Building AI Agents for Internal Teams

0:00I feel like those three things create a like interesting little recipe for your internal team to embrace this versus fear it. Welcome to Higher Ed Pulse, your No Fluff Guide to Artificial Intelligence in Higher Ed. I'm Mallory Wilson bringing nearly 20 years of ed tech and marketing expertise straight to your beds. Higher Ed leaders are expected to master AI overnight, but let's be real, no one has time to sift through every headline, hot take, or research report. That's why every Monday all breakdown what actually matters, fast, clear, and to the point. Want to sound like the smartest person in the room at your next team meeting? Here in the right place. Let's dive in. Welcome back to Higher Ed Pulse.

1:00Today's episode is a blueprint. Clark University is developing seven purpose bill AI agents for their advancement team with defined roles, budgets, governance, and human oversight. If you've heard the hype about AI agents and have been wondering how to move from pilot to practice, this episode's for you. Today, I'm joined by Joe Manic, vice president of advancement at Clark University and founder of globalfulanthropy.ai. After eight years at MIT, leading principal gift programs, Joe has led fundraising strategy at Clarks since 2023. His work blends philanthropy, governance, and emerging technology all grounded in a clear belief. Relationships stay human and infrastructure gets smarter. How do I know that because Joe is developing these seven agents and I'm so excited to talk to him about that today. Joe, welcome to the Pulse. Hi, Malay. Thank you for having me. All right. So we have to confess something to our listeners before we hit record. You were telling me about your unique relationship with Rotten Tomatoes.

2:04Yeah. So we were talking about like what do we do for fun and I really respect and appreciate great shows and the ratings and rankings from places like Rotten Tomatoes. But when things get really tough or like I'm having a tough week or a tough day, I usually look at Rotten Tomatoes upside down and really look at the worst shows ever. And I pick a movie or show that like I really have no attachment to I don't care about finishing it or not and it's so therapeutic. It's so helpful. I feel great doing that. I mean, you said it was like your form of meditation and that resonated with me. I was like, we all have our unique forms of meditation and like terrible television is such a good one. It's amazing. And like you're literally just living the moment and so invested in that like one hour and what's going on. And you generally don't care about anything else, but also you don't care about that show

3:06movie or they release this and there's a plot twist and like all the thinking that it comes with great shows that of course we watch as well. I mean, I think listeners know at this point that I am obsessed with reality, television. I mean, but when this episode drops survivor 50, we'll have just premiered and oh boy, like let's go like a bad re competition reality TV. I won't call it bad. Competition reality TV is my version of your upside down rotten tomatoes. It's too good. Well, we are here to talk about AI agents and folks, if you've been paying attention to how large enterprises are deploying these agents, there is a clear pattern that's emerging. At Morgan Stanley, for example, 98% of advisors now use an internal open AI powered assistant every day. It's embedded in how they work, but here's what's interesting. They did not launch AI everywhere at Morgan Stanley. They launched it with discipline, with controlled access, a very defined tool, tight identity

4:06and permissions and human oversight was built in from day one. So there's a model and the organizations I believe seeing real results are starting with their internal workflows and they're clearly defining jobs for their AI agents. So they begin with those support roles and then they let the agent assist before it acts independently. And that's what led me to Clark University. Joe was posting on LinkedIn recently about these seven very clear, agentech roles from a research coordinator to a donor stewardship officer in a few in between. But how did he choose them? How is he evaluating partners? What are the guardrails he's going to put in place when I saw his post on LinkedIn? These were the questions that immediately came into my mind and that is why I knew we needed to get Joe on this podcast so that we could dig into his process and really learn in a higher eye context, how can you build AI agents to support your internal team? So Joe, I have to imagine that you didn't just wake up one day, look at your upside-down

5:11rotten tomatoes list, sip your coffee and say, today I'm going to build seven AI agents. So let's step back because I have to imagine you were evaluating that things were happening inside your shop that led you to this. What was it? Great question. And thank you for the framing. So to start with, I didn't start with AI agents. So I started with traditional campaign, with traditional consultants. We ended up with really, really, really good plan that I was super proud of was reviewed by the board in May 2023. And then I got the most annoying question by the president who was involved in this plan. And he was asking me, are we going to be proud of this campaign when it's over? And is this the best campaign that we can do? Is this the best version of the campaign? My mind automatically went into the data points, right? We have financial resources, we have human resources, we have tech resources, and we maxed

6:11out every single one of them. We optimized, and to me it was like, yep, there are things I can't afford right now. Technology was one of them at the time we were running on Razor's Edge since then we upgraded to NXT. But that's the best I can do right now. I will use AI and technology, because that was like months after Chargibilly started. And he said, take a few days to think about that. And if that's the answer, let's do that. But before we jump into implementation, I just wanted us to be first one about it. And around that time, I went to a case conference and I met multiple colleagues there who were thinking about the same thing. And CBT was literally a month, six or seven, and we were all posing and thinking. My background is computer science. And at some point in the, in the route, I moved from computer science and working on advancement operations to become frontline fundraisers in New York. And I remember every person I knew who knew about advancement was telling me, this is

7:11the point of no return, either stemming on the operation side or you're moving to the fundraisings. Are you sure this is what you want? And then when I moved to MIT, I was on the, on the principal gives sign. And then when I moved to Clark, and because of this question, I realized that actually, at MIT, you were working on being bilingual. And that involved, like, can you be fluent in computer science and biology, computer science and sociology, computer science, urban planning, and like build those bilinguals who can understand both and start using AI. So we never use that in an advancement context, because what we usually do is, in large organizations, we don't have time. And in small organizations, we don't have money. While at Clark, we don't have either. But I, that president saying, go experiment, go fail, do it in a content environment, and try to be unconventional, so to speak, which is part of our motto. So with that in mind, we built a lab, an AI lab where we could experiment with this technology, knowing that we have no idea, agentic AI is going to be a thing.

8:16But knowing that technology is going fast and things like agentic AI are going to be part of the hype cycle, and some large companies are going to invest in it, which means it can be easily commercialized at some point. So we worked on multiple things, but going back to like the technical part of your question is, we didn't focus on technology or AI, we looked at the bottlenecks. And the bottleneck was like specifically a friction point. It takes us days or at least 10 hours to produce quality, detailed profile, research profile. It takes us like sometimes minutes to find the right PDF in a huge campaign toolkit that might have hundreds or thousands of documents, a few large organizations. The deeper issue wasn't like lack of effort, so to speak, to like find this information, it's just like it's structurally difficult to get what you want. And it takes you a lot of hours, and that's like draining our team.

9:19And we realized that I think the problem is more structural than looking at this new technology, as like this is like the email you can have it as an assistant, or this is like Zoom, where you can like save on your commute. This is much more transformational. And when we looked specifically at AI agents, we realized that what we've been experimenting with for almost a year and a half on the research side, and that would be the equivalent of having a research assistant has grown with technology becoming more available, but also with our knowledge of how to use it to maybe not have a research coordinator. That's why you see in that some of them are officers, some of them are coordinators, and that has to do with where we are in our readiness in these areas, and what are we comfortable with, and what kind of knowledge do they bring to the table. And the reason we use human titles is it really helps us better understand, like this is the complexity of this AI model, that intelligence of that model, and the learning of that model is at this level of a human. And we will always be like the

10:25final say in terms of how we implement things, or how we review the content that comes out to fit, and everything in our case is 100% human. Wow. Just listening to you talk like about 20 different thoughts popped into my head. One of the first was you started with the problem, right? You identified your goal, and then you went and found the tools that were going to help you solve the issue, and not the other way around. And I think when people are getting stuck, or are moving slowly with AI, it's because they're approaching it from the other way. They're saying, I need to apply AI, because I'm hearing about it, or because I'm getting pressure from somebody, and they're not being as thoughtful as what you just articulated. So that was so fascinating. And another stat that you said stuck out to me too, that you might spend 10 hours, or multiple days just doing research. And we know an agent can do it in the background while your humans are off doing other things. I mean, the incredible amount of time that this is going to save your team

11:30is huge. And I have to wonder if the combination of like your thoughtful framing with the clear like you must be clearly articulating these benefits to your team members. And even the psychology behind naming the agents with human roles, I feel like those three things create a like interesting little recipe for your internal team to embrace this versus fear it. And I'm just curious if you could sound off on that. Yeah, I completely agree on that. The adoption part of any technology has ups and downs. So the first few times like we adopted some tools like everyone else was doing automating contact reports, summarizing next steps, etc. The adoption was lower. And one of the things we tried to do is like to use something a trick from Facebook so to speak when they they face scarcity. They would have like made it a community open to everyone, but they started by saying we're going to pilot this with only Harvard students and it's

12:34only for Harvard.edu and then they opened it to any student with the dot.edu and then they opened it to the world. And at that time, I remember like we were craving more as opposed to like we all can do is like next month, next week, like anytime that it's available or it's accessible to me. So we did that scarcity a little bit that we piloted certain things where we only had three licenses or five licenses and that didn't create competition but created more tolerance and adoption. But also in pattern, we piloted one project at a time. Even when we have a lab now and we can test multiple things at the same time, the research component I personally spent a lot of my personal time like coming up with a detailed prompt that kept making it close to a super prompt. That's not only would put your first name, last name and like job or company, which we would usually put on need to search for you if we're engaging with you. If you want to learn more about you and the

13:35board members, you're somewhat obvious. But also after getting to that detailed prompt, I ran it against the council for advancement and support of education's codes from like guiding principles of how we work, our admissions statements, as organizations, the association of fundraising professionals, code of ethics, just to make sure that it's if in compliance with these things, run it against GDPR, the privacy law in Europe, against like the California laws and against some path laws being drafted right now and five or six jurisdictions. And the reason we wanted to get to a place was like if we're using this, that should be as easy and as ready as possible for the human who has the final decision of what to compile and what to do. So the two routes you can go there is you can go with I'm going to do this. And then we're going to get to a place where I can run the team on a third of the researchers. So to speak now in case we're small, we can't do that. We have two great colleagues and we can't like trim it even further. But if you're a large

14:40organization, you might decide to do that. We went to the exact opposite approach. And I believe, and I hope my team agrees on my colleagues agree, that we actually are getting to a place where what we want is that same human colleague who used to have the bandwidth to do 50 high quality end depth reports for the president to hear to do 2,500 or 2,400 for every single volunteer young alone or someone that when we're engaging them, we're trying to learn as much as we can on them, they're interest and try to like make their experience as as meaningful as we can. So giving everyone the Royal treatment that we usually get to our top donors or the top 50 donors and giving to everyone else. So we're trying to scale our impact as opposed to trim the team and the team is seeing that more and more. I hope like I'm seeing that also how they're adopting it and testing things on their own as well. That's incredible. Now boundary definition has to be part of the process of course. And I'm curious that like as you made decisions about

15:43these seven agents, how are you thinking about the information that they will or won't be able to access and the actions they will or won't be able to take without some human oversight? So let's take the research example because it's like using charge BTA deep research. So it's using a deep research function that goes deeper into getting the data and then it gets you after each sentence a footnote that when you click on it, it will tell you from this article, this is a quote that Joe said publicly or this is something that's in my bio. This is Clark you.edu. So if that's the case, then a human researcher can do exactly their job. We're not putting guardrails. It's just the first draft is coming to them and instead of them going to the data and browsing the internet for hours to filter for this information. The information is coming to them as a first draft, but nothing is going to the president. Nothing is going to the fundraiser. Nothing is communicated with the donor is just instead of using Google and seeing

16:44them in a certain format, you're seeing it in an exactly opposite format of it's in a word document already. It's already formatted and the concept is video. You don't like the source or the reference that's used to just take it out. So that radically simplified the review process to like 20 minutes, 30 minutes and then generation for 10 to 15 minutes. So now what we're doing with the agenda is instead of putting first name, last name and company and do that one time and then my human colleague will review it after 15 to 20 minutes. With the agenda, you can put 100 names and you're only sharing first name, last name company and saying literally go to the 6L file, pick first name field, last name field and corporate field and run this whole process 100 times or run it for 2000 names and every time you finish it, export to word and notify the human who's running this process. So instead of that person spend thousands of hours a year, all of this is coming to their inbox and we can't pace it anyway. Now if you think of it as a human

17:48and that's why we use some terms to remind us that we don't bring a human and just have him start a job. We do onboarding. We try to test things, we train them, put metrics for them. We do performance reviews on the regular basis, we fine tune these things and in this case, and things go bad or the fundraiser or the researcher, the human basically feels that this is not working for me to just turn off the agent. It's not in control, it doesn't have the data, it doesn't have anything, just turn it off and put it on hold or shuffle it until we figure out what to do and everything we've done with it is already locked there and we have an extended version of what did we do, what did we capture, how we did how we did that, and that's archive for for future years. Nice. Gonna hit pause for a second to share some awesome news. If you or your team are looking for practical strategies to crush operational bottlenecks and scale engagement with AI, then the Engage Summit 206 in Charlotte needs to be your destination this June the 15th through

18:52the 17th. From hands-on sessions to main stage keynotes showing how AI transforms enrollment in retention, Engage Summit is the place to level up your institution's game in ways you probably didn't realize were possible. You'll walk away with tools, frameworks and connections that actually move the needle and people who will keep pushing you forward long after the conference ends. The good news spots are still available and the better news and roll-ify listeners can get 50% off the ticket price. Yes, half off. Just head to Engage.Element451.com and use the code Mallory50. That's M-A-L-L-O-R-Y-5-0. Now back to the show. I'm curious, and I guess I don't know if this all occur given the roles you have outlined, but I'm wondering what you anticipate doing if an agent recommends something sensitive.

19:57That's an amazing question. That used to make me lose a lot of sleep when we started, and when you think about it, it was also one of the top five questions I would have whenever I talked to colleagues, and then every time we're at a panel or talking publicly or posting something, the questions that we get, we get around the effects of using AI, how you're using it, how do you build kind of borders? What I started with is thinking about the Department of Defense, because what they do is they have the defense readiness conditions, which I used to see in those bad Rotten Tomato movies. When you look at the defense readiness, they say the United States is in DEF-4. The president could make it DEF-3. The second he says that, every person who works in the government building, in an airport, in an embassy, on an air base, they know exactly what that means. They know that they're allowed to take a vacation. That clarity around readiness,

20:57we have it in other things, so we have it in industries, we have it in like environmental conditions for companies working with chemicals. We have it for product readiness, but we never did it for our industry, so we developed, and that's why we created global philanthropy.ni, we developed an AI readiness framework for the nonprofit sector, so for universities, hospitals, other nonprofits, and then we built around an effects maturity framework, and then if you answer 25 questions or so on each one of them, or if you just look at the chart and you can see where you are, you can't think of like basically what's the next step for you. The first thing we did, basically, but at the heart of your questions, anything related to AI decisions cannot happen with advancement. So, as a VB, when I have the legal right to actually get vendors, we have a process internally on how we vet them, we have like a group of people who would like decide which vendor to go with. I bypassed that right for anything related to AI, and I gave it to a set of colleagues

22:02outside of advancement to do that independently of us, so that they're deciding not only on the vendor, but also this particular tool is effectively acceptable at-clone. And if we share this at Cornell, or at MIT, or any other organization, they might have a different decision. So, everything is filtered from that ethical lens of our institution, and we deal with it just like when we have any other conversation. Should you accept money from this core corporation or from this donor, usually you have that conversation internally, and you have a set of people and a set of values on guiding principles, and it's how to make that decision. And we put that in the guest acceptance and advancement policy, and we also have the board approved that and give us the mandate to use AI with an oversight from the advancement committee of the board on every tool that we're using every six months, they get to review it, and see if it's doing what's it's intended to do just like they review everything else we do. From our short conversation, I have to say, I am not surprised at how thoughtful and thorough you approached that from what I'm learning

23:06about you, Joe, like that that seems to be a through-lite for how you just go about things. Thank you. It's really, it was a just a question from the president, so the question triggered the strain reaction, but it's a web of colleagues, particularly the president, our advancement committee of the board. The head of the committee was the first person to bring the topic of AI to us, and her husband, Kevin, her name is Erica Pan. Kevin and Erica helped us a lot in the first conversations how to think about AI and who's using it in his company and how privacy and security and the guardrails are extremely important. So it took like probably 30 people to get to that. I'm just happy to be the person delivering the message and helping with sharing the version. Fantastic. Well, you brought up the word vendor, and so I got to ask you about partner evaluation because the post I saw was, you know, your advertisement for, hey, we're creating these seven agents and we would like to evaluate partners to help us do that. So tell us a little bit about

24:08that process. So whenever we're assessing anyone as Clark or assessing any product or service, we value the kind of part that we're bringing to the table. So that's Clark thing, and I wish you to be in something that I personally value as well. But the main thing was we're trying to not move our data at all. So for the first part when we established the Clark Advancement Club, the reason it ended up being the ever to Clark Advancement Club is we looked at what kind of the company they are, what did they do in the past 15 years? Are they learning and is their product evolving as well? Or is this just like the same product they had 10 years ago, they just added an AI layer now because there's a hype? And there are startups that we work with, but they have the same mindset, they have the same values. We meet with them just like when you interview someone to join your team, like we do the same thoughtful multi-layered multi-purpose conversations to try to learn what kind of person they are, what kind of organization they are, try to meet multiple people

25:12there, see, start it over. So we do all of that before we kind of partner because we think this topic is so sensitive, and if they make one step on that, on the FX maturity, that's a little bit more aggressive than what you're comfortable with, it could damage our reputation. So we're extremely conservative with that, and whatever true we found, we found the real partner in this. Now, when it comes to other vendors, for example, we work with another gallery for a product called activated. So this is a tool that will help do some sort of online coaching. So you use AI, you're not sharing any donor data. So that was like a starting point. Mallory was among the people who are extremely skeptic about AI, so that was another great point. And then the third point was she built a product that presented her values and her values are public. She's been talking about them for a long period of time. So what we ended up with is I'm using AI to figure out before I talk to you, or I make an ask that you're a consistent donor,

26:17and you're upset about something, and like you put the persona of that donor, then you're having a human to AI conversation that's all you're driven. But then I'm netting an evaluation of how much did I talk, did I ask the right question, and kind of doing an assessment for you, and then making recommendations. It's still me deciding how I'm going to approach that. It's still the human walking to the donor, but that level of coaching for some people it might be like much more easy or much more relaxed. So it's more like trying to mimic what we do with personal relationships, how we select donors, how we select team members. We're just trying to replicate that with vendors, as opposed to just looking at terms of preference and looking at statement of works and so forth. Yeah. I'm going to have to meet this other Mallory. Hello. There aren't a lot of us out there. Absolutely amazing. She is also among the co-founders of fundraising.ni, and when they worked on it, they were the first in the space. They met at the conference, and then they started ideating, and it's one of the largest conferences

27:19at ecosystem around nonprofit fundraising. Cool. Very cool. So one thing that you mentioned to me right before we started recording, which we got to talk about it. I am obsessed with the fact that you are hiring somebody to manage your AI agents, like brilliant. Absolutely brilliant. Tell us more. Absolutely. So the position itself was like promoting a position that we already had as an FDA. So we added values and responsibilities to it. And now it's a senior executive director for advancement inside an AI strategy. As I mentioned earlier, I've been doing this kind of on the side, enjoying the design part of it. My weekends, this is what I do for fun. But at the same time, we have to institutionalize that beyond the lab and beyond the tools that we're using, and we're launching a campaign in May. So I need to have the whole lab be one by one person. And now that we're hiring seven AI agents, those agents have to have direct human oversight.

28:20And the oversight has to happen from two layers. One, from someone who understands the technology, and we're hoping to hire someone who gets the technology. And then the person was the last to face before that is being used, whether with a senior officer or internally or being used with a donor or an alumni volunteer. What kind of experience are you looking for someone to have in this type of a role? We're really getting out of there is the job description and skills and all of that, but you're really getting out of the business of being to rigid around experience, because what we're realizing is this technology is moving too fast. And even for fun, why are you asking? Exactly. And how we're doing fundraising is also changing fast. So we're looking more around like people who want to learn on a regular basis. I know we say that as universities that we're hoping or we're working towards having lifelong learners, but we're genuinely looking at people who have our values, our lifelong learners, and they want to make an impact on the world. Otherwise, they'll give a for-profit sector doing their own impact in the world, or they'll be

29:24in government doing that kind of impact in the world that representing the United States or national interest or protecting us. In our case, we want someone who's interested in the mental and profit sector as a sector, as the primary reason. They actually are interested in learning, and they genuinely want to make an impact. The rest of the things we can learn from each other, they can do that, and then six months later the job will change. They'll have that flexibility of learning and adopting. So hey, we got to talk ROI, because like, let's flash forward. You've implemented these AI agents. We're a year in. How are you going to say and know that the work you did was successful? You've spoken like my colleague Dan was the CFO. Like it's like my president and the board. So absolutely, what we're doing is we're trying not to go into the business of doing things that are cool, but like to look at it from the perspective of what's the ROI, and if we can't quantify it now, how much time do we need for this pilot to either sunset,

30:25or like to actually turn to something that we can quantify the impact of having it on the team. And my best example, I can think of is sender blocks. Going back what you said about like, you're starting from the problem, sender blocks been there for like almost a hundred years, they used to be like an actual block that was heavy, that was not stable as a structure, and was expensive to produce. And to solve for these three things, they made them hollow in the middle wider, they became cheaper and lighter. That is the kind of things we're looking at. As opposed to, and it's one of my favorite products in the world in theory is Google Glass. It's the most advanced technology we ever had way before any of us imagined it, but at that point, they were not solving a problem. They were just bringing technology in a place that all these technologies can be brought together, but they didn't solve a human problem at the time. And that product was just almost no one. Or like people started shutting it down before it in some municipalities and cities and restaurants or whatever before

31:27it was commercialized. So when we're looking at ROI, we're looking at what are we solving? And anyway, how did we quantify that before? So part of the reason why we picked also the human related titles for comparability, how did we do that when it was a research assistant announced a research coordinator? So we're looking at the quality, we're looking at efficiency, how much we're saving in terms of hours. We're looking at inclusively because part of like the approach is if you just work on the efficiency, then you get from having 10 researchers to three researchers and just produce the same output. If you want to be more inclusive than instead of using the research power that you had with the humans to produce only for a tier of donors, you can produce the same quality for every person that you work with. For every volunteer given that tutorial treatment that we all aspire to be able to get in our own institutions. And then specifically because we're an advanced workshop, they're giving level. So on the fundraising part or the monitor part, what's the portfolio velocity? Are we moving

32:32faster in conversations? How are we moving from identifying people? How we're if we're using in the future predictive analytics? How can we quantify how much time are we trimming from searching some funds for days to like hours of getting who should we talk to next? And then how much are we moving from identifying that vein to like reaching out to them, qualifying them and so forth? You know, Joe, as we wind this down, I'm putting myself in the shoes of the leader who is listening to this conversation and sitting there saying, you know, I really want to do this too. What's your advice to them? Like, what is their very first move that they can go do this afternoon? It's something I live by every day. I start with writing a personal position statement. I wish I did that before I started downloading absolutely an iPhone, but it's something personal. So I was okay with it. But working on something like this is like a technology that will have a

33:32huge impact if you have children, how your kids work or not. If you have colleagues, how they interact with each other and like the future of our community and our mental health and well-being in terms of productivity, how they interact with donors and how efficient and how good of stewards we are for the resources they trusted us with. So I sat down and I heard something very private. I'm not sharing it with other people, but I go back to it on a regular basis and try to be true to what I'm comfortable with, what I'm not comfortable with and things that I think we shouldn't be using to be the best advocate there is for that. Once I completed that, I still repeat a process on a regular basis. I started one pilot at a time. I don't care about what anyone else is doing. So I don't care where we are in terms of like we're top of this space or we're not using the space at all. We might be bypassed by everyone else. It's more about us and our base and doing things in a solidified way and move to the next step. So I'd start with the pilot or bring in some team members

34:38specifically people who are skeptical about this technology, pick one thing at a time and then solidify that or spray it and then look at the next and then whenever you're doing that, you start seeing patterns. You get to see what you like and you dislike how your team appetite goes and then adoption and then you can slow down, you can accelerate. As long as you have clear school, you have the human oversight and you have because which you can decide to shut down the project anymore. Yeah, that's that is the blueprint friends like right there. That is the blueprint. Amazing. Well, Joe, it the last half hours just flown right by. I so appreciate your time and chatting and giving all this great information to our pulse listeners. I have to imagine someone's going to say where can I find Joe because I got to ask him 20 more questions. Is LinkedIn the best place to find you these days? Absolutely. Just add me to LinkedIn or or teach out if you have any other way to reach me just like do that as well. So grateful for you Mallory. This is absolutely amazing and you're one of the most

35:39of the people I'm at really. Like the way you approach this has been amazing and the way you're looking at the future of our industry is really amazing. Thank you for having me. Thanks, Joe and listeners. We'll catch you next week. Bye-bye. Thanks for listening to The Higher Ed Pulse. The show is a proud member of the Enrollify Network home to higher ed's largest collection of podcasts. Enrollify is where higher ed goes to grow. So be sure to check out our other podcast webinars, video clips, blogs, and more. Visit Enrollify.org and subscribe for all the latest details and content drops that directly to your inbox. Enrollify is brought to you by element 451, the digital workforce platform for higher ed. Learn more at element 451.com.