Campus Technology Insider Podcast March 2026
Episode: Navigating AI Partnerships in Higher Ed: Governance, Procurement, and Scaling Student Success
Host: Rhea Kelly, editor in chief, Campus Technology
Guest: Bridget Burns, CEO, University Innovation Alliance
Episode Overview
In this episode, Rhea Kelly and Bridget Burns discuss how generative AI is transforming student success and driving cross-institution collaboration, including key risks, governance challenges, and practical frameworks universities can use to responsibly adopt and scale AI initiatives.
Key Questions & Takeaways
Why does the University Innovation Alliance emphasize collaboration around AI?
Institutions face many of the same AI-related challenges — including governance, privacy, literacy, safety, procurement, and student success — and collaboration can help campuses identify shared problems, run experiments together, and share what they learn.
Where can AI support student success and institutional operations?
AI shows promise in automating student outreach workflows, tutoring and CRM functions, accelerating transfer-credit articulation, improving student messaging and pathways, supporting professional development and coaching, and helping institutions analyze websites and communications for inconsistencies.
What are the biggest risks and challenges with AI adoption?
Risks to watch out for include governance gaps, unvalidated efficacy, privacy concerns, long-term data implications, intellectual property issues, contract risk, capacity issues, and uncertainty around student data usage, storage, and safety responsibilities.
What questions should institutions ask AI vendors before signing agreements?
It's important to ask where student data is stored, who can access it, whether student data is used to train models or shared with third parties, whether institutions can opt out of certain uses, whether there is evidence the tools improve learning outcomes, and who is accountable when AI-generated recommendations cause harm.
How can institutions scale AI initiatives successfully?
Define the specific problem and population being served, and adapt solutions to different institutional contexts rather than simply “making them bigger.” Establish cross-functional teams and co-captain leadership models, set measurable goals, monitor outcomes, and share lessons learned — including failures and risks — across institutions.
Topic Index
00:00 Welcome and Introductions
00:32 UIA Mission and Model
01:49 Generative AI Changes
05:07 Promising Campus Use Cases
07:22 Top Risks and Governance
09:23 ASU Early Mover Lessons
11:11 AI Vendor Decision Framework
15:28 Procurement Questions That Matter
18:54 Community Over Perfection
21:42 AI Readiness and Culture
25:53 Scaling with Guardrails
31:56 Closing Thoughts and Wrap Up
Transcript
Rhea Kelly 00:07
Hello and welcome to the Campus Technology Insider podcast. I'm Rhea Kelly, editor in chief of Campus Technology, and your host. And I'm here with Bridget Burns, CEO of the University Innovation Alliance, to talk about AI in higher education and the work her organization is doing to help institutions navigate the landscape of AI providers. Bridget, welcome to the podcast!
Bridget Burns 00:29
Thanks for having me. I'm super excited to be here.
Rhea Kelly 00:32
So to start, maybe just introduce yourself and your background and the work that you do.
Bridget Burns 00:37
Yeah, so I have the privilege of helping support 19 large public research universities around the country do a very highly unnatural act, and that is collaboration in higher ed, a highly competitive field, especially when it comes to innovation, a place where there is an incentive to, you know, keep it to yourself, to not necessarily share, and try and move your institution ahead. We're trying to run a kind of counterculture movement where institutions actually team up together to think about what kinds of experiments we should be running and what kind of work we should be advancing if we were operating like the R&D lab for the sector. And then trying to give away everything we learn, because we need to serve millions more students, and we need to not just serve them so they can get a degree. We need to actually ensure that they get to post-college mobility. So I would say that's what the UIA is about, is operating as higher ed's kind of, you know, national laboratory for student success, innovation, and transformation. And we're committed to trying to model how you do that work and make it so that it's accessible and useful for any institution, anywhere, with no money.
Rhea Kelly 01:49
I think it's interesting that the UIA launched back in 2014 I believe, pre-ChatGPT. And now that AI is so, like, intertwined with, I think, a lot of student success efforts, how has the UIA's work changed with that advent of generative AI?
Bridget Burns 02:07
Well, I think that generative AI has accelerated a few different dynamics. Like, first is, you know, they can, campuses can now automate workflows when it comes to, you know, student outreach and CRMs and tutoring and things like that, but each campus, again, would be incentivized to go it alone to figure out their approach. And I think there's plenty of spaces where we could be collaborating, especially in this phase where institutions are pretty obsessed with efficiency as the primary play for AI, but at the same time, the pace and, like, opacity of these tools raises serious governance, privacy, literacy, safety issues and questions that existing procurement and academic process and how institutions operate, that any one institution is not set up to handle. And so what we have found is there is a need for, a greater need for collaboration in this space. There are models that have already been proven to be highly successful. I would shout out the EDSAFE Alliance, which has been convening K-12 for a while to clarify standards around AI literacy and safety. And that's where our work, we can see we're being asked to step into, is to, to just work with them to create the higher ed equivalent of the higher ed safe alliance. But I would also just add that while those, you know, there's the, the way that generative AI is affecting the sector, but it was also, I would flag that I think we were in 2016 that we implemented AI-enabled chatbots on our campuses using large language models. So this is not as, as new to us in terms of we've already socialized that we have to find ways to use technology at scale. But I do think that what you're seeing is that campuses, regardless of what you're talking about, are having very same, the same problems. And I think the challenge for us is always to look at, like, what's the big thing we can only accomplish together?
Rhea Kelly 04:11
What I'm kind of hearing from you is that the collaboration is almost more important than whatever the technology is that's enabling the student success.
Bridget Burns 04:20
Yeah. I mean the, the pace of change in higher ed is set to pretty slow normally, and it's because collaboration is an unnatural act. And so if you think of whenever you're measuring four to six thousand, seven thousand institutions across this country, if they were all tinkering in silos, then you're getting a pace of change that is just fundamentally not what students…. Students need more. They need us to actually see what needs to translate, where we need to pull together. There is just too much insular behavior, and it's, that is what I think is the most revolutionary thing we can do is to efficiently, very quickly, identify shared problems and the big goals that we can only accomplish together, and build the right capacity to help support institutions tackling those issues.
Rhea Kelly 05:07
Are there opportunities for the use of generative AI that are, that you're excited about?
Bridget Burns 05:13
Oh, yeah. There's, oh, I mean, the stuff I'm seeing from individual campuses is particularly, it's just really impressive. Obviously, ASU is completely out front when it comes to especially the efficiency plays. What they have identified around the credit transfer space and articulation, and being able to map courses to, that students are coming in with, so much faster than any process prior, and being able to do that like immediately, is really going to help a lot of students when it comes to transfer. I think that there's a lot of things that we do in higher ed that we could do so much better in terms of helping support our staff, our faculty, thinking about internally when it comes to professional development, accountability, and coaching. There's so much when it comes to the student success space, when it comes to thinking about, like, it was so edgy for, you know, 12 years ago, predictive analytics, this idea that you would possibly have digested your past ways that students had been tripped up at your institution. Using AI you could actually be able to evaluate and look through right now, crawling on your website to be able to find the ways that you are actually contradicting your messages, where you could actually be helping create far better, clearer messaging and pathways for students. I think there's just, there's, there's so much stuff that's already happening, I would say. I think the bigger get is institutions pulling together to put forward a very strong point of view on this literacy/safety stuff, because what we're seeing is the problems are always the same. And we recently released materials to support campuses navigating the decision-making about, about the vendors that are coming at them. We can talk about that, but I would just say there's a very relatable experience right now, which is every campus is being asked: Sign away your life. Choose which one of these that you're going to sign an enterprise agreement with. They're also being asked by their faculty about cheating. They're asked, they're being asked about how they should use it. I think all of these are very similar challenges, regardless of your institution, that is what people should be teaming up on.
Rhea Kelly 07:22
Can we dive in a little bit more in detail about those challenges, like, are there, what would you say are sort of the, you know, if you could rank them, the most common challenges that institutions are facing as they sort of engage with the potential of AI for student success work?
Bridget Burns 07:39
Oh, yeah. So I would say governance gaps. You've got, like, efficacy that's been unvalidated. You've got privacy issues. You've got long-term data. We are signing a contract giving away students' data that we cannot possibly fathom right now. There is an intellectual property issue that people are not thinking about. There is a lot of contract risk, capacity issues. I mean, just think about this. Like to work at an institution, you're a mandatory reporter. If you sign an enterprise AI agreement with any vendor, they are going to be having, like, there's a relationship between one of your students and this product that you have signed a contract, and we are already seeing some of the safety risks in terms of the types of conversations, and what models are inclined to do or not do, that, if you think from a mandatory reporter perspective, or any of these kind of safety precautions that we have long built into running our institutions, there is a massive gap in terms of how we are equipped to navigate. And so I think what you're going to have is, and you're seeing, is some institutions are freezing and just waiting for everything to get super clear, and then you have some that are racing toward it. And I just think, whatever your approach, I don't think you should freeze, because I think we have to figure out how we help support our students, that they are now entering a workforce where this is not just a maybe it's a certainty. And I think we also have to be really keeping our eyes open about the, the workforce effect. I think we're seeing that, I think it's a, it's a canary in the coal mine. I think it is actually a real serious thing. I'm not a, I'm not a doom, a doom person when it comes to AI. But I do think, I think that how long it's taking students to get hired is real.
Rhea Kelly 09:23
I can think of institutions like ASU, they were super early in signing, like an institution-wide agreement, I think, with ChatGPT, so they must have, you know, had to figure out a lot of that stuff on their own.
Bridget Burns 09:37
Yeah, they got to actually determine what many of the contracts that others are considering say, because they figured out with them, right? So there is a first mover advantage, but there's also, I would assume that that relationship was set up, that there are certain episodic moments where they are going to review. I mean, I don't know personally, but I do think that they have the right approach and are pretty you know, they're kind of thoughtful partners who are willing to kind of engage with technology, and they have the right, I think, framework around piloting and testing. And so I would assume that if they, if they can, they probably sign with everybody. They probably, they probably test with a lot of folks, and have, have a backup plan. But the, the actual interesting thing about a, about ASU's approach to AI, I observed, is that they started as soon as GPT came out, as soon as they signed that contract, they launched a campus-wide competition that students, faculty, staff, anyone could submit an idea for how you could use AI to improve something, to solve something. And they have funded a lot of those things. They have built a lot of those things. And the, the scale and the volume of really incredible pilots and tests that have already been, there are, they already have the data from. That's the reason why, is that they really unleashed the creativity, and they kind of backed it up by resourcing it. Of their faculty, of their students, of their staff, I love, especially seeing they have so many AI products and things that have been created by students that they're actually, they're resourcing and supporting.
Rhea Kelly 11:11
So earlier, you alluded to, you know, helping institutions evaluate these vendor agreements with the AI decision framework that the UIA created. So can you kind of break down like, what is the framework? What does it consist of?
Bridget Burns 11:27
So most of my work is just identifying shared problems, right, where more than one institution tells me they're struggling and okay, now I'm, now I know that there's something here. Because I know that we're just the earliest ones, but this is relatable and real everywhere. And we saw that campuses were being asked, and the question is, which office is being asked to sign this contract to evaluate this, and it was very clear that presidents and provosts and chancellors and CIOs and CISOs, all of these folks are not actually equipped to navigate this really new terrain, and neither is our legal team. I mean, it's just, it's all in real time happening, which is the perfect opportunity for us to pull up together. So we started accumulating the questions that we were hearing, that were actually useful, and worked with Erin Mote from InnovateEDU — she is a lead figure for the EDSAFE Alliance, which is the K-12 group I shared about — and Lev Gonick, who's the CIO at ASU, to just identify what questions, since ASU had already gone through this, what, what do they wish they'd known before they started? What questions would they recommend? And it was very quick that we generated that list and then tested it and tested it and tested it, and then our method is to just give it away. And so we launched it and shared it, and it's, I think everything we do should be kind of an open source solution. We just wanted, you know, for me, I think if there's an avatar of who I think about regularly, it's an under-resourced campus in a rural community that doesn't have, that everyone's in an interim role, and nobody has money or time, and now they're possibly thinking that if they don't sign a contract, they're harming their students, they're setting, that they're going to be behind. And what if, you know money's on the table, right, that the scarcity of the moment could cause you to, you know, agree to something, and they need to have just the wisdom and knowledge that, that others have figured out the hard way. And that is how the field should operate. We should be, you know, spotlighting things, you know, be not so stupid. You know, follow, you don't go down this path or, you know, here's what I wish I'd known. And I would just say the best question that we found was you should ask, whoever the vendor is, because it's very clear that there are competitors, right, you got Google, you have anthropic you have ChatGPT, Open AI, and the question, I would just say, is ask Open AI, what should I ask Google? Ask Google, what should I ask, what should I ask Open AI? And that is going to tell you, they know their models best. They know their weaknesses and their strengths. It's their job. And so help me, you know, help me help you. And that's the one I would give you, is, because presidents need something they can just hand off to their team, and they actually know that they're not just throwing them some huge thing that they don't know how to handle. And they know how distracting that would be. So this is, you know, your contracts office, procurement, whoever. Here are the questions. Here's the ways you need to think about, especially around the data privacy stuff. And it had to be conversational enough that someone like me would even understand, because I am completely like, this is not my jam. This is not where I spend my time. I mainly am just obsessed with trying to find ways to help higher ed and to learn from like the things they're struggling with.
Rhea Kelly 14:47
That is a really clever way to pit the vendors against each other to get the information that you need.
Bridget Burns 14:54
Yeah, and I didn't add, so Anthropic was a latecomer, so I don't have them in it, and well, so I'm hoping, you know, I think just, I'm gonna have to update it, or others need to contribute. But we shared it on our website and our newsletter with 10,000 folks on it, and I just, you know, again, barely threw our logo on it, because it's not important. What's important is that there are real, serious, long-term decisions being made, and we need to support students, no matter what institution they are enrolled. We need to make it so that we're all on the team, the same team of student.
Rhea Kelly 15:28
What is different about the procurement process for, you know, an AI platform, as opposed to just any education technology, like, if an institution is evaluating an LMS, like in theory, they have a process for piloting and getting stakeholder input and just all of those things, but I suppose that's a lot slower than maybe the AI process would need to be to keep up.
Bridget Burns 15:55
I mean, I don't think that anyone who works in procurement has been trained on, you know, the data workflow. But I think the question is, you know, very specifically, what are, is the student data that is going to be flowing into the system? Who can access it? Where is it physically going to be stored? That is a very important question. I want to know the country. I want to know where is this happening? Because there are different laws. There are, there are different, yeah, like, there's just a lot here. And if you are giving access to information about your students, like this is, there's people use this, and they provide highly personal information. So we have to really be the guardian here, but we also have to be on the leading edge. You know, they're trusting us to set them up to compete in a world where this is normal. So I would say, is the vendor using the student data to train the model or to share derivatives with third parties? That's a really huge one. If Open AI now, is going to be selling ads — we all saw that at the Super Bowl, but I think there's even more questions that are going to be coming. Can we opt out? What things can we opt out of? I think the other is like, is there any evidence that shows that the tools can actually improve learning or retention of information for the students that we serve? I think that is still out there. I think asking for validation studies, efficacy studies, disaggregated impact data, something like that. I think who is accountable when the tools, recommendations harm a student? Who is, like, define decision rights and human-in-the-loop controls. Because there are some where you have, like, a sentence that says, you know, I think we just saw about the Meta's glasses, that they are, actually have human beings, I think in Kenya, who are actually, if you have Google, if you have the Meta glasses, that it's actually a human that's identifying these things, not, not a computer. And because they have a sentence that says that a human is, there will be human review at some point, they are essentially like, a lot of really private things are being shared. I mean, I think there's just a lot in terms of that. But also, how will the model be updated? And, like, what is the audit access? Like, there's just all this stuff so, so just, just that list, which, again, like, I'm way above my pay grade here. This is not like my, I'm not a technical expert in any way. I think the, that the real thing we have to think about is the human beings that we are trying to build for are processing a ton of contracts that day, about a ton of things. They're worried about a boiler being replaced. They're worried about vendor, like, have we had enough bids? And then you're asking them to evaluate this, which has like, real questions that we should be asking that are not necessarily their expertise. I don't think that we have time to build that expertise into every specific lane in the institution, so we have to pull together.
Rhea Kelly 18:54
I like what you said about like asking if there's sort of measurable impact on student outcomes. It reminded me of, I don't know if you saw Educause's recent survey on the impact of AI on work in higher education. It was sort of a follow up to their AI Landscape Study from last year. And one of the things that stood out to me was only 13% of respondents said their institution is measuring return on investment for work-related AI tools. So no one's like testing if it does any good whatsoever, even though, I think it was like, almost everyone uses the AI, it was like 95 or 94% have used AI tools for work, you know, in the past six months.
Bridget Burns 19:38
Yeah, when things are moving really fast, and you really can't afford to slow everything down and just freeze, this is why community and partnership matters so much. And I don't mean between institution and vendor. I mean this is why you already have small communities of practice throughout the sector. You already have your athletic conference, your accreditation, other institutions we're going through the same model, you have, in your campus system and your state system and your, there are peers and partners. And this is a time for us to actually lean in, because there is no possibility that an institution is going to get it perfectly right. And I think that these are the right, we need to be raising questions, and we need to have a healthy discourse and dialog about these things so that we're just doing our job for students. At the minimum, just, just, let's start there. Like, what the responsible thing is, yes, this is all very edge of the frontier, and we don't want to, we don't want to make the wrong choice, but we also don't want scarcity and fear to stop us from moving. And so I think figuring out, like, how frequently you're going to check in with your buddies, how often are you going to, like, how do you make sure that your, the folks on your team, understand that their job is not just to run their lane at your institution. It is actually to actually seek out community and partnership with strategic allies who are at other institutions, who are going through similar things, right? Because this is where you're going to actually need that. And yeah, I think lot of experimentation going on, and I think the number of questions we should be asking only is going to grow. And at the same time, I do think that people have personal anecdotes of great things that they've figured out. I think that we should be sharing practices that are helping. But I also I think that this is a space where we are like, we have to, in real time, act out as we're navigating something uncertain, knowing that our students are expecting us to figure this out because they are going to have to do the same thing once they leave and go get a job. So I think, yeah, I just, I think that we have to be really honest about what we know and what we don't know.
Rhea Kelly 21:42
We talked a lot about questions to ask of the providers. Are there any questions that institutions should be asking themselves, just about, are they, maybe about AI readiness?
Bridget Burns 21:53
I think that thinking about how we are going to, what is going to be our philosophy about engaging with this technology. I think: Are you a critic? Are you going to be investigating it, interrogating it, or are you actually, you know, trying to set yourself, like I said, setting yourself up to be part of an improvement network or community of practice of some kind, to be probing? I think the posture at the leadership level really matters. I think that we have all seen how this happened, where each of us has engaged with AI work slop, where you have a colleague who's gone way too aggressive, and you're starting to get e-mails and content from them that's ChatGPT, and you're like, Oh my God, I've lost respect for you, I can't…. Like, very quickly, right? We're seeing that happen. It's happening everywhere. And then you're seeing those who are figuring out the sweet spot of how to actually, when to use it and how. And I think the question is, at, at the institutional level, like, how are we cultivating reflection and learning about this? So where is there professional development needed? Where is there, you know, we used to convene every year and the same things, the same time of year. You know, there are certain things you could set a clock to in terms of universities. We have our strategic retreat, our strategic planning period, all these things. What is going to be your habit around AI as an institution? What is the frequency, what new meeting needs to exist, what meeting needs to die, and what are the kinds of questions you should be asking? I think the questions I think about are, where is there strategic partnership from the folks who are thinking from the contract world, the, like, who've actually looked at these contracts and understand the risks and liabilities, the technology implementation teams, whether it's CISO, the CIO, or otherwise, and then the academic leads, right, like they're thinking about things, they should be thinking about things from a very different level. This is going to be happening and affecting different disciplines differently, and I think faculty are going to have different levels of comfort and familiarity. And I think, so I just think from an institutional lens, I want to ask myself, What is my posture going to be? What are the practices I'm going to implement to support myself and my teams? What new partnerships and teams need to emerge that are consistently guiding and governing and overseeing this AI experiment that we're doing at this moment? Because it should evolve over time. But I think the other is, I would just say scale and use AI with guardrails, so you want to validate before you, you scale. That's, you know, we love a pilot. We love a test, quasi experimental models, validation reports, all that kind of stuff. But really focus on governance and contracts. You need to know that stuff. That stuff like is super important. The negotiation check, checklist we gave is really important, and it's providing the kind of protections that students and institutions need for control. There are a lot of resources with the EDSAFE policy labs, the EDSAFE Alliance, that you can use. A lot of the stuff in the K-12 space, they have figured out. And we just need to, you know, copy, paste, and then add to it, because we are doing, dealing with things at a different level, in some ways. But I would say, if it's enterprise AI, student-facing, there's a lot of this risk stuff that's been analyzed. And then I would say, you know, build capacity for, like, sustained use. Like, really think about AI literacy for faculty and advisors and, and IT. And HR, okay, because that is a place where I'm seeing some of the most interesting stuff is HR with efficiency. Because first off, that's a really painful part of the institution, and I think there's a lot that you could, that you could benefit from. So again, the EDSAFE Alliance has created a blueprint for AI literacy and safety that folks can, can use and download. And I think we should be regularly sharing with each other and trying to team up on this.
Rhea Kelly 25:53
So let's say an institution has successfully signed a contract. They've found some innovations that work that are having an impact on student success. What are the key next steps to scaling that up?
Bridget Burns 26:07
Oh, well, when it comes to, so, whether it's AI-enabled or not, I think you need to adapt any, so it's not like you just, like, inject it with more money. When it comes to scale, you have to actually think about, you know, the evolution of whatever it is, so that it's going to adapt to solve the problem. So getting very clear about what is the bigger problem that you're trying to solve, what is the bigger population you're trying to serve. What are the additional factors that you need to be thinking about? So for us, what we've learned is the type of scale we have focused on, is scale from place to place. Like, how do you take an idea that works at Central Florida or Purdue, and how do you scale it at Iowa State or Oregon State? And so it's a lot more about adaptation. And how do you kind of need to riff on the idea? How do you need to shave off the corners? But when it comes to AI interventions, I think you want to, are there additional risk factors? You got to go back to first principle on, you didn't just, we analyzed it already, we're good to go. There might be something different that you need to be thinking about. I think setting clear targets for what you want to accomplish, instead of just kind of like, well, we're going to go bigger. And I think you have to always think about who is the cross-functional team that needs to exist to be able to shepherd this work, because it cannot be one entity. We are addicted to putting one person in charge of something. We love it. It does not work. It has not worked, because the biggest threat to all progress is transition and turnover. And so for us, we implement a co-captain model. You have to have two people in charge. If you have one person in charge, you are not doing it right, because that person will find another job. They will move on. Something will happen, and now you are stalled. And so build a bench, starting with co-captains, a cross-functional team that is going to support the scaling into this new environment. Set a very clear target. What is it we're actually trying to do? Not just go bigger. Like, we're going to add this, now this is going to expand to this department, okay, then we actually know the bounds of that. We know the size of that department. We can actually, you know, think about, what are the ways that this population might be different, so that we can draw upon what we know worked here. And then I think you got to, you gotta map the data. You always got to actually, like, you know, when are we going to weigh in and see if this actually worked? Those are just the pieces, I think, for, for scaling, and that's within an institution. But I'm, I think that folks have a lot of expertise about this. They've been doing it for a long time, and I feel like this is the place where you're, I'm more interested in where is the environment where people can, warts and all, share about their experiments with this kind of technology and what they learned. Because we are in a culture of competition. Don't admit that you failed. Polish it up. You know, lower risk. And we are going to miss out on the learning collectively, and that harms all of our students. So we need to create spaces where people can actually talk about like, Oh my God. Because I already know, there are some examples of people like, they released their AI into their data systems because they wanted to clean things up and didn't know that there are a range of files that the sharing setting on those files was, nobody had looked at in a long time. And so you have a model that's accumulating, a large language model accumulating information about a university that is drawing upon confidential files. Confident, you know, think about all those Google Docs that you've ever made, and all of a sudden, if you didn't, you know, have the time to, you know, review them all, that if you were accumulating a large language model for your institution to search a database, that you could really have some spicy stuff in there that you don't want in there. And that happened, and that has happened, by the way, at more than one institution. And that is the kind of stuff we need to be sharing, is like, you need to stop first. Don't release it. Go in and actually look at your privacy sharing settings, identify the documents you actually want it to draw from, and what is a practice that could accelerate that experience so that you could still do what you wanted to do? But let's not, you know, have the confidential HR files, you know, from a, you know, someone getting a PIF or a performance improvement plan pulled into your, your LLM.
Rhea Kelly 30:24
That's why you said the HR part was extra hard.
Bridget Burns 30:28
Oh, yeah, totally. But it's also place where I'm seeing really cool stuff, where at ASU in particular, they figured out that they could use like, you know, this is a space that's very difficult. You need people to do a lot of the same stuff very consistently. And you could actually see data at a different level. You can provide coaching, guidance, check-ins. You can actually help people improve at a level that, you know, we've just never been good at. There are lots of stuff that we're not good at. We're not good at training people how to manage. We're not good at actually following up on KPIs. We're not really good at, you know, all that stuff. We're good at really a lot of other things. So there are places where AI can be super helpful.
Rhea Kelly 31:07
I really like what you said that scaling up doesn't just mean making it bigger. That's kind of like, that made my brain explode a little bit,
Bridget Burns 31:16
Because there's a totally different context. And, and, so we actually also just released — sorry, I feel like I'm plugging again. All our stuff is free. We don't have like an end game other than we actually just hope it's useful and you actually use it. But we actually created a documented method for scale and a, e-learning modules and actual framework to help any institution scale any idea in any context. So it asks, it gives you the questions that you should give your team about how you adapt. There's culture adaptation, there's actual like, you know, process and, and like, procedure adaptation, and then how do you actually sustain and actually get to successful implementation? So I'm happy to share that as well.
Rhea Kelly 31:56
Such an amazing resource. Well, thank you so much for coming on and thanks for enabling the collaboration that's so important.
Bridget Burns 32:03
I am super honored to do this work, and I know there are a lot of other folks who are leading really important collaboration all around the country. It's just, big fan of higher ed I would say, I just want to, I find it endlessly fascinating, and the work that people are doing is just so important. And if, you know, I would just say that's a higher calling I hope everyone else steps into, is looking for places where there are shared problems, because there's no shortage of need for this, of seeing where you see the trends and themes, and stepping in to make it easy for people who are in the trenches, really doing the hard work, to have the benefit of the wisdom of their colleagues. So such a privilege. Grateful to do it. I'm totally on the recruitment path, other people should step into helping to lead and support collaboration.
Rhea Kelly 32:50
Thank you for joining us. I'm Rhea Kelly, and this was the Campus Technology Insider podcast. You can find us on the major podcast platforms or visit us online at campustechnology.com/podcast. Let us know what you think of this episode and what you'd like to hear in the future. Until next time.