Exposing L&D’s Operating Model: Navigating the Most Significant Disruption in L&D History with Tom Kupetis and Brandon Dickens

The Learning and Development Podcast presented by 360 Learning. Hello listener and welcome to the Learning and Development Podcast. I'm David James and today I'm speaking with Tom Capitas and Brandon Dickens about how AI's exposing L&D's current operating model as unfit for purpose and what we need to consider instead. Brandon, welcome to the Learning and Development Podcast and Tom, welcome back. Thanks David, good to be here. Yeah, great to be back. Go ahead and pleasure to chat with you David. All right, let's not be about the bush. We'll get straight in with the hard question name because AI is forcing organizations to rethink how work gets done and how departments run. So as I mentioned in the intro, in what ways is this exposing L&D's current operating model as no longer fit for purpose? Well, first of all, let me just preface the answer by a prayer lit of saying, what what a unique concept to have a podcast talking about AI and L&D. Those have happened in minutes, right? No, I just, but there's a point to that which is that space is just overwhelmed with points of view perspectives, information, data, new tools, new tech, new approaches, new methods. And I think it's it's frankly overwhelming for L&D organizations. And I'll I'll start with my waxing poetic and then give Brandon, who's the brains of the operation kind of really give you a more proper answer. But what I see happening in terms of how departments are running right now is a real a real error of uncertainty, right? And oftentimes, and I've said this for decades now, L&D functions for whatever reason tend to gravitate towards technology first. And I see that happening yet again, it most recently happened with the skills craze, right? Everyone's like, oh, we've got to get a skills platform. We've got to go license attacks on. I mean, we need to stuff, right? Somehow stuff equates to progress and advancement. But oftentimes, that's not the case. The challenge in the AI world is there is an overwhelming, you know, a paradox of choice in terms of the options. And when some of the things are truly groundbreaking, exciting opportunities that are harnessing the power of the large language model. And some of it is just the same old tool with a different wrapper around it and everything in between. And that's exacerbated by the fact that the technology itself, the native technology of large language model, artificial intelligence is advancing so rapidly. I don't know how I need tool provider or technology company is going to attempt to keep up. And so that's not a direct answer, but it's the foundational elements that are causing the concern and confusion within the L&D function. Yeah, and I agree with all that. And I mean, just to dive in on a couple of a couple of things. I mean, I think one big piece of disruption here that we haven't seen before is that, you know, AI is sort of striking at a core capability of L&D that they built up over time, which is content production, the management of content production, the management of static courses, all that kind of stuff. And, you know, AI is making it, I mean, today, if you re-engineer processes the time required to create a static courses, you know, under a day. It's measured in hours. Right? So all that organizational overhead around creating static content, it's not going to need to be there. On the other hand, you know, there's, you know, in all of the kind of analyst reports, you know, people talk about the kind of C-suite's lack of faith in the value that they're getting from A&D because we can't measure. And I think AI changes that picture pretty dramatically as well. And when you put those two things together, you're talking about a whole new world that's pretty hard to imagine. And if you couple that with the silo collapse, we're seeing starting now, and it's just going to rapidly expand, is AI allows a single person to do several job roles. Like we're seeing probably most, most pointedly in the, you know, kind of product manager role in tech, right? That role is now three or four roles historically rolled into one. And everyone's scratching their head about what to do. That's going to continue. So I think L&D's knowledge of what the business is going to be is beyond shaky. You know, no one knows what it's going to be. So I think you put all those things together, and you're just looking at something that's a level of disruption we've never seen before. You know, we've seen disruption previously. I was in running L&D at Disney during the financial crash of the late 2000s. We lived through COVID-19. And things largely reverted back to old ways. We can, you know, pretty much safely say across the board, as far as learning developments concerned. So what makes this moment different? I can take a swing at that. And then if you want to jump in, Tom, I think that if you look at how, I mean, I'm going to zoom kind of a little bit, cosmic care for a second. If you look at how our educational system was, what it came to be, right, it really almost every decision we made was based on one constraint, which was the, the scarcity of expertise. Right. For millennia, we had apprenticeship models. Those worked really well. We knew they worked really well. But when we had, you know, a million citizens, we had to, we had to instruct. Obviously, we can't put an expert with every individual. And that led to classrooms, that led to static courseware, that led to curricula, right? They are all of our organizing principles in L and D come from that. You know, what what Gen A is doing, and I don't know that people had fully understood this yet, at least not a lot of people, it's, it's putting expertise on tap, basically. And so that core constraint that we built everything around is gone. And when that, when that goes, you know, the, the, the level of change you're going to see is not the same as, oh, we have Web 3.0 now, or even, wow, we have, you know, online training now. It's just much more foundational. And it challenges a lot more of how we've decided to go about the job of, of training people. Yeah, I couldn't agree more. And that's going to have reverberating effects. There's two points I would make in addition to what Brandon said to reinforce that message. The other, the other construct that's always sort of been there and I'm saying this a bit tongue in cheek, but there's always been this, oh, learning, that's just learning. Anybody can do learning. Like I'm an expert in topic XYZ. I can build the best training ever. And then you get what I call the death by the eight point PowerPoint font, right? You know, it's just regurgitating all the knowledge they know. Well, now, and in L and D, people will get their hands on that and say, this isn't good instructional design and this nobody's going to learn from this and they weren't wrong. They're absolutely correct. But now it's happening as you're putting the power of these large language models in the hands of people that may or may not be subject matter experts and they're able to crank out stuff on their own, whether it's good, whether it's bad, whether it's accurate, whether people will learn from it, more importantly, whether people will take it and apply it on the job and change performance. Again, total unknown and question mark, but the ability to do it as rapidly as you can. And you've heard countless people talk about, oh, I just created a course in five minutes. Okay, well, you created something in five minutes. You know, is it a course? Is it a communications document? Is it a, you know, set of questions? But point being, that constraint of taking the time sitting down in front of your laptop, opening up PowerPoint, you know, writing that stuff is gone. Now you just feed in source documentation and you get something back. So that's a dynamic that Ellen Dees never dealt with before. And I'm more sophisticated. I was mentioned about the apprenticeship model being the basis for how we learn going back to ancient Greece and then evolved into the current classroom. We're seeing that play out. So St. Charles is, you know, David does a lot of work in the professional service world, you know, the big strategy firms, the big accounting firms where they in particular have embraced an apprenticeship model for hundreds of years in terms of developing their people, particularly at the lower levels, the classic professional services pyramid. And we're having a lot of conversations with client about how that pyramid is going to change into more of a diamond shape, right? Because they're not going to use many recruits right out of school. And the jobs that those people are going to be doing to brand its earlier point about product management, they're going to be more managing the technology and people and apps through a process. And what we see there from an apprenticeship is a big gap, right? The particular big gap we've identified in the last few weeks and months is around professional judgment, right? because when you do those menial. basic foundational tasks, they might seem repetitive and you know, but you're actually learning professional judgment through that process and being shoulder to shoulder with someone who's been there and done that before. So now that's gone, right? And particularly in those terms that creates a level of risk, right? So if you're signing off on, if you're auditing a publicly traded organization, and the partner has to sign audit workpapers, but isn't confident that the the teams that are doing the heavy lifting have the professional judgment to make good decisions, how do you how do you get around that? And so we've got to I won't reveal the answer to that question, but we've got a solution to that. And surprise, AI is front and center in that solution to become in some ways the apprentice, right? So it's a it's kind of an existential thing I was trouble getting my head around, but you get the point. Yeah, absolutely. It was the way that you you both frame this. It's almost as if we could we might look back on the last 30, 40, 50 years as a blip. You know, we've acted a certain way because to your point brand and because of scale and because of limited resources and perhaps crude technology that that scale became different versions of one size fits all. But the apprentice the apprenticeship model which worked because it was focused on the work it provided very personalized and adaptive guidance and support in service of the work. Back then he might have been done one to one person to person, but an apprenticeship model using more advanced tools. It's almost reverting back to what what worked for thousands of years before. Am I am turpentined that right or? That is that is that is 100% my take. I mean, I I quickly became convinced that I saw the dark ages lifting from us. When we started first experimenting, you know, three and a half four years ago with this technology, I do think it's going to be a blip. And I do think it's going to be remembered as a sort of dark age. I mean, we did the best we could. We certainly lifted a lot of people up, you know, in the process. It was the best we could do. It was not nearly as good as we can do today. So I definitely see that. Well, and the last thing on the apprenticeship model is if you think about if anyone's ever hired a bad carpenter to come to work for you, right? That person learned from someone else, right? They were in that apprentice program at some point. Well, there are no bad carpenter's teaching other carpenters with AI when you think about the power of it. And the amount of knowledge it can harness and the speed at which it's advancing in terms of its ability to process and synthesize data. And so not only is that apprenticeship model now available at scale and will be available on a one-to-one basis. But the level of I'll use the word genius for lack of a better term that is going to be afforded to people is it's almost incomprehensible. And so I suppose that makes a mockery then of the responses that we're seeing of of organizations looking to centralize L&D to drive efficiency and cost savings because AI helps to do things faster and cheaper. But to say that that's full-hard is is an understatement, wouldn't you say? Yeah, we're going to develop the best static course where we've ever developed in the history of L&D right as it becomes completely obsolete. So if you're indexing your L&D strategy around producing content cheaper and faster, that speed and cost is going to approach zero at some point. And you're not going to be needed anymore. So I definitely think I definitely think that that is an understatement. I mean I think it's a fatal decision, frankly at this point. You know I think the the big challenge for organizations is that their budgets aren't changing, right? L&D's budget is L&D's budget and they've got to keep running today. But somehow they have to build a parallel stream within that budget that's moving towards the future. And I think a lot of people, a lot of departments, a lot of learning leaders that I talk to are just stuck. You know they don't know how to navigate that particular divide. And so they're doing what's getting them kudos, right? Which is great. You produce 30 more hours of content and what it did, nobody knows. But you know they're not they're not really I don't think coming to grips with how important that second stream is to get going. Yeah and the conversation we've been having with clients around being more efficient and reducing your cost. It's the cost of the traditional legacy operation. The very next words out of our mouth are, if you're going to do that, you have to go get executive buy-in that you're not going to give that savings back to the business that you're going to reinvest it in what the future of this organization needs to be. And honestly, even if you were to drastically reduce cost which many organizations have done and successfully because there's always a lot of waste, even that reinvestment may not be enough to completely get you where you need to go but without it to brand its point, it's like anything else. I harken back to when eLearning was invented back in the Stone Age just when I was a young pup. And you know people like wow this is so so expensive to build an hour of this stuff and the argument was yeah but you can touch thousands of people you know right at their desk when before you'd have to give them to a classroom you need I used to build tools that were Excel based that had these fancy ROI models right. Now it's like that's a whole new game and thinking about how that reinvestment takes shape is important. So yes being efficient and saving money that's a drumbeat and a pulse that will continue to beat but my message to L&D leadership is make the case turn on or reinvest that in yourselves to transform as fast as possible. So thinking about that reinvestment in ourselves as L&D, how do you see roles needing to change as AI reshapes the business? Well I would talk about and Brandon I think we could agree let's just take the concept and this is very hot topic right now is this concept of enterprise wide AI adoption AI fluency right where people are saying we need to get tens or hundreds of thousands of people across the globe in a Fortune 500 Fortune 100 company up to a baseline level of knowledge of AI and what they really mean is they don't want knowledge they want knowledge usage application in the context of the role that they do today and we're already seeing a classic breakdown of that model and Brandon I don't know if you want to expand on that or you want me to but we've got a very strong point of view around this that we're pretty passionate about that directly speaks to the question yeah I mean you know the kind of the core of that is that you know we're we're looking at the big challenge and the challenge I think that L&D hasn't groupled with enough is really kind of the abstraction transfer problem you know that people do not transfer any level of abstraction very well back to their jobs you know and I think that that goes to one set of roles that are that are going to change pretty dramatically and that's that's the role of facilitation so you know when you're trying to get a team to really adopt AI and champion it and figure out how to make the most of it you know you you have to create that aha moment where they see it in their context they can feel it they have a thing they they're going to go back and use and brag about and feel great about and the only way you create that that that moment is with something like action learning you know we're kind of learned by doing approaches so you know what what we're seeing in the facilitation role needed there and I think it's going to be in a lot of other places as well you know you you need something a lot more like a business consultant somebody you can sit down with a classroom and really understand what workflows are you trying to accomplish here you know what tools do you have available to you you know okay how do we break that task down and think about how to create an AI workflow around it you know you have to actually get them doing that in the moment you can't just talk about it so I think I think that that's one central change is we're going to have I think less of a view of facilitator as a person who stands up and reads slides and a lot more of an idea of a facilitator who is rolling up their sleeves going shoulder to shoulder with folks and and really you know again implementing that more apprenticeship like model um to what they're doing yeah in the context of the role that the person performs so that they can go back to their job after the learning occurs and not only apply that that thing they did in the classroom that's directly relevant but it what we 've seen and proven is it opens and frees their mind to think about they can pattern match them what they experienced with AI in that particular task role or process to other tasks roles and processes and now you've got this exponential thing and the beauty of it is you can actually measure that David right so you can actually say At 30, 60, 90, 180 days after people have gone through this, this, what we're calling Praxis lab concept, Praxis being the ancient Greek philosophy of learning by doing that, that they're actually advancing the application and use of AI towards changing the way work happens, which is what everybody ultimately wants. But what do we see? The vast majority of L&D organizations are putting out curriculum, right? Everybody take this e-learning course on good prompterating. Well, the problem is those are written generically. Every company has a specific tool set that they've adopted. The prompts don't work the same. Oh, by the way, they've put various guardrails around those tools, depending on the organization, the industry they're in, their level of compliance, their approach towards risks, their approach towards data, privacy, data security. So, you know, all those specificity factors are limiting. And so putting out generic courses, well, again, you can point to consumption data and say, yeah, we've trained 78% of the organization on basic AI prompting and fluency skills. If you're not moving the needle and you can't point to it, we're back to that at all of it, conversation. Totally. And I mean, you know, kind of zooming out from that more specific case. You know, if you think about, if you buy the premise that, you know, static content is not going to be the game anymore, and you buy the premise that we don't measure very well today, but we're going to be able to measure really well in the future, that tells you a lot about what the L&D Department of the future is going to look like. It's going to be a lot more focused on deep intimacy with the business. It's going to have a lot more focus on rigorous learning science, which we've just not been serious about, I think, you know, across the industry. And it's going to be a lot more about data analytics than it is today, because we're going to be able to measure, we're going to be able to prove ROI and so forth. So you're going to see roles come into play that are focused around those things. And I think and I hope that that's going to drive the conversation away from cost and for the first time into the arena of value, which is, you know, really how we should be looking at AI is, you know, we can get a two sigma improvement in the performance of our population. We know that. That's what one-on-one direct apprenticeship does. You get a two sigma improvement. Like apply that to sales, right? What does a two sigma change in, you know, closure rates mean for an organization? It's enormous. It's a huge value. And so as soon as we can start talking that language and we have the tools in place and the people in place to do it, I really think we're going to, we're going to turn into a value center instead of a cost center. Yeah, I love conversations when we pivot. We see the optimism because I'm sure that there is an listener who's engaged right now who's not thinking that they want to be more impactful, that they want to build credibility based on demonstrable impact rather than how any particular stakeholder feels about what was delivered for them. But you mentioned tools there. What types of tools are you seeing right now or perhaps are being prototyped that you think could have the biggest impact today on L&D? Well, I have all kinds of feelings and opinions here. I mean, you know, I think probably the most exciting thing happening today is really around the agentec tools that are coming into play. I'm thinking very specifically about cloud code and what it can do. And you know, what it's done for us is, you know, last year, this time, last year, we were doing, you know, these very big open-ended business simulations that are powered by AI, kind of infinite practice machines, you know, AI coaching, you know, all the kind of interactive stuff that you can do with AI right now. And the timeline that I quoted to get to something we could get our hands on was somewhere between three and six months, right? Cloud code made a big release in December and we've been creating scaffolding in that for some time so that we can work faster. We're now at a place where one of our designers can pick up cloud code and in this kind of scaffolding system, they can create a prototype in four hours, deployed to the web. And I mean, that speed up is unthinkable. And I mean, from our perspective, the modalities in the future just haven't been designed yet, right? We don't have the patterns, we don't have the routines, we don't know what they look like yet. So it's a time of kind of wild experimentation and it has to be. And the faster we can run those iteration cycles, I think the faster we're going to find that that future. So that's a big thing. I think that, you know, what we're seeing in the roadmap of companies like Anthropic suggests huge, huge changes coming. I mean, they just, they leaked Cloud code, source code last night so people have been reverse engineering the roadmap. I mean, today, if every L&D employee had the Anthropic kind of ecosystem at their disposal, it would 10X what they're doing immediately. So I think the impact of those tools just today are under our undersold in a lot of ways and underutilized by businesses, you know, going into the future, I mean, the stuff that I'm seeing that's really exciting me are these infinite practice engines. And just the level of fidelity and, you know, alignment with business and the level of measurement, you can get out of solutions like that. I mean, when you take somebody and you put them in an interactive open-ended simulation, you are creating data that is the same as observed performance or very close to observed performance. You can have an AI looking or large language model looking at that performance, rating it very reliably against a rubric. That's the kind of thing that would have taken, you know, a big ethnographic study in the past. And you would have gotten some tiny portion of the audience. Now you're getting it by default as people are interacting with the system that you've deployed. And that combination of interactive practice that's going to feel very real and the data that comes out of that and the way you can create a virtuous cycle between those two things, that's what I'm most excited about. And that's why I think that, you know, we are going to see a world in which the kind of current mode that is courses, curricula, static stuff is just going to slowly lose share, you know, to this interactive stuff. And that's kind of why I think that we're going to make the best possible static content we've ever made exactly at the moment that nobody wants it anymore. And there's another facet to this related to the market in general and those companies that are providing technology tools, SaaS-driven platforms today. I was chatting earlier this week with a friend who's a CEO of a company, you know, and not a huge company, you know, $25 million revenue. This person's a serial entrepreneur and very much an innovator and an embraceer of new technologies has been for decades. He pulled up on his screen and he showed me, it wasn't really a learning application, but it's a learning company that he runs, but he's passionate about all sorts of things. And he basically created this tool that was a enabler of a CRM that essentially made CRM's irrelevant and it was able to pattern match the six degrees of Kevin Bacon. So Brandon knows somebody, I know that same person, you know that person, I can get an introduction, you can analyze your pipeline. And he said to me, I developed this in three weeks and I've used a billion tokens in Claude code to do it. And I thought, wow, a billion tokens. Well, Brandon, what's a billion token? $10,000. It's a good week. Yeah. It's not a tremendous amount of money that was invested. And this tool, when I looked at it, I thought, this could make organizations like HubSpot irrelevant. And HubSpot is a big, fast, software CRM platform, right? And it wasn't that huge. So I mean, there's this race to become the best mouse trap out there, you know, embracing AI and building these technologies. And I know a lot of, you know, close friends that I've grown up in this industry with, who I wish all the best, nothing but success to. But I just don't know where it's going to end up. I mean, and what we're doing is sort of native outside of any one platform and we've created an ecosystem of things that live behind our curtain. And you know, my friend's example of what he did in the sales CRM relationship management space, it's just, it's hard for me to get my head around. But it's incredibly exciting, right? Yeah, he's really exciting. And you know, and you can feel, it's a lot to feel anxious at the same time because if this is, if these are challenging, if it's challenging long established and successful, you know, infrastructure like HubSpot, you know, who's to say the learning development is, is safe here. But I suppose this is, this is something we can, we can thrash out here. And what it brings to mind now is, so what, what doesn't work or needs adapting when AI enters the picture. What I'm thinking are long established processes in learning and development, the things that perhaps we build our teams around whether that be ADD or agile. What doesn't work anymore? There's so much. It's almost impossible even catalog at all. I mean, on the process side, just think about all the infrastructure, all the process, all the support required for static course review. It's enormous. Again, you're producing a course in two hours. Do you need all that infrastructure? You think a little bit further down the road. How much of what we do is administering an LMS? Is there going to be an LMS in the future? We're probably not going to have courses. We're probably not going to have curriculas and organizing concepts. AI is probably going to be the interface. That means the interface is that the LMS provides us. Maybe you're going to be a lot less important to us. What does that whole administrative service do in the future? Going from the premise that a lot of what we do is organized around the static course and the static curriculum. Everything we have today that's supporting those, the roll out, the creation of, the management of, the maintenance of, which is most of L&D. It's just not going to be needed. I think the agile versus ADD, agile debate. It's definitely not ADD in the traditional sense, but it's not agile either. Both of those things together combined, but it's just, once you train the large language model, Dejure, on both of those things, it's done. It'll just operate in that regard. Those that prompt the best. I'm not even talking about people using cloud code. They prompt well because they do it in an agile way. Agile is a series of sprints with a series of reviews. Well, I'm not a great prompt or by any means. Brandon is one of the best I've ever seen. But in the early days, if you just, if you treated it like Google, you've got Google like output, which was a whole bunch of stuff that you would have sorted through. But if you treated it like a conversation with someone who knew what they were doing and you iterated, things got better and better and better. Well, that's an agile process. It just happens every 30 seconds versus every two weeks. So these things are all there. They're embedded. They're they're becoming part of the fabric and the DNA of how we use AI. But we don't stop and say, what stage of ADD am I in and do I need to apply? It's just there. It's easier. Well, the other thing is that I mean the whole, the whole, what we're managing is going to be very different. I guess is the key thing and why it's a little difficult to map it into what we have now. So, you know, if a course is created effectively instantaneously and, you know, processes are adjusted so that approvals can happen as they shouldn't quickly and so forth. You're talking about a one day project. Is that a project? You know, and so and and when you go further into the future and you think, okay, it's all going to be a femoral content. It's going to be in the moment coaching. It's going to be in the moment, Sims and so forth. What are you even creating at that point? Right? You're you're you're honing, you're tweaking, you're you're adjusting over time. But, you know, the project that you're running is really probably something more like an analysis and revision process on outcome. And so trying to figure out like how that maps into either waterfall or Agil is very, very difficult because it's something much bigger than that. It's more like an organizational operations approach, as opposed to something that's an individual work package because individual work packages are just going to be trivial to produce. And, you know, so it makes it really, really difficult to think about, you know, what what model someone will use when we're really talking about, you know, kind of an apples and oranges comparison. What we do today versus what we'll probably be doing in five years from now. More like grapes and watermelons. There you go. One of the things that strikes me there is, you know, we were talking about Adi and Agile, which I think pre pre AI. So pre pre 2022, I think was the, you know, the Agile, fix some of the deficiencies in Adi in the the ability to to quickly deploy it because you spend more time on understanding what the problem was, the ability to iterate rather than something being more static. So, so speed was the real differentiator. And it always seems to me that the one of the the advancements of this with that with AI now is going to be in listening and understanding to what the actual problems are, listening rather than being alerted or being requested for for some help, listening to understand what the critical points of failure are to point resources in the right direction, which could certainly address many other deficiencies around learning and development, not being able to to measure the impact and then therefore be more performance focused. Is this how you're seeing this happening in, you know, with with L&D or or perhaps business or performance data, reading the organization to alert L&D and and and if so, what what's the potential there? It's enormous. I mean that this is a this is a huge area of thought and we're doing we're doing a lot of in for build out around this right now. You know, and I love I love that that you sort of highlighted kind of failure modes and detecting those because I do think that's going to be very central. You know, I do think that that analytical skill set is going to become much more important in L&D. I do think AI is going to perform a lot of work that that helps prop that function up. You know, what what what we've noticed when we go into, you know, our clients like say a mining company we work with this huge mining company and they have reams and reams and reams of near-miss reports that they generate every day. Right? It is like flat tire, you know, in the pilbarra region or something, you know, everything gets written up. That just sits in piles. Nobody looks at it. Right? How much intelligence is in that material? Right? Common patterns, you could do root cause analysis. I mean you can get to mistake patterns repeating across audiences and geographies and that that's all incredibly important. AI can do that root cause analysis. It can hoover up all that information and it can dashboard it for you and say look, here's an emerging pattern. Like, you know, here's what I think we should do about it. Do you want to do this thing about it? Here's what I think the return is. And we have we have we have tests running where we're seeing it work. So it's definitely not sci-fi. And I do think that's going to be that's going to be really huge. And when you think about the other component of that, that being, you know, these kind of infinite practice engines where people are in there actually practicing and we're observing their performance. Now we have that performance data coming in from where it used to be siloed and we didn't have enough people to look at it. You can out correlate it. Right? So training becomes a lot more predictive of what's going to happen in the future because we're going to be able to tie what's happening in the training to what we see in those kind of more experiential data reports that are piling up everywhere. I mean call centers, reams and reams of data, customer service has, you know, complaints from client from customers coming in. There's just there's so much of this data out there. It would take a team bigger than the whole L&D team today. It's just to go through it. And, you know, AI is just going to be able to do that 24/7. So I think that is enormous. Exactly what the human role is going to be in that is a bit of a question mark still. Because AI does a really great job. You're going to need a human oversight. How much, you know, that's the big question mark. Is this going to be one data analyst looking at dashboard? Is this going to be, you know, a big team kind of tuning the algorithm behind the scenes to make sure it's all really firing at maximum potential? Interesting. Now here's a blue sky question for you. If you had licensed to redesign a corporate L&D function from scratch today to be fit for purpose for today or tomorrow, what would it look like and how would it operate? Wow. It's a big one. Hey question. I'm afraid we've already scared the audience so much. Because anybody who's still here. Now, but in all seriousness, we've never had a time to be more optimistic and excited, right? And frankly, it's easy to get eyeballs on LinkedIn by, you know, predicting Armageddon and Doom and Clume. But it truly is one of the most exciting times if you're willing to embrace the change, which can be a scary concept and thought. I think if I had a blank canvas, I would really start with with that linkage to the business. And, you know, I'm a. serial consultant I have been for decades, right? And I've always asked two main questions that starts every potential client conversation, which is what's the problem you're trying to solve? And how are you gonna know when you solved it, right? Which is the linkage to the business, like what metrics, what performance, what need do we have? And how are we gonna measure it and be able to evidence it? So the investment that we made, we can show how to return on it. And that's a basic, the most basic consulting principle there is. But to me, that basic principle will last for time in Memorial, and that's the basis at which I would build an entire L&D organization around is, how do you become part of the business? And I've got another good friend who was a CLO and still very prominent in the space and he's running around purporting, there won't be an L&D organization. I don't think we're gonna go that far, but his point is, it's gonna be so infused and integrated into the business and tied into what the business is doing, that it won't feel like this separate standalone thing. And that's what I would build. I would build this that's so integrated and woven into the business, that you don't think about, well, now I'm talking to a learning and development person, you're talking about that person's gonna help you enable the performance that you need out of the humans and the technology. - And I sort of think about it from the experience space and build out from more of the experience side of learning. So I mean, if I got to just wave a wand and create the L&D department of the future, it would start with making sure that every learner has a very capable AI tutor coach, partnered up and that everything's worked out so that the experience of that is wonderful and people actually like the personality and wanna interact with it. I would give that coach the ability to spin up in the moment, learn by doing simulations so that it could actually incorporate that into coaching. I would connect that coach into the kind of relevant data stream so it could create the organizational model, the learner model, the goal model, all those various models it's gonna need so it needs to have Microsoft Graph access. It can see the emails, it can do all that stuff. And then on the other side, creating that kind of sensing engine that looks across all the experience data coming in makes good kind of pattern matching and root cause analyses and makes good recommendations about that and just shorten the path between that sensing engine and the coach being able to implement the recommendations that fall out of that engine. I would build my entire department, my entire approach around those three kind of core components and making sure that those are propped out and that they're designed in the way that they should be designed and that they're able to be operated again to just maximum impact. - Love it. And as a final question, if you were advising the listener today on what decisions they need to start making in the short to midterm to avoid being left behind but also to capitalize on the opportunity in front of us, what would you advise them? - I can take first stab at this. I mean, I think the key thing in this, I've been saying it consistently for three years, start experimenting. So get yourself a playground that has access to a lot of tools, figure out whatever you have to do internally to make that safe. But get people asking themselves the question, what do I not believe I can do and have them try to get AI to do it? And after five rounds of that, where there isn't a thing they can think of that AI can't handle when they really work at it, you see the mindset shift and you see the AI adoption just go through the rub. And I think that's it. Put hands on tool, get access to as many of them as you possibly can, create a safe playground and start doing experiments that are trying to get somewhere you don't think you can get today. - I'm pretty much home. - That's exactly where I was going. The thing I would add to that is, don't presume what you're doing is even good 'cause there's somebody out there who's doing it much better, much more sophisticated. And what I mean by that is be proud of what you're doing and embrace it, but listen and learn. And don't let your hubris get in the way of your learning, and ask your peers, ask the people who, forget your title, your status, your tenure, right? And there's no, I know younger generations in the workforce that haven't adopted it and I know people who have. And it kind of breaks all the demographic stereotypes, right? It's, people are passionate. And it's almost like, I've even met some of them, even on our own team who are almost closet passionate about it. We asked the question like, "Hey, who wants to help with this?" And also I'm like, "Look what I've been doing in my personal life." Like, why didn't you say something? Like, you've got this nailed, right? So just, there's so much to learn and none of us have all the answers. And I think we're a community of people that are learning and development. And there's this great opportunity to learn from each other and share. And there's no competitive advantage or differentiator at this point. So I think we all just need to be completely transparent and share all we can. - Yeah, I love it. Thank you. This has been really enlightening, if not mind blowing at times. But all is left to say is he's Brandon and Tom. Thank you very much for being guests on the Learning and Development podcast. - Awesome. Thanks, David. - You know, pleasure was all I was thinking about always. (upbeat music)