Campus Technology Insider Podcast June 2026
Episode: Why AI Readiness Can't Wait
Host: Rhea Kelly, editor in chief, Campus Technology
Guest: Jay James, cybersecurity operations manager, Auburn University
Episode Overview
In this episode, Jay James discusses closing the AI literacy gap, offering students hands-on AI-embedded work experiences, building institutional AI readiness, and more.
Key Questions & Takeaways
Why can't AI readiness wait?
AI adoption is accelerating faster than workforce preparation, and students who graduate without meaningful AI fluency risk entering the workforce already behind.
What does effective AI preparation for students look like?
Give students real work, real tools, real accountability, and real outcomes, with AI embedded where appropriate and opportunities to validate, challenge, and act on AI output.
How should institutions get started with AI readiness?
Start with a use case: find one program where AI-embedded work is possible, identify a faculty member ready to try it, pilot the approach, document what happens, and use what you learn to shape broader policy and strategy.
What support do faculty need?
Create a structured, ongoing faculty AI development pathway that moves instructors from using AI themselves to integrating it into their disciplines and helping define what AI readiness should mean for students.
How can institutions keep AI preparation aligned with workforce needs?
Maintain ongoing, bidirectional relationships with employers through mechanisms such as industry advisory councils, internships and co-ops, and AI-embedded capstone projects.
Topic Index
00:00 Podcast Introduction
01:00 Keynote Roadmap
02:18 George Story Setup
04:22 Why Readiness Matters
06:12 Workforce Data Reality
10:12 AI Fluency Surge
14:55 Opportunity and Equity
17:10 Cybersecurity Skills Gap
20:22 Auburn SOC Model
22:32 George Phishing Dashboard
24:54 Five Ingredients for Success
29:08 Scaling Beyond Cybersecurity
30:58 Policy and National Strategy
33:24 Campus AI Versus Teaching
35:54 Readiness Levels Framework
37:37 Four Move Playbook
41:46 Closing Call to Action
46:00 Podcast Outro
Transcript
Rhea Kelly 00:08
Hello and welcome to the Campus Technology Insider podcast. I'm Rhea Kelly, editor in chief of Campus Technology, and your host. At our May 2026 Tech Tactics in Education conference, keynote speaker Jay James, cybersecurity operations manager at Auburn University, highlighted the widening gap between students' AI literacy skills now and where the workforce needs them to be. Educators and practitioners are uniquely positioned to close that gap, he argued, by offering students a chance to do real-world AI-enabled work with real consequences and real learning. For this episode of the podcast, we're presenting the recording of his keynote entitled "From Classroom to Workforce: Why AI Readiness Can't Wait. I'll let Jay take it away from here.
Jay James 01:00
Good morning, everyone. I am glad to be here with you today. And over the next 40, 45 minutes or so, I want to take you through three things. And so, first, I want to talk more so about the data. What is actually happening in the workforce right now? What employers are facing, why the urgency around AI readiness, and why is this real and not overstated? And secondly, I want to tell you about a real story. You know, what it looks like when an institution builds an environment where students do genuine AI-embedded work. And also, too, I want to talk about what is produced on the other end of that. And lastly, and third, the playbook. So, leaving you with four concrete moves that any institution, regardless of size or budget, can take to make a effort in closing the gap between where your students are and where the workforce needs them to be. So, what I want for you today is clarity and momentum. And by the time that we are done, you should know at least somewhat where your institution stands and exactly the moves you should make first, and who you need to call to help you on that journey. But with that, before I give you all of the data, there's someone I want you to meet. And that someone is George. To the right of the slide, you see a picture of George and I at the Student Involvement Awards at Auburn University. He was fortunate to win the award for the campus, and I am very proud of him and the work that he's done at Auburn, but the work that he's also doing now. When I first met George, he was a college student working part time in our security operations center. He was a computer science student who had the curiosity, drive, and access to a program that gave him real tools in a real environment. I remember his first week. He was there with real tools in the network, with real alerts that we had coming into our SOC to respond to, with real threats we had to respond to on a daily basis, and most importantly, there were real consequences in the decisions that he made. I watched him take a breath, lock into the screen, and get to work. No script, no case study, just a student with a real opportunity. And that moment really stuck with me because I realized that we built something that many students, institutions haven't at the time. It was a place where all of the students could show up, do meaningful work, and grow in ways that a classroom alone simply cannot provide. So today, George is doing the work that many seasoned professionals, from my experience in, in my realm, they can't do because we gave him a chance to learn this AI-augmented security operations before most organizations even knew that was a thing. So I'm going to come back to George, hold on to his story, because everything I'm about to say, every data point, every policy, every signal, every call to action, all comes back to students like him, students who are ready, students who are waiting, and students who just need someone to open the door. So let me ask you a question, and I want you to stick with it and sit with it for a moment. Who are we preparing our students for? We build programs on what the workforce needed five years ago. We designed assessments and competencies that have honestly aged. We graduate students in a world that has already moved past assumptions we made when we designed their programs. And here's what makes this particularly hard. We don't always know it's happening, right? We have curriculum drift that is slow. The gap between what we're teaching and what the workforce needs doesn't announce itself, and it shows up in different places like the first performance review, or it shows up when the hiring manager says, "Well, the candidate looks great on paper." It shows up where graduates who get the job and then they spend their first year catching up on skills they should have already had.
Jay James 05:31
The workforce students are walking into in 2026 is not the workforce we design our curricula around, and the AI disruption curve didn't give, give us a warning; it just arrived. And the gap between where our students are and employees need them to be, it's widening. It's widening fast, and it will keep widening unless we make a deliberate choice to close it. So we're past the debate if AI matters, and we're past whether students should be exposed to it. The question is now: How fast do we move, and whether we move enough or with enough intention to actually make that difference? So that is what today's conversation is about. All right. So now let's get into some of the data, and here's where we are: 79% of workers say they feel unprepared to use AI at work. Not uncertain, unprepared. And 65% say their employees have never provided a single hour of AI training. That is two-thirds of the workforce never a single hour of AI training. And I want you to sit with what that means for a moment. These are people who show up to work, they saw AI being deployed around them, and they were never given the tools, the time, or the support to develop the skills they needed. The organizations moved; the people were left to figure it out alone. And here's the contradiction that should really keep every educator in this room up: 78% of the organizations are already using AI, but 92% plan on increasing that investment by 2028. So almost universal adoption on one side, based off of those metrics, and almost no preparation on the other side, and the gap is not closing on its own. It's really widening because adoption is accelerating faster than informal catch-up efforts can match. So employers are not closing that gap fast enough, they're hiring, hoping, and somewhat struggling, and they are looking to us, to higher education through K through 12, to solve a problem they do not have the infrastructure to solve themselves. That gap lives in our classrooms, and if we don't close it there, it doesn't get closed. Every graduate we send out unprepared is another person who will spend the first years at work trying to catch up to something that we could have given them before they walked out of the door. And unfortunately, the metrics don't get much better if we look at Workera's 2025 State of Skills Intelligence Report, it found that only 14% of employees believe their organizations are on track to develop AI skills they need, even though 99% of organizations claim to use AI in some form. 99% claim adoption, 14% believing they're actually being prepared. That gap is a crisis of trust. And employees know the tools are there. They know the investment has been made, and yet they still don't believe the skills are being built. So organizations adopted AI tools because those tools were available. We see so much of so many different tools coming out every single day, and we're seeing whether it's their peers or their competitors are adopting them immediately. So the issue is what most of them did not do and still not have done is invest in the human development side at anywhere the near scale of them implementing these technologies. They built the runway without teaching anyone how to fly, and that trust gap does not start at the workplace. Students who leave our institutions without genuine AI fluency arrive at their first job already behind. And here's something to think about as well. Students, they know they're behind. They can feel that gap. I experience that with my students all of the time, and the students within our SOC and our workforce development program. They see peers who came from programs that invested in real AI fluency, and they understand immediately that they are starting from a different place. You know, beyond just that professional disadvantage that they have, it's a confidence wound that takes time to recover from. We can prevent that. The institutions in this conversation have the power to change the outcome entirely, and we have to take this problem seriously as a question of preparation and trust, and act on this with urgency as the timeline demands.
Jay James 10:28
So now to get into AI fluency a little more, when we are looking at this pace of shift, it's really staggering, and the number of workers and roles where AI fluency is explicitly required grew sevenfold in just two years. So we went from 1 million in 2023 to 7 million in 2025. That is a labor market shift happening faster than any curriculum revision cycle was designated to accommodate. So when we have 88% of organizations who now use AI, which is up from 20% in 2017, a massive jump, only 1% of leaders consider their companies truly mature in AI deployment. So we have this adoption without mastery everywhere, through every industry, through every location, all throughout the world. And for those of you that are in the K through 12 wondering, is this something that I should worry about? The pipeline that we are creating does not start at the age of 18. Students who graduate from high school without any meaningful exposure to AI thinking, AI tools, or AI ethics already arrive at college behind. And when those students arrive behind, the higher education system has to catch them up while simultaneously trying to advance them. So to say this more plainly, if the K through 12 doesn't build a good foundation, the higher education will spend its time on the remediation of that instead of advancement. We are one pipeline from K through 12 all the way through the workforce, and if any part of that pipeline is broken, the whole thing will suffer. The World Economic Forum tells us that 39% of workers' core skills will change by 2030, and AI will transform 86% of businesses. That countdown has already started, and every year that we wait, it is another year where graduates enter the workforce unprepared in a world that honestly exists today. So let me give you a number that really woke me up when I first read it: 5.5 trillion dollars. This is the projected economic loss from AI shortage by 2026, which is this year. Over 90% of global enterprises are expected to face critical AI shortages, and companies budgeted for AI transformation, bought software, and built these, you know, strategy decks around it, and then discovered they do not have people with the skills, as we said before, to make any of it work. So once again, these tools exist, and the investments have been made, and the humans are not ready. Many organizations do have the vision, so I don't want to discount them for that. But they are struggling because the talent pipeline did not produce what was needed, and they are waiting on the programs that we design, on our graduates that we send them, on the decisions that we make, and about what belongs in programs and what gets left out. So something else I find striking about this number is not just the scale, because 5.5 trillion is almost impossible to fully comprehend. What I find more striking is the timeline. This isn't a warning about 2035, 2030. It's somewhat a projection about what happens if we continue on this current path for another decade. It's looking at this year, right? We have a shortfall that is already here, and the losses are already being counted. And every institution that graduates another class of students without genuine AI fluency, is really contributing towards that number. So you're starting to see a theme here with all of the data points collected within the industries, in, within our economy of what we're seeing forward. So if someone says to you, we can get to this next year, you need to tell them that we are already in a hole. The clock ran out while we were deliberating and debating whether, whether to include AI in our learning outcomes or not. So now let me flip this around because this is also a story of extraordinary opportunity. We sometimes spend much time on the urgency that we also forget to talk about what actually is possible for the students that we serve, and thinking about how are we sending them out in the world. If we look at PwC's 2025 Global AI Jobs Barometer, they found that AI-skilled workers command 56%, 56% wage premium over comparable roles, double the premium just from one year prior across every industry.
Jay James 15:35
So we are talking healthcare, manufacturing, education, finance, every sector, job postings required AI skills grew 7.5 percent year over year, and even as total job postings fell by 11 percent, the labor market is contracting and shrinking everywhere else except for where AI fluency lives. So the median annual salary for AI-related roles in Q1 was $157,000. So I want to be concrete about what it means for a, for example, first-generation college student, or for a student from a community that has historically had less access to higher-wage careers. A wage premium for AI fluency is not concentrated in a narrow slice of elite roles at large tech companies. It runs across industries, it runs across geographies, and it is accessible to any graduate who comes out of their program with a genuine demonstrated AI skill. That is not, that is not something that we should take lightly. It's an equity argument that is much as an economic one that we have. Our institutions that build real AI literacy into their programs are opening doors for students who have historically had fewer of them, and the institutions that do not are quietly assuring that access gap widens. So we have the power to determine which side of the divide our graduates land on. That is the work that we have to do. So I do have one more number for you before we start to shift gears and talk about some of the work that I've done and some of the things that I have been seeing in this space, especially for those who are leading institutions with those security programs. And this hits home for me, especially given the nature of cybersecurity in the world that we are in today. 4.8 million unfulfilled cybersecurity jobs worldwide, a 19% increase just from the year before, and the industry is nearly twice its current headcount, or it needs twice its current headcount just to meet this existing demand. So, one of the more popular cybersecurity organization, Fortinet, they had a 2025 skills cybersecurity gap report. They found that 54% of breaches are attributed directly to the cybersecurity skills gap. So if we hire, we would have less of those incidents. More than half of all breaches are happening because we have not produced enough prepared people to stop them. So the attackers have gotten much more sophisticated. The pipeline did not produce enough skilled people, and this is really showing you how much of an education problem in the space that we have. So here's the part that doesn't make headlines, but it really should. This is a solvable problem. You know, the tools to train people exist, and the curriculum to, that is develop the right skills, that actually does exist, and I've seen it in many spaces. And employers' demand to absorb prepared graduates exists at a scale that we have almost never seen in any technical field. So the gap is a talent production gap, and the talent production is what educational institutions exist to do. We think about what it means that 54% of breaches trace back to a skills gap. That number represents real organizations that got breached. So real data that was exposed and real consequences for real people. AI is rewriting what the cybersecurity job fundamentally is. The analyst can work alongside AI, so they can be someone who not only prompts but validates and challenges it, and act upon its output. This is a completely different professional than someone who does not have those skills. And having someone looking at this number, and I know many of you know many organizations and the staff that they need and how they have gaps similar to this 4.8 million. When you empower a student who can work alongside with AI, you are giving them what they need to help that number in ways that the industry cannot do on their own. So we have to think about this for the skill gaps that we already have and how AI can help empower that. So now to talk about one of my favorite topics, Auburn's security operations center. We made a deliberate decision, and I want to be honest about it because it was not an obvious choice. We staffed it with students, real students doing real work on a real enterprise network that supports 10s of 1000s of users.
Jay James 20:46
Some colleagues thought it was a mistake, and that argument against it is larger, logical, right? You know, enterprise security is too important to entrust to people who are still learning. The stakes are just too high. But the argument that I made then, and I will make time and time again: you cannot learn security by watching security. You cannot develop the judgment in real work that requires you without doing the real work. So, if we want graduates who arrive at their first job ready to contribute on day one, we have to give them real work before they graduate. And I can admit that first year was really terrifying. We've made many mistakes. Our students made mistakes. But we built processes, then revised them. We had moments where I questioned whether we had made the right call to even do this, but we stayed with it, and we established supervision frameworks that allow students to operate meaningfully without creating unacceptable risk. There will be risk, but we want to make sure that we avoid those unacceptable risk in our environment. So by the end of the first year, we had something that I had not anticipated, because we created this to help support us at Auburn University to be more secure. But what came out of it were graduates who were there that employers wanted to hire: people already producing value from day one in a model that I knew can be replicated not only in cybersecurity, but across different disciplines as a whole. So let's go back to George for a moment. George was assigned a project months into his time at the SOC, and this is after we just implemented some new AI-related technologies. We had a real operational need at the time, where we needed visibility into our phishing activity, which many of you know about, across campus. So we needed to know who was being targeted, what patterns were emerging, and how the landscape was shifting over time. In many organizations, that kind of analysis can sit in a queue for weeks. You know, the output is usually spread out in a spreadsheet that gets emailed to a few people, and honestly, probably never will get looked at again. George took it upon himself to approach it differently. He used AI to query, clean, and synthesize the log data, and create a dashboard out of it. He used AI to identify patterns that would have taken human analysts days to surface. And then, and this is the part that I want you to really remember, he went back to and validated that data. He cross-referenced the AI's findings against what the raw data actually showed. He asked whether those patterns made sense, whether there were alternate explanations, and whether those conclusions were defensible. What he produced was visual, executive-ready phishing trends on a dashboard built in the fraction of the time it would have taken a senior analyst to be able to put it together, and then we shared that directly with our leadership. And it was useful enough to actually change a lot of our decisions that we made. Now we have evolved our processes since that dashboard was created, but it was something that was very consequential in how we developed our strategy moving forward. This was something that a student did. I remember looking at what he put together, and I was thinking, this came from a student who was given the problem, tools, and trust, and who rose to meet all three of those. So this is a moment that I knew the model worked, and this was a moment that I started to think about how can we replicate this, but not just in our SOC, but anywhere. So, what made this possible? And there were five things that I wanted to really point out here. First, real tools in a real environment. George is working with our actual analytics deployment against our actual data on our actual threat landscape. The stakes were real, like I've mentioned before, and that reality changed how people engaged with their data and their work in ways that no simulation that you might have can fully replicate. I have been an educator in several programs that we work with simulations that are very beneficial, but there are some things that a simulation cannot replicate in a live real environment. Secondly, AI embedded from day one as the baseline for an operating environment.
Jay James 25:47
So the question was really never should you use AI. It was always how do you use it well, how do you evaluate its output, and how do you know when to override it. And I wanted to add here as well, every process that you do in your organization does not need AI embedded, enabled, or integrated. But for those that do with the use case, you want to make sure that you're using these factors to be able to use it well and use it in an efficient way, leveraging AI literacy. So third, and this is my favorite because I am really passionate about mentorships, having a structured mentorship. So someone invested in the professional development, not just checking whether their work cleared the minimum bar. There is a real difference between a supervisor who tells you what to fix and a mentor who helps you understand why something needs to be different. Fourth, we have a culture of failure tolerance. So George could get it wrong. George could iterate through the issue, and he can try again in an environment that had his back. The real learning is requiring you to have a space where you have the ability to fail safely. We built that deliberately. So, what that looks like is having tiers of activities, and those different tiers have different risk thresholds. Those with the lower thresholds, you can give students more work in that space because if they fail, it won't be that detrimental. So you need to have that conversation with your team of how do you build this culture of failure tolerance? And fifth, having visible and consequential outcomes. That dashboard that went to our leadership, his work had already a real audience with real stakes, and that changes everything about how a student shows up and how that grow and how they grow, and also the network that they build. Now, I do want to be honest about something. None of these five things are required with a large budget, right? We do see, and you might be wondering, well, you need a large budget to have all of these different AI tools and to be doing all of this work that is integrated, but we didn't start that way exactly. We start with a lot of tools that we had accessible to us. None of them also required, you know, a new building, a specialized lab, grant funding initiative, which grants do help. But what they required was a decision: a decision that students would do real work, and a decision that AI would be the baseline, not the advanced topic. The decision that mentorship was meant to be a genuine investment in the person's personal development. So, once again, I mentioned this before. I wanted this to be more than just a SOC experience. You don't need to build an AI-augmented cybersecurity SOC to apply this model. The SOC is the implementation. The principles transfer to any discipline, any institution, and at any level of the education. A journalism program where students use AI research on live editorial projects, real stories, real deadlines, and publications. A business program where students build AI augmented financial models for actual local nonprofits. An education program where pre-service teachers design AI literacy curricula that real teachers will try with those students. And here's what I find remarkable about the institutions that have done this very, very well: the discipline almost does not matter. What matters is that the work is real. So hopefully you see this theme of having this real experience for your students and going beyond in breaking out of our comfort zones of actually giving them something that they can actually make real consequential changes. So whether the stakes are real, whether someone is genuinely invested in the students' growth, this is an area where we need to put time into deliberately and intentionally looking in these different spaces of how we implement these technologies. So, from my experience, the moment students understood their work would be seen by a real audience, that is where everything has changed and the quality of thinking went up. The questions that they asked went deeper, and the ownership they felt over their work became visible. So the same principles that I've repeated over the past few slides every time, having the real work, real tools, real accountability, and real outcomes.
Jay James 30:57
When those four elements come together, the students stop learning about their field and start practicing it, and this is exactly the change that the workforce is waiting for. So, to think about how we're looking at this as a country, the federal government put a very explicit stake in the ground on this issue. In April 2025, the president signed an executive order for advancing artificial intelligence education for American youth. It established this national strategy to promote AI literacy in K through 12, directs federal agencies to prioritize AI pathways, and calls on educators, industry leaders, and employers to partner with building programs that equip students with essential AI skills. So, in February of this year, the Department of Labor released a formal AI literacy framework. So, I highly recommend you looking at that. It has five foundational content areas, seven delivery principles, and several guidance for workforce development on how you integrate AI literacy into their programs. This is a federal government operational operationalizing this policy, and the AI, America's AI Action Plan explicitly frames K through 12 and higher education as a core infrastructure to the national AI competitiveness strategy. So, when Washington calls education institutions infrastructure, they are making a claim about accountability. And infrastructure is expected to work, right? When bridges fails, we don't shrug and say the, the bridge program, you know, is too hard. We investigate, we rebuild, and we invest in what is needed to make sure that it doesn't happen again. That same standard is being applied to talent pipelines, and institutions that are not producing AI-ready graduates, by their standard, are failing this infrastructure. So the message from Washington is clear: we are the pipeline. And if that pipeline is not producing AI-ready graduates, that is a national vulnerability. Every institution in this conversation is a part of that infrastructure, and infrastructure either works or it fails. So let's talk about within our institutions. So AI adoption within our own institutions have jumped from 49% to 66% in a single year. From experimentation to institutional strategy within that 12-month time span, so there is a distinction getting lost in the adoption numbers that I think most, is a most important distinction within higher ed right now. An institution that uses AI and an institution that teaches students to use AI are doing two different things. Using AI to optimize the administrative operations, to personalize student communications, and to improve retention modeling, is a legitimate and valuable use. But it doesn't transfer to the students themselves. A student who attends an institution that uses AI extensively may still graduate with no meaningful AI fluency. And this is because the AI was used to serve themselves, and no one made sure they could use it themselves. So the question I keep coming back to is: Are we only using AI in our institutions, or are we also teaching those students to use AI in theirs? So here's a practical test that I want you to consider and to think about. Think about the last three significant AI investments your institution made. Were they primarily about making operations more efficient, or were they primarily about putting students in direct contact with AI tools in a structured, assessed learning context? And if it's the former, that tells you exactly where the gap is. Using AI well as an organization is a legitimate goal, and building students who can use professional, use it professionally is a different goal. So both matter, but only one closes the readiness gap. So you probably heard the term of scaffolding AI literacy. So when we're looking at scaffolding AI literacy from awareness to application to critical evaluation, it honestly takes design, faculty development, and an actual institutional decision that this is what it means for us to get a degree here. That decision is the missing piece, and only we can make up for that. So I want to also give you a framework that you can orient yourself to, to honestly assess whether, where you're starting from. So level one is awareness, talking about AI, faculty experimenting it individually, maybe a policy draft, students using AI tools informally, and you may or may not know the extent of it. Level two is integration, so AI embedded deliberately into a specific course, an institution policy may exist, some faculty may have been trained, and you are collecting outcome data on AI fluency.
Jay James 36:30
This is where intention becomes very visible. And level three is the transformation. So, AI literacy runs through the curriculum as a thread that connects what it means to graduate from your institution, and students leave with demonstrated skills employers can verify. Industry partnerships are active and bidirectional, and this is where impact becomes systemic. So most institutions right now are at level one, some are reaching level two, and very few are at level three. So the distance between level one and level three is measured in whether leadership decides this is a priority and acts accordingly. So the institutions that move fastest make the clearest decisions and then build towards it with intention. And I want you to think honestly about where your institutions sit right now — not where you want to be, but where it actually is. Are you at level one, level two, or level three? Because that honest assessment is really where you start to build that strategy and where meaningful change starts. So here's the first move for institutions that want to lead. You know, you don't start with policy. You start with using, use case. So when students is, so when students are here, when something is new and carries risk, the instinct is go get a policy first. You know, convene a committee, draft a framework, and get through shared governance, and then pilot something. That process is not wrong, but you will need that policy, and if you make that policy a prerequisite for the use case, you will still be working on that policy when the window closes. So institutions that are leading on AI readiness right now start with one faculty member that's willing to try something, one program that are willing to put the students in contact with those real tools, and one administrator willing to say we don't fully have the full framework yet, but we are willing to have enough to start. So we really started our SOC with asking that question of what if we gave our students real work, and you want to make sure that you start with your use case. You find a program where real AI-embedded work is possible, and you find the faculty member who is ready. Pilot it, document what happens, learn from it, and use that proof to build policy and a broader strategy around it. So students can only be as AI ready as their instructors are willing and equipped to take them. And if we are not addressing the system design, we are not actually addressing the problem. So every institution needs a faculty AI developed pathway that you create that is structured, ongoing, and supported. Moving faculty from users to integrators to people helping their institution think of what AI readiness really means in their discipline. It does not have to be expensive. All it has to be is intentional. So when we're looking at the metric from the Association to Advanced Collegiate Schools and Biz, of Business, they published guidance in 2025 on AI literacy scaffolding as a core institutional competency, and the argument applies far beyond business education. I think about where, what we require of students and why, and we require writing, you know, because we believe that the ability to communicate is fundamental to the function, functioning in this world, and we require quantitative reasoning because we believe that of the ability to work with data and numbers. We need to be looking at AI and big data in the same way, and looking at AI literacy within that category, because the ability to work with AI tools is quickly becoming as fundamental to the function in today's world as everything else. And the question is not whether we will wait another decade to figure that out, but it's more so about the decision to just start it now. And that final piece of the playbook that I wanted to talk about is a genuine, ongoing, bidirectional relationship with industry. You cannot design AI-ready graduates in isolation, and you cannot sit in a quarterly committee meeting and produce what employers actually need today without knowing what they need today. So we not, we need to think beyond what they might have needed a few years ago and what we planned for, but more so of what happened this semester.
Jay James 41:14
PwC found the skills sought by employers are changing 66% faster in AI-exposed roles than any other role, so we as institutions have to build a genuine feedback loop between what they are producing and what the world needs. That is the result of a decision to stay in a relationship with people on the other side of the bridge. So we have this pipeline. We have to create a relationship that we sit and maintain, and we can do this through things like having our industry advisory councils, having co-op and internship pipelines, and then also creating these AI-embedded capstone projects. So I want to end where we started with George. After one year after the phishing dashboard that he built in front of our leadership, one year after a college student produced analysis that changed how a major university thought about its own security posture, George had an offer before graduation. So we have what we need to build what we need for the students of our future, and we have all of the parts starting with the raw material. You know, the students ready to rise if someone gives them a chance. We have the faculty who do care deeply about the people in their classroom, and we have the institutional standing to create environments where the real work happens. The only question is here is whether we decide it's time or not. Higher education must train people to critically evaluate the AI output, cross-check it with human expertise, and know when to not trust the machine. And that is something that only we can do. You know, the workforce was not waiting for us to get comfortable, and employers are also not waiting for us in our internal companies. Students are not waiting for us in those policies that we are creating, and it's important to know that AI isn't changing what education is for. Education is going to be help, help being a enabler to leverage problem-solving and critical thinking. So we are in a world where AI fluency is not optional, and the gap is between being prepared and unprepared is really coming from us and what we already do in our fields. So I do believe there are thousands of Georges right now, right? The students have the same combination of extraordinary potential, genuine curiosity, and a willingness to work, and a need for someone to give them a real opportunity. Our actual job, the reason that our institutions exist, is to build an environment where George can happen, right? Where extraordinary students can get extraordinary opportunities for all of them and everywhere. So we are the bridges. We are the bridges that build, and the bridges that we build or decide not to build will determine whether the next generation walks confidently into a AI-powered workforce, or if they will get left behind. So AI readiness is the central educational challenge of this decade. We know how to do it, and we have the proof. We just have to decide with real commitment and real sources. And that is the time that we need to think about. So I want to leave you with three things before we close out this talk. There's three things that you can do that has no budget approval, no policy commitment needed, no permission slip, just a little effort. One, identify one course or program at your institution, where AI-embedded work is, is possible. Two, find one student who reminds you of why this work matters. So, a George at your institution. Tell them what's possible and give them a real opportunity. And three, share what you're building. The institutions leading on AI readiness are not hoarding their playbooks, they are publishing them. The problem is too big and too timely for any institution to solve alone. So your peers need your stories. So with that being said, I thank you for being here, and I really, truly thank you for the work that you're already doing in your spaces. Thank you for taking this seriously and thinking about the possibility of your institutions that could be a part of the answer to really one of our most important workforce challenges of our time. Thank you all for listening to this talk today and looking forward to seeing what you produce on your end.
Rhea Kelly 46:00
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.