AI is changing how organizations approach learning and development, but questions remain about what’s really working and how L&D teams can apply AI effectively.
In this episode of The Business of Learning, recorded during the Training Industry Conference & Expo (TICE) 2026, Tom Whelan, Ph.D., facilitates a panel discussion on what’s working with AI in L&D today. Claire Cole, Mike Groesser, Maria Rodriguez, Ph.D., and Artrell Williams, CPTM, discuss navigating AI adoption, supporting learners and preparing for the future of work.
Check out the episode now to learn:
- Strategic capabilities that will remain uniquely human in an AI-driven future of work
- How early AI adopters can help inform and accelerate broader AI strategies
- How L&D teams can support learners throughout their AI adoption journey
More Resources:
- [Toolkit]: AI in Training Toolkit
- [Wiki]: AI in Training
The transcript for this episode follows:
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Tom Whelan, Ph.D.: Good morning, everybody. I hope you’re all ready to discuss more about AI and how it gets used in L&D. I just wanted to begin by saying over the past three years, Training Industry has done a nauseating amount of research on AI. What organizations are doing with it, the different things you can do with it, how learners feel about it. How do you sell it? I mean, all the different things. The token economy that Nick mentioned, like, one of the biggest gotchas, I think, affecting a lot of organizations right now. But in all the research that we’ve done, there’s three themes that I wanted to talk about before we introduce everybody on the panel. The first is that AI is a continuum. It’s a continuum of attitudes. It’s a continuum of applications. One of the things that constantly recurs in the research that we do is if you ask people how they feel about AI, how comfortable are they with AI, what is their organization’s disposition to using it? You always get a range of answers. Yes, you get a good dollop of people that are, you know, gung ho and firing on all cylinders, but you also get a crop of people who resist it. And then there’s also a muddling middle that are just hanging out and I think waiting to see what goes on. But that is one thing that we consistently find. The next thing I wanted to point out is that I think it’s very easy to talk about AI like it’s this monolith of a concept. Like, when we invoke those two letters, we’re talking about one thing, and we’re not. Basically, we’re talking about the cereal aisle at the supermarket. If you go there and you’re like, “I need some cereal,” you go down that aisle, there are so many varieties. If you want something healthier, they have it. You want something really full of sugar and technicolor, they have that too. So anytime we’re talking about AI, we are talking about an array of different solutions meant to do different things, in addition to the kind of Swiss Army knife, large language models that were introduced three years ago. The last thing that I wanted to point out, and this comes from the research that prompted this panel, is that AI is very much not an IT problem. And I think if we think about it only as an IT or a technology problem, we’re missing a lot. I mean, it is a technology problem, let’s be honest, but it’s a culture problem for a lot of organizations. You know, AI affects the way people work. It affects the tasks that they do. In some places, it’s affecting the work environment. It’s invoking all these changes in how we work. So I think if we approach the challenge of AI as a cultural thing and as something that’s much broader than just, is there an API that we can get to work with this, the outcomes that we’ll get will be that much greater. And with that, I’ll sit down, stop yapping, and we’ll introduce everybody on the panel. Artrell, if you’d begin, tell us who you are, where you’re from and one word that would describe the AI journey that you’ve been on.
Artrell Williams, CPTM: I’m Artrell Williams, and I’m based out of Charleston, South Carolina. I do leadership development consulting, so I get to work with lots of different organizations in learning and development capacities, whether it be designing or delivering content. One of the things that I think of when I think about my own AI journey is agility, just being able to be nimble enough to navigate these spaces that I’m speaking about and knowing, just like Tom mentioned, that AI is not this one thing, that we’re using it across different functions, across different industries, at all levels. And so as learning individuals and professionals, we just have to be prepared to be able to speak AI in all of these spaces in which we go.
Claire Cole: Good morning, everyone. My name’s Claire Cole. I head learning and development at Bitly. I saw some Bitly short links around at TICE this week, so thank you, TICE, for using our product there. One word for our journey, with all things AI at Bitly, I would say is earned. We have a very high adoption rate, however you decide to measure that. I know we’re going to be talking about that a little bit later. But we’ve really worked for it, so I feel like we’ve earned where we are and how we’re using AI at Bitly.
Mike Groesser: Good morning, everyone. I’m Mike Groesser. I lead learning and development at Fidelity Investments for our technology function. I’m actually local, so I live in Fuquay, and the office that I work out of at Fidelity is right down the road on Davis Drive. So good to see all of you. I would say the word I would best describe Fidelity’s adoption of AI is measured. I’ll just leave that as a tease.
Maria Rodriguez, Ph.D.: Good morning, everyone. I’m Maria Rodriguez. I am out of Orlando, Florida. Have you been? Ever heard of it? I work with Carnival Corporation. It’s the parent company for Carnival Cruise Lines and seven other brands. And dealing with 150,000 employees, AI is giving us the ability to innovate. So that’s my one word.
Tom Whelan, Ph.D.: Thank you, everybody. All right, so we’re going to start our discussion with basically I think what we’ve already all been talking about anyway. We often hear that everyone is using AI, or that seems to be the drumbeat that we tune into. Yet, again, our data suggests that most organizations are using it in only a fraction of possible training applications. And so the first question I wanted to pose to the panel is, what do we think explains this gap between perception and reality? And how much of an actual problem is this gap if we think it exists? Mike, let’s start with you.
Mike Groesser: Sure. Happy to take that one. I think there’s really two things I would think of to answer this question. One is research and the other is relationships. So I can speak from my own experience on this one that I think all of you have probably tried to look and see what’s the research about AI for learning? What are the outcomes? Who does it benefit? How does it benefit them? What interactions matter most? Is it voice chat? Is it text chat? Those kinds of things, right? And I think what we’re seeing right now in just the general literature out there is very mixed. You’ll get very different responses depending on what you look at, and a lot of the research that’s out there is paid for, at least behind the scenes, by companies that benefit from you adopting the AI. So I think that’s causing some of the confusion for people like us that actually care about neuroscience and how people learn and what is the right way to approach learning. It makes it kind of challenging to know which tool should I use and for what purpose. I think we know that neuroscience doesn’t care about AI. The way people learn is the way people learn, and AI will probably help enable some parts of that, but there’s a lot of questions about how and why. So just as an example there, I’m partnering in our organization with some universities to do some of our own research to understand. And so I just encourage all of you, ’cause we need more research out there, if your company is willing to do that, work with some of these organizations that help you partner with universities ’cause we need more input on what’s actually working and what’s not. So I think that’s one. I think people have a hesitancy to adopt in certain areas, and it’s hard to prove to your executives if you don’t have hard data to say, “Hey, I’m going to spend a couple million dollars on this, and this is why I know it’s going to work.” You might have something from Anthropic that says it’s going to work, and your CFO will laugh at you. So that is the other piece then, is the relationships. And so I think if you are struggling with AI adoption in your L&D organization or getting the tools that you want, I would guess that your relationships with your technology organization and your finance organization probably need to be a little bit stronger. Those are the two key people that you need to get to know that you probably haven’t had to work with a lot in the past. Technology, because they are scared of everything you’re trying to do. If you’ve heard of Mythos, you understand why, right? So they know what these capabilities can do, and they’re terrified of it, and they’re trying to protect the organization. Your CFO cares because they know that everything is offered as a free lunch right now, and right around the corner, it’s going to cost a lot of money. We’re already starting to see that with certain tools and applications. So they want to know, what is the ROI going to be here, and how do we have price protection? Is this going to become part of a business process that we can’t get out of? And that’s some of the resistance I know that we’ve seen at least, and how we’ve taken a measured approach is let’s really think about what are we doing here? Are we sure that this is going to have the long-term value that we want it to have?
Maria Rodriguez, Ph.D.: I would add that, you know, with the partnership, you also need to partner with your privacy, your ethics, your cybersecurity teams, because some of your projects will touch all of these different sectors. And if you’re not considering all areas of this when you’re trying to bring a project to life, then you could be stuck. You may have the best intentions, but if you are not understanding the laws and the regulations or the risk that you may be bringing in with this project, then you wouldn’t get the adoption anyways. So make sure you’re having those relationships and speaking with various sectors of your organization.
Tom Whelan, Ph.D.: The next question I wanted to pose to the panel is that AI has proven to be both immensely useful but also annoyingly sloppy quite frequently, to the extent that it takes a subject matter expert to interpret the quality of many types of output. And we end up with this weird chicken and egg scenario where it’s like, how do you create an AI agent that’s really, really good? You need an expert to do it. It’s like, well, what happens when you don’t have the expert to create the agent that you need to train everybody else to do? Like, it’s a terrible loop. But what do we think learning leaders need to be vigilant about when it comes to how AI gets used in training so that we are staying on the right side of the AI divide and more or less keeping it human focused? And Artrell, would you like to tee off on this one?
Artrell Williams, CPTM: Sure. When you used that word vigilant, it made me think of Batman. We should give out capes. Everybody deserves a cape in this situation because we’re giving them a superpower that they have to learn how to use just like the superheroes we’ve seen the journey where they get the power and they’ve got to figure out how to use it, and it’s real clunky coming out, they’re hurting people and all that kinda stuff. We’ve got to understand that that’s part of the process, that in the beginning they might hurt somebody or something. So we need to have the right level of oversight, if nothing else. We don’t necessarily have to control it. But as learning leaders, we have to make sure that we’re not trying to be the gatekeepers of AI for the organizations. We shouldn’t be the ones that tell them how and what to use it and not tell them that they’re using it, but still using it within their learning programs. ‘Cause everything that we use has AI built into it these days. The learner doesn’t have to know that AI is there, but they should. They need to understand what role it plays, and the subject matter experts can come in and apply that critical thinking that we’re afraid that everybody’s losing. I don’t know if we’re losing it, but I know that it is shifting the way that it looks. And so we have to be prepared for critical thinking to be done a different way than it used to be in the past. But you have to have that human in the loop, and I love that Nick earlier mentioned that we need to empower the people. Let the people understand that AI is being used, help them understand where it’s being used so they can look for some hallucinations if that’s the case, help them understand how they may be able to use it as well so that it’s not just our own individual superpower. ‘Cause when I started using AI tools, I would just go in my office and bang out a 20-page report in, like, four hours, and my boss was like, “Wow, it used to take weeks to do this.” And other people who hadn’t learned about AI and spent as much time with it as I had were trying to figure out, how did he do this? Where did this come from? Is he now a superhero? We’re all gonna be superheroes. We all are superheroes. And we have to understand the power that we have, and then help those that don’t know as much about it, help guide them through the process of adoption.
Claire Cole: I’ll add too from a skilling perspective, we’ve all gotten that report from a colleague or from a friend. They’re planning a trip, and it’s like a 10-page-long document, and you’re like, “Wow, this is … They really worked hard on this.” And then you get into it, and you’re like, “What am I … What is this? Like, what am I reading? This means absolutely nothing.” So in our world, what I’ve found is from a skilling perspective, it’s not enough for us to focus on the technology. We also have to pair that with universal skill training, such as judgment, empowerment, as Artrell said, using our critical thinking skills to determine, how am I using this tool? Is this first draft? Am I using this for refinement and proofreading? Am I using this as a creative partner? Because it’s not enough to say AI just does the job. It does a portion of the job, and that’s where we’ve found a lot of help, or a lot of ways to help our leaders, is pairing the technical training with the universal skill training so that we’re not getting these wild outputs that mean absolutely nothing. And I love that you talked about hallucinations. My husband has just gotten into AI recently, and he called me the other day, and he said, “I got my first hallucination. I’m so excited.” And he was pumped because he had heard about it, and I was really proud because we talk so much about hallucinations. And what you want is you want your team to come to you and say, “I got one. I got a hallucination, and here’s what I did to fix it.” So training the universal skill side is just as important as the technical side.
Mike Groesser: You mentioned the sloppiness piece, and I think some of that is just natural and something that we need to be okay with. MIT recently hosted this L&D summit, and I had the fortune of attending it, and one of the things that they talked about was for your senior leaders especially, there’s sort of a progression that they need to go through to learn AI and to become comfortable with it, and the first step is play. And they said, “Who in your organization wants to play the least?” It’s your executives, right? They don’t want to do that. But it’s a natural phase that all of us have to go through. Probably a lot of you have. You’re playing around with the apps. You’re making Gandalf pictures. You’re doing stuff like that, right? It is fun, and this is how we start to understand what is this tool possibly able to do. But along with that play comes a lot of sloppiness, right? You start to get things with the 10-page report that has those em dashes everywhere, and you’re like, “I know what this came from.” I see the em dashes. So I think it’s just a natural part of the process.
Tom Whelan, Ph.D.: The next question I wanted to pose is that our data consistently shows that even high-performing organizations tend to be selective rather than exhaustive in their AI efforts. So those that are having success aren’t taking a spray and pray approach and just inserting AI into every nook and cranny of the organization they can. They’re being, kind of as Mike said, they’re being measured about it. They’re being careful. They’re understanding, I think rightly, that this is an investment. And as we’ve already alluded to, like, if you’re gonna stand this up long term, the economy underneath that isn’t likely to stay the same as it is now. So wherever you place those bets need to be the right places to put your chips. But kind of said another way, these high-performing organizations are seeking out a limited set of high-value AI use cases rather than putting it everywhere. So the question is, how do we decide when to say yes to a new AI initiative versus when to say not yet? And Maria, can you start us off on this one?
Maria Rodriguez, Ph.D.: Yeah, absolutely. I think you … It was part of the question, is that value. Where is the value? And if you can indeed show that value and the impact, that’s an easy yes, let’s move forward, because it does take resources. Yeah, it costs, and it costs people, it costs time, it costs technology. So the yes sometimes comes a lot easier because you can look at this as a, this is a really big value it’s going to bring to the organization, it’s going to innovate the way we do things. It’s a strategic move. It’s what we want. The not yet is broken down in several ways. It could be not yet because we haven’t considered all of the things we talked about earlier in regards to the risk and other things that need to happen. It could be not yet because we’re gonna be driving on a legacy system which won’t even be here for the long term. It could be not yet because you don’t have proper alignment. So your not yet is not a no, but it could mean, hey, we have more work to do. We need to look at our processes. We need to look at our alignment. We need to look at our technology to get to that yes. So when you get the not yet, when you get the, “Mm, let’s think about it,” and three months later and you have no answer, then go back and say, “Why not yet? What exactly is hindering this adoption or this process or this piece?” And make sure that you’re having those conversations. I think we said that at the very beginning. You want to have the conversations with the right people in the organizations based on your relationships, your connection, so that when you propose such a project, you know you’re having the adoption, the acceptance, the agreement and the approval of all the right departments and business units before you move this forward. The not yet can also mean it’s not enterprise-wide, but it’s really good for your team, and your team can take on the pilot. Your team can do the prototype. Your team can do the adoption so that you can then bring forward data to show if we scale this, this is what can happen. So it’s okay to have the not yet, because it costs to do the yes. And your company’s going to go bankrupt if it says yes to everything. Just think about it. The resources, the time, the effort, the energy, the strategic move, the project management, all of that is for the yes. So there’s nothing wrong with the not yet. Accept it, embrace it, and find how do you just make it into a smaller element and then take it to the organization. So don’t be scared of not yet. It’s part of life. It’s part of playing with the program, right? You start playing with it, you find what works, and then you worry about what the organization can do.
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Artrell Williams, CPTM: And it’s tough because you’ve got a different answer for different sized organizations. An organization’s size doesn’t necessarily mean the number of people that you have, but how large, how much scope you may have within your sector. Because you want to be fast enough to be able to get on board, and some of the smaller organizations may need to take note of what other larger organizations within their industry may be doing so that they can know where to hop in and hopefully learn from some of those mistakes. But then you also don’t want to move too quickly. And what I’m seeing from organization to organization is that you’ve got pockets of people who are using the tools on their own. And that’s kinda dangerous because if the organization hasn’t set some type of internal parameters about how AI is to be used, then you got a group of people over here using ChatGPT, and other people are using Claude, and other people Perplexity, and some of the other tools that are out there. And nobody’s on the same page, but they’re all vying to have their tool be the one that everybody else uses. But by then you’ve got these power struggles. It’s almost like AI cliques that take place. And you’re out of alignment. So when the organization finally does get on board and they’re like, “We’re using Gemini,” and none of the people have been using Gemini. So all of a sudden everybody’s gotta shift from where they were and where they’re comfortable, and probably where they’ve already dumped a whole bunch of your proprietary information into this new tool that finally the organization has said it’s okay to use. So we really have to still be quick enough to not allow the Wild Wild West to take place within our organizations, but still slow enough to understand what the impact can be on the various, whether it be departments or functions within your organization may be.
Mike Groesser: I was just going to add two quick things that I found really helpful as part of this discussion. Some of you, if you’re a big enough organization, you probably have an AI governance committee. If you’re smaller, you probably have one person, right, who’s responsible for that. But it’s the person you gotta get through, right, to prove that this is okay to do. And one framework that we’ve been using that’s really helpful is, I always love a good four box. So the two axes would be, is this everyday AI? So is this stuff that we’ve been using for 10, 15 years? ‘Cause AI’s not new, right? We’ve been using this for a while. So is it everyday or is it transformative? Is it the kind of thing that we can’t explain, we don’t know what it’s doing? And the other axis would be, who is this touching? Is this externally facing, like, is this something that’s touching external clients or customers, whatever it might be, or is it just internal and the impact would only be internal if something went wrong? So you can imagine the quadrants that that creates, and it just creates a much different discussion with your governance team of, “Hey, this thing’s pretty safe because it’s everyday and it’s internal only,” or, “This thing’s transformative and I want to use it externally.” You’re going to have a lot higher bar to jump over to be able to do that. And then the other dynamic, I mentioned the finance team earlier, but they do continue to be a key piece of this. Sometimes the evaluation framework that they’re using is they have a certain number of dollars to spend, and is what you’re proposing worth it? They’re looking at other opportunities that are coming from non-L&D sides of the house that are saying, “Hey, this opportunity is a hundred million dollars.” Maybe theirs costs five times as much as yours, but it’s worth it ’cause it’s a hundred million dollar opportunity versus 50,000. So it’s that balance too of understanding what are you asking? Like, is your ask worth the risk? And I think that’s the kind of thing that we as L&D have to get more and more used to doing.
Tom Whelan, Ph.D.: Absolutely. I was going to add as well to Artrell’s point about, in research, we called them shadow AI teams, these nefarious groups of people just going rogue and using whatever tool they want to. That’s not conjecture or a fairy tale. That exists. I’ve asked a question about that on a survey in Q1 of this year. And when you look at the results between, like, yes, this is happening all over the place versus, like, good Lord, no, we keep it under wraps, it was a flat graph. So that is 100% happening in many organizations. All right, the next question I wanted to pose to the panel is, from the vantage point of a practitioner, what’s one thing that the broader market seems to get wrong about what, quote, unquote, “AI adoption” in L&D actually looks like in real life? And Claire, let’s start with you. Bitly seems to be very AI positive.
Claire Cole: AI positive, but we’re not without challenges, right? So two areas that I see the broader market, and Bitly is no exception to this either, where we’ve gotten it wrong. Number one is AI adoption as only technology implementation, and number two, how we’re talking about AI. So the first one, AI as technology implementation, I think that’s where most of us start, right? We want to implement a new tool. Let’s go. Let’s have training. Let’s teach people the skills, how to use the tool technically. And our biggest challenges at Bitly have not been about the tool implementation. They’ve been about the people side. So how do we help folks shift their habits? How do we build trust with the new technology and how we’re using it? How do we help people feel empowered, to Artrell’s point, without feeling replaced? I think yesterday our time with Jess was a really helpful connection for me in understanding some other ways that we can be talking about AI. For example, if you have a mandate about we’re going to use AI, here’s the tool we’re using. Go, use it. Success is using the tool, right? And we’ve all heard those mandates, whether it was your company or somewhere else. That’s where we’re going to see that extrinsic motivation that Jess was talking about, right? I’m using it. I’m checking the box. I’m doing it. I’m probably running up my tokens because you’ve told me success is using it. So now I’m using it a lot, but I’m not using it in a meaningful way, and that’s a problem. Where we want to shift is using change management for AI adoption so that we can demonstrate if you use these tools in a way that is meaningful, you’re going to find ways to work more creatively, find ways to work more strategically, take things off your plate to open you up to do more of what you love, what you enjoy, explore new areas. I cannot say this enough. Mandates do not work. Mandates do not work. Mandates do not work. And our job as L&D professionals is to help our stakeholders, whether that’s clients, customers, learners, executives, whoever it might be, understand that mandates, they don’t work. They don’t work. So point number one is AI adoption is not just technology implementation. It truly is change management. And the second area, we’ve actually made this shift recently at Bitly this year. For a long time, the common misconception and where we were getting it wrong is we were talking about AI as a productivity tool. It is. Of course, it is, right? You can potentially find savings of time. But going back to what Nick shared earlier, that flow, if you really get into your flow with AI, now all of a sudden you’re burning the midnight oil building these cool agents and skills and all these things that now you can do double what you were doing. You’re also opening up your human capacity to be more strategic, be more creative, and it’s giving us time to now do the things that only humans can do. And I know that’s probably controversial because we’ve heard that eventually AI is going to do everything for us, and I do not agree with that. AI is never going to be as strategic or creative or as good at motivating teams or leading people as humans are. So that’s the second area I think we need to get a little bit better at, is how we’re talking about AI. It’s not just a productivity tool. It’s truly a human capacity tool.
Artrell Williams, CPTM: It’s funny that you say mandates do not work. I recently had the fortune of being able to work at the Department of War, formerly known as the Department of Defense, working with a group of leaders over there, and they’re going through a lot of the same things that we’re going through in the public sector. There’s banners, and I don’t know if I was supposed to share, but it’s a thing where they’ve got the banner similar to Uncle Sam saying, “We want you,” but this character is saying, “We want you to use AI.” And they are really struggling within the government, the U.S. government, with getting people to use it, and I think using it the right way as well. And so there’s a lot of sentiment inside the government similar to what we’re experiencing in corporations, nonprofits and everything outside of that, that there are people who don’t want to do it. There are people that are afraid of what it’s going to do with us. And mandates do not work, right? But we gotta encourage people. We gotta get people comfortable with the notion that it’s here, whether we want it to be here or not. It’s in our phones, it’s in our GPS devices, it’s in tools that we use readily and willingly. So if it’s there, help us understand the role that it’s going to play going forward and hopefully assure us that there’s not going to be a chip put in us. ‘Cause that’s what a lot of the people who are afraid of it, that’s what they’re afraid of, is that it’s going to be forced upon us. And it’s not that people don’t want to change, they just don’t like to be changed. And that’s what mandates do, is force them into a change that they may not yet be prepared for.
Maria Rodriguez, Ph.D.: I think you have an opportunity to use the early adopters in your organizations to really showcase and help with some of that. You will always have your early adopters that can get things done. And the people-to-people connections, the people within your network that you share that information with, it gives them one little idea. It’s, “Hey, if Maria can do it, I can do it.” Right? So it doesn’t have to be always coming from the top saying, “You will do this.” It’s more so utilize your early adopters, whoever they are, and say, “Okay, let’s get some projects. Let’s highlight the projects you’re working on.” And maybe at the next town hall, the newsletter, whatever you do to communicate, share some of those success stories. “Hey, this person in such and such business unit has used AI to do this. This is the output.” And whatever testimonials, whatever information, whatever sharing that you can do, it helps the others that may have a little more difficulty, saying, “Eh, I’m not there. I’m not ready. I don’t have that appetite,” to say, “Okay, maybe if you understand it, can you work with me? Here’s what I’m thinking.” So use the early adopters, use the other folks within your organization so it’s not just a you versus them, or it’s not just a leader saying, “I want you to use AI.”
Tom Whelan, Ph.D.: All right, looking at the clock, I think we’re at at least the last question for the panel before we open it up for Q&A. And here we’re going to change the format up and do a round robin. Okay. So the question for everyone is, for organizations just getting started with AI, our research suggests that there’s no single right use case or no single best point of entry for every organization. So if you had to recommend one starting point that balances impact and feasibility, from your perspective, what would it be and why? And I guess we’ll start with you and go down the line.
Artrell Williams, CPTM: All right. Well, I’m biased. We start with the learning folks. We’re the ones that would like to introduce it and hopefully be able to get people from day one. Obviously, organizations that exist, it’s not day one for them anymore, but there can be a day one of AI. If you implement AI usage into whatever orientation, onboarding, training, and get people used to seeing it around in the different things that they do, that’s the entry point that I feel would be best for all organizations. To go back to one of the topics of conversation that Claire was talking about there, too, is that there are a lot of people who confuse AI with automation, and they think that they’re one and the same, when in reality you gotta look at them as separate, completely separate concepts. Automation is something that you can do with AI, but you don’t need AI to do it, and AI is not all automation, especially when we talk about processes like in manufacturing and things like that, or even the way you set up your email. You know, you can use AI to get your email to do certain things automatically, but you don’t have to have AI to do that. So just helping people understand that it’s the automation they should be more concerned about taking your jobs than it is the AI itself. But the AI is something that we can use as individuals, and the automation is something that’s often used to cut out some of the individuals. So just don’t let people confuse those terms.
Claire Cole: Yeah. Plus one, plus one to that, Artrell. I am not a technical person, which is very uncomfortable working at a SaaS organization. But I found myself learning different ways to implement AI into my workflows as an L&D professional, even helping me with instructional design. Don’t get scared, IDs. Don’t worry. It’s just a starting point for me. But that being said, the thing that we have found most successful, and where I wish we had started a little bit sooner, is yes, we can build out content. Yes, we can find training to support the technical skills that are needed with AI, but where we have seen the most impact to our users adopting the tools is group learning. So having sessions on the calendar on a regular cadence. We call them learning labs. And we started with topics, and would say, “Okay, we’re talking about Claude today,” and you just get into Claude, and you play, to your point. And it’s amazing because you’ve got folks like myself, I’m not a technical person, but I have found a really creative way to do things in my world that could translate to other disciplines within our organization. So I’ve got IT saying, “Wow, that’s really creative,” and I’m like, “Amazing. I’ve never had that happen before, that IT is impressed with something I did that was technical.” But likewise, we’re able to sort of learn together. This has been sort of a fun exploration for me in my career journey to say, “Let’s just put time on the calendar and see what happens,” and it’s been pretty cool to see the things that have happened. So our learning labs have actually transitioned into just open AI conversations and tooling together. And I would recommend if you don’t already have that built out in your learning structures at your organization, go ahead and build that structure now and get people used to the format because it does take time. It’s uncomfortable for people to show up and not have a topic. So if you’re able to sort of demonstrate, like, “Here’s how we do this,” early, it’s going to set you up for success once you get to those tools and start implementing at a larger scale.
Mike Groesser: I’ll give you a quick answer. I mentioned research earlier. There’s not a lot of good research out there yet, in my opinion. However, there was a recent meta-analysis that was actually in a peer-reviewed journal published. And so from that, what I would suggest, where you should start with learning to see the most impact, from what they found at least, was early career, so young people, using text-based tools, so not voice, for the highest impact. Doesn’t mean voice is not helpful, but text was more helpful for young people to learn technical concepts. So if you wanted to see, what’s the highest lift I could have with an AI tool and a group of people, research right now is saying at least start with young people trying to learn technical things, because it’ll help them more than it would help a more experienced person trying to learn a non-technical thing. You’ll just maybe have more success that way.
Maria Rodriguez, Ph.D.: Absolutely. I think we have the benefit of creating experiences that touch the entire organization, and you shouldn’t take that for granted. Because a lot of times, the other business units don’t get to go across the board, but you do. And when you create those experiences, those learning experiences, incorporate your AI into that so that now they see a different experience altogether. And don’t forget to promote, “This was created with AI.” So they can actually start seeing examples that you’re producing from your business unit out to them, and everyone is now starting to see, “Oh, you can do that with AI? Oh, you can create a video?” So I take a policy and I make it a rap. “Oh, how can you do that?” Whatever it is, get creative yourself. Encourage your team to think outside the box. You’re no longer just a learning and development. You’re a communications expert. You’re a social media expert. You’re a marketing expert. Bring it all together and give them a totally different learning experience, one that they haven’t really seen before, or one that’s incorporated into what they’re used to. And by that, you’re showing them they’re actually living the experience of AI innovating what they already do. Not everyone signs up like, “Yeah, I want to take compliance training. Eh, I’m in line.” No. But if you start bringing it as a podcast, a poem, as something completely different, it’s still, you’re still learning the policy. You’re still learning the content. You’re still learning the topic. But in such creative ways, now they were like, “Oh, I didn’t know AI could do that. I didn’t know AI could take my report and do this with it.” Start being an example and modeling what you want to see from your employee population. You have the platform already. You give them content already. They complete your training already. Put it in there and then say, “This was created with AI,” so that they can start seeing examples and living it and experiencing it. Challenge to you all.
Tom Whelan, Ph.D.: To follow up on what Maria was just saying, AI won’t give you the best bars, but you can fix the lyrics if you’re trying to turn something into a rap. It will absolutely do it, though, and you’ll be impressed with the results. All right, well, we are over time, everybody, but please join me in thanking everybody on the panel. Maria, Mike, Claire, Artrell and also Nick.
