We all know that changing the way we work with AI takes time and is a bit of a tricky thing to do. That's why it's so important to do it with friends, with colleagues, and to learn from our peers during this journey. This is why one of the first things we should do when we roll out something like Copilot and prepare for it is we should establish the right people to start Copilot with, help them understand and support the change and transformation that it drives, so that they, they can be our champions to drive success across the broader organization.
This is something that we've already talked about, where you can use collaboration signals and more to understand who are these super collaborators. Who are these people with high digital debt that can have a lot of impact and a lot of ready impact around Copilot? Because if we can make them more performance and them more productive, they can unlock potential in many others around them. Well, the secret is that this has always been a bit of a hard thing to aggregate and analyze, not just for the initial preparation phase but also during the pilot.
As you scale your pilot to your next wave and your next wave, it becomes more and more important to understand how are people using this technology and what kind of impact is it driving, and how could we nudge or target specific groups of users to help them more, to provide them additional guidance or support, or to help influence our champion community so that they spend more time with some of these people or on some of these gaps that exist in the organization. That's what I want to talk about today using the Copilot dashboard.
Let's look at the Copilot dashboard. The first thing to understand is that the Copilot dashboard is an experience that's not just limited to the example I'm showing here within Viva Insights, but it's something that can be provided within Power BI and of course, in the admin center. If you're an admin, you're going to see some of these reports light up in your admin center over time.
Now, in this particular set, I want to talk about the readiness and why it's important to understand the kind of data that you get in the Copilot dashboard. The first thing the Copilot dashboard gives us is it gives us information around who are the users who are technically ready, who have the right technology teams, outlook, office applications, and they have a good amount of usage, so they make sense for us to target in our next rollout of Copilot users who don't already have active usage are definitely not the right people because they don't have as much need for digital fitness and the capabilities that AI transforms.
At the bottom of this, we also see things like update channels. It's important to understand that the latest technology makes Copilot work, and if we don't have that, we're limited in how we roll it out within the organization. Now, these are pretty technical things and pretty straightforward, but because this picture changes over time, it's really great that we have a set of tools that can help with that.
What I really want to talk about is the adoption section of this because in the adoption data we have some really interesting and rich insights showing us not just how people are using it, right with usage over Teams and Outlook, Word, Excel, PowerPoint, et cetera. But we can also see how this changes over time. How people are using Copilot today in Excel might be a smaller number than we want, and so we could target more effective scenarios and examples.
Again, it's interest and awareness. Plus, shared understanding leads to eventually sustained commitment. You can't drive the change that AI represents without building interest and sustaining interest and awareness and without building and sustaining understanding.
One of the things that we've learned is it's really important to help give guidance, give data to your champion community and your other communities that are going to support AI change in your organization by saying things like, hey, we are seeing that there's a lot of potential we haven't realized with usage in Excel. There are a lot of people we see using Excel, but only a smaller subset are actively using Copilot in Excel. Maybe we need to spend more time on education in there.
Maybe we need to nominate some really effective champions and actually support them in coaching other groups and business units and things like that. So these are things that you can do with this data. At the same time, we want to understand how people are using these technologies a little bit more as well.
Understanding the number of times that they're using it to do these activities can be a really good baseline. We could actually and often do associate numerical values with this, like potential time savings or cost implications for this. And that can help with our ROI analysis over time, as well as understanding not just these four technologies that you see here, but actually many other technologies over time, like how it's used in Viva or other technologies as our usage in loop and other technologies over time.
Before I move on, actually, let me just go back a little bit. In this particular report, I also want to highlight that the actions per user is a really useful part of this tool. It gives us insight not just on how people are using it today but how that might change over time.
Today, you might use AI in PowerPoint to create presentations and summarize presentations, but a new signal that might make sense to add there is how people use it to generate imagery, and how is that used in Word or PowerPoint or things like that? And when we start to aggregate that, we could start to assess, well, how much does that reduce the load or enable new patterns of usage outside of what a creative team or somebody else in the organization might have serviced before. So it's really useful to start thinking about your own scenarios that apply here.
And of course, watch for the ones that Microsoft adds. Quick and easy reporting, because this will become something that changes very rapidly over time, just like the technologies represented here will change over time quite rapidly.
Well, let's talk about impact then. Impact is also something we want to measure and understand. The good news here is that there's quite a bit that Microsoft can do to improve the way we understand our current impact. In particular, there are two different data signals that we can support. The first one is a direct data signal. This means a comment or a survey response in something like Viva Glint that we can use to improve how people use our understanding of how people are using Copilot and their satisfaction with it.
Here, as an example: when we run those surveys, we can analyze that to understand over time how these numbers might change, not just at an organizational level, which, to be honest, isn't that useful, but at a population group level. Within a department or a region, if we have a population that's large enough, we can actually analyze where favorability is running into challenges. We learned actually early in our pilots, in our preview work with customers, that there were some regions and areas that really weren't satisfied with Copilot, and we correlated almost always that that was actually because we didn't have champions and we didn't have people proactively doing education sessions and supporting them.
When we adopted that little change, it actually made a big difference in those feedback scores. Now, that's one of many potential answers. But again, this data is really helpful in helping you target the right investments at the right time in areas of the organization. That's not the only way we can target.
Another big one, is user behavior. Digital signals that are indirect around how people work are actually captured today in Viva Insights, and we can use that information to enrich our understanding of how Copilot might be changing how people work. Now, we have a lot of ideas of how it changes them.
What Microsoft is doing here is helping you understand how these might be correlated, right? Because people are using Copilot, we know it's going to change how they meet, how they create content, and we know it has a huge impact on email responsiveness and drafting of emails. But, some of the things that you might be surprised by is how it actually affects employee networks. If you use it for Viva Engage, it allows more people to participate, a lot more people to actively engage in Viva Engage both in learning awareness and connecting with employees.
That actually strengthens the speed of which employee networks grow within the organization. Similarly, the way people can quickly write and create emails creates a deeper level of connection with many other employees because they can focus less on transactional, low-value collaboration. Like me asking you, hey, where's that document?
Instead, focus on high-value collaboration where it's like, hey, I know you have access to this document, I have access to this document, and we've been working on these documents together. What can we learn and how can improve this process? Again, more shifts to high-value collaboration is something that you can actually see represented in both direct and indirect data signals from things like surveys and places like Viva Insights.
Now, this was just a taste of what's to come in the Microsoft Copilot dashboard, but what's really important about it is the way we use it. We need to use tools like this to help us augment and adjust our trajectory over time. Because when you start with Copilot, it's not going to be one quick project, checkmark done. It's going to be a change that you manage over many years, potentially even decades into the future. It's really critical that we come up with a strategy or use data to improve that over time. The Copilot dashboard is one of many tools that you're going to use in that strategy.
Thanks so much for your time, and I hope this was helpful to you. I look forward to hearing your comments, maybe in the stream below, help us understand what are you using to measure? Because we are seeing a lot of interesting ideas from our customers. Perhaps we'll share some of those responses as well.
I'd love to understand what you are using to measure AI effectiveness across your organization, and what parts of the Copilot dashboard align with that, and what parts do you think are really not that useful, or maybe even making a mistake in how they're correlating some of those insights.
Thank you so much for your time.
