{"id":22539,"date":"2026-03-05T09:05:00","date_gmt":"2026-03-05T17:05:00","guid":{"rendered":"https:\/\/www.microsoft.com\/insidetrack\/blog\/?p=22539"},"modified":"2026-03-13T14:36:34","modified_gmt":"2026-03-13T21:36:34","slug":"powering-the-new-age-of-ai-led-engineering-in-it-at-microsoft","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/insidetrack\/blog\/powering-the-new-age-of-ai-led-engineering-in-it-at-microsoft\/","title":{"rendered":"Powering the new age of AI-led engineering in IT at Microsoft"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">When generative AI burst into the mainstream, it landed in our IT engineering organization like a shockwave.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There was excitement, curiosity, skepticism, and no shortage of questions about what this technology meant for the future of IT.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">At Microsoft Digital\u2014the company\u2019s IT organization\u2014we didn\u2019t start with a grand transformation plan. Instead, we started with a realization: AI wasn\u2019t just another tool to roll out. It was a fundamental shift in how engineering work could happen.<\/p>\n\n\n\n<aside class=\"wp-block-group aside-for-guide has-white-200-background-color has-background has-global-padding is-content-justification-right is-layout-constrained wp-container-core-group-is-layout-3f1abf08 wp-block-group-is-layout-constrained\" style=\"border-radius:10px;padding-top:var(--wp--preset--spacing--spacing-12);padding-right:var(--wp--preset--spacing--spacing-12);padding-bottom:var(--wp--preset--spacing--spacing-12);padding-left:var(--wp--preset--spacing--spacing-12)\">\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-298f84b7 wp-block-group-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"132\" height=\"132\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/10\/Engage-with-our-experts_blogs.png\" alt=\"\" class=\"wp-image-20636\" style=\"width:48px\"\/><\/figure>\n\n\n\n<p class=\"has-body-lg-font-size wp-block-paragraph\"><strong>Engage with our experts!<\/strong><\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--spacing-4)\">Customers or Microsoft account team representatives from Fortune 500 companies are welcome to <a href=\"mailto:msitstaff@microsoft.com\">request a virtual engagement<\/a> on this topic with experts from our Microsoft Digital team.<\/p>\n<\/aside>\n\n\n\n<p class=\"wp-block-paragraph\">For years, our IT teams have been focused on scale, reliability, and operational excellence. Those priorities didn\u2019t change. What changed were the possibilities.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Suddenly, engineers could draft code in seconds, summarize complex systems instantly, or automate work that had once consumed hours or days. It was an opportunity to take the skills and capabilities of our people and amplify them with AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That realization forced us to step back and ask harder questions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">How do you help thousands of engineers understand what AI can actually do to impact their day-to-day work? How do you move from experimentation to trust? And how do you adopt AI in a way that strengthens engineering fundamentals instead of eroding them?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The answer came in the form of a phased journey grounded in people, culture, and continuous learning.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 1: Awareness and access<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">It might sound surprising when speaking about engineering processes, but our first challenge wasn\u2019t technology; it was understanding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When generative AI entered the conversation, most engineers saw the headlines and dabbled in various tools, but few understood fully what it meant for their work. Some were excited, others were wary. Many simply didn\u2019t know where to start. That gap between awareness and practical value was the first barrier we had to address.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We realized early that top-down mandates wouldn\u2019t work. Telling engineers to \u201cuse AI\u201d without context or relevance would only deepen skepticism. Instead, we focused on something both simpler and more difficult: Exposure.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We started by making AI visible and accessible in the tools engineers already used. GitHub Copilot. Microsoft 365 Copilot. Early copilots embedded directly into engineering workflows. The goal wasn\u2019t immediate productivity gains. It was familiarity. Letting engineers see, firsthand, what AI could and couldn\u2019t do.<\/p>\n\n\n\n<figure class=\"wp-block-image alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Mukul-Singhal.png\" alt=\"A photo of Singhal. \" class=\"wp-image-22543\" style=\"width:150px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Mukul-Singhal.png 500w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Mukul-Singhal-300x300.png 300w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Mukul-Singhal-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cWe encouraged tool usage and adoption so people would at least play around with AI. And once they did, they started seeing the value. That\u2019s when the mindset shifted from \u2018AI might replace me\u2019 to \u2018AI can be my companion.\u2019\u201d<\/p>\n<cite>Mukul Singhal, partner group engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Just as important, we talked openly about limitations.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI wasn\u2019t perfect. It hallucinated. It made confident mistakes. And that honesty mattered. By framing AI as an assistant, we reinforced the role of engineering judgment. Engineers didn\u2019t need to fear losing control. They needed to understand how to stay in control.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We also made experimentation safe.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No quotas. No forced adoption metrics. Engineers were encouraged to try AI on low\u2011risk tasks: summarizing documentation, generating test cases, or exploring unfamiliar codebases. Small wins built confidence, confidence built curiosity, and curiosity drove organic adoption.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As that experimentation took hold, the mindset began to shift.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cWe encouraged tool usage and adoption so people would at least play around with AI,\u201d says Mukul Singhal, a partner group engineering manager in Microsoft Digital. \u201cAnd once they did, they started seeing the value. That\u2019s when the mindset shifted from \u2018AI might replace me\u2019 to \u2018AI can be my companion.\u2019\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Over time, conversations changed from \u2018Should we use AI?\u2019 to \u2018Where does AI help most?\u2019<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Engineers began sharing prompts, tips, and lessons learned with one another. What started as individual exploration turned into community learning. Awareness gave way to momentum.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Phase one was about providing access to explore, to question, and to learn. And that foundation made everything that followed possible.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 2: Culture shift<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Access created awareness and awareness created curiosity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As more engineers began experimenting with AI, we noticed a pattern. Some teams were moving faster, learning faster, and reducing friction in their day\u2011to\u2011day work. Others stalled after initial trials. The difference wasn\u2019t technical skill or capability, it was mindset.<\/p>\n\n\n\n<figure class=\"wp-block-image alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Veera-Mamilla.png\" alt=\"A photo of Mamilla. \" class=\"wp-image-22544\" style=\"width:150px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Veera-Mamilla.png 500w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Veera-Mamilla-300x300.png 300w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Veera-Mamilla-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cPeople started shifting from the mindset of \u2018Will AI work?\u2019 to \u2018AI is working for me.\u2019 I think that was a very transformational shift, to where I believe a lot of engineers in the organization started believing in AI.\u201d<\/p>\n<cite>Veera Mamilla, principal group engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">To move forward, we had to shift how AI was perceived from something optional or experimental to something that was simply part of how modern engineering gets done.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That meant normalizing AI as a trusted partner in the engineering process.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Leaders played a critical role in that shift. Rather than positioning AI as a productivity shortcut, they framed it as a way to strengthen engineering fundamentals: clearer design discussions, better documentation, faster feedback loops, and more time for deep problem\u2011solving. The message was intentional and consistent. Using AI&nbsp;wasn\u2019t&nbsp;about cutting corners, it was about&nbsp;reimagining how work gets done.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We also had to address a fear that surfaced early: that AI adoption was a signal of replacement rather than empowerment.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cPeople started shifting from the mindset of \u2018Will AI work?\u2019 to \u2018AI is working for me,\u2019\u201d says Veera Mamilla, a principal group engineering manager in Microsoft Digital. \u201cI think that was a very transformational shift, to where I believe a lot of engineers in the organization started believing in AI.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That framing mattered.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As engineers incorporated AI into their workflows, success stopped being measured by output alone. The focus shifted to outcomes. Did AI help you understand a system faster? Did it surface risks earlier? Did it free up time to focus on higher\u2011value work?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Over time, AI stopped feeling like a novelty. It became part of the engineering fabric. We reinforced it through leadership modeling, peer learning, and shared success stories. Teams no longer asked whether AI belonged in their workflows. They asked how to use it responsibly and effectively.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 3: Upskilling and role evolution<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Once AI moved from curiosity to expectation, the challenge of skill building became unavoidable.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">From the start, we made a deliberate choice: This would be an upskilling and reskilling journey, not a wholesale replacement of roles. The goal wasn\u2019t a new workforce. It was an investment in the one we had.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That decision shaped everything that followed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Early upskilling efforts focused on practical entry points. Prompt engineering. Tool literacy. Understanding how copilots and early agents behaved in real engineering workflows. We treated these as something every engineer needed to experiment with, regardless of discipline.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But it quickly became clear that skills alone weren\u2019t the full story. Roles themselves were starting to evolve.<\/p>\n\n\n\n<figure class=\"wp-block-image alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Ragini-Singh.png\" alt=\"A photo of Singh. \" class=\"wp-image-22545\" style=\"width:150px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Ragini-Singh.png 500w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Ragini-Singh-300x300.png 300w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Ragini-Singh-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cYour title might still be software engineer or principal engineer. But if you\u2019re acting like an AI engineer, what does that actually mean? That question helped us start defining how these roles were evolving.\u201d<\/p>\n<cite>Ragini Singh, partner group engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Across software development, service engineering, and cloud network engineering, the work was shifting from manual execution toward orchestration and oversight. Engineers were no longer expected to do every task end\u2011to\u2011end by hand. Instead, they were learning how to guide AI, review its output, and decide where automation made sense and where it didn\u2019t.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As part of this shift, we began researching how the industry itself was redefining engineering roles. Leaders examined emerging job descriptions from across the market and compared them with Microsoft\u2019s own role frameworks. At the time, there was no formal \u201cAI engineer\u201d role in the internal job library. Rather than creating a new title, the focus stayed on evolving expectations within existing roles.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The idea of an \u201cAI\u2011native engineer\u201d emerged not as a job description, but as a mindset.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An AI\u2011native engineer still understands systems, architecture, and risk. What\u2019s different is how that expertise gets applied. Routine tasks are delegated to AI. Judgment, design, and accountability stay with the human. Engineers move from doing all the work themselves to supervising work done in partnership with AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cYour title might still be software engineer or principal engineer,\u201d says Ragini Singh, a partner group engineering manager in Microsoft Digital. \u201cBut if you\u2019re acting like an AI engineer, what does that actually mean? That question helped us start defining how these roles were evolving.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This evolution looked different across disciplines. Software engineers focused on AI\u2011assisted coding, test generation, and spec\u2011driven development. Service engineers leaned into AI for incident response, knowledge capture, and operational decision support. Cloud network engineers began moving from manual intervention toward intelligent orchestration and agent\u2011assisted troubleshooting. The common thread wasn\u2019t identical tooling, it was a shared shift toward higher\u2011order work and reduced toil.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 4: Embedding AI across the engineering lifecycle<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">By this phase, we knew individual productivity gains were simply the starting point for larger and broader benefits.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Early on, most AI usage showed up in familiar places: Code suggestions, documentation summaries, quick answers. Useful, but fragmented. The bigger opportunity emerged when we stepped back and asked a harder question: What would it look like if AI were embedded across the entire engineering lifecycle, not just used at isolated moments?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We stopped thinking in terms of tools and started thinking in terms of flow. Design. Build. Test. Deploy. Operate. Improve. AI needed to show up across all of it, in ways that reinforced how engineers already worked.<\/p>\n\n\n\n<figure class=\"wp-block-image alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Sudhaker-Sadasivuni.png\" alt=\"A photo of Sadasivuni. \" class=\"wp-image-22546\" style=\"width:150px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Sudhaker-Sadasivuni.png 500w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Sudhaker-Sadasivuni-300x300.png 300w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Sudhaker-Sadasivuni-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cIf AI is only showing up at one step, you don\u2019t get the full value. The real impact comes when it\u2019s integrated across the lifecycle, where engineers can design, build, operate, and learn faster as a system.\u201d<\/p>\n<cite>Sudhakar Sadasivuni, principal group engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">In software engineering, that meant pulling AI earlier into the process. We began using it to help draft requirements, reason through design options, and review code with broader system context to accelerate how quickly we could get to informed decisions. Coding assistance mattered, but it was no longer the center of gravity.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Testing and quality followed a similar pattern. AI supported test generation, defect analysis, and code review, reducing repetitive effort and helping issues surface sooner. That gave engineers more time to focus on quality and architecture instead of cleanup.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In service engineering, we embedded AI into incident management and operational workflows. Engineers used it to summarize incidents, surface relevant knowledge, and analyze signals across systems. In cloud network engineering, AI helped shift work away from manual intervention toward orchestration and intelligent troubleshooting. Across disciplines, the principle stayed the same: AI should reduce friction, not introduce it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As we scaled this approach, one thing became clear. Embedding AI wasn\u2019t just a technical exercise. It was a systems change.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cIf AI is only showing up at one step, you don\u2019t get the full value,\u201d says Sudhakar Sadasivuni, a principal group engineering manager in Microsoft Digital. \u201cThe real impact comes when it\u2019s integrated across the lifecycle, where engineers can design, build, operate, and learn faster as a system.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As AI became part of core workflows, engineers remained accountable for outcomes. AI output was reviewed, tested, and validated like any other engineering input. Embedding AI didn\u2019t lower the bar for rigor. It raised expectations around judgment, oversight, and data quality. We became more deliberate about responsibility and governance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Over time, these integrations created compound benefits.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Faster design cycles reduced downstream rework. Better testing lowered operational noise. Improved operational insight shortened recovery times. AI stopped being something we used occasionally and became something the engineering system itself was built around.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Phase 5: Eliminating toil and accelerating outcomes<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">At some point, every AI story hits the same test. Does it actually make engineers\u2019 days better? For us, that proof showed up fastest in elimination of toil.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Across Microsoft Digital, engineers have always spent time on work that was necessary but draining. It included tasks such as manual troubleshooting, repetitive diagnostics, log analysis, and routine operational tasks that kept systems running but didn\u2019t move the organization forward.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI gave us a chance to change that.<\/p>\n\n\n\n<figure class=\"wp-block-image alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"500\" height=\"500\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Beth-Garrison.png\" alt=\"A photo of Garrison. \" class=\"wp-image-22547\" style=\"width:150px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Beth-Garrison.png 500w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Beth-Garrison-300x300.png 300w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2026\/03\/Beth-Garrison-150x150.png 150w\" sizes=\"auto, (max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cToil reduction is the biggest thing. That\u2019s where engineers\u2019 eyes light up. If we can eliminate toil, people engineers will flock to use AI. I really believe it.\u201d<\/p>\n<cite>Beth Garrison, principal cloud network engineer, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">In cloud network engineering, for example, troubleshooting used to require manually reconstructing what happened, such as logging into devices, chasing configurations, and piecing together context after the fact. As we began introducing agents and machine learning into these workflows, that work shifted. Instead of spending time assembling the picture, engineers could generate the views they needed faster and focus on resolving issues.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The same shift showed up in how we used operational data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Rather than reacting to incidents after impact, we started using machine learning to analyze logs, identify patterns, and surface anomalies earlier. That moved teams from reactive response toward proactive monitoring and prevention.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">One thing became clear very quickly: Toil reduction wasn\u2019t just a benefit; it was the catalyst for adoption.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cToil reduction is the biggest thing. That\u2019s where engineers\u2019 eyes light up,\u201d says Beth Garrison, a principal cloud network engineer at Microsoft Digital. \u201cIf we can eliminate toil, people engineers will flock to use AI. I really believe it.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Service engineering followed a similar arc.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Across governance, operations, productivity, and cost management, we began applying agents and automation to simplify complex work and reduce manual review cycles. Governance and compliance workflows became faster and more consistent. Operational processes benefited from guided remediation and earlier insight. Knowledge capture improved as documentation and remediation guidance could be generated and updated automatically.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When we removed repetitive work such as manual triage, rote diagnostics, endless documentation cleanup, we transformed how engineers spent their time. More focus on design. More proactive problem\u2011solving. More energy directed toward improving systems instead of just maintaining them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Toil reduction made the value of AI tangible. It\u2019s the moment AI stopped being interesting and became indispensable, and our engineering teams started asking where else we can apply it next.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Measuring what matters<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">By the time AI was embedded across our engineering lifecycle, a new question came into focus: \u201cHow do we know it\u2019s working?\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In the early days, we paid close attention to usage. Which tools engineers were trying, where adoption was growing, or where it stalled. Those signals mattered and adoption was the leading indicator that people were getting comfortable and starting to integrate AI into real work.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\">\u201cAdoption was always the starting point. But we were clear from the beginning that usage isn\u2019t the destination. The real goal is impact; more time for engineers to focus on the work that truly matters.\u201d<\/p>\n<cite>Ullas Kumble, principal group software engineering manager, Microsoft Digital<\/cite><\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">But using AI doesn\u2019t automatically mean better outcomes. So, we shifted the conversation and started asking, \u201cWhat\u2019s different now that our engineers are using AI?\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That change reframed how we thought about measurement. We began looking beyond tool activity to understand impact across the engineering system. Faster design cycles. Earlier defect detection. Reduced time spent on repetitive operational work. Shorter incident resolution. Clearer documentation. Fewer handoffs. Less rework.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These weren\u2019t abstract metrics. They showed up in the flow of work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We were intentional about not forcing a single definition of value across every role. Software engineers, service engineers, and cloud network engineers experience impact differently. What mattered was that each team could point to tangible improvements in how work moved through the system.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That perspective shaped how leadership talked about success.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cAdoption was always the starting point,\u201d says Ullas Kumble, a principal group software engineering manager at Microsoft Digital. \u201cBut we were clear from the beginning that usage isn\u2019t the destination. The real goal is impact; more time for engineers to focus on the work that truly matters.\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Over time, this approach changed the quality of our conversations. Instead of debating whether AI was worth the investment, teams talked about where it was removing friction and where it still wasn\u2019t delivering enough value. Measurement became a tool for learning and prioritization.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Moving forward<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Looking ahead, one lesson stands out: this journey isn\u2019t complete.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">AI tools will continue to evolve. Agents will become more capable. Roles will keep shifting. What it means to be an engineer will continue to change. And that means our approach must stay grounded in the same principles that guided us from the start: invest in people, reinforce fundamentals, embed AI into real workflows, and stay honest about what\u2019s working and what isn\u2019t.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We didn\u2019t set out to build an AI\u2011driven engineering organization overnight, we built it phase by phase.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By meeting engineers where they were<br>By reshaping culture before redefining roles.<br>By embedding AI across the lifecycle, not bolting it on.<br>By reducing toil and measuring impact where it mattered most.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The result is better engineering: powered by AI, guided by human judgment, and built to keep evolving.<\/p>\n\n\n\n<div class=\"wp-block-group has-global-padding is-content-justification-left is-layout-constrained wp-container-core-group-is-layout-c0392459 wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-7db9d80f wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<figure class=\"wp-block-image alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"190\" height=\"190\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Key-takeaways-badge.png\" alt=\"\" class=\"wp-image-19493\" style=\"object-fit:cover;width:75px;height:75px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Key-takeaways-badge.png 190w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Key-takeaways-badge-150x150.png 150w\" sizes=\"auto, (max-width: 190px) 100vw, 190px\" \/><\/figure>\n\n\n\n<p class=\"has-body-xl-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--spacing-24);margin-bottom:0;padding-top:var(--wp--preset--spacing--spacing-24)\">Key takeaways<\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Here\u2019s a set of approaches you can take to establish AI-led engineering for your organization:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li class=\"wp-block-list-item\"><strong>Start with access and understanding. <\/strong>Give engineers safe, easy access to AI in the tools they already use so curiosity and confidence can develop organically before you push for outcomes.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Frame AI as a partner, not a replacement. <\/strong>Position AI as an assistant that strengthens engineering judgment and fundamentals rather than a shortcut or a threat to roles.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Normalize experimentation without pressure. <\/strong>Encourage low\u2011risk experimentation and peer sharing instead of mandates, allowing adoption to grow through visible, practical wins.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Invest in upskilling. <\/strong>Focus on evolving skills and expectations within existing roles so engineers learn how to guide, review, and stay accountable for AI\u2011assisted work.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Embed AI across the full engineering lifecycle. <\/strong>Look beyond isolated productivity gains and integrate AI into design, build, test, operate, and improve workflows to unlock system\u2011level impact.<\/li>\n\n\n\n<li class=\"wp-block-list-item\"><strong>Measure impact where engineers feel it. <\/strong>Move past usage metrics and track outcomes like reduced toil, faster feedback, and improved flow so teams can see where AI is truly making work better.<\/li>\n<\/ul>\n<\/div>\n\n\n\n<div class=\"wp-block-group has-global-padding is-content-justification-left is-layout-constrained wp-container-core-group-is-layout-c0392459 wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-7db9d80f wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<figure class=\"wp-block-image alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"190\" height=\"190\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Try-it-out-badge.png\" alt=\"\" class=\"wp-image-19492\" style=\"object-fit:cover;width:75px;height:75px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Try-it-out-badge.png 190w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Try-it-out-badge-150x150.png 150w\" sizes=\"auto, (max-width: 190px) 100vw, 190px\" \/><\/figure>\n\n\n\n<p class=\"has-body-xl-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--spacing-24);margin-bottom:0;padding-top:var(--wp--preset--spacing--spacing-24)\">Try it out<\/p>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/features\/copilot?OCID=InsideTrack_Product_10816\" target=\"_blank\" rel=\"noreferrer noopener\">Try GitHub Copilot<\/a>.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-group has-global-padding is-content-justification-left is-layout-constrained wp-container-core-group-is-layout-c0392459 wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-7db9d80f wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<figure class=\"wp-block-image alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"190\" height=\"190\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Related-links-badge.png\" alt=\"\" class=\"wp-image-19491\" style=\"object-fit:cover;width:75px;height:75px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Related-links-badge.png 190w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Related-links-badge-150x150.png 150w\" sizes=\"auto, (max-width: 190px) 100vw, 190px\" \/><\/figure>\n\n\n\n<p class=\"has-body-xl-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--spacing-24);margin-bottom:0;padding-top:var(--wp--preset--spacing--spacing-24)\">Related links<\/p>\n<\/div>\n\n\n\n<ul style=\"margin-top:var(--wp--preset--spacing--spacing-20)\" class=\"wp-block-list\">\n<li class=\"wp-block-list-item\"><a href=\"https:\/\/techcommunity.microsoft.com\/blog\/appsonazureblog\/an-ai-led-sdlc-building-an-end-to-end-agentic-software-development-lifecycle-wit\/4491896\" target=\"_blank\" rel=\"noreferrer noopener\">Learn how an AI-led SDLC builds an end-to-end agentic software development lifecycle with Azure and GitHub.<\/a><\/li>\n\n\n\n<li class=\"wp-block-list-item\"><a href=\"https:\/\/www.microsoft.com\/en-us\/microsoft-cloud\/blog\/2025\/10\/13\/fyai-why-developers-will-lead-ai-transformation-across-the-enterprise\/?msockid=118fa29f92986f38157eb59b969864e9\" target=\"_blank\" rel=\"noreferrer noopener\">Discover why developers will lead AI transformation across the enterprise.<\/a><\/li>\n<\/ul>\n<\/div>\n\n\n\n<div class=\"wp-block-group has-global-padding is-content-justification-left is-layout-constrained wp-container-core-group-is-layout-c0392459 wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<div class=\"wp-block-group has-global-padding is-layout-constrained wp-container-core-group-is-layout-7db9d80f wp-block-group-is-layout-constrained\" style=\"padding-right:0;padding-left:0\">\n<figure class=\"wp-block-image alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"190\" height=\"190\" src=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Wed-like-to-hear-from-you-badge.png\" alt=\"\" class=\"wp-image-19490\" style=\"object-fit:cover;width:75px;height:75px\" srcset=\"https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Wed-like-to-hear-from-you-badge.png 190w, https:\/\/www.microsoft.com\/insidetrack\/blog\/uploads\/prod\/2025\/07\/Wed-like-to-hear-from-you-badge-150x150.png 150w\" sizes=\"auto, (max-width: 190px) 100vw, 190px\" \/><\/figure>\n\n\n\n<p class=\"has-body-xl-font-size wp-block-paragraph\" style=\"margin-top:var(--wp--preset--spacing--spacing-24);margin-bottom:0;padding-top:var(--wp--preset--spacing--spacing-24)\">We&#8217;d like to hear from you!<\/p>\n<\/div>\n\n\n\n<ul style=\"margin-top:var(--wp--preset--spacing--spacing-20)\" class=\"wp-block-list is-style-list-no-bullets\">\n<li class=\"wp-block-list-item\"><a href=\"mailto:msitstaff@microsoft.com\">Want more information? Email us and include a link to this story and we\u2019ll get back to you.<\/a><\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>When generative AI burst into the mainstream, it landed in our IT engineering organization like a shockwave. There was excitement, curiosity, skepticism, and no shortage of questions about what this technology meant for the future of IT. At Microsoft Digital\u2014the company\u2019s IT organization\u2014we didn\u2019t start with a grand transformation plan. Instead, we started with a [&hellip;]<\/p>\n","protected":false},"author":92,"featured_media":22541,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_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":[71,882,1],"tags":[199,868,881,820,597,850,237,827,849,848],"coauthors":[550],"class_list":["post-22539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-featured","category-it-strategy","category-uncategorized","tag-ai","tag-ai-deployment-and-adoption","tag-developer-tools","tag-device-management","tag-employee-experience","tag-end-user-services-and-support","tag-governance","tag-microsoft-365-copilot","tag-network-and-infrastructure","tag-security-and-risk-management","m-blog-post"],"yoast_head":"<!-- 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