{"id":24946,"date":"2026-07-30T09:00:00","date_gmt":"2026-07-30T16:00:00","guid":{"rendered":"https:\/\/research.codeghost.online\/insidetrack\/blog\/?p=24946"},"modified":"2026-08-04T13:16:45","modified_gmt":"2026-08-04T20:16:45","slug":"unlocking-the-value-of-microsoft-365-copilot-and-agents-at-microsoft","status":"publish","type":"post","link":"https:\/\/research.codeghost.online\/insidetrack\/blog\/unlocking-the-value-of-microsoft-365-copilot-and-agents-at-microsoft\/","title":{"rendered":"Unlocking the value of Microsoft 365 Copilot and agents at Microsoft"},"content":{"rendered":"\n

In Microsoft Digital, our company\u2019s IT department, we get to share our internal learnings on deploying and using Microsoft 365 Copilot and agents with many of our enterprise customers.<\/p>\n\n\n\n

During in-person briefings, I often remind our customers that generative AI systems like Copilot and Copilot Cowork are still quite new, and that they upend decades of human-computer interaction patterns. For over 40 years, we’ve trained knowledge workers to memorize patterns in the GUI to support their productivity. <\/p>\n\n\n\n

With Copilot and agents, you can type nearly anything as a prompt and instantly get a detailed response, or create an agent to autonomously complete a business process on your behalf. It’s a very new way of working, and it\u2019s no surprise that many knowledge workers\u2014and even engineers themselves\u2014are still adapting.<\/p>\n\n\n\n

More than anything, our customers are looking for one key insight: They want to know how we measure and define the business value of Copilot and agents at Microsoft, especially through the lens of increased productivity and cost savings. After all, AI systems represent a significant investment for our customers, and leaders want to have confidence that they’ll yield sufficient ROI to justify the cost, knowing that the payback period isn\u2019t immediate.<\/p>\n\n\n\n

Six steps to proving value<\/h2>\n\n\n\n

So, how do you prove the value of AI-powered tools in the enterprise? There are six main steps that I share with customers that I believe are critical, based on the empirical evidence we’ve gathered in Microsoft Digital.<\/p>\n\n\n\n

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This graphic shows the six main steps for measuring Copilot and agent value in an organization.<\/figcaption><\/figure>\n\n\n\n

Our big mistake? We didn\u2019t instrument all the arduous business processes that slow us down and impact effectiveness before<\/em> our deployment, so that we could more easily prove the value of AI across our enterprise after<\/em> deployment.<\/p>\n\n\n\n

Here are further details on the six measurement steps to keep in mind:<\/p>\n\n\n\n

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  1. Before you deploy, talk to people who are doing the frontline work in your organization. <\/strong>These should be hands-on managers or influential individual contributors (ICs), not executives or mid-level managers who are abstracted away from the work (although these individuals can be useful in helping identify the right SMEs to talk to).<\/li>\n<\/ol>\n\n\n\n

    For each role, identify three to five everyday pain points that could benefit from Copilot or agents. These could be operational, business, or technological processes, all of which can be improved by thoughtful applications of AI. This phase provides a great chance to use Six Sigma skills or other continuous improvement (CI) methodologies to evaluate your processes and identify waste, then consider how a combination of CI and AI<\/a> could make them more efficient.<\/p>\n\n\n\n

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    1. Once identified, instrument and measure each of the processes to get baseline data on the average time it takes to complete them.<\/strong> Then the hard work begins\u2014you need to think deeply about how AI could make those processes better. This could be through defining a series of step-like prompts that take away the toil. It could be through further AI-powered automation to make some of the steps go away. It could be applications of AI that validate outputs to ensure that rework is minimized. More and more, it could be an agent that completes the work on the employee\u2019s behalf.<\/li>\n<\/ol>\n\n\n\n

      If you lack detailed telemetry for the process in question, you have two options. The first is preferable: Build end-to-end telemetry, so you have observability throughout the process in question. While this is always the best way to reliably measure productivity gains at scale, there is also a second option: Identify a cross-section of ICs and measure them the old-fashioned way as they complete the process in question\u2014with a stopwatch. Take the average of several people to get a better sense of how much time is typically needed to complete the task. Then consider how AI could make that process more efficient, while also improving the resulting business outcomes.<\/p>\n\n\n\n

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      1. Concurrent with your investigation into ways that AI can improve productivity and reduce toil, you need to <\/strong>invest in enterprise AI skilling<\/strong><\/a>, ideally by role. <\/strong>The fact is, engineers are going to use AI differently than operations staff, who are going to use it differently than sales and marketing pros. Yes, there are common skills for each, but the best training is tailored to the role, grounded in the experience of using Copilot or agents to address challenges or opportunities that commonly arise in their day-to-day work.<\/li>\n<\/ol>\n\n\n\n

        It\u2019s also important to ensure your employees understand the strengths and weaknesses of current AI models. No model is perfect, and understanding where reasoning errors can occur will help them avoid accepting AI-generated output that may be inaccurate. Human discernment and observability is a critical step in validating AI outputs.<\/p>\n\n\n\n

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        A best practice is to gate access to generative AI; require employees to engage in both general and role-specific training prior to their provisioning to ensure they immediately unlock value in their work.<\/p>\n<\/blockquote>\n\n\n\n

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        1. After you\u2019ve trained your employees and given them the skills to succeed with AI and agents in the enterprise, <\/strong>begin your deployment in earnest<\/strong><\/a>.<\/strong> For tools like Copilot, we recommend deploying in cohorts by role, focusing on employees who will immediately see the greatest benefit. At Microsoft, we started with our sales team and then gradually deployed to the remaining employee population over the course of several months. This enabled us to scale up our skilling programs, governance strategies, and support function in anticipation of increased volumes. For agents, deploy to support the biggest pain points first, then monitor and observe agent performance before scaling to additional business or operational processes.<\/li>\n\n\n\n
        2. Post-deployment, give it a few weeks, then return to those same processes you identified in the first step and measure the average time savings. <\/strong>If time savings aren\u2019t as significant as you hoped with either Copilot or agents, go back to process evaluation and employee skilling and continue to refine your approach.<\/li>\n<\/ol>\n\n\n\n

          This whole process is intentionally iterative\u2014you\u2019re not likely to get it exactly right on the first attempt. But if a process that used to take 30 minutes can now be completed in 10, you\u2019ve obviously made a big difference that will save a lot of time when extrapolated across an entire fiscal year. Even better if that end-to-end process is now being completed by an autonomous agent, with a human reviewing and validating the output.<\/p>\n\n\n\n

          After you\u2019ve succeeded in one domain, identify different role leaders and find another batch of processes that could be improved with a combination of continuous improvement and AI. There\u2019s really no limit to the scope of ways you can positively impact your employees if you maintain a sharp focus on continually improving their experience with Copilot and agents.<\/p>\n\n\n\n

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          1. The final step: Reclaim the value from those productivity savings and apply it to new business challenges or opportunities. <\/strong>Let\u2019s be clear\u2014most enterprises aren\u2019t investing in AI solely to give their people back time. They\u2019re trying to do more with less, enabling their workers to be more productive and generate more value for the company.<\/li>\n<\/ol>\n\n\n\n

            In this crucial final phase, you need to be thoughtful and deliberate about how you\u2019ll use the time saved with AI. If your people are saving two hours per week on average, how will they use that time? There are innumerable ways you could redeploy that time to address new business challenges or opportunities. The key is to be intentional in maximizing value, so it aligns with your team and your company\u2019s goals and ambitions. Then report those savings to the appropriate members of the leadership team, to help them understand how their AI investments are paying off.<\/p>\n\n\n\n

            There you have it\u2014you now possess the tools to quantify the value of your generative AI investments in the enterprise, using them to recapture value and drive more business impact thanks to the power of Copilot and agents.<\/p>\n\n\n\n

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            Learn more<\/strong><\/strong><\/p>\n\n\n\n

            Dive deeper into this topic with a framework that our organization put together to measure the impact Copilot is having at Microsoft.<\/a><\/p>\n<\/div>\n<\/div>\n\n\n\n

            Boosting AI adoption with structured change management<\/h2>\n\n\n\n

            As you can see from the graphic above, it\u2019s important to surround the AI value measurement process with structured change management and ongoing employee skilling. In Microsoft Digital, we\u2019ve learned that even the most useful or intuitive technologies won\u2019t see widespread adoption without a deliberate and sustained change management effort that\u2019s localized to meet the disparate needs of a global organization.<\/p>\n\n\n\n

            In our case, that meant cultivating and supporting a community of Copilot Champions who have become the backbone of our global change management strategy. This community is now 10,000+ strong<\/a> and even has its own Viva Engage forum, where our Copilot enthusiasts answer employee questions and share useful prompts.<\/p>\n\n\n\n

            Unfortunately, just doing AI skilling once won\u2019t be enough. The pace of change with Copilot and agents is simply too great to do one training effort and then move on. Building an AI-forward culture<\/a> takes time, and ongoing opportunities to learn and improve skills are one of the best ways to help your employees build the AI habit.<\/p>\n\n\n\n

            The Microsoft 365 Admin Center has an “AI adoption score”<\/a> that can help you see\u2014by cohort\u2014how successfully people are building that habit. Having employees who use Copilot three times per week in any way is enough to build an AI-focused workforce, enabling your company to unlock the value of AI in the enterprise.<\/p>\n\n\n\n

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            The promise of generative AI is significant, and there\u2019s no doubt that you\u2019ll see qualitative benefits in your organization even if you don\u2019t instrument every process. But in an era of tight IT budgets, having the quantitative data necessary to calculate the ROI of your investments in Copilot and agents will make it far more likely that you\u2019ll get financial support from your executive team to deploy and succeed at scale.<\/p>\n<\/blockquote>\n\n\n\n

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            Key takeaways<\/h3>\n\n\n\n

            Here are some key things to remember as you embark on your own Copilot and agent value measurement efforts:<\/p>\n\n\n\n