{"id":24374,"date":"2026-06-18T09:05:00","date_gmt":"2026-06-18T16:05:00","guid":{"rendered":"https:\/\/www.microsoft.com\/insidetrack\/blog\/?p=24374"},"modified":"2026-07-09T09:08:36","modified_gmt":"2026-07-09T16:08:36","slug":"guiding-our-ai-deployment-with-a-set-of-employee-councils","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/insidetrack\/blog\/guiding-our-ai-deployment-with-a-set-of-employee-councils\/","title":{"rendered":"Guiding our AI deployment with a set of employee councils"},"content":{"rendered":"\n
The AI adoption curve gets steeper every day, as the technology continues to advance at lightning speed.<\/p>\n\n\n\n
At Microsoft Digital, the company\u2019s IT organization, we\u2019re using a set of employee councils and connected capability groups to guide and accelerate how we deploy and adopt AI across our enterprise. Our goal is to focus our energy on the AI-enabled scenarios that matter most, reducing duplication, strengthening accountability, and making sure our investments create measurable value.<\/p>\n\n\n\n \u201cOur AI decisions and direction must be grounded in business strategy. AI councils provide guidance and enablement for our organization, ensuring that our investments in AI generate tangible benefits to our business. It\u2019s not just developing technology and then looking for a problem to solve with it\u2014we start with the opportunity.\u201d<\/p>\nDon Campbell, principal group technical program manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n That focus matters, because AI success doesn\u2019t come from usage alone. It comes from connecting strategy, enablement, data readiness, responsible AI, continuous improvement, change management, and measurement into one driving force.<\/p>\n\n\n\n That\u2019s how we\u2019re moving from experimentation to repeatable outcomes and from AI enthusiasm to AI accountability.<\/p>\n\n\n\n \u201cOur AI decisions and direction must be grounded in business strategy,\u201d says Don Campbell, principal group technical program manager in Microsoft Digital. \u201cAI councils provide guidance and enablement for our organization, ensuring our investments in AI generate tangible benefits to our business. It\u2019s not just developing technology and then looking for a problem to solve with it\u2014we start with the opportunity.\u201d<\/p>\n\n\n\n Our council-based approach is helping us accelerate our Frontier Firm transformation. The councils work together to set direction for AI adoption at Microsoft Digital, ensuring that our business needs drive solution development that can keep up with the pace of AI change. This work includes building visibility into all our AI solutions, including agents and Model Context Protocol (MCP) servers, while establishing governance and proven practices; developing training and learning pathways; and connecting teams together that are working on similar solutions across the enterprise.<\/p>\n\n\n\n We\u2019re excited for a future where our employees use intelligent agents and human judgment together to work smarter, move faster, and unlock new value for Microsoft and our customers.<\/p>\n\n\n\n AI councils at Microsoft<\/strong><\/p>\n\n\n\n Check out our series on how employee councils are guiding how we use AI here at Microsoft. <\/p>\n\n\n\n Effective AI needs both enterprise guidance and business-owned direction. That\u2019s why we\u2019re using councils and connected capability groups as the operating model for our AI deployment.<\/p>\n\n\n\n Each group has a distinct role, and none of them work alone. Together, they help us connect the strategy for AI to the work currently happening across Microsoft Digital.<\/p>\n\n\n\n These councils help us see across the business landscape through the lens of AI. They enable us to reduce duplication, scale what works, and make better decisions about where AI can create value. That allows our teams to keep moving fast without letting activity get ahead of accountability.<\/p>\n\n\n\n Our strategy council<\/a> helps us decide which AI-enabled scenarios deserve the most attention, which investments align to our business priorities, and how we\u2019ll know whether the work is creating value. It gives leaders a practical way to look across the portfolio and keep our AI work tied to the outcomes we\u2019re accountable for.<\/p>\n\n\n\n \u201cBusiness strategy defines the what and the why. AI defines the how, enabling execution of the strategy and delivering real value. We should use AI to advance our business strategy, not the other way around.\u201d<\/p>\nQingsu Wu, principal group product manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n This is important, because broad experimentation is useful early on in your AI journey. It helps teams learn and build momentum. But experimentation has to mature into focus. Without that shift, organizations can end up with too many different agents, agent skills, MCP servers, and other artifacts, without a clear view of what\u2019s actually impacting the business.<\/p>\n\n\n\n We\u2019re using the strategy council to keep that from happening.<\/p>\n\n\n\n \u201cBusiness strategy needs to lead the AI strategy,\u201d says Qingsu Wu, a principal group product manager in Microsoft Digital and an influential member of the strategy council. \u201cBusiness strategy defines the what and the why. AI defines the how, enabling execution of the strategy and delivering real value. We need to use AI to advance our business strategy, not the other way around.\u201d<\/p>\n\n\n\n That principle shapes how we work. We use the strategy council to identify our top AI-enabled scenarios, clarify the value we expect to create with each one, and connect that work to a monthly operating rhythm. Product owners still manage delivery and the council keeps the portfolio focused, visible, and aligned.<\/p>\n\n\n\n Our AI Center of Excellence (CoE)<\/a> is at the heart of our approach to enablement. It helps us translate enterprise AI priorities into practical guidance and execution support for teams building AI-enabled solutions.<\/p>\n\n\n\n \u201cWe can see patterns that a single team can\u2019t. We\u2019re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization\u2019s context. That allows us to align priorities and make sure our biggest bets are actually landing.\u201d<\/p>\nRia Khetan, senior program manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n The AI CoE extends the reach of the strategy council. It gives teams what they need to build, govern, reuse, and scale what matters, while the strategy council assists us in deciding where to focus.<\/p>\n\n\n\n That connective role is central to the broader council model. The strategy council identifies the top AI-enabled scenarios. The AI Center of Excellence connects strategy to execution across the organization, operating as a cross-functional coordination layer that sets direction and creates shared accountability.<\/p>\n\n\n\n \u201cWe can see patterns that a single team can\u2019t,\u201d says Ria Khetan, a senior program manager in Microsoft Digital, who is a member of the council. \u201cWe\u2019re translating AI CoE strategy and enterprise priorities into clear execution plans that work in each organization\u2019s context. That allows us to align priorities and make sure our biggest bets are actually landing.\u201d<\/p>\n\n\n\n The COE helps teams move those scenarios forward with answers to important questions:<\/p>\n\n\n\n It also helps reduce fragmentation. When teams build in isolation, they can solve the same problem in different ways. They can choose different patterns, interpret standards differently, or create solutions that don\u2019t scale beyond a single context. Enablement gives us a shared way to look across that activity and ask better questions.<\/p>\n\n\n\n \u201cWe use the CoE to bring consistency to how AI work gets done,\u201d Campbell says. \u201cIt gives us a way to step back and ask whether we\u2019re solving the right problems and whether we\u2019re set up to scale.\u201d<\/p>\n\n\n\n \u201cHigh-quality, well-governed data is essential to accelerate AI implementation and adoption, and to ultimately unlock its full value. Data quality, accessibility, and governance are imperatives for AI systems to be reliable, scalable, and business-critical. Recognizing this principle is propelling our data strategy.\u201d<\/p>\nMiguel Uribe, principal PM manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n Our AI scale depends on trusted and reliable data. That makes our data council<\/a> central to our council-based approach. This council makes sure our teams work with data that\u2019s governed, discoverable, accessible, and ready for AI.<\/p>\n\n\n\n \u201cHigh-quality, well-governed data is essential to accelerate AI implementation and adoption, and to ultimately unlock its full value,\u201d says Miguel Uribe, a principal PM manager in Microsoft Digital and member of the data council. \u201cData quality, accessibility, and governance are imperatives for AI systems to be reliable, scalable, and business-critical. Recognizing this principle is propelling our data strategy.\u201d<\/p>\n\n\n\n We\u2019re applying a data mesh mindset to balance domain ownership with enterprise consistency. Teams stay close to the data that they know best. Shared standards for governance, quality, metadata, and compliance provide a framework to make that data useful across Microsoft Digital.<\/p>\n\n\n\n Microsoft Fabric and Microsoft Purview are key to that approach. Microsoft Fabric unifies our siloed data in a shared data mesh. Microsoft Purview enables governance and best practices to ensure that we manage our data responsibly through discovery, classification, protection, and monitoring.<\/p>\n\n\n\n Our goal is AI-ready data that\u2019s available, complete, accurate, and high quality. Our data council also works with the AI Center of Excellence to strengthen data and AI fluency through learning pathways, operational practices, and community programs.<\/p>\n\n\n\n \u201cOur capacity to drive process improvements has been crucial to our AI transformation as a company. We\u2019ve adopted a \u2018CI before AI\u2019 approach to ensure that we don\u2019t end up automating inefficient processes.\u201d<\/p>\nDavid Laves, director of business programs, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n AI works best when it\u2019s applied to the right problem. That\u2019s why continuous improvement<\/a> is part of our council-based approach. Before teams automate a workflow or build an agent, we want them to understand the process, identify waste, and decide where AI can create measurable value.<\/p>\n\n\n\n \u201cOur capacity to drive process improvements has been crucial to our AI transformation as a company,\u201d says David Laves, director of business programs in Microsoft Digital and a member of the Continuous Improvement Center of Excellence. \u201cWe\u2019ve adopted a \u2018CI before AI\u2019 approach to ensure that we don\u2019t end up automating inefficient processes.\u201d<\/p>\n\n\n\n Continuous improvement helps teams make sure the underlying work is worth scaling. That\u2019s when a continuous improvement approach can help. It encourages practices like Gemba walks, Kaizen events, bowler cards, and monthly business reviews that allow our teams to understand where work gets stuck and where AI can help.<\/p>\n\n\n\n Continuous improvement keeps the council model grounded in real work. We\u2019re applying it where the process is understood, the value is clear, and the outcome can be measured.<\/p>\n\n\n\n Our compliance council encourages the application of Responsible AI<\/a>, so our teams can move faster with confidence. As our AI work scales across Microsoft Digital, responsible AI has to connect directly to the same council ecosystem that guides strategy, enablement, data, process, and measurement. That connection helps teams understand what they\u2019re accountable for before they build too far, too fast.<\/p>\n\n\n\n Our responsible AI work focuses on compliance, inclusiveness, fairness, transparency, reliability, privacy, security, and accountability. It\u2019s grounded in the Microsoft Responsible AI Standard and supported by responsible AI champions who help teams apply those expectations in real development workflows.<\/p>\n\n\n\n This approach gives teams structure. It allows them to assess impact, identify risks, document decisions, and bring in the right reviewers. It also creates consistency, as more AI agents and solutions move from experimentation into enterprise use.<\/p>\n\n\n\n The goal is to enable AI project teams to move in the right direction with the right safeguards. Responsible AI gives the strategy council, the AI Center of Excellence, the data council, and product teams a shared standard for trust\u2014to turn ambition into accountable execution. It also makes sure the AI systems we scale are worthy of the trust that employees, customers, and the company place in them.<\/p>\n\n\n\n Our councils choose the right AI work, support teams as they build, strengthen the data foundation, apply responsible AI, and improve processes before we scale. But we still need to answer the most important question: What changed because of the AI investment?<\/p>\n\n\n\n That\u2019s why we have built a common value measurement framework<\/a> across Microsoft Digital. Our teams use the framework to define expected value before they build. With it, they can establish a baseline, track results, and review what they learn with the right business and AI owners.<\/p>\n\n\n\n We organize AI value across six areas: Revenue impact, productivity and efficiency, security and risk management, employee and customer experience, quality improvement, and cost savings. Not every initiative needs to deliver value in every category. The point is to create a shared language that leaders and teams can use to compare investments, make tradeoffs, and understand progress.<\/p>\n\n\n\n Measurement also pushes us past simple savings claims.<\/p>\n\n\n\n If AI saves time, reduces cost, improves quality, or increases coverage, we want to know what happens next. Did teams reinvest that capacity? Did service improve? Did risk go down? Did quality increase?<\/p>\n\n\n\n AI accountability depends on that full loop. We define value, measure results, review progress, and adjust. Then we use what we learn to guide the next round of decisions.<\/p>\n\n\n\n Our AI councils make a difference because each group has a different focus.<\/p>\n\n\n\n \u201cWhat got us here won\u2019t get us to where we need to go next. We started with broad experimentation\u2014getting teams excited and building\u2014but now we\u2019re evolving as an organization to think about scale, alignment to business goals, and making sure our investments are driving the right outcomes.\u201d<\/p>\nMyron Wan, principal group product manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n Strategy assists us in choosing the right priorities. Enablement helps our teams to build with shared patterns. Data readiness gives AI systems a trusted foundation. Responsible AI allows us to move faster with confidence. Continuous improvement makes sure we\u2019re improving the work before we automate it. Measurement tells us whether the investment changed anything meaningful.<\/p>\n\n\n\n Together, this system means we can operate AI as a business-driven enablement system.<\/p>\n\n\n\n “What got us here won\u2019t get us to where we need to go next,” says Myron Wan, a principal group product manager in Microsoft Digital. “We started with broad experimentation\u2014getting teams excited and building\u2014but now we\u2019re evolving as an organization to think about scale, alignment to business goals, and making sure our investments are driving the right outcomes.\u201d<\/p>\n\n\n\n There\u2019s more work ahead. We need to keep scaling enablement, improving data readiness, increasing high-value use cases, showcasing measurable impact, and tightening alignment across teams.<\/p>\n\n\n\n We also need to keep asking the hard questions: Where should we invest? Where are we reducing risk? Are we reinvesting the value that AI creates?<\/p>\n\n\n\n Our council-based model allows us to answer those questions with discipline. It helps us connect AI ambition to business outcomes and move from experimentation to repeatable enterprise value. And it provides a practical model that other IT organizations can adapt as they guide their own AI deployment.<\/p>\n\n\n\n Here are the core actions organizations like yours can take to align your AI efforts to business targets and scale them responsibly:<\/p>\n\n\n\n The AI adoption curve gets steeper every day, as the technology continues to advance at lightning speed. At Microsoft Digital, the company\u2019s IT organization, we\u2019re using a set of employee councils and connected capability groups to guide and accelerate how we deploy and adopt AI across our enterprise. Our goal is to focus our energy […]<\/p>\n","protected":false},"author":92,"featured_media":24376,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_hide_featured_on_single":false,"_show_featured_caption_on_single":true,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[912,882],"tags":[199,868,137,822,89,850,853,237,852,827,848],"coauthors":[550],"class_list":["post-24374","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-councils","category-it-strategy","tag-ai","tag-ai-deployment-and-adoption","tag-change-management","tag-corporate-functions","tag-digital-transformation","tag-end-user-services-and-support","tag-enterprise-data","tag-governance","tag-it-and-business-operations","tag-microsoft-365-copilot","tag-security-and-risk-management","m-blog-post"],"yoast_head":"\n
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Why we use councils to guide internal AI efforts<\/h2>\n\n\n\n
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Aligning AI strategy to business value<\/h2>\n\n\n\n
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Tuning strategy into repeatable execution<\/h2>\n\n\n\n
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Building AI on trusted data<\/h2>\n\n\n\n
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Improving the process before applying AI<\/h2>\n\n\n\n
Scaling AI responsibly<\/h2>\n\n\n\n
Measuring our AI outcomes<\/h2>\n\n\n\n
Operating as one connected AI system<\/h2>\n\n\n\n
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Key takeaways<\/h3>\n\n\n\n
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Try it out<\/h3>\n\n\n\n
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Related links<\/h3>\n\n\n\n
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