In Conversation with Josh Bersin: Why 2026 Changes Everything About How We Work

[00:00:09] - [Speaker 0]
Hello everyone and welcome to the Talent Equation podcast. This episode sponsored by InfoPro Learning and always I'm your host, Nolan Hout. Joining me today, we have Josh Burson who probably does not need an introduction, but I'm going to give you one anyways. If you've worked in the HR talent training space at all, you've probably quoted Josh Burson in a meeting. If you say you haven't, I don't know if I'll believe you in that.

[00:00:32] - [Speaker 0]
He started Burson and Associates back in 2011, sold that to Deloitte, but then spent the last two decades really shaping how the world thinks about work, learning and talent. And his team just launched Galileo Mars, which is a big leap from the original Galileo product that they had put out. I'll let Josh talk a little bit more about that, but in short, it really transformed what was already a super, a powerful AI agent into something called a super agent. In today's episode, we'll learn a little bit more about Galileo Mars while also learning from Josh about where the future of our industry is headed. So without further ado, let's meet our guests, Josh Burson.

[00:01:08] - [Speaker 0]
Welcome to the talent equation.

[00:01:10] - [Speaker 1]
Thank you, Nolan. Great to be here.

[00:01:12] - [Speaker 0]
Yeah. And before, yeah, yeah, absolutely. So let's get into it before we go into the, the Galileo Mars, which I know everybody is really excited to learn more about. We'd love to just, for those that don't know your story, if you could just take thirty seconds and kind of explain how you got into this field in the beginning and how it's kind of led, led you to where you are today.

[00:01:33] - [Speaker 1]
I inadvertently tripped into this wonderful profession, in 1998 when I was leaving a tech company to work for a startup that was building an online learning system. And in 1998, the Internet had not barely been coined. But people were starting to do training over the internet. So I spent, a couple years at that startup. That company was sold to a bigger company.

[00:01:59] - [Speaker 1]
I got to know a bunch of chief learning officers and realized that there was gonna be this huge, transformation from instructor led traditional training to online, which just seems like so long ago now. And so, and the part of it that I was particularly interested in was studying it and understanding the best practices and writing about it. So I started getting involved in research and that evolved into, a company that as you said, we sold to Deloitte. But, around eight, nine years into this, we realized that the HR stuff and the learning stuff were interrelated and that all of the research we were doing in L and D was related to much, much more going on in leadership development and succession and all that other things that goes on in the Asia world. So for the subsequent years, maybe, I don't know, ten, twelve, fifteen years, I we we we leverage that research to study all the different domains of HR.

[00:02:59] - [Speaker 1]
And, and now it's all about AI. So, we're going through the reinvention again.

[00:03:06] - [Speaker 0]
What do you think it is, Josh, that made you stick into like this, this, this lifelong of like learning, you know, I'm sure at that point you could have taken a job somewhere else and worked as a COO. What?

[00:03:21] - [Speaker 1]
Of all, there were no jobs in 2000 when I got laid off. Was a .com in The Bay Area. Nineeleven had just happened. There were no jobs. But anyway

[00:03:30] - [Speaker 0]
Okay.

[00:03:31] - [Speaker 1]
For me, first of all, I really like what I do now. So this turned out to be a career that I sort of perfect for me that I didn't realize that I could do. But I think my nature, my father was a scientist. And, you know, so I grew up in a world where we were always asking questions like, why do you think the sky is blue? And why why do you think, you know, this happens?

[00:03:55] - [Speaker 1]
And so we were always thinking about understanding and, learning. So, I think my nature is, and I think the people that work with me know this, is I never assume that the answer we currently believe is correct actually is correct, and there's probably other ways to think about it. So, in the human capital and training and HR domain, there are no perfect answers to any question. Everything's open to discussion.

[00:04:26] - [Speaker 0]
Yeah. I mean, deal with people, you deal with complexities.

[00:04:30] - [Speaker 1]
Totally. I mean, and I've learned so much over the years and I'm still very humble about it. I don't expect, I don't believe I've ever mastered all this stuff, but I've learned a lot and things repeat themselves in different forms over time. So I've become more, I guess, seasoned in understanding all these issues.

[00:04:48] - [Speaker 0]
Yeah. And you know what I thought was, what's really interesting, I want to talk about now is, is when AI came about, you kind of saw some people who were thought leaders in this space, you know, some kind of ignored it for a while, but you really jumped in really early on and recognized the potential that it had. And, when you you launched Galileo, don't remember when you launched Galileo, the original one.

[00:05:14] - [Speaker 1]
Three, two and a half years ago, something like that.

[00:05:16] - [Speaker 0]
Yeah. So very early on, like, you know, it might as well be, you know, zero BC in the age of AI.

[00:05:22] - [Speaker 1]
Yeah, it was early. Well, okay, so let me tell you what the reason where I think this will help people understand what's going on. So I spent a bunch of years doing mainframe PC computer stuff at IBM, Then I spent a bunch of years at a database company. And one of my jobs at the database company was running business analytics and data warehousing. So I was learning in those years about the different forms of data and how data is manipulated and multidimensional analysis of data and things like that.

[00:05:53] - [Speaker 1]
But up until AI, all of the data in companies was siloed into functional areas or databases. Databases are very structured row by column storage, containers, and they're transactional. And the problem that companies have had since I was at Sybase and really at IBM is we've got all this data, who's gonna put it all together and make sense of it? It's changing in real time. So we had big data, we had data science, but it was really still just updated performance of the same old problem.

[00:06:31] - [Speaker 1]
Along comes AI and AI says, give me the whole dataset and I'm gonna vectorize it. And and the the vectorization is really important. What what the literally what the AI does, and because I have a background in science, I understand this to some degree, is every piece of data is connected and, indexed to every other piece of data. So it's a it's a multidimensional, multidimensional, multidimensional system where anything that happens over here, we know what the impact is over here. Now think about that in a business context where we have data about skills, data about jobs, data about safety, data about experience, data about college degree, all that stuff that we never could figure out how to relate it all to each other.

[00:07:19] - [Speaker 1]
The AI sees all of it as if it's one thing and may and understands the relationships between it. And even more, it can update itself through what is called learning as the data changes. So all of a sudden, all that stuff that I did for twenty five years is a 100 times easier to use and get value from. Now you apply it to l and d where, you know, basically L and D is a very old fashioned idea. We've got a problem.

[00:07:52] - [Speaker 1]
We're gonna diagnose the learning needs. We're gonna teach people about the problem and hopefully they're gonna get better. You know, that's a very old paradigm of pedagogy. But imagine that the AI sees all of that and knows what good looks like because it can see the people or the content that refer and can apply that to an individual through a course or a video or or it's so, it's so big, it's so fascinating. I, I couldn't stop.

[00:08:22] - [Speaker 1]
So what happened to us is we had been publishing research primarily in PDFs and, and videos and stuff for many years. And so we were kind of running a publishing company, with a very advisory culture to it. And, you know, we were always frustrated that somebody would call us up and say, do you have any research on this? Like, yeah, we have like 50 reports on that, but I hate to make you read them all. Let's just talk about what you're trying to do.

[00:08:49] - [Speaker 1]
Well, now we don't have to do that because what happened was when we put all of our content into AI, the AI suddenly became intelligent about everything that we've ever done for thirty five years. It's almost as if all of the research we've ever done by all of the analysts, some of whom don't work for me anymore, are all there for you to use in a form of asking a question or in a form of learning because the Galileo platform has a learning engine so it can generate courses and videos and podcasts and checklists and stuff. And you can just talk to it. So, I mean, I kinda got a sense this was coming when I first heard about, you know, ChatGPT. But within maybe three or four months, I saw the opportunity.

[00:09:39] - [Speaker 1]
And and then I think the other thing that we did that I think a lot of the people on the podcast have been going through too is, I had to convince our company that this was worth our time. And so, the original project that we started where we put all our content into ChatGPT, everybody said, well, why are you wasting time on that? We already have a search engine. Well, it's not a search engine. Everybody thought it was a search engine.

[00:10:05] - [Speaker 1]
So once we lit it up and got it working, everybody goes, woah, what did we just do here? So, so in some ways, Galileo for me is the, the pinnacle of my career where I can take all of this work I've done all these years and I can bring it to people in a form that they can consume, at a very, very low cost at a very high scale that we can never do before. So it's really powerful. And then, and there's hundreds of applications of it.

[00:10:36] - [Speaker 0]
Yeah. And I mean, the first step, when I remember when Galileo was originally launched, I thought, and every company kind of made this decision of do you cannibalize your own business or not? Because, and those, I think that, that, that realized that it was going to happen, whether they wanted it to happen or not, are the ones who ended up iterating and learning the fastest and being able, you know, like, like Galileo did to be able to say, listen, yes, maybe if I give away my IP and this tool that can answer questions better, they were not going to, I'm not going to be able charge my consulting rate, but at the end of the day, they're going to get that knowledge eventually. So why not be the source of that innovation instead of being worried about what it does to my bottom line? So when, but when we look at this latest release, Mars, which came out roughly a month ago, this came out, I think March, mid March, end of March.

[00:11:35] - [Speaker 0]
Tell us a little bit about what changed. There was a really cool line in there that I love. I don't know who came up with it, but you're like, we're going from agent or, you know, what's an assistant and then an agent to super agent. So talk to us a little bit about what that big change was and why it matters.

[00:11:52] - [Speaker 1]
Well, and this is, this is very relevant to people in L and D and people listening to the podcast. So, when we first built Galileo, it was a really, really powerful assistant. You could ask it any question and it would teach you things. It would answer questions. And a lot of the questions people asked we had or we, you know, the system would manifest a good answer.

[00:12:13] - [Speaker 1]
There were some things we didn't have. So we got some new data in there. We got a bunch of salary data, jobs data, turnover data, things like that. But it wasn't able to solve a problem. It was able to explain to you how you would solve the problem.

[00:12:30] - [Speaker 1]
So what was what companies were doing with it is they were connecting it up to their, corporate systems and they were loading it with information like their skills model, a list of all their employees and their job titles and their job levels, their salaries. And the system was starting to prove that it was a system of action, not just a system of answer answering questions.

[00:12:53] - [Speaker 0]
Yeah. Q and As.

[00:12:55] - [Speaker 1]
So So, what we did is in the Mars release, there's a big, big new feature called Workflows where you can prompt and develop basically applications in Galileo to solve problems. And we just did a big demo this morning internally here. For example, one of the workflows in Mars is a workflow that says, I forget what it's called. It's called something like talent redeployment. And what it does is it asks you a bunch of questions and it's, you know, what it, what it, what's the state that you're trying to move from to?

[00:13:31] - [Speaker 1]
You know, I'm trying to read you know, upskill a bunch of salespeople or I'm trying to, you know, move from sales to consulting or whatever it is. And it steps you through a series of questions. And then if you give it the list of job titles and people, even details about the people, it will literally show you who should be trained for what role and what the development plans for each of these people are. And so we've know, I remember many of the clients I've talked to about this, they didn't believe me that this was possible. And because it sounds kind of too good to be true.

[00:14:04] - [Speaker 1]
So we took, you know, data from their company about their people and we stuck it in there. And sure enough, it developed a new development plan. It showed who's gonna be most suitable for what role. It gave people skills gaps based on their experience. And then lo and behold, even more interestingly, we use Galileo to record all our company meetings too.

[00:14:28] - [Speaker 1]
So it's filled with all sorts

[00:14:28] - [Speaker 0]
of Yeah.

[00:14:29] - [Speaker 1]
Textual data with about people talking about their jobs and stuff. Then you throw that data set in there. Now all of a sudden, it, it knows about people's real skills and real work activities, and it can make even smarter decisions. So, so that's a lot of what Galileo is, is its ability to use the work. We have about 400 of these workflows built out, and then you can build your own.

[00:14:52] - [Speaker 1]
So if you're in there doing a bunch of stuff, you can store it in a sense in a what if, you know, kind of a program. And then when you run it, and the interesting thing about it, this has been even more fascinating, is when you run one of these workflows, not only does it do a lot of great stuff, but if you click the button, it'll show you how it's making the decisions it's making. And you can go back and say, Hey, that was very good, except at this step you should have done It's this instead of really amazing.

[00:15:21] - [Speaker 0]
So in what is so fascinating about this and just the space in general. So what you're talking about, that one workflow, right? Is like a $50,000,000 company.

[00:15:33] - [Speaker 1]
Oh, every company's got that going on all the time.

[00:15:36] - [Speaker 0]
And that is, that is the beauty of how a connected ecosystem where if you, you know, we talked a lot about the importance of data, but, but, but AI is where you hear about all this time where, where these large AI agents are essentially consuming all of these startups because they're able to just release a new thing like that workflow you just talked about. I know the CEOs of some of these companies, that's all their whole platform does. You're able to build an agent out for it or a workflow

[00:16:06] - [Speaker 1]
for Nolan, one of the things we did with Mars is we announced a version of Galileo called Galileo for Consultants because what we also did is we put in a bunch of our consulting models that we use, our change management stuff we go do with clients, Because it is now a consultant. I mean, it can take a problem. As long as you can explain the problem and put the data about the problem into the system, it will diagnose the problem and step you through the solution with a very small amount of prompting. And the thing that's been happening the last couple of months is now that companies can see what this product can do, they want to connect it up to their corporate systems. So, so we've got a version of Galileo that's running on the Microsoft Copilot.

[00:16:53] - [Speaker 1]
We've got a version that runs with ServiceNow. We have the version we have works with Workday because Workday bought the company that we had built it on. We have a version that works with SAP. So, you know, depending on where you are and what kind of company you work in, you can get your company data into Galileo very, very easily now and then use the intelligence there to do these kinds of activities.

[00:17:15] - [Speaker 0]
So let's talk about data for a second, Josh. That is, and I, you know, as AI has matured over its short history, I feel like I have, it was like the tip of the iceberg of the problem that I started realizing. Like at first, when it first came out, was like, okay, well, if I give it a bad prompt, it's going to give me a bad answer. Okay. So bad data in bad data out.

[00:17:41] - [Speaker 0]
Like we know this problem, this problem has existed since the beginning of compute. And when we, so now that we have these super agents like Mars, Galileo Mars, how important is our data and connecting that data to be able to leverage these tools?

[00:18:00] - [Speaker 1]
Well, the data's important obviously because the system won't make the right decisions unless it has the right data about your company, about your people. The more data you can give it, the smarter it will be. But I actually have found that the challenge isn't usually the data because most companies have, you know, you can usually get the data, you have to decide what data you want. Correct. First of all.

[00:18:25] - [Speaker 1]
That's a little bit of a brainstorming process. And then you have to but you don't have to organize it that well. It's it's interesting. These systems are very good at understanding the structure of data without you having to tell it too much. So if the data's in Excel or in a flat file, it'll make sense of it if you label it reasonably well.

[00:18:43] - [Speaker 1]
What I think is a more interesting thing that's also going on is the prompting or programming process is now much more like consulting. So, you know, what you can, so for example, the way I think about it is you give Galileo a task or a project, but you also have to give it a rubric of rules because otherwise it's gonna just solve the problem in whatever way it thinks it should. But one of your rules may be, we can't raise people's salaries more than 10%, or we can't move people more than, you know, 200 miles from their home location or we never lay off more than 10% of the people at a time during a transformation or we like to lay off lots of people, so don't bother reskilling the people that don't look like a good fit because we like to lay them off. I mean, you give it We're reaching a point where these systems where you give it sort of Or here's our corporate leadership model. Here's the seven things that we teach all our managers.

[00:19:47] - [Speaker 1]
Make sure you're considering these seven things in the solution. And believe it or not, if you give it these, we call them rubrics or rule books, the AI sort of becomes like your company. And I think where this is gonna go over the next couple years is the AI is gonna tell you what the rubrics are. Gonna say to you, the way, I've been observing what's going on in your sales organization and you're quick, you're very quick to judge some of your salespeople when they actually need more time.

[00:20:18] - [Speaker 0]
Yeah, no. You know, a manager may

[00:20:20] - [Speaker 1]
not even realize he's doing that.

[00:20:23] - [Speaker 0]
Yeah. We, we just yesterday, I'm a part of this product that just got launched called One Guru. And it's like the idea of it is these, we call nudges because we realize so much of it is still like, tell me what I got to do. Like, know, you just tell me what I got to do. But the reality, as you said, it's so good at interpreting data and interpreting once you use it a little bit, or if it's in a company, it can interpret how the company uses it a little bit.

[00:20:53] - [Speaker 0]
It will be in a position to say, you're not hitting your targets this quarter. Have you thought about taking this negotiations course or what we know, whatever it is to start being embedded You as a proactive

[00:21:06] - [Speaker 1]
know, I think one of the big L and D use cases that is gonna become much more common as these things roll out is we've got a bunch of people doing similar jobs, sales as an example, or customer service, whatever. Yeah. Here's a bunch of people that are killing the numbers, they're just super successful, great customer feedback, high revenue, whatever. What are they doing that we don't know about?

[00:21:34] - [Speaker 0]
Absolutely.

[00:21:34] - [Speaker 1]
You you know, can talk to them, you can ask her, you can go on a sales call with them, you can listen to them, but they don't always know what they're doing.

[00:21:42] - [Speaker 0]
Yep.

[00:21:42] - [Speaker 1]
They're just good at it. Well, the AI knows, the AI could figure out the behaviors, the activities, how they're spending their time, whatever. Things like that are gonna be really transformational. And then there could be an alert to the manager that says, hey, you got a bunch of people that are falling behind in these domains because we see what the high performers are doing. You might wanna talk to them about it.

[00:22:05] - [Speaker 1]
So, I think it has a potential to become much more proactive.

[00:22:10] - [Speaker 0]
Yeah. And I remember at an Informatica conference, which is like a big data, you know, for those of them.

[00:22:16] - [Speaker 1]
Know them.

[00:22:17] - [Speaker 0]
Aren't they? So

[00:22:18] - [Speaker 1]
I know the founder of that company.

[00:22:20] - [Speaker 0]
Oh, do you? So, I was at

[00:22:21] - [Speaker 1]
one of It's side base. We actually invested in him. Yeah.

[00:22:24] - [Speaker 0]
Oh, really? Well, Informatica, they ended up getting bought, right?

[00:22:28] - [Speaker 1]
They got bought by either Salesforce Yeah. Or Oracle or

[00:22:32] - [Speaker 0]
Yeah. So, I was at a conference and they were talking about big data. And what they had said is they did a research with one of the medical, I think maybe the Mayo Clinic or something like that. And they had put sensors in the carpet of this retirement home and like thousands of sensors. And they took all of that data every day and used it to try to predict when somebody was going to have an event, whether that was they were going to trip and fall or a fall or something and predict ahead of time.

[00:23:07] - [Speaker 0]
And they were able to show that based off of like how deeply people put, like once their gait changes, it was a predictor. But what they said, said, isn't this a great use case? Now, obviously at scale, we can't really do this because we have to have somebody like analyzing these results that, you know, if we blow that up to what it is today, this it lives in everybody's laptop. Like the ability to do that lives in your laptop. Yeah, You can take all absolutely.

[00:23:32] - [Speaker 0]
Of those You're going to walk, you know, for me, every time I'd walk by a hot wing joint, it's going to say like, Hey Nolan, they've got a sell on chicken wings. Get your butt in there. Some chicken wings, which is good. That's what I want it to do.

[00:23:48] - [Speaker 1]
No, it's, it's, you know, to give you another idea, Nolan, of something else we're doing here. So in addition to all the Galileo stuff, which we use a lot, we have another tool which is which we call a digital twin. It's kind of a strange name for it, but what it does is it keeps it's AI. It keeps track of it looks at we use Microsoft. So it looks at all of the data in our Microsoft Outlook system and personifies each person's emails into a persona.

[00:24:18] - [Speaker 1]
So I can send a I can go online and I can have a text message with Barbara, who's one of our salespeople, and say, Barbara, when was the last time you talked to HSBC? What did you talk about? Well, what is there any are there any open issues I need to know about? And it responds to me as if it's Barbara based on her activities. Now, she can turn off things she doesn't want people to see and they you can set privacy settings.

[00:24:44] - [Speaker 1]
So I think this, this idea that the AI is almost ambient in the company, that's maybe a year or two away, but if you look at what Microsoft's doing with WorkIQ, which is their big system for connecting data and creating context, it's gonna be easier and easier for tools like L and D tools to diagnose problems and implement solutions almost on its own.

[00:25:08] - [Speaker 0]
Yeah. I mean, you talk about a big problem with the, with skills, right? It's validation. A lot of people will tell you like, how do we really validate somebody's skills? Well, a big use case of that type of tech is to say we can now interpret Nolan's skills because we are seeing what he does.

[00:25:28] - [Speaker 0]
Like we are seeing what he uses in Excel, like how he works in Excel, how he responds to an email, how long it takes to respond to an email. What does he show up like in meetings? Right. A lot of calls are being transcribed at the very least. How do they, how do they show up?

[00:25:45] - [Speaker 0]
And so it's really, like you said, it's it, it now knows you probably better than you end up knowing yourself because you're not able to pay it. You know, it's like you do those. I just took one yesterday. I did a coaching or a you know, one of those leaders, what type of leadership style are you? I had somebody on the podcast and they came up with the new model and I like, I love doing them.

[00:26:04] - [Speaker 0]
I'll take them. You know, it's like these 30 questions, you end up finding something else about yourself. You're like, I, that's probably right. I didn't think about that. You find your blind spot.

[00:26:13] - [Speaker 1]
We talked to, I talked to a CHRO the other day who records, he works for a tech company, records all his meetings and stuff and stores it in his own local AI. And he every Monday, he asks his AI, what did I do well last week and what did I do poorly and what could I do better based on these things I'm trying to accomplish? He used to think AI is like his personal coach.

[00:26:35] - [Speaker 0]
Oh my gosh. I never thought that is such a phenomenal use case. There's a, the, the gentleman who's the product owner of Airtable. He uses it. He loads in, they record every single customer conversation and he loads it into a table and he says, like, give me feedback about my product.

[00:26:53] - [Speaker 0]
Like, what are the, and it's like, like, and he's like, I can't even explain to you, Nolan. I don't even know how I would get to these answers in the past, like dig through millions of

[00:27:04] - [Speaker 1]
pages You'd talk to of customers and you'd assume that you got the right answer.

[00:27:07] - [Speaker 0]
Yeah. And assume those 10 of the right, but then you got to go do that every month. What are you kidding? But to be able in real time to track month over month, what is my sentiment of this product? I was like, woah, so really fascinating stuff.

[00:27:18] - [Speaker 1]
It's not a good idea to take the customer support calls and use them for product. Yeah. Yeah.

[00:27:23] - [Speaker 0]
Yeah. Yeah. It was really, fast. The way that they actually leverage it is these, know, Gong, I don't know if you're familiar with that, automatically records it all, throw it into a table, really good stuff. And they kind of built like a clay, if you know clay for marketing, but they built it more for like any use case and product is one of them.

[00:27:40] - [Speaker 0]
So, you know, anytime that we talk about AI and a lot of this technology and we know a lot of things we're talking about today, we've said humans couldn't even do that. That we're not even really in the past. Nobody did these jobs. They were just so hard to do. But there are a lot of things that is doing, like you mentioned, one of the things that Galleo Mars can do is to create content for me.

[00:28:02] - [Speaker 0]
Like my, me personally, Nolan Howell, like, hey, oh yeah, we recognize you need to learn a little bit more about this skill called data processing. You can go in Mars and say, can you tell me everything I need to know about data processing? Instead of just giving you an answer, can kind of build a course for you. And if you're an L and D, think of doing that at scale. It can actually build the course for you and you can edit that course and do everything.

[00:28:26] - [Speaker 0]
So with these things that are assisting us, what do you think, how will that actually change the L and D in the HR industry as a whole? Like the workforce, what is it going to look like if everybody is leveraging super agents?

[00:28:41] - [Speaker 1]
Well, that's a really good question. So, we've been talking about it for over a year and I'll tell you what we come up with. The phrase we use to describe where this is going is what we call dynamic enablement. So, many, many things that we do in HR and certainly in L and D are, we might call them development or training, but they're really there to enable somebody to do something better.

[00:29:08] - [Speaker 0]
Yeah.

[00:29:08] - [Speaker 1]
Or safer, or more quickly, or more productively. And, and we do that in an episodic, periodic way. We diagnose a problem, we come up with a solution, and then we try to train or improve the performance of the individuals. Imagine if that happened in real time, in the near real time, by the the learning and enablement platform, and it would be there to help you on a regular basis. Sometimes you might ask it a question and it might just answer for you and you might say, well, that's an interesting answer.

[00:29:45] - [Speaker 1]
I'd like to learn more about that. I have five minutes. Can you give me a five minute overview on that topic because I don't quite understand this aspect to it? Or, you know what? I don't understand it at all.

[00:29:57] - [Speaker 1]
I'd like to take an hour next week. Book me an hour on my calendar and teach me about it and develop me a thirty minute or forty five minute course. And and that's the way people actually learn. I mean, spent a lot of you you know this. I mean, everybody in L and D has read all the books on learning styles and, you know, there's so many things written about it.

[00:30:18] - [Speaker 1]
What I've learned about learning is that it is personalized. It's different for everybody. People learn by talking to others. Some people wanna be in a meeting and discuss it as a group. Some people wanna sit and read a book.

[00:30:30] - [Speaker 1]
Some people wanna listen, You know, the, the, the Notebook LM podcast from Google is unbelievably good

[00:30:36] - [Speaker 0]
for Unbelievable.

[00:30:37] - [Speaker 1]
I mean, unbelievable. Who would have guessed that two voices talking to each other would be the most interesting paradigm to learn? Mean, there's something about that paradigm that we had not thought of before, this sort of debating between two people. Yep. So, if you apply that to all of the issues that you have in a bank or an insurance company or a manufacturing company or anything you've got this, this, this enablement issue going on all over the company all the time.

[00:31:05] - [Speaker 1]
And, and AI basically takes you from a, a traditional training paradigm where you're building a solution and trying to personalize it for each person, which is very hard to do, to a completely different situation where everything is personalized for your needs. And when we show this to people with Galileo, it doesn't quite sync. What they often say is, Oh, I can build courses faster. No. That's the old way of thinking about it.

[00:31:36] - [Speaker 0]
Yeah. Yeah. The course doesn't the course builds itself in real But can time courses as solution. By the

[00:31:43] - [Speaker 1]
that's what your company likes. And sometimes people just want a course, they don't want to think too much, just teach Especially them what I need to if it's compliance. And then you sort of think of the word enablement. You know, one of the issues in enablement is, am I in the right job? Am I in Mhmm.

[00:32:01] - [Speaker 1]
Am I, is my manager the right manager for me? Am I working too hard? Is my shift correctly correct? Or am I schedule? Many of the things that we try to optimize in the whole areas of productivity at work that aren't just training things, other things, tools, have to do with enabling somebody to succeed.

[00:32:22] - [Speaker 1]
So, if that word help helps people understand this, that's, that's the sort of the best framing we have for the future. And then what happens to the L and D team is a whole bunch of people that were locked in a closet building courses, having a lot of fun, you know, kind of playing with stuff are now out there helping people enable themselves, watching what they're doing, watching what questions they're asking and saying, wow, you know, there's a whole bunch of people in the company that don't understand this. We need to build more curriculum. We need to get more expertise. We need to do a webinar, whatever it may be.

[00:32:58] - [Speaker 1]
So so we think L and D will become more distributed, more decentralized, more in the business. Because if this stuff works as well as we think it does, many of these enablement problems are gonna be done by the enablement group in the business, not by L and D. And so either L and D will be redistributed into the business or the functional areas, or there will be enablement managers that feed the AI to make sure that it's current on the latest things going on in each domain. So if I'm a if I'm a legal department or the, IT security department, I don't wanna call L and D when I wanna train people on something. I just wanna take the new stuff and put it out there so everybody can get it.

[00:33:42] - [Speaker 1]
So, think it's going to end up with a much more business centric, decentralized L and D function.

[00:33:49] - [Speaker 0]
Yeah. I think it's going to, you know, you called the enablement, I think like the enablement performance, whatever you really want to call it. Think we're talking the same thing. It's, it's the idea that, you know, maybe you're going to be partnered with a marketing, you know, in the marketing stack or, you know, for the, for the digital marketing team, whatever. Your job isn't to build programs for digital marketers.

[00:34:12] - [Speaker 0]
Your job is to look at the talent that lives within the marketing team and understand who's stuck and why and what do they do? And, and, and is everybody stuck on the same for the same thing? Why are they stuck? And I mean, eventually, right? A lot of these tools will even give you that nudge, you know, to say, Hey, I think your team might be stuck here.

[00:34:31] - [Speaker 0]
Let's go ahead and do that and say, Oh, okay, well they're facing the same problem. What is the answer they're getting? What is the content that they're pulling from? So really your job will be focused on how do I lift the performance of this org or of this department or of these groups of people. And you're now given this superpower, this like super tool, super agent,

[00:34:51] - [Speaker 1]
It's to use your true because I mean, I think some percentage of an L and D or HR person's job now is to encourage people in the company to feel like they're super powered people and to get the answer without waiting for somebody to spoon feed them. Yeah. Because it is much more accessible. I mean, I mean, I think the, the people that are the more creative, value creating, you know, maybe self confidence is the right word, individuals are gonna thrive in this world.

[00:35:28] - [Speaker 0]
Yeah. Every CLO, CHRO I talk to, when I ask, what are those skills? It's all, you know, do they know my business? Do they know performance? Are they curious?

[00:35:39] - [Speaker 0]
And like, are they willing to work with ambiguity and kind of live in that gray space? You know, being consulting, like I just, I, it's the consulting mindset. I think that's really going to get a flourish. Well, you know, Josh, as we look to wind up the podcast, I'd love to just kind of take a, maybe a broader look, you know, we've taken it at a micro level, what's happening with the tools and technology. What is that going to change the workforce?

[00:36:03] - [Speaker 0]
If we look at maybe 2026 and the, you know, maybe just starting with 2026, what do you think those huge trends are that are real or what are the huge trends that are on the minds of these talent leaders, the problems they're trying to solve?

[00:36:19] - [Speaker 1]
Okay. Well, report we call, HR 2030, so you can look ahead, but maybe just taking where we are today. Right now, what every company is struggling with is I got a bunch of legacy stuff. I've got Degreed, I've got LinkedIn Learning, I've got Cornerstone, I've got Saba, I've got- what are we going to do with this stuff relative to the new stuff? Are we going to wait for them to upgrade everything?

[00:36:45] - [Speaker 1]
Are we going to start over? Or are we going to just I don't know. So, to give you an example of what we did, and I'm just talking about L and D. So, we had an online academy that took, I don't know, five, six, seven years to build with hundreds and hundreds of things in it. And we we we get the Galileo stuff up and running, we're scratching our heads and we're thinking, what are we gonna do with our academy?

[00:37:11] - [Speaker 1]
And my original reaction was, I think it's gonna take us at least a year, maybe two years, to get all that stuff into the AI because it's very carefully put together and, you know, meta tagged and everything. Well, we did it in four months.

[00:37:24] - [Speaker 0]
Oh, my word.

[00:37:25] - [Speaker 1]
And thank God I had somebody here who pushed me to do it. I think there's a lot of fear about moving to the new world, and people Mhmm. Some companies are moving much more slowly than they probably should. But as one of the clients said to me the other day, this is not a situation where we do a pilot and see how well it works. This is more like rip off the band aid, describe the future, and start going down there that that path now.

[00:37:55] - [Speaker 1]
And in 2026, everybody is caught right now in confusion. Mhmm. Because the vendors are throwing out lots of prototype y, near perfect but not finished products. And it's not clear if we're gonna go from an old platform to a native new or keep the platform we have and wait for it to evolve. And I think that's very company dependent on how much risk the company can take or wants to take.

[00:38:22] - [Speaker 1]
I'll just tell you, in my situation, we're not a super big company, so maybe it was a little easier for us. Thank God we did this and we pulled off the band aid because we're in a new world. Can iterate and grow so much faster now. And we're figuring out the new business model, you know, now that we have this more scalable solution.

[00:38:41] - [Speaker 0]
Yeah. It's interesting. I've the, the arc that I've seen our company take is, you know, cause you have like a micro adoption and a macro adoption like micro, how did you adopt to it? Macro, like how did everybody else adopt to it? But they all fall like that same trend of like, Oh, this is cool.

[00:38:58] - [Speaker 0]
Let me check it out. Oh, this sucks. Everything they give me is wrong. Oh, I was the one that sucked. It's just doing whatever I gave it.

[00:39:06] - [Speaker 0]
Oh, this is the greatest thing I've ever seen now that I know how to use it and give it the right answers. And so I'm seeing like this arc and I, I'm noticing it even within my own team, we just pushed everybody to the single enterprise LM and, and it is kind of the tipping point for us where I'm saying, listen, you know, like the world, you must now live in this LLM. You have to, like, you don't have an option and, and it's going to change how a lot of things are. Cause like you said, I don't really think there is another option to just have a little, you know, there are a lot of companies that are trying to kind of bolt on AI into all of those tools you just talked about, right? My bolt on Degreed AI, my bolt on AI to LinkedIn, bolt on to this, but I don't, you know, even if that's an option, that's an okay option.

[00:39:53] - [Speaker 0]
But I think the ones that are going to get the most bang for their buck are the ones who are realizing that the more centralized I can, you know, bring all my data in and keep my AI in one location, the better it's going to be because it's learning. It will just continue to learn and get better.

[00:40:10] - [Speaker 1]
The other issue is the company itself. I mean, we were in Asia with a big bank, a big US bank, and we were showing a bunch of people the new stuff we have with Galileo, and they were blown away. They're like, Oh, this is so fantastic. We could do this, we could do that. And I said, well, do you guys wanna you want us to send you a proposal?

[00:40:27] - [Speaker 1]
They said, well, we can't do anything until IT says it's okay. So I'm sorry, we can't even issue you an RFP right now. We're waiting for them. I'm like, okay, I gotta go all the way back there and talk to them or maybe it's not worth my time. Yeah, yeah.

[00:40:43] - [Speaker 1]
And that's the way some companies are, but other companies are saying, hey, you go come up with a good idea and if it works, we're gonna spread it around. And, and I think I think there's more focus on central decision making now because these decisions are starting to pile up. Oh, these guys bought this and these bought this and these guys bought this. So, it could slow down a little bit while some of the standards get established, but, it's it's happening very fast.

[00:41:10] - [Speaker 0]
Wonderful. Well, Josh, if people want to learn more about Galileo Mars release and just the overall insight, what's the best place for them to go?

[00:41:19] - [Speaker 1]
There's a whole website called getgalileo.ai has all the Galileo information in it. We have a whole success center with videos that show you how different things work. And then the main website of ours is joshperson.com. It's it's we're we're redoing it, but it's filled with information about all of these topics. And then we have a very, very highly subscribed podcast, which I do a lot myself, that has just tons and tons of education about the market and AI and L and D.

[00:41:53] - [Speaker 1]
You know, keep up on that. So those are all good resources for people.

[00:41:57] - [Speaker 0]
Lovely. Well, Josh, thanks so much for joining us today. It's been a pleasure having you on.

[00:42:01] - [Speaker 1]
Thank you, Nolan.