Work Forces
Audrey Ellis, Founder and Principal of T3 Advisory, discusses the strategic and human-centered implementation of artificial intelligence across higher education. Drawing on extensive experience in college policy and institutional effectiveness, she explains how institutions can move from isolated experimentation to a comprehensive AI strategy that supports all learners. The conversation highlights findings from a national study titled AI for Institutional Transformation, which was conducted in partnership with Complete College America and funded by the Gates Foundation. Audrey describes artificial intelligence as an accelerator of existing institutional challenges, such as disorganized data and limited human capacity, while identifying specific opportunities for positive impact, including in the areas of student advising and financial aid. She introduces open practical tools for leaders, including an AI adoption rubric and a survey suite designed to capture feedback from students and staff. Audrey encourages leaders to take a proactive approach to AI use, emphasizing the importance of establishing clear rules to foster safe and effective experimentation. These efforts can lead to the automation of routine administrative work, allowing educators more time to build the human connections that support student success. Transcript Julian: Welcome to the Work Forces podcast. I'm Julian Alssid. Kaitlin: And I'm Kaitlin LeMoine, and we speak with innovators who are shaping the future of work and learning. Together, we unpack the complex elements of workforce and career preparation and offer practical solutions that can be scaled and sustained. This podcast is an outgrowth of our workforce's consulting practice through weekly discussions. We seek to share the trends and themes we see in our work and amplify impactful efforts happening in higher ed, industry, and workforce development all across the country. We are grateful to Lumina Foundation for its past support during the initial development and launch of this podcast and invite future sponsors of this effort. Please check out our Work Forces podcast website to learn more. And so with that, let's dive in. Julian: Kaitlin, AI has become such a pervasive topic. It seems to make its way into every conversation we have on this podcast, on this planet, from skills validation to credential transparency to workforce development. But there is a conversation that we haven't had in some time. How are colleges and universities approaching AI implementation? How are they approaching it not just in the classroom, but across the technical infrastructure that supports running these large complex institutions? Kaitlin: Yes, that is a critical question. We hear a lot about AI experimentation from faculty piloting tools to students using AI when studying or completing assignments, but less about the coordination and decision making challenges underneath it all. How do institutions move from pockets of experimentation to a holistic strategy? And how do they address the human capacity, governance structures, and academic considerations required to scale AI responsibly? Julian: Exactly. And that is where our guest today comes in. Audrey Ellis is the founder and principal of T3 Advisory, where she partners with higher education institutions on AI strategy, change management, and student centered process improvement. Her research focuses on AI adoption, institutional transformation, and addressing initiative fatigue in the higher ed. Audrey has co-authored numerous publications on AI and student success and frequently delivers keynotes and workshops on technology, equity, and education. Kaitlin: Audrey holds a doctor of management in community college policy and administration from the University of Maryland Global Campus, an MS Ed from the University of Pennsylvania, and a BA from Tufts University. Audrey brings both consulting and firsthand institutional experience leading effectiveness, strategic planning, and student success initiatives at community colleges. Welcome to Work Forces, Audrey. We are so glad to have you with us today. Audrey Ellis: Thanks. It's great to be here. Julian: And though we gave a bit about your background, we would love to hear you tell us a bit more about who you are and the journey that led you to founding T3 Advisory. Audrey Ellis: It's an honor to be here and really excited to talk about this ubiquitous topic of AI in every industry, really. And I think that's what makes it interesting in higher ed, right, because we serve all the industries. So it not only is appearing there, but in how we make all of our decisions on campus these days. A little bit about me, I started T3 a little over three years ago. I am from New England, Massachusetts. We are focused on supporting institutions that are access oriented, so they really serve the vast majority of students in this country in their postsecondary experience, but that often don't get the same resources allocated to them as the elite or more well-funded institutions. So that looks like community colleges, minority serving institutions, and regional publics primarily. Especially because those institutions are making a tremendous amount of decisions that need to be made around AI and often with less resources than, you know, their peers at other institutions that we hear about more often in the news. So really trying to create more knowledge sharing among those access oriented institutions so we can all learn together right now, because I think we need to face the reality that we're all a little bit of a student when it comes to AI. Feel like every morning I'm starting a new course because the tool changes overnight, it seems. So back to basics every single day with Claude and ChatGPT, Gemini, starting new features every morning. Julian: Yeah, it is just dizzying, isn't it? Audrey Ellis: It sure is. Julian: So your organization recently released a national study examining AI implementation across colleges and universities. Would love to hear about the study and some of the biggest challenges that higher ed is facing. Audrey Ellis: Yeah, we conducted a national study called AI for Institutional Transformation. We were really generously funded by the Gates Foundation and we worked with Complete College America to conduct interviews with senior leaders at over 30 institutions across the country from that segment that I just described. Our goal was really to understand how institutional leaders are kind of navigating a dual path. So we have institutions both trying to figure out today, tomorrow, this semester, next semester, what does AI mean for us? But we also are trying to understand, you know, dream big, wave a magic wand, what does AI mean in three years or five years? With the exponential rate of change and that comes with opportunity, and so we wanted to understand kind of both sides of that conversation: policy, governance, practical changes, but then also really big picture what could AI mean for how we do work in higher ed. And so all of this work was considered public goods funded in their investment strategy at Gates. So that's really exciting because it means that everything that we have published is available to the field for free. So I appreciate you having us on. I just really want to make sure that institutions know that this body of research and all of the tools that we've developed are available and can be downloaded, repurposed, edited, modified today. So we'll be sure to share the link and hopefully you can share that out with the listeners, but really excited to kind of get into what we learned. Kaitlin: Yes, so please tell us a bit about, you know, what do you see as some of the biggest—I guess you could start at either end of the spectrum—what are some of the biggest challenges or the biggest opportunities? Why don't you take us where you'd like to go first and then we can discuss the whole spectrum. Audrey Ellis: Sure. Let's start with the challenges. I think I'm a practical person and there's no doubt there's a lot of opportunity, but I think in order to be around to play with the opportunity, we have to figure out right now. So let's start there. We need to kind of, I think, what we've heard and what we've seen from the institutions that we've interviewed and studied is that very few institutions are taking a coordinated strategic approach to their AI adoption right now. So what that means is that kind of as you described in the intro, there are a tremendous amount of pilots and experimentation happening at institutions. And I feel pretty confident saying that probably every institution has at least a little bit of AI experimentation. I don't think that any institution could say they have no AI happening on campus anymore. But what that looks like at most institutions is a very decentralized, dispersed, kind of scattershot approach to AI adoption and AI innovation. Which isn't bad. So I'm not trying to suggest that institutions have made the wrong move so far, but that's come out of kind of a couple of steps that leaders have made that has led them to take this more decentralized approach. So we developed a rubric that helps institutional leaders think about kind of all the different decision areas or dimensions of AI adoption, everything from budget investments to mission alignment. All of those play a part. And when we saw our institutions that are kind of the most far ahead with their institutional transformation, they had addressed all of the different areas, whereas we saw that institutions, and really like around 80 percent of the institutions that we interviewed, are more in this scattershot area of implementation. And that's often happened because there's been a real focus on things like policy and academic integrity, which is certainly an important thing to consider, and I don't want to diminish the value of the conversation around academic integrity. But often it's led to kind of kicking the can down the road on any other decision making around AI. Like, we're going to stand up a committee or a task force and until they have the policy, we're doing AI, that box is checked, and we'll get there later. And what that's leading to is a growing divide between institutions who are taking this more strategic centralized approach around AI adoption where they're thinking, you know, what are the biggest strategic plays that I can make right now? Whether it's workflow efficiency or improving the student experience or kind of like winning back time that, you know, from an initiative fatigue standpoint, like our stakeholders on campus have been, you know, saying that they've been wearing multiple hats for decades with all of the reform and work that we do. So there's a lot of strategic opportunity here. But importantly, the divide is happening not only between decentralized and centralized or strategic adoption, but that is happening across kind of the resource lines of institutions. So those institutions that are more well resourced, which we used endowments as a proxy for resourced, those institutions that have those massive endowments are taking a centralized strategic approach and that gap is going to further widen if institutions don't figure out how to wrap their arms fully around their institutional strategy. A couple of other kind of big findings we saw is that, you know, staff are really not being considered in the training equation. You know, in higher ed, and I'm sure from your workforces angle this resonates, like we call ourselves the connector to the workforce, like we that is a huge part of our mission. But our institutions are also part of the workforce. Like, we employ so many people in our region. And we've actually often overlooked a really significant part of our own workforce in this moment when it comes to talking about AI skills and AI literacy. So we found that almost none of the institutions that we interviewed had intentional training for their staff. They were, you know, leveraging teaching and learning centers, professional development for faculty, but because there's often not a comparable center for staff, those folks were getting pretty overlooked in the training conversation. And that's just an example of the fact that AI is in a way just an accelerator of the existing inefficiencies and process challenges that we have at our institutions. And it's shining a new light on challenges that are not new. Whether that is, you know, trying to put dirty data into AI that has been dirty and causing inefficiencies or challenges with reporting for decades, but now AI is making you realize the data quality issue that you have, or whether it's that you don't have, you know, kind of standalone institutionalized professional development for your staff. There's just a number of existing challenges that AI is really illuminating right now. I'll end it here with, like, the big risk is that our institutions are feeling the pressure of having to keep up. Like, they're not ignorant of this widening gap. And that puts them in a bit of a vulnerable position when it comes to their relationships with vendors who often bring really important expertise and solutions to campus. But without having kind of senior leaders' arms wrapped around their strategy, it lends itself to a more impulsive vendor strategy that might not be thinking about the comprehensive picture that an institution needs to consider before they get into a long term contract. Julian: So it really is sort of, it sounds almost like it's really surfacing just what we already know to be these huge issues and inequities and so on. So, what do you see as some of the bigger areas of opportunity? And I guess and I'm particularly interested—I'm interested in all the institutions—but particularly interested in the institutions that are more challenged. You know, we do a lot of work, for example, with community colleges and, you know, they just don't have the resources and and and probably and are never going to. What are the opportunities for all institutions? Audrey Ellis: First let me just say that I don't mean to sound like doom and gloom. I realize I just ran through so many challenges only to just kind of paint the picture of the importance of taking a strategic approach. So I think in order to take advantage of the opportunities that AI can present, which I'll talk about in one second, I just want to make a plug for the resources that we've developed that are all public goods. So everything from toolkits, talking guides, facilitation resources, how to develop staff training around AI. All of those challenges I described have free resources to try to help institutional leaders wrap their arms around this. But I think that the opportunity is huge, right? Like, I having studied initiative fatigue for a long time can't tell you how many times I've heard faculty, staff, senior leaders say, "I would love to do more of this best practice you're teaching me about right now," whatever it be, like let's just take a faculty professional development about universal design in the syllabus or about memorizing your students' first names so that they feel a greater sense of belonging. And then faculty say, "Yes, like you've sold me. This is a good practice. But exactly when am I supposed to fit this into my schedule? Like I have already am, you know, working late or traveling between campuses because I'm an adjunct or because I have to work multiple roles because I do work at an institution that's, you know, lower resourced and maybe doesn't pay as much." So I think there's a real opportunity to offload some of the transactional work to make more time for the human. Which is definitely like a different narrative than what you would get from, you know, maybe like the most recent Mission Impossible movies where it's like AI takeover, global domination, we're all going to be irrelevant in a few years. Which don't get me wrong, I'm not an AI evangelist and I'm not here to say like that AI is going to be the perfect solution and there's a lot of challenges that it brings outside of what we're discussing today. But I do think that, like, we have an opportunity to do more of the mission-driven human work that I at least when I worked on campus wanted to do more of. Like, I didn't, I know that the advisors that I used to work with would have rather spent more time talking to students than dragging and dropping courses in Banner or Degree Works. They would have rather had that time to say like, "Hey, you know, Julian, what do you want to be when you grow up? Why did you come to this college? What made that a decision for you?" But they were often faced with the reality of a long line of students out their door waiting for them and that student who needed to register, and so it became more transactional like, "Okay, let me just get make sure you get into these courses." The challenge is really significant though with that, like, we know that students often pick let's say the wrong major on their application. If we're rushing the conversation then around what classes they should get into and don't help them course correct, they might end up having excess credits in their first semester or not seeing a connection to the college and not being retained. Not to mention from a staff sense of belonging engagement standpoint, it starts to feel really repetitive and not like what you've signed up for. So I think there's a lot of opportunities there. That one gets me really excited. Kaitlin: It is really tricky, right, to think about the layers here around, like, you mentioned earlier the vendor relationships. And the fact that each of these vendors, right, are also innovating and experimenting with AI and embedding AI in their own tools, and then what does it look like to start to think about how to holistically integrate tech systems that are also kind of changing and evolving in this landscape? And as, right, as you started today's conversation and saying using that word ubiquitous, right? Like it's everywhere. So what does it look like to be kind of in the center of this changing landscape trying to utilize these tools effectively, but the tools are changing? Right, and I appreciate what you just said about almost like well what can we start with? Where can we, like, where can we focus our energy on the things that matter most like engaging with students effectively? And can we draw, like, lessons learned from there or can we can we draw, like, places to start from the core of what matters most around students and engagement? Audrey Ellis: Yeah, I think you're really getting at at the real crux though, which is like if we don't decide what we want to use AI for, vendors will decide what we use AI for. Because every vendor is adding AI. That is how they will stay relevant in this moment. If you already have technologies, which you undoubtedly do, we all use technology in our day to day. And you don't take an active stance in this conversation and in your procurement and in your strategy, you will ultimately become a passive consumer of how vendors have decided you should use AI. And so I think that that is why it is so important right now to pause and think about what do you value about your job? What value do you provide that technology cannot, which is substantial in my opinion. Like, humans should absolutely still be at the fore of education and the driver. So then, then where are the biggest opportunities to leverage AI? And that's something that institutions who are taking a scattershot approach are not benefiting. Like, that exercise is not benefiting those institutions. And what we found is when we conducted our interviews, we had this heat map, I guess, that we asked institutions to vote for both where they have seen AI, where they are using AI, like from an org chart standpoint, but then also where they thought AI could have a lot of value and there was a there was a gap. Because I just talked about advising. Because it is a very obvious example of a place where AI could have a huge impact. And also, for what it's worth, advising is always the front line of every reform. So, you know, for better or for worse for the advisors from an initiative fatigue standpoint. But we also heard from a vast majority of our interviewees that strategic finance and financial aid are other areas where there's a lot of opportunity for AI, but almost no one is leveraging it. And so if we don't kind of demand or force the experimentation and the innovation in those areas, then we won't benefit from from it or someday, you know, the tools that we use to do our our financial aid packaging, for example, will roll out whatever they think is best for AI integration. And so I think there's a real opportunity to also work with vendors right now rather than just be kind of passive consumers of the AI washing, like whatever they're just embedding in their functionality. Julian: So my last job working for other people was as an administrator at a leader at a community college system. So my mind takes me to, okay, so what is working? What are examples of how this can be organized? Because, you know, you can be strategic even if you don't have a lot of money. And you can experiment and be thoughtful about how you pilot and grow and scale and and and drive change and policy and work with vendors. So are there in your research, in your travels, are there examples, structures that are working? You know, obviously the president needs to be the leader of leaders, but who typically is driving this work? And especially if you if institutions are thinking about it as both what we do on the learning educational teaching side and throughout the rest of the institution crossing to the admin and everything in in between? So I don't know, it's a lot to cover, but I'm curious to know if there are examples that stand out to you? Audrey Ellis: Well, ironically, there isn't like a monolithic AI lead in higher ed. That made our research really hard to be honest, because you're kind of like shooting fish in a barrel with an org chart. Like, I'm going to reach out to this person who I think maybe is in charge of AI and then they say, "No, I don't have anything to do with AI, talk to this person," it gets kind of kicked around. The alternative institutions are starting to assign or hire for like AI innovation leads, but I am kind of not fully in on that model. I don't think that it's I think a lot has yet to shake out in how that like AI or Chief AI Officer kind of role works. I think the risk just quickly is that it becomes kind of like "Oh, that is just that person's job and therefore it is no one else's." But with such a ubiquitous technology, it is everyone's job. And so what I usually say is we all need to be leaders in this moment and every supervisor should be thinking about what AI means for them and their direct reports. And that's why, you know, doing capacity building exercises like leveraging there are so many free AI training modules and content out there. Encouraging your leadership leadership broadly, being really like I said supervisors, to think about how AI could look in their unit is a really big opportunity and pretty low cost. I think that one very free next step that institutions can do is assess what their AI adoption currently looks like on their campus. So we developed a survey suite and I just recorded a long webinar about this, so feel free to put that in the show notes, but we developed a four part survey suite that includes a presidential reflection, leadership or department unit lead survey, an individual user survey, and a student survey. And the idea is that if you conduct all these surveys and I'll just interrupt myself to say I used to run institutional research office. So I'm not trying to blindly push surveys. I know that survey fatigue is very real. But AI is being used in offices that we don't know about in ways that are potentially very innovative or potentially very problematic. They may or may not be benefiting us as an institution and we don't know. So it's really important for senior leaders to get that kind of heat map view of what their institution looks like or their system. So Julian, I would say that is like the immediate next step. You can start with the rubric though. What I suggest is like take the rubric to your cabinet. Have everyone independently score your institution and then compare your scores. What does your VP of Academics think compared to your CFO and your CIO and how are you aligned? But then getting that more distributed look is just as important because, you know, I hear all the time about like, you know, the student workers who are also students and using AI in their work for school and then, you know, they already have the account set up and so they just start using this their current account to maybe do what their job is in their student worker role a little more clearly. It goes all the way down to, you know, frontline staff that could be using AI but also could be really creatively using AI and perhaps and I'm not saying do this in an audit or punitive way. So I think in addition to the survey, the most important secondary action leaders can take is getting really tight on like what is actually out of bounds from an AI use standpoint and then otherwise encouraging experimentation. So, you know, the institutions that are, you know, private, really well resourced, I've been kind of watching this moment unfold for the last three years. And I remember, you know, in the first six months of ChatGPT on the scene, there were senior leaders who full on banned AI and then there were others who said, "This is your kind of temporary notice. Do not upload FERPA protected data. Do not put, you know, confidential information into AI. Otherwise, experiment and tell us what's working." And that I think is another free action that's actually setting institutions apart right now as far as how far they've gotten. A lot of institutions that I've worked with have spent a lot of time making AI policies that by the time they get approved by the board are not relevant. And so, you know, I would encourage leaders to not overplay the policy angle and and think about how you might incorporate one or two potential additional lines in existing policies. Like AI isn't new. If you've used GPS for the last 10 years, like our technologies include AI already. So in theory our policies should for the most part cover the ways that AI rolls out. And let's not use it as kind of like a delay tactic anymore. Kaitlin: There is an element of this ongoing learning that I think it would be more comfortable if we could say, "Well, when I'm an expert in this, then I'll use it," or "When I understand it enough, then I'll apply it here." And there's this reality that that's not what we're facing and so, you know, I think it really is complicated, right, how to get comfortable with that as you're saying from just like a human development and capacity standpoint. Like how do we all like gain some comfort with that because the world keeps moving and our jobs keep, you know, every day we wake up into our jobs and so what does it look like to apply this ongoing learning mindset to something that I think it can feel a little uncomfortable on an individual level never mind an organizational level. Audrey Ellis: Yeah, actually from my initiative fatigue research, that is a really important takeaway is that below all of these institutional decisions or maybe like a history of failed initiatives or poor communication or mismanaged expectations, there's the human element where your stakeholders on campus are honestly just like "How does this affect me in my day to day and will I still have a job?" And it doesn't help that with AI that is also the narrative in our society and, you know, like there's a lot of fear mongering. And so that's why I say that every supervisor has a role right now because especially in the absence of a presidential statement around AI, every employee is going to look to their boss for signals around "Should I be worried right now? Should I take a growth mindset or a fixed mindset in this moment?" And that is not something to underestimate. Like if you have a growth mindset, you're seeing that you bring I think intrinsic value because of who you are and your your humanness and your mission drivenness. And then you are saying "How can I continue to do this better with this new technology?" If you have a fixed mindset, you're saying "As I am today is protected and so I cannot change or else what I am right now is as under threat, I guess." And I think that is very much like being rinsed and repeated in the public sector right now and so I think leaders need to lead by example. They need to say, "I am scared of AI too. I am trying to learn and you should learn; we will learn together. We will learn with our students and maybe from our students," rather than just being silent because I think that's when the real fear happens and then folks kind of freeze. Julian: It's so interesting because it does on one level harken to so much of what, you know, those of us who've been trying to reform education have been dealing with for years, which is career pathways, yeah, that happens down there, or workforce development, yeah, there's an office I think they're in that other building. And this idea of how do you just integrate this work in your everyday? And it's really scary when something this powerful has emerged. We have not experienced anything like this. And and I'm almost interested in as we wind down the conversation, you know, we want to hear from you again and more about how listeners can follow and continue to learn more and follow your work. I also like there's a part of me that like wants to talk to you again in six months, talk to you again three months later, talk to you again three months later because I think it could be a very different conversation, you know, that's just the reality we're in. Audrey Ellis: Yeah, and I know I would love to keep the conversation going. It certainly is only ramping up and is not slowing down. So on our website, t3advisory.com, there's an section or a tab that's called AI for Institutional Transformation. All of our resources are available to download and use on that page. That also includes our recorded webinars and upcoming webinars. I'll be presenting at a bunch of conferences this year and really happy to hear from folks if they want to continue the conversation. We hope to do a second round of this study, like you were just saying, Julian, like this conversation is not going away. You know, luckily the way I talk about it is constantly updating and being informed, but our actual data was collected last year. So I really hope that we'll be able to collect even more data formally officially in the coming months as a continuation of this research. And, you know, we work with a lot of institutions and systems on their AI adoption strategy, and we are also thinking about developing some solutions to support institutions with this in a more scalable way, so maybe we can check back in in a couple months. Kaitlin: That sounds great. Yeah, thank you so much for taking the time to connect with us today, Audrey, and to share this work and yeah, looking forward to remaining in touch. Audrey Ellis: Yeah, thank you so much for having me and really excited to be part of the conversation. Julian: Thank you. Kaitlin: We hope you enjoyed today's conversation and appreciate you tuning in to Work Forces. Thank you to our listeners and guests for their ongoing support, and a special thanks to our producer Dustin Ramsdell. If you're interested in sponsoring the podcast or want to check out more episodes, please visit workforces.info/podcast. You can also find Work Forces wherever you regularly listen to your favorite podcasts. If you enjoyed this episode, please subscribe, like, and share it with your colleagues and friends. And if you're interested in learning more about Work Forces Consulting, please visit workforces.info/consulting for more details about our multi-service practice.
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