\u00a0nearly\u00a0doubled,3\u00a0while the number of\u00a0users\u00a0engaging with\u00a0multiple\u00a0Copilot features grew\u00a0by\u00a0triple\u00a0digits.4\u00a0And\u00a0the fact that\u00a0average\u00a0weekly engagement with Copilot is\u00a0now on par with Outlook and\u00a0Teams5\u00a0speaks\u00a0to the speed at which AI is becoming a part of everyday work.\u00a0\u00a0<\/p>\n When we look at what people ask Copilot Cowork to do most in a given week, we\u2019re seeing a rise in requests that span multiple steps, like analyzing a set of data and drafting the email that explains it or researching a problem and writing the code to solve it. When we account for these multi-step workflows, analysis-related work accounts for 49% of all tasks, up from 29% when analysis is performed as a standalone task.6 In other words, the data suggests AI is moving beyond helping people complete tasks to helping them complete entire workflows.<\/p>\n Zooming out, early adopters like manufacturing, banking, and software\/technology remain among the industries with the highest number of active agents in the Microsoft 365 ecosystem. But what\u2019s surprising is that industries like automotive and healthcare are emerging as some of the fastest-growing agent adopters,7 signaling that agent value is spreading well beyond the early leaders.<\/p>\n The clearest sign of this momentum may be how quickly organizations are scaling Copilot across their workforces. The number of our customers with more than 50,000 seats has increased more than 7x year over year. Meanwhile, the number of enterprise customers deploying Copilot to the majority of their information workers grew nearly 75% quarter over quarter, a signal of how central Copilot has become to their operations. And the time it takes to reach high usage, meaning monthly active usage above 80% across a customers\u2019 user base, has fallen from months to just days over the past year.8 Taken together, these shifts indicate that organizations are increasingly embedding AI and agents into the flow of everyday work.<\/p>\n What AI makes possible<\/p>\n \n At its most basic, AI-enabled speed creates efficiencies. But the organizations that are getting this right are moving beyond efficiency for efficiency\u2019s sake and starting to see how AI changes the work itself\u2014and can even expand what an organization is capable of doing.\n<\/p>\n For example, Microsoft\u2019s own Cloud Supply Chain team took a \u201clean before agents\u201d approach. It first mapped and simplified six end-to-end workflows and built a shared data foundation, then deployed more than 70 purpose-built agents across planning, sourcing, fulfillment, and logistics. The result was a 75% reduction in cycle time across selected workflows, with the lesson baked in: simplify the work itself before you automate rather than layering agents onto a broken process. <\/p>\n \n Cycle time is one measure of progress. The quality of the decisions underneath it is another. At intelligent power management company Eaton, root cause analysis had long relied on time-intensive investigations. To lighten the load, the company has begun to use an AI-powered quality agent that\u2019s helping experts review reports on manufacturing quality issues to quickly surface potential root causes, trends, and supplier insights. Applied across approximately 5,000 reports so far, it has accelerated analysis, strengthened data-driven decision making, and enabled earlier risk identification and prevention actions while keeping Eaton\u2019s expert team at the center of every decision.\n<\/p>\n Other organizations are discovering what\u2019s possible by empowering those closest to the problem to build the solutions. Premera Blue Cross set out to let employees create their own AI agents to take repetitive, manual work off their plates and tackle growing backlogs and manual workflows. The health plan, which serves members across Washington and Alaska, started with putting Microsoft 365 Copilot and Copilot Studio into the tools their people already used every day, backed by training and clear guardrails so employees could experiment without putting member trust at risk. The team has since built more than 900 agents and counting, including a contract exhibit agent whose intelligent document processing cut a manual workflow from 30 to 45 minutes down to about three\u2014freeing up employees to focus on members who need access to care or answers to critical questions.<\/p>\n Further along the spectrum, AI can unlock the ability to do work that previously couldn\u2019t be done at all. At Levi Strauss & Co., where AI is being woven across the business from consumer insight to human resources (HR), an agent analyzed 1,100 finance standard operating procedures and cataloged 18,000 individual tasks in a day, creating a detailed view of work that wouldn\u2019t have surfaced otherwise. \u201cThe agent sized an opportunity I\u2019d assumed was impossible to quantify,\u201d said Lisa Sterling, Vice President of Finance at Levi\u2019s. <\/p>\n And at global consulting organization EY, an Autonomous Sourcing Agent (ASA) automates low-cost, low-risk transactions across the procurement process, from requisition and negotiation to purchase order creation. It can negotiate with one or many suppliers at once across cost, delivery timelines, and warranty terms while keeping humans in the loop for validation and escalation. Since its October 2025 pilot, the ASA has supported more than 200 transactions and is expected to handle 1,500 over the next year. The result is a faster, more efficient procurement process that helps teams get more value from suppliers while ensuring important judgement calls remain with people.<\/p>\n Specialization at scale<\/p>\n The difference between the organizations seeing changes like these and those still waiting isn\u2019t how\u00a0widely\u00a0they\u2019ve deployed AI\u2014it\u2019s how well they\u2019ve tailored it to the work. Yet the most common deployment pattern is still a general-purpose one: give everyone the same tool and let them figure it out. The organizations creating the most distinct value are doing something different\u2014starting with how specific roles actually work, then building AI around those patterns.<\/p>\n One example: S&P Global Commodity Insights is showing how AI can make deep, specialized expertise accessible to more people by bringing trusted intelligence directly into the flow of work. As demand for AI-ready data grew, the company used Microsoft Copilot Agent Builder and Microsoft Graph connectors to create a Copilot agent in Microsoft Teams, giving customers a simpler way to access and interact with its commodities research and market insights. By delivering 95% faster data extraction and 98% faster comparative analysis, the solution helps customers turn vast amounts of information into actionable intelligence more quickly, empowering faster, more informed decisions across global energy and commodities markets.<\/p>\n Microsoft\u2019s sales team mapped distinct seller personas to dedicated AI agents\u2014Researcher, Analyst Agent, Sales Agent, Deal Agent, and MSXi Copilot (a sales intelligence and reporting agent)\u2014each designed to fit specific moments in how sellers spend their time. With agents taking on more administrative and coordination tasks, seller time dedicated to customer-facing activities doubled, from 25% to 50%. The pilot group saw 9.4% more revenue per head and 20% faster deal closure, and in a survey reported a 3x increase in adoption of priority use cases.9<\/p>\n Kantar took a ground-up approach. At the marketing data and analytics firm, a \u201cpeople agent\u201d for HR queries (really a system of about 10 agents with 60 topics and related flows working together) is being built and expanded through collaboration by people that actually handle and support these requests across its People team. It already resolves a growing share without human intervention: an employment verification letter that once took three days is now returned in 120 seconds. Kantar reports the share of HR queries handled this way has risen from zero in February to about 40%, against a year-end target of 95%. <\/p>\n Microsoft\u2019s People Operations team runs more than a million employee interactions a year across more than 100 systems, doing work that carries real weight for people. They are using Frontier Tuning to scale HR expertise across their processes and employee moments rather than improving one task at a time, capturing the expertise of practitioners as tasks, skills, and rubrics that define quality standards. The result is an AI-powered system that can take on operational work with the context, standards, and institutional knowledge necessary for success, transforming their practitioner know-how into organizational capability. The team can see how the agent performs on real work and tune the levers\u2014tools, context, skills, and the model itself\u2014to ensure consistent performance. The agent developed with Frontier Tuning is now live in People Operations, delivering 5x improvement in successful task completion compared to a baseline general-purpose model.10\u00a0As routine activities are increasingly handled by AI, Microsoft HR\u2019s People Operations experts can focus on the complex employee interactions, nuanced decisions, and strategic work where human insight matters most.<\/p>\n What\u2019s next<\/p>\n \n As AI technology advances and organizations everywhere begin to see real value emerge, the measures that matter are changing as well. We\u2019ll see increased focus on depth of use, measurable change in the work itself, the growth and governance of agents, the redesign of roles and workflows, and the emergence of capabilities that didn\u2019t previously exist. The organizations building durable advantage are the ones where technology, operating models, and human agency are evolving together. That is the Frontier Firm taking shape.\n<\/p>\n Footnotes\u00a0<\/p>\n 1Microsoft Q4 Earnings call.\u00a0\u00a0<\/p>\n 2This analysis was conducted by internal Microsoft teams. The analysis compared 125 test runs across a total of 12 light, medium, and heavy work data prompts in Copilot Cowork and Claude Cowork with their Microsoft 365 connector, both using model Opus 4.8. For Copilot Cowork, the costs were calculated using variable rates based on relevant models, context, tools, and runtime from our internal logs. For Claude Cowork, the costs were calculated using publicly available API rates based on token usage and Microsoft 365 connector usage. We used these costs to get a total price per run and compared the prices across light, medium, and heavy prompt sets between Copilot Cowork and Claude Cowork with their Microsoft 365 connector. Cost savings results based on tests conducted in June 2026. Actual costs and potential savings may vary depending on usage, configuration, time, and other factors.\u00a0\u00a0<\/p>\n 3Microsoft Q4 Earnings call.\u00a0\u00a0<\/p>\n 4Based on anonymized, aggregated Microsoft 365 Copilot productivity signals, count of monthly-active M365 Copilot users who use 2 or more unique Copilot-based features over a 28-day period. Includes all countries and enterprise customers in June 2025 and June 2026.\u00a0\u00a0<\/p>\n 5Microsoft Q4 Earnings call.\u00a0<\/p>\n 6Based on anonymized, aggregated Microsoft 365 Copilot productivity signals, Cowork telemetry from July 19, 2026 to July 26, 2026, analyzing the most common tasks customers delegated to Copilot Cowork during the reporting period.\u00a0\u00a0<\/p>\n 7Based on anonymized, aggregated Microsoft 365 Copilot productivity signals, Monthly Active Agents measures the count of unique active agents observed through telemetry across Declarative Agents, Custom Engine Agents, and SharePoint Agents. The metric is calculated over a rolling 28-day window. An agent is counted as \u201cactive\u201d if, within that 28-day period, it meets at least one of two criteria: (1) it has at least one day of user-initiated usage, or (2) it completes at least one autonomous run. Each qualifying agent is counted once regardless of how many times it was used, ensuring a de-duplicated measure of the active agent base. Growth is assessed over a 12-month period spanning June 2025 through June 2026, comparing the active agent count at the start and end of the period.\u00a0\u00a0<\/p>\n 8Microsoft Q4 Earnings call.\u00a0\u00a0<\/p>\n 9Internal Microsoft sales team data based on 687 sellers of Microsoft 365 Copilot from January to June 2024, as compared with sellers with low usage of Copilot.\u00a0\u00a0<\/p>\n 10Success was defined as a \u201cperfect run\u201d: the task achieved a 100\/100 score against human-defined evaluation criteria. Tasks were evaluated across multiple executions to account for variability in completion paths, task sequencing, and tool selection, with only consistently perfect outcomes counted as successful.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"In yesterday\u2019s earnings call, we shared that Microsoft 365 Copilot has surpassed 30 million paid seats, with net…\n","protected":false},"author":2,"featured_media":124317,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[24,420,7853,416,8638,320,7852],"class_list":["post-124316","post","type-post","status-publish","format-standard","has-post-thumbnail","category-microsoft","tag-ai","tag-azure","tag-azure-copilot","tag-copilot","tag-copilot-for-work","tag-microsoft","tag-microsoft-copilot"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/124316","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=124316"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/124316\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/124317"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=124316"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=124316"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=124316"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"Statistics](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/07\/Telemetry-Inline_c-1024x297.webp\")
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