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So let's talk about the management side. So AI is also changing management, and I don't have as many examples here because I do think it's a bit early, but I wanted to at least highlight a few.
One of the biggest ones that we see for accelerating and optimizing management is how you actually work. So let's say you start your day and you come in, you want to know what happened around X, so what happened around this customer yesterday, or what happened around this department yesterday, what happened around this team yesterday? And all of a sudden it can do that semantic search that we mentioned earlier and start to make sense of all the signals from activities within SharePoint OneDrive to conversations and meetings and teams. And instead of me having to go and make sense of those and go look at this recording and ask these people about these things, I can get that aggregated for me.
And this alone has a fundamental change in terms of how we work, because it's aggregating signals across many things that are happening and then helping us prepare for a meeting that's coming up, or helping us figure out how we support some change in the organizational process. And this has big implications because while this works at a localized level, right, I'm working on my needs and my customers and things like that, this also works at a larger level as well. So when you look at it at an organizational level and you're starting to think as an HR person about the things you're trying to change, you can start to use these tools to really help you get a sense.
Of what's happened this last month or this last week or this last period in some of those spaces that we manage, that we facilitate across surveys, across all these different tools. Maybe you use Microsoft Forms and you have that data being stored in lists and those are in a SharePoint site. Again, just by asking these questions about what's changed, it'll find those recent submissions, it'll summarize them instead of just showing one or two, it'll actually aggregate like, say, the six other ones that changed with feedback and say, here's all the feedback we received recently about our policy processes or something like that.
Here's what we can do about it. So again, these have significant implications in helping us speed along the way we manage our own work and the way we manage our departments. Here's another example.
So let's say you want to use a great tool like Viva Goals, but for a lot of us, we have plans and we have quarterly plans, we have yearly fiscal plans for our departments and things like that, but we never take the time to really convert them into a tool like this. That makes it easier for us to track, to create visibility, to do check ins. But now with Copilot, we could potentially do that much easier.
Or AI tooling essentially can help us accelerate that. In this example, what it's really doing is it's taking the skill. And it is a skill, it's actually an expensive skill today to help people take something like their goals and objectives and things like that and map them and document them and put them into these types of tools.
Now it's making that much, much easier for them to create and even suggesting ways in which you could drive those initiatives through common understanding of language and things that happen across industries. So, again, this is really very valuable, and we're seeing a lot of success with early stages of viva, goals, access. So what this means is optimizing management, because it gives us skills we didn't have before, and it kind of makes us superhuman in some ways as managers to be able to keep up with everything.
The requirement for both those examples, though, is the content must be digitized in the first place. Right. It has to be in a format where digital tools like AI can benefit from it.
So that's often the gap that we have to address. Are these managers working in a digital form factor? Are they digitally fit today? And if they are, then adding these AI pathways becomes much more accessible.
