The Copilot Rollout

Host Dave is joined by Brian Curtis (CIO), Irene McCarty (Sr. Director of Technology Innovation), Susan Geiger (Sr. Business Planning Analyst), and Megan Bickford (AI Operations Specialist) to discuss Southern New Hampshire University's Microsoft Copilot rollout to 5,000 staff members.

Key Topics

• Framing the rollout as an AI literacy opportunity, not a problem to solve
• A people-centric, change management approach to driving adoption
• Why meaningful adoption takes 8–12 weeks — and how to set expectations accordingly
• Early wins: meeting transcription, document summaries, and SharePoint/OneDrive search
• The Cody role — volunteer AI champions who support peers and bridge the rollout team
• How success metrics evolved from raw usage to excitement, peer sharing, and proactive use

Top Takeaways

• AI literacy takes time — give people room to experiment and fail
• Don't over-prescribe; let people discover what works for their role
• Peer-to-peer influence is one of the strongest drivers of adoption
• Stay curious, challenge the tool beyond the basics, and ask: What am I missing by not using AI here?

Transcript

SPEAKER_03 0:09

Hey everybody, and welcome to this week's episode of The Prompt Report. Today we're going to be talking about the co-pilot rollout that we're in the middle of here at Southern New Hampshire University. And I am joined today by some of my esteemed colleagues, Brian Curtis, our Chief Information Officer, Irene McCarty, our Senior Director of Technology Innovation, Susan Geiger, our senior business planning analyst, and Megan Bickford, our AI operations specialist. Brian, would you mind uh introducing yourself quickly for the audience?

SPEAKER_00 0:44

Yeah, so as Dave mentioned, I'm the chief information officer here in Southern New Hampshire. And my role on the Copilot rollout is executive sponsor. And what that essentially means is I'm here to make sure the team is getting the support and whatever else they need to make this rollout effective. So my role is very much a supporting role and ensuring that we are doing what we need to do, getting the resources, and making sure the support is there that we can make this an effective rollout for all involved.

SPEAKER_03 1:18

Thank you, Brian. Irene, over to you. Would you mind introducing yourself quickly?

SPEAKER_01 1:22

Hi, Dave. My role with the co-pilot rollout is to work with this fabulous team to get co-pilot rolled out to SNOO to 5,000 staff members. Overall, my role is kind of as a program manager, but really looking at how we're effectively managing the rollout and tracking our adoption and usage. And similar to what Brian said, helping to move some hurdles out of the way as we move along.

SPEAKER_03 1:53

Susan, over to you.

SPEAKER_04 1:57

Hi everyone, my name is Susan Geiger, and I'm the project manager for the Copilot Rollout. Um, my role has been uh setting up the planning and execution of the project, just typical PM work, like creating the charter requirements, etc. And that's how I've been involved and how we continue to work forward.

SPEAKER_03 2:18

Thank you, Susan. Megan, please introduce yourself.

SPEAKER_05 2:22

Hi Dave, thanks. Yeah, so I'm Megan Bickford. I'm the newest member of the AI team, and I'm our AI operations specialist. So I actually have only recently begun and have been able to uh see how this rollout is a very well-oiled machine that has been organized so beautifully by the people you're talking to here and handled so well. I'm starting to immerse myself in the support work for the Codies, and I'm really loving learning beside the members of the rollout myself, if I'm being honest. So it's a great place to be in, to be able to be testing it with the cohorts that are going through the rollout. But yeah, that's how I've been supporting so far.

SPEAKER_03 3:04

Thank you all so much for your introductions and for being here today. Brian, over to you. The first question I have is when we're thinking about co pilot, what is the problem that we're trying to solve with co-pilot? Is there one problem? And why was this moment the right moment to act and to start really working on bringing this tool into our larger tech ecosystem at Snow?

SPEAKER_00 3:27

Yeah, thanks, Dave. I love this question in terms of the first part of that, like what problem we were trying to solve. Really, it's not a problem we're trying to solve necessarily. It's an opportunity I would say we're trying to take advantage of. We all know that AI is out there, that people are using AI, people within SNOO and with Southern Hampshire are using AI. What we're really trying to do with this opportunity is take advantage of the tool sets and tools that we have available to us to really start to teach our workforce how to best take advantage of AI in what they do on a day-to-day basis, how to responsibly use AI because these aren't the only tools, as we talk about co-pilot being rolled out. There will be other AI tools that'll be available to us in the future and having a foundation of understanding how to best interact with these types of tools in a responsible way and how they can help us in our day-to-day lives, both at work and actually to some extent, even in our personal life, is really what we're trying to do. We become more literate in these tools. So we want to eventually become a very AI literate workforce so that when we are supporting our learners, which is our priority for this organization, we also understand how to best use it, you know, teach them, make them understand responsible use. And in addition, we know they're coming, and probably more so, they're very familiar and even to some extent more than we are. So in interacting with us, they might have a little bit of heads up on it because they're willing to play around with this. So we're trying to basically not only play catch up, but also lead the industry and making sure our entire workforce is preparing itself for the future. So I wouldn't say it's a problem. It really is that opportunity that we want to take advantage of.

SPEAKER_03 5:23

I love that call out for AI literacy because, yeah, copilot is very much a vehicle to help deliver that content, right? It is, uh I found it personally very useful in my day-to-day work, but I think more than anything else, just watching people go through the six or so weeks of copilot. And the beginning, I don't really even know how to make a prompt and how to get something meaningful out of it, too. At the end, where they're helping their peers to solve a problem because they feel comfortable enough to do that. It's really, I think, uh quite transformative.

SPEAKER_00 5:56

Right. I think that's what this team has done in terms of the approach of this rollout. So again, I think that's going to make us a better workforce, number one. But I also know there are examples in my own, you know, beyond work. I've used it in my personal life in terms of some things, and it's been beneficial there. So not necessarily making me more efficient, but it really helping me out in various areas. Great. Thank you.

SPEAKER_04 6:21

So the biggest challenge from my perspective as a project manager has been managing the rollout with a smaller project team. So we've got so many moving parts and in-the-moment decisions that are needed on a daily basis. But what's really helped in this area is that the project core team is so flexible. Everybody's willing to take on work to make the rollout a success, to help others at any time, fill gaps. So that has really been instrumental in keeping the rollout on track and making it a success.

SPEAKER_02 7:09

Awesome, thanks so much.

SPEAKER_03 7:10

Now over to Irene next. Irene, would you mind talking a little bit about how you help teams and individuals, I suppose, even to see Copilot as an immediately useful tool rather than just another tool that may sit and collect dust in the background.

SPEAKER_01 7:28

For this rollout, we've taken a very people-centric approach, trying to bring our practices around organizational change management to the program. So ensuring that folks have an awareness of what we're doing, try and build some desire around using AI and copilot, helping them gain knowledge and ability, and then providing a lot of reinforcement and opportunities to get more information more just in time as they need to. That's been our approach from the very beginning, and it seems to be working well. I'd say that we didn't actually promise copilot to be useful immediately, though. You know, what we found through the proof of concept that we did early in 2025 was that people really needed eight to 12 weeks with co-pilot in changing their behavior and getting used to the tool in order to see benefit in any of their work day to day. So we've set expectations in the program that you may not see immediate benefit, but give it time, have some patience, give yourself some room to experiment and really continue to try the tool over several weeks. That said, as we started the training, I think we really focused on a few high-impact areas where folks could see a big benefit, hopefully, with Copilot. And I think a couple of the quickest wins is using the transcription and AI summary tools within Teams. People really saw how that was very beneficial, especially in a lot of their project work. And also doing summaries of documents and finding information on SharePoint in their OneDrive. That's where people seem to see a lot of benefit very quickly. So we focused on those things up front to help engage folks and build some excitement through some practical wins of using those tools in that way so people could see some of the benefit and start to see it take hold and try a little bit more.

SPEAKER_03 9:37

I like that call out that it's not an immediate, like you get copilot turned on and everything is suddenly sunshine and puppy dogs. I really like that it is a process, and to your point, it's about thinking how you do work and how you can remove pain points in the way that you do work, and how can you leverage copilot to assist with removing those pain points? And I think that there can be a lot of introspection along using it, right? Because you force of you to think about what's the part of my work that I love I would never want that I would never want to give up. And what's the part of my work that if I could click a button and make it go away today, that I would do that with? I think we all probably have pieces of our jobs that we're absolutely in love with. That's why we're here. And pieces of our jobs are like, man, for me, it's like the meeting note transcriptions. I am so bad at taking notes, and I find that Google Pilot is immensely helpful in that and really helps to make sure I'm on top of things. I know what my kind of takeaways are from a meeting. So thank you. Thank you for that. Yep. Megan, over to you. Would you mind talking a little bit about because folks listening to this might not be familiar with the Cody role that you mentioned during your introduction? Would you mind talking a little bit about the Cody role and explaining what they do, why Codis are part of this process, and perhaps why it's important?

SPEAKER_05 11:04

Yeah, absolutely. Um Cody's are folks who have generously volunteered to really be a champion for the work. They are willing to learn the platform a little bit earlier than maybe their peers in their department or their spaces. And then they really try to be a bridge between the rollout team and the folks who are participating in the rollout. So they're a great resource for us. They're really interested in learning and in AI, and they're really invested in doing a good job learning it. And they're able to help support the questions that folks have because it is a large rollout. It's a very large rollout. And so it's good to have extra people who can answer questions or think of things in a different way that help to support the work being done. And they're there to build relationships. Irene alluded to the fact that this was a really relationship-focused project. And I can say coming in in the middle of it, that rings very true to me that I can see how intentional it was in the build of this, that there be points of connection and relationship for everybody along the way. And I think that is making a difference that Codies feel empowered. They feel proud of the work they're doing, they have opportunities to share it and to be recognized for that. And I think that gives them a lot of excitement about continuing to do that and being communicative and then sharing it out with the folks that they're working with. And that's the best way we know at SNOO that um you can hear a lot of things from your leaders, and there's different ways that communication lands. But when your peers are invested, it makes a huge difference. It creates this culture of change that really can make or break a project like this. The Codis are critical. And I would say that just a couple of the things that you guys shared, I think are really important to call out again is that like it literacy, AI literacy does take time. Like anything else, it's a new skill to build. And I think we have this perception with AI right now that it's so easy or fast, right? That you're just gonna learn it and know it and it's gonna make your life better. And I think it's really true that it takes time. And the Codies, again, are great bridge to that, that they are able to say, yeah, it took me a little bit to learn this. I tried it this way and this way, and then it didn't, it didn't happen. And now, here, I got this refined prompt and this works great for doing this, or I built this agent and you're gonna love it for what you do. I think there's those elements. And I think you talked about introspection, Dave, a little bit and this idea that um people really need to think about their role. And I think it also involves a lot of reflection in your language and your communication and how you think about how you want to say things in prompts. And so I think that's helping us all as an organization too, just as a side note. Yeah.

SPEAKER_03 13:58

I read, and I want to go back to you for a second, and then Brian, you're up next. So can you, now that we're about just under the halfway mark of the rollout, we're looking at right now about a thousand people licensed, give or take. Was there any indicators for you that the adoption has become meaningful as opposed to just experimental? Thinking back, like especially, we were, of course, involved with the initial proof of concept, which was a year ago, thinking about what we were trying to accomplish there versus what we're trying to accomplish now, perhaps.

SPEAKER_01 14:33

Yeah, it has been meaningful. And I think I could summarize how we've seen it be meaningful versus just experimental and kind of a quick thing that people try and then walk away from in two ways. First, in all the conversations that we're having. Every week we have two office hours, one workshop. We might have other meetings with specific teams, be it the Codies or other teams, and every single time we hear in the conversations during the meetings and in the chats, uh, where people are finding wins with Copilot, real concrete wins and how it's helping their day-to-day work. It's helping to save them time, it's acting as a virtual thought partner and helping them try new things and iterate quickly. Um, those stories we hear over and over and over again, which is really fun and exciting, but I think it's also demonstrating that it's very meaningful and making a difference for folks. I think the second way that we've seen that is really in the data that we're collecting and tracking. So, you know, we're doing a survey once a month, kind of a sentiment survey, and we've seen sentiment move from disagree to strongly agree on statements like co-pilot helps me with mundane tasks, it improves the quality of my work, reduces mental load, and makes people feel more productive. And so that's to me really demonstrating how it's making a meaningful difference for folks. We also see usage continuing to increase once people use the tools for a bit, like I mentioned before. You know, that as they use it and they become more frequent users, that that just seems to kind of create a snowball effect where they use it more and more and maybe try it in different applications that they don't use as often. So these two things combined really the stories we're hearing as well as the data that we're gathering through this program, we're really seeing how it's not just a quick thing that people are trying and then abandoning because it's not adding a lot of value. They're seeing that it is helpful to them and really more meaningful, I think.

SPEAKER_03 16:52

Brian, going back a little bit, would you mind talking a bit about guardrails were essential early on in the co-pilot rollout to help build trust without slowing momentum? I asked this thinking about we do, there are a lot of people skeptical about AI and the benefit that it can bring, especially when we're thinking about the data-rich environment that SNU is, and that balance of making sure that we protect the data, especially things like personal identifiable information, privacy, respect for user privacy, things like that.

SPEAKER_00 17:27

So thanks, Dave. The guardrails we try to put in place here is there's difficulty in, you're right, there's a balance between being taking risks and then also protecting to make sure you don't go beyond that and be cavalier about anything. So we try to make sure that our data was protected and secure as best as possible. So prior to this rollout, and even prior a little bit to the pilot or an early phase of the pilot, we had folks purposely do things to see if they could find in some of their questions and using these tools some of the more critical and secure data that we didn't want people to find. So we really went down the path of making sure that we protect it where we could. Now, that being said, that doesn't mean that, like with anything in our environment, that certain things aren't available to folks that could be detrimental to us. So we put carrels in place to make sure we're doing the best we can to protect it, but also knowing that we are heading down this path. We can't keep ourselves from trying and using these tools to be efficient and effective. So it's a balance. And we're learning still along the way. We will be addressing things as things come up that may be of concern of ours, but we try to take a thoughtful approach to look first and secure it, but also know that we cannot stop it because it is so useful and will help us. So, yeah, that was really the garb rows, the balancing, the risk versus reward sort of thing, but also being a little more thoughtful at the forefront to go figure out can we see stuff before we turn it over to everybody else, right? And again, that was sort of the piece we put in place. We know in working with both the AI team and with Irene and things like that, that we are working to, again, as we talk about this rollout, teaching people how to use AI in a responsible fashion because it is a very powerful tool. So that was also part of this whole thing of making people AI illiterate as we move into the future. So I hope that answered your question there. But that was really the primary guardrail. And then I think the other guardrail is to make sure that our approach was solid, right? So as we do this, there are other organizations just buy licenses and say, okay, the first 300, 500 people that sign up, it's yours. Well, we're learning from each other and the guidance that you give and that Irene gives and that others give in terms of their use of the tool. We're learning as we go along. And I think the other guard rule we put in place is to make sure that we had a very thoughtful approach to this rollout that ensured people could use it properly, took care of the fears of like, oh, I don't like AI, it's going to replace you know my job or something like that. But no, it's an enabler for you, it's a helper. So I think that part of it also was one of the things we want to put in place to make sure we had a solid, thoughtful approach, not just like, hey, we're doing AI training, please sign up, you got a license, now go, right? So I think that was the other sort of I'll say guard where we put in place to ensure a better chance and opportunity of success for what we're trying to do here.

SPEAKER_03 20:50

And one more question for you, Brian. Thinking about success of this rollout, now that we're about halfway through, give or take, has how you view the measure of success changed over time? And if so or not, how do you view success of this rollout?

SPEAKER_00 21:08

I do think it's changed over time because I think when we started out, usage uh was one of the things we were looking at in terms of success. And what do we mean by usage? I log in once a week, twice a week. I use Copilot once a week, twice a week. Success has changed in that people are actively seeking to use the tool, right? They're benefiting from the tool, they're seeing the efficiencies from the series of tools that Copilot presents. So to me, it has changed. It's not about necessarily the usage, it's more about the value and the proactive nature and what people are doing with it, how they're seeing it, how they're sharing with each other. So I think, you know, from my perspective, it has shifted from the initial thought of like, okay, let's get 85%, 80% usage. That's great. But for what reason? Now I'm Seeing much more excitement in the people that are using it. And again, that varies with different folks and stuff like that. So at this part, not quite halfway, but you know, in terms of this rollout, I think that's what's really shifted my thinking in looking more towards it's not just the active use of it, but it's the excitement around what I can do with this tool and how it's helping me.

SPEAKER_03 22:24

Yeah, that makes complete sense. I love that approach to success and how success itself can be fluid because we don't know, like you said before, we don't know what we don't know, and we're learning as we go, and our expectations are changing as we go too. Right. Um more question for the group. What's one big lesson or takeaway, I suppose, that you've learned from the throat that you would encourage other folks from around the institution, perhaps teams looking to stand up their own AI proofs of concept. What something they shouldn't overlook? What's something, an important thing that you've learned that might help support others in their AI adoption journey?

SPEAKER_02 23:06

I mean, let's start with you.

SPEAKER_01 23:11

I think something to consider with not only co-pilot, but all AI is the need to be curious and the need to be flexible because it's new for everyone. It's new in the industry, it's new in relatively new in the world, it's new for a lot of roles, it's new for a lot of people, and it can do a lot of different things. And in some ways, you know, we don't know all the ways that it can be helpful. And so I think there's a lot of curiosity that's required, an ability to, you know, play with something and be like, that didn't work, I gotta try it again. And that can be frustrating for some teams, some institutions, some individuals, but I think it's part of what I'm learning. I need to have more of and help through this process, helping people to get more comfortable with that and saying it's okay. Uh to be a little nervous, but you know, still stay curious. That's kind of the perspective I think that's needed with AI in general.

SPEAKER_03 24:36

Wonderful, thank you. Brian.

SPEAKER_00 24:38

I'm gonna add on to that because I do think that's a key part of it, being curious. I would say at this point, is you have to challenge yourself and challenge the tool, right? So go beyond what you typically do. What we see is, and I think there have been very good examples for those that are using it that have done it in different ways that we haven't thought of before. So some people, well, you know, I don't know if this will help me. Maybe I can transcript my meetings. Okay, that's great. That is helpful, no doubt about it. But but that's not the only thing. So I would say challenge yourself, try things, you know, the experimentation piece that Irene was talking about, but try things. Challenge yourself in terms of how could I use this in an entirely different way that I would think I would even do what I typically do on a day-to-day basis. And then I would say, you know, challenge AI. Challenge can it respond to you in a different way or do things differently that you you're you're surprised at. So I think in terms of even some of the office hours, really good ideas have come up because people have thought of doing something that really wasn't part of what they would typically do. So to me, the one lesson learned is don't stay with the basics. Challenge yourself, challenge the tools, experiment at a much deeper level. And yes, things won't work out, or you know, when it comes back, it's like, wait a minute, that's totally off the mark of what I was looking for. Okay, learning. Now let's go back and try something different again. So to me, that is the biggest learning that we can do because if we stay with the basics, we're not taking full advantage of what this thing can really do for us. So let's go beyond the basics, challenge ourselves, challenge the technology, and see what comes out of it.

SPEAKER_04 26:26

With this size of a rollout, there's going to be teams who have something come up and suddenly they can't roll out on the timeline that we had set up for them. So it's to critical to allow some of it is with the person themselves of just going into this with a mindset of it's going to require flexibility, control what you can, and then with everything else, uh, just be flexible.

SPEAKER_05 27:11

Those are really good lessons from the rollout to consider. I guess I would say the thing that I would add to that is um I think it's it's two pieces. Use this co-pilot rollout as a roadmap to doing your own thing in your own department in terms of the support that was talked about here, like how it we found safe guardrails, but we learned that you have to accept some risks. We're putting these guardrails in place and we're giving you the support along the way to make sure that you feel safe in them and that you're guided down the road. So you're not driving down a winding road on your own with full accelerator. You've got these guardrails in place, and we're helping you figure out like you're starting slow and then you're accelerating faster. So I would take the roadmap from the team that put this together and really tailor it to your own team in that way. And then I think the second piece would be we had a great conversation, Dave, earlier in the week with a dean or a representative from campus who really posed a great question at the start of it of what are you missing out on if you're not using AI in some spaces? And I think when you really challenge yourself to think in that way, that it shouldn't be used for everything. And there's lots of things to think about with the use of AI, but that doesn't mean not using it altogether. So really challenge yourself to think about in every situation where you might have paused, what am I losing out on by not trying AI with this? And how does that impact my ability to do my job and to thrive in the world and to really thrive in this new environment that we're all living in?

SPEAKER_03 29:02

I think I would add just one thing through thread for a lot of these. Empower the people that will be using it on the day-to-day basis. Give them flexibility. I think Tyreen, something you said to be curious to experiment, listen to them when they say this is something I'm struggling with, or this is something that's not working well for me. Um, I feel like one thing that we've really done well as part of this rollout is not to be too prescriptive. Because if we can come in and say, this is how you use copilot, and then that's how people are going to use copilot. Brian, to something you said, like really, once they start challenging it and they start challenging themselves, that's when people start to see, oh, this thing is more than just a meeting summary tool or create an email based on these bullet points. It can be really a powerful co-creator, thought partner, and really help us, I think, reflect on our own practices. Brian, Megan, Irene, Susan, I wanted to take a minute again and thank you so much for being here today. Thank you, everybody, for tuning in and listening to this week's episode. We always appreciate your feedback and thoughts. Anything that you would like to uh see on the prompt report in the future, let us know. And if you'd like to be featured and talk about your own work in the AI space, you can always reach out. Thank you, everybody.