Episode Transcript
[00:00:00] Happy New Year, everyone.
[00:00:02] It's the first week of January and I just finished listening to a long series of discussions of university presidents talking about the future of their educational programs and the role of AI and careers and how difficult it is to find a job. And so I thought one way to kick off the year was to talk about how do we learn and educate ourselves in this world?
[00:00:27] Because there's enormous complexity and confusion about what we need to learn, how we learn it, and what the relevance of it will be. And I'm going to give you a lot of information from my own experience studying learning, but also our experience as a learner, as a learning organization, and as a small company.
[00:00:47] The first thing that I think you have to remember, regardless of how big the AI market is, is that the value of AI, or any technology for that matter, is problem solving.
[00:00:59] And the problems that we solve in our companies are not technology itself. They're problems of products, customers, industries, internal operations, people, culture, or other innovations that we bring to market as a drug company, as a manufacturing company, as, as an engineering company, as a construction company, as an entertainment company, or as any other company. And these domain specific problems in your industry are the reason your company exists. They're the reason your company was founded. Because you wouldn't exist if you didn't do something in that domain that was new and different.
[00:01:38] Now, given that we all, as business people, need to understand that domain or some part of that domain. If you decide to choose to study data science or machine learning or computer science, that's great. That is a profession. But that is not a problem. That is a domain or a functional specialty, like my functional specialty in mechanical engineering, that can be applied to other things.
[00:02:05] So if you look at the problem that computer science students face today, where it's hard for them to find jobs, that isn't because computers aren't important. It's because the number of jobs building computer systems to sell them per se isn't that high. It's somewhere around 5 or 6 or 7% of the workforce that builds the technology. The other 90 to 95% of us use the technology.
[00:02:29] So that's the first thing I would say, is don't ignore your knowledge and expertise about the domain you live in and the domain of your business just because AI is so popular and interesting at the moment.
[00:02:43] Number two, your experience and ability to learn in general. You know, for me, having studied engineering and being sort of a bookish person, I think I, I don't know where I got it. Maybe it's genetic I always enjoyed and found myself leaning toward understanding something deeper. And it may go back to my family, but. So when I studied engineering, I actually did read the books, I did do the homework, and I did ask the teachers to explain to why something was happening or why something was true. And that gave me a particular style of learning where if something is not clear to me why it's happening, I have a little bit of an itch. And I will go back and I will look at a book, I will go to YouTube, I will go online, I will ask ChatGPT or Gemini, and I will try to figure out what is going on. So this problem solving, understanding, grokking, grok is a word, by the way, that came from Robert Heinlein, not from Elon Musk was born in me, maybe when I was born or early in my life. I think that's number two. You have to believe in yourself that you can learn new things. And you have to set aside the time or the energy to teach them to yourself or explore them. Now I have this debate a lot of times with my wife about silly things like how to use the PC. And what I oftentimes discuss with her is the reason I know how to do some arcane, stupid little thing is because I took the time to learn it, because I knew that eventually I would use it a lot. And that's what this exploratory curiosity, domain of learning is about is this belief that if I take some extra time to read or learn or ask or watch or take a course on some topic, that I will eventually use it a lot and it will make me a better professional or a better person. And I think we all have this. I know those of you that have chronic diseases or medical issues or things going on in your family or kids, why do we read books about how to raise kids? Why do we read books about how to get better exercise and reduce our heart pressure or whatever? We're always trying to learn something to make ourselves better. And that curiosity that nature is number two. Now, it turns out right now, because AI makes information so accessible, you can learn things faster than ever before. Because you can literally ask one of these agents a question and it will show you the resources and find them for you. So that's number two.
[00:05:17] Number three is depth.
[00:05:19] You know, I think most of you know this, but let me just reinforce it. The key to a high productive, high value, high reward career is to be really, really, really good at something, something, whatever it may be, and it may be something that's fairly narrow or unique to you, or maybe something generic. But the challenge that we face at work is that there's a lot of dilettantes everywhere you look. There's a lot of people that think they can do whatever it is you think you can do, and then you get left out of the conversation. But if you're really good at something or really expert at it, or really experienced at it, what, or really deep at it, you're going to stand out and you're going to find opportunities to do things that nobody else can do. That's what happens in universities. The reason universities reward PhDs, which take years, and college professors who study the same domain for their entire career is they become more and more and more expert and more valuable over time. I happen to be doing a lot of research on genetics the last couple of years, and I was just reading a magazine from Yale about a Russian professor who's becoming quite an expert at the use of peptides, which is a chemical compound that contributes to the protein systems in our cells. And he basically is a learner about all sorts of things that got him to this point. In other words, he studied chemistry, he studied biology, he's studying economics, he's studying cell creation, he's studying genetics, because it's making him better and better and better at this research to try to solve some of the problems of chronic disease.
[00:07:00] My father, who was a physicist by training, got involved in chemistry when he was working in the manufacturing of silicon wafers in his career and never stopped reading books about chemistry. He just was always more curious about different aspects of the chemical process and how they could possibly be applied to this domain that he was working in. That goes for change management, that goes for leadership, that goes for recruiting, that goes for assessment, that goes for pay. That goes whatever it is you're doing, if you are going to do it, do it all the way. Go for it, just do it, whatever phrase you want to use. And if you don't want to do that thing in depth, maybe it's not the right career for you. Because when people find things that they love, they don't mind spending time becoming experts at them, whatever they may be. You know, the problem, you know, that a lot of young people have is they find a domain that they really want to learn a lot about, but they're not sure if there are jobs in that domain. They're not sure who's going to hire them. That's okay. I mean, my experience as an older person is that pays off over time, regardless of what the job market looks like at the moment. So that's number three.
[00:08:11] Number four is building an ecosystem of learning with other people and other sources of information.
[00:08:19] There's the word diversity has become sort of a dirty word, unfortunately. I hopefully this period will pass, but I think diversity is the right word for learning. Many of the things that teach you a skill or a job or a career or a profession that make you more useful and successful come from places you don't expect.
[00:08:42] Going to conferences in cities or countries that you've never been to before. Learning about an industry that has nothing to do with the industry that you're in.
[00:08:51] Meeting someone who's in a company that is radically different from the one you work in. Talking to people of different ages who have different experiences in a certain domain. Young people see the world differently because they don't have the maybe legacy experiences that we have. Sometimes our experiences actually make us a little bit less of a learner because we think we already know things. This need or interest in diversity I've seen in my career pays off as you move deeper into your career. Now, most senior people know this, and that's why there's so many conferences and events and workshops and so forth for senior execs. That's why executive education exists and so forth, is to create those connections. But I think, you know, if you look at a way, the way a university works, the reason the university is called a university is because they have learned that by bringing multiple disciplines together into one place, the state of knowledge, the state of learning, the state of creativity, the state of innovation moves much, much faster. I spent a lot of my time growing up in Berkeley, California, and I spent a lot of time on the Berkeley campus as a student, as a graduate student, and as a business person selling stuff to them, actually. And it is one of the most interesting multidisciplinary places I've ever been. And many of the people I met when I was younger were from many, many different domains. And they, they had a culture, and they still do, of many, many interdisciplinary things. And so we can learn a lot about that, our own personal careers from that experience that universities have also discovered. The final thing that I would mention in terms of learning is experience or skills. I hate the word skill. I think it's a loaded word. It gets used for many, many things. And I'm not going to spend a lot of time talking about it. But all of these things that we learn or read about or talk to people about or discover or take tests on are no good if we don't Use them. Because it is in the use and application of these new capabilities that we actually discover the nuances, the bended corners, the dead ends that really, really make us successful. And I learned this, by the way, during my seven years or so in consulting at Deloitte. I didn't realize at Deloitte that there was no real expertise database. I thought when we were acquired by Deloitte that I was going to join this company and find armies of experts in different domains. And interestingly enough, they're there, but there's no list of them, there's no database of them, there's no university of credentials at Deloitte, at least not when I was there. But there are people who have huge amounts of experience at different things. And those individuals are well known for their expertise because of their projects, their clients, the things they have done. And if you look at your career, you look at your LinkedIn profile, your bio, what you tell people about yourself when they ask you, what you tend to value is what you did, not what you learned. Because it is then the doing that we learn the most. So that means you need to do things, you need to try things, you need to get involved in new projects, you need to join a team that's doing something that you've never done before. You need to volunteer or experiment or take a class that forces you to build something or do something or understand something. That's why we do. Case studies is such a fundamental part of our research. You know, the way our research works, we really don't sit around and pontificate on what we think the future of HR or leadership or learning or anything else is going to be. We do a little bit of that, but most of it is talking to you and identifying discoveries that you are having every day. Because in our domain, in the world of work and human capital and HR and technology, the discoveries and the applications are the learning. If you look at a product manager at Workday or SAP or UKG or any one of these companies, if they have not been in the field working with companies, doing things in different direct, in different industries, they can't design a product. They don't really know what to build. And that's true for us in hr. And the most most interesting and inspirational HR people that I run across, and there's many, many, many of them come to me to talk about things that they have done or projects they've worked on or experiences they've had or challenges they're facing. And we always talk about what other companies have done. And so the fifth thing I would say about your career and your learning is jump around, try new things, change companies, change roles, work in and out of the business, get involved in projects, team with people that you admire, and you will learn enormous amounts just from the experience. You know, when I studied engineering back in the 70s and I went to work for an oil company because I wanted to work in the energy industry, I didn't really know, and I was very naive that most of the engineering sciences that I learned, which I was fascinated by, were never used in the actual applications of engineering in the oil industry, because 90% of the engineering that goes on the oil industry is done out of a book of best practices that was developed by scientists and engineers many years ago. And the actual engineering that's going on is the application and use of stuff that somebody else already invented. And for me, as more of a theoretical thinking type of person, I was very, very disappointed. And I ended up going back to school and eventually ended up where I am. But that's the way I look at our jobs in business. You can read a book or go to a course or go to a lecture on any particular domain, whether it be culture, psychology, leadership, learning, assessment and so forth. And it's. It's going to teach you a lot of fundamental principles, but unless you've used them, you're not going to be that good at that domain. So that would be my final sort of recommendation is in the world of AI, get your hands dirty, try it, use it, get involved in projects, and you will find that you learn enormous amounts from that. And that's why so many educational programs involve projects and homework assignments and things that get people to do things, not just learn about them. Now, we're going to be launching on January 21, our imperatives for 2026.
[00:15:25] And there's lots and lots of stuff in there about lots and lots of things. I won't bother to try to summarize it right now, but the bottom line is this year that we are entering is going to be a year of many, many new domains. There are going to be many things that are going to pop onto your radar screen and you're going to scratch your head and say, I don't even know what that word means. I don't understand that. What does that mean to me? Should I ignore it? And because we're at such a, an experimental, in a sense, stage of AI, I was having during New Year's Eve, I was talking to somebody I know about AI. He was asking me my opinions, and we were going back and forth. And I said, look, I don't think we're even in the first inning of this yet. I think we're in the first half of the first inning. The discoveries and the applications and the solutions and the technologies that have yet to come are going to be acceleratingly fast. They're going to go faster and faster and faster before they slow down. So your career, whatever role it is, is going to be very, very dependent on your ability to learn. That's the reason I decided to put this into a podcast. I also think we're entering a world where really much to our benefit, we're not going to be just buying solutions, we're going to be building solutions. And this is a big theme of the imperative study is AI is a tool for building things. It's a tool set or a technology that allows you to take the domains of expertise and knowledge and data and business processes that you've been working on and institutionalize them and automate them and make them more autonomous and make them smarter and make them more integrated and build people better experiences from the things you do in your company. The tools we buy from the outside, from the vendor market, are not total solutions in the world of AI, because every AI application is very dependent on what you do in your company and the data that you have and the experience and the knowledge and the direction your company is trying to go. I've talked about this in the last podcast. A generic AI that does insurance risk analysis is no good if it doesn't have your company's insurance policies and insurance information in it. Same thing for recruiting, same thing for learning careers, pay equity analysis, whatever it may be. So we're going to be entering a world for the next few years where a lot of the solutions are going to be things you buy and build, not just buy and implement. And that's going to push you to really hone your learning skills even more. Okay, it's going to be a very, very exciting year. We are absolutely thrilled to be here to help you and support you and try to educate you as much as we can. My experience in our company of about 45, 50 people is that we are learning, too. You know, we're not PhDs in computer science. We know enough to be dangerous, but we learn, we ask questions, we try things, we experiment, we get a lot of information from third parties, we ask you for help. And I think, you know, maybe we are, in some sense, are a microcosm of the team and the group and the department and the organization that you're a part of that in 2026, at least. @ least for the next couple of years. We're going to be learning a lot. So think about these principles in the year ahead. I hope you have a nice holiday and we will start next year with a big bang starting next week. Thanks for listening. Bye for.
