[EP 9] AI and the Art of Problem-Solving with Kian Gohar

Foreign. Welcome back to the Jack Canfield Podcast. I'm Jack Canfield and today I have a very special guest joining me. Someone I'm proud to call a dear friend in the world of transformation and leadership. Kian Gohar is a visionary futurist, I love that term. A best selling author and a key member of the Transformational Leadership Council which is a gathering of about 150 people that I started of leadership leaders in this transformational world that are making a huge difference in the world. We were originally going to record this in Ireland at our meeting and somehow we couldn't make it happen. We were talking before and Kian, who's an expert in AI, said AI is easy, AV is challenging. So but here we are, it's working today. Now, as a former executive director of the Xprize foundation and a faculty member at Singularity University, Kian has a wealth of experience in innovation and cutting edge technologies. And as the founder and CEO of geolab, a well respected leadership development firm, Keon has a wealth of experience helping organizations tackle complex challenges. And his work includes having made a profound impact on the leadership teams of numerous Fortune 500 companies, guiding them towards a future focused approach that embraces change and opportunity. Keane also recently co authored the best selling book Competing in the New World of Work with Keith Ferrazi which focuses on how to become and stay competitive in the new post pandemic context. Today we're going to dive into Keon's latest research on generative AI. AI of course being artificial intelligence and its potential to augment human creativity and transform team collaboration. So Kian's a futurist and he's made an unwavering dedication to empowering individuals and organizations. And I'm looking forward to discussing Kian's exploration into this field which promises to open all of our minds to the boundless possibilities that lie ahead of us. So welcome to this conversation with Kian Gohar. Welcome Kian.

Thanks Jack. It's a pleasure, pleasure to be here.

So let's start with you sharing a little bit about, you know, I've introduced you a little bit, but give us more about your background that led you to becoming a best selling author, a futurist and a founder and CEO of Geolab. And most people probably don't know what the XPRIZE is and what Singularity University are, so if you'd include that in what you share.

Yeah, sure. So I have an unusual background, not only professionally, but also personally. I was a child war refugee and we moved around many countries when I was very Very young, and I went to 10 different schools before the sixth grade. And all of this, as you can imagine, had a lot of impact on the way I think. And it made me very nimble and adaptable to change and wired my brain to how I see the world. And I see the world. That change is constant. It's something that you can't avoid. And so we as humans have a choice to make of do we try to resist the change or do we try to make the change for ourselves? And so my work really helps teams across the world, organizations large and small, understand how to be more innovative and harness change and disruptions to our benefit. So, you know, I've reinvented myself many, many times in my life. Early in my career, I was in venture capital. I was the youngest business school professor ever in China. I was an entrepreneur, I was a non profit leader. And you know, now I do team business transformation work, as you mentioned. So my background over the last 10 years, I was an executive director of the XPRIZE foundation, which is a nonprofit based in Los Angeles that crowdsources the world's genius to design moonshots to solve complex problems for humanity. So what does that mean? That basically means we put together competitions, anywhere from 10 to $100 million, and we ask the world to solve these technology challenges. And teams get together, come with ideas, and they experiment with them, and then they launch them into the world. And if they meet the objective criteria, they win the X Prize. And the X Prize is most well known for our first prize, which was a $10 million prize for opening up the space frontier to private space travel, which ultimately was won by a company that became Virgin Galactic. I simultaneously was an executive director at Singularity University in Silicon Valley, which is an institution founded about 15 years ago to teach about the convergence of these technologies like AI and robotics and bioinformatics, and really help train individuals, entrepreneurs, organizations, how to learn to use these exponentially growing technologies to really transform the world for the better. And after doing this for many, many years, I came to the realization that the best way for us as humans to compete in a world that we have to work side by side with AI is if we lean into those parameters that give us competitive advantage as humans. For example, complex problem solving, creativity, empathy, emotional intelligence, resilience, all the soft stuff is actually pretty hard to do. And so my work now really takes the best of all. I've learned about technology and innovation over several decades, but then applies human behavior transformation best practices to it so that we can learn how to Live successfully in this new bionic age.

Wow. I like it. This new bionic age. The Bionic man was a TV show like God knows how many years ago. All these terms are coming to fruition now. Your research on what we're calling generative AI sounds fascinating. Can you talk about exactly what generative AI means and why you find it so compelling as a topic to be studying?

And so generative AI is a type of artificial intelligence that's really designed to create new kinds of content, like new text, new images, even new songs from scratch. And unlike other types of AI, which is really useful for training and recognizing and classifying data, generative AI is really focused on creating something new. We've had AI for many decades, and there are many different versions of or useful implementations of AI, and generative AI is the latest one that has gotten a lot of hype. And that's partially because over the last year, there has been a flurry of new applications and softwares which your listeners will be familiar with, like ChatGPT or visual image generators like Dall? E and Midjourney. And these have made it very easy for the average individual to. To use generative AI to create complex new things. And I'll try to explain it in a very simple way, because it can be very complex. But basically, what generative AI is, it scrapes the web and millions for millions of images and millions of words by looking at patterns between words and in a sentence. So imagine, for example, generative AI is like your Google auto search, but like on exponential crack, it's trying to better predict what comes next in a series of words. And the way it does this is every word in a paragraph or in a sentence or in a phrase is broken down into a very small segment, what they call tokens. And then generative AI creates a mathematical model of what is the most probable word that should come after the previous word. And this is based on having scraped billions of words across books and the open web and Wikipedia and Reddit to really learn how to understand natural language. And once it has this mathematical model, it can then predict what comes next in a word or what comes next in a sentence. And so it's really powerful for us to think about how we can use generative AI to come up with new ideas, to analyze existing problems, and even to really come up with new forms of creativity. And I'm just so excited about the potentials for how it can help us do things that we have never before been able to do as quickly give.

Us an example of that. I Mean, I've played around with like, you know, write a limerick using my wife's name. And, you know, I asked it to create a painting of the end of time at the edge of a volcano with horses dancing on it in the style of Salvador Deli. Deli. And it did it, you know, and so, I mean, I've had fun with it. But talk about some of the more, let's say, commercially valuable, you know, working in the companies you do and so forth and then talk about that, then we'll talk about some of the dangers. And now we can avoid that. Make sure we use this for the good. But go ahead.

Yeah, sure. I've been researching generative AI for the last year with my dear friend Jeremy Uttley, who teaches at the Stanford Design School. And we've been trying to understand how can generative AI be used in a corporate environment to solve new problems. And so we partnered with nearly a dozen companies all across the world and we facilitated ideation workshops with hundreds of participants from industries as far ranging as energy to finance to manufacturing. And we're really trying to understand how can teams, whether they're innovation teams or any kind of team, incorporate generative AI into their human workflow for problem solving. And so some of the problems that they were trying to solve for, for example, include how do we develop this new product, or we have this idea for a product, how do we go to market with this new product, how can we, for example, train our employees to be better at customer service? How can we train our employees to be better at sales? And so we created a very interesting data set of companies from many different industries, different kinds of problems. And we're really trying to understand, okay, how can it help us solve some of these problems? Because that's ultimately what innovation is. Innovation is, is about coming up with an effective solution to a problem. And what we found, if we just finished the research over the last couple weeks and we're about to publish, we're really excited to publish the results is that generative AI is a very useful co pilot in the problem solving sphere. So if you are trying to come up with, let's say, the best idea possible to solve a particular problem, you need to think about two factors. One is the quantity of ideas you come up with and also the quality of ideas. So why does quantity matter? Quantity matters because we know from our research that if you have a gradient of ideas, let's call them A ideas, like amazing ideas, and average ideas like B grade or C grade, and then D grade ideas which are not so great ideas. When we think about problem solving, we want a entire spectrum of ideas across all of. We want really good ideas and we also want really bad ideas. Because effective problem solving is a, is a result of having a lot of ideas, including talking about, for example, the elephants in the room or the really bad ideas, which are just a step away from having a good idea. So generative AI can help us actually generate a lot of ideas very, very quickly. So that's important. And then you want to good ideas. Now, in order to get good ideas, that requires actually context and human judgment and human discernment. And so our research actually found that if you leave generative AI to the tool of solving a problem by itself, it tends to actually generate average ideas or good enough ideas. And the reason for that is because, like I mentioned earlier, the models are based to give you the most probable answer or probable next word in a phrase. So the average answer, and so that in itself isn't that interesting. But we found in our research was that if you learn how to use generative AI and reinvent your workflow of how you as a team solve a problem, when do you bring in the tool to ideate and come up with better solutions, and then you as humans choose which of these solutions are the best ones. That's when you're able to actually have ideas that are consistently more towards one end of the spectrum, which are A and B, great ideas. So we found that generative AI can really help us come up with new ideas to solve problems. But the caveat is that we as humans need to learn how to incorporate it into our workflow. And we're right now not doing that very well because we're going to, for example, ChatGPT and we're asking it for an answer. And, you know, it's, we're basically thinking of it as like an oracle or as like an encyclopedia. And those kinds of conversations tend to give you average answers. What we found in our research was that if you have a conversation iteratively back and forth with a large language model like ChatGPT, you end up coming with much more nuance and interesting ideas than if you just ask it to give me, you know, how many states are there in the United States, like an encyclopedia answer. So all this to say is that teams have to reinvent their workflow of how they work as humans, how they bring in generative AI when they bring it in, and then how they evaluate those kinds of ideas. And that's the kind of work that we're doing. And we just finished this research and so excited to publish the results very soon.

I'm curious just for our viewers to take this out of the, I don't want to say philosophical, you know, broad scope conversation to grind it down into reality a little more. So let's take for example, you said you, you could ask it how to improve sales or how to improve customer service and then you get a bunch of ideas. And then you said you, you know, iterative. I assume you mean you ask another question based on the, the answer you got and you keep asking more questions to get more deeper, better, wider answers. Give us an example of something that you did that would give us a sense of like how that was unfolding.

Yeah. So I'll give you a model of how you can use it. A very simple five step process. We call it Fix it and Fix it. It's an acronym, F I X I T. And I'll walk you through it. Very simply, F is start with a focused problem. So in this situation which you said, how do we improve sales? That is a too broad of a question. And so you want to focus on a narrow question. So for example, if I am developing a new product and my goal is to improve sales by 10% in this market and this product, give me some recommendations of how I can do that. So you can see how we narrowed the question significantly. We focused it. It's very, very important. You don't want to try to boil the ocean. So that's the first step. The second one, that's a good one, really, honestly. The second one is I and it stands for individual ideation. So we want you to work solo first because we have human biases, we have expertise, we have knowledge. We want everybody to start thinking on their own, spend 10, 15 minutes to try to come up with solutions or ideas to a problem individually, without your team and without AI because we don't want the bias to come in first. That's really, really critical and very important in the innovation process. The third step is X. It stands for context. When you are trying to solve a problem, you can't just go to ChatGPT and say, okay, again let's use that sales example, help me improve my sales by 10%. Well, it doesn't have context and so you have to actually input information to the training data to be able to, for it to give you better output. So you know, output is a result of good input. So what we recommend is you have to feed a training model or large language model context. And it's really simple. We would recommend a minimum of like 4 to 500 words of a description of the specific problem that you have. Maybe it is a marketing document you already have, or maybe it is a PDF or maybe there's some data set. You can upload this very easily to ChatGPT or any other kind of large language model to give it context. Then it has the nuance to give you actually better answers. So that's the third step, give it context. The fourth step is again, I for fix it. And that is for interactive conversations. Like I said earlier, you want it to ask you questions and you're having a conversation like you're having with somebody because that's going to get you better answers for you asking a very encyclopedic answer. And so think about like how you and I are having a conversation. We're having like, you know, a friendly conversation, but then we can veer off and talk about like, you know, personal stuff, or we can talk about having emotional conversations. We want to have a natural language conversation with a chatbot. Remember, it's called a chat bot. It's not just a bot. So focus on the chat part. And that's what we mean by the interactive interactions in the conversation. And the fifth step stands for T. And that's the team incubation. That's when you finally go to your team and say, okay, I thought on my own, here are the ideas I generated. This is the conversation I had with AI. Now let's all bring it together, put it all on the table and let's identify and prioritize based on our goals, which of these ideas make sense. And we found in our research that you have to have one of two kinds of people in this T team incubation process. They do the same role. One of those individuals is an internal person who has the understanding of these AI capabilities to be a really good facilitator. And right now that's a pretty rare commodity. Or you can hire somebody as an external facilitator like us to help you actually curate these sessions to allow you to then figure out, okay, we've identified all of the ideas that are relevant. Now let's remove the biases and let's facilitate the right kind of next step. So fix it. Remember, fix it is a five step process. Focus, individual, solo thinking, X for context, I for interactive interactions. And then five, you go to your team and have a final conversation. And this has been really powerful for our clients and our research partners to now figure out, okay, when and how do I bring in generative AI into my problem solving methodology.

And when, when you've done this with clients and also in the research aspect, that is how long a process? I mean just, I imagine it's different for the different kind of problems and so on and so forth. But you know, to get from the F to the T, what, what's that look like?

So you can do this all in a couple days. So the first part of it, which is, you know, the team leader or the problem owner says, you know, this is, we need to focus on this particular statement. And so that's the role, the responsibility of the problem owner or the team leader, whomever it is, says, you know, we have this narrow problem, we gotta, we gotta, we gotta solve for that is the role of that individual to identify the narrow focus. Then what we recommend is giving each person on the team two to three days to work on it on their own. You know, during work or maybe asynchronously if you're working remotely. This is just quiet time for yourself. And this is a about, I would say again 15 minutes at the very minimum of individual thinking to then having a conversation with generative AI model that could be about an hour of your time over the next two to three days. And then what we recommend is you actually schedule that final piece, the T, the team incubation conversation, two or three days after everybody has had time to do the individual work first. So you can schedule this over a five day period, but it's not a five day workshop. It is a interactive process that can be done asynchronously for most of it until the very last step, which is the team facilitation process. And that individual, that team workshop can range anywhere from 90 minutes to 3 hours depending on the complexity of the problem that you're trying to solve.

For right now you also focus on leadership in your work. How do you see AI if it can reshaping the future of leadership and organization? What challenges and opportunities do you think it has there?

Well, I mean one of the things that I just mentioned is like, you know, you have a problem owner and now they have a new leadership role of identifying what that specific problem is and then being able to outline that for your team to solve a problem. We have never had that before. At least you know, in this technology world. We now have a new problem using. How should I use generative AI to solve a problem? So new leadership goals right there. What we found really interesting, we measured teams behaviors as they were going through this research process. So we're trying to find out how are teams feeling? How do they feel like that this is impacting their behavior one on one and with their bosses? What we found was that teams that use generative AI showed a 21% improvement in their confidence levels to solve a problem. So that just by itself means if you start using generative AI, your team is feeling more connected and willing to actually think that they can solve a problem better. So all of a sudden, you've improved a little bit of workflow. But there was a twist to this research. We found that the teams who thought generative AI was magic and was solving these problems so easily actually came up with kind of average answers because they were settling for good enough. They were just checking the proverbial box. But the teams that came up with really good answers to their problems actually felt like using generative AI was more work. And it was more work because they had to step back, reassess some of their assumptions, and learn new behaviors like they're learning a new sport. So all of this is the new world of work. And as a team leader, as an organizational leader, we have to be aware that people have to learn new behaviors. It just doesn't happen overnight. And so you have to facilitate that kind of training and that kind of practice to help your team actually reach the potential for using AI in a. Well, in a way that's very, very effective. That's really on the team level. Now, if you just go macro a little bit and think about, you know, what is the impact on the quote, unquote, future of work. Now, I honestly hate that phrase. I don't think it means much because I think the future of work is the president of work. Right. And so, yes, there's a lot of hype in the media. There has been for generations about technology taking away jobs. And the truth is that absolutely is a case for many kinds of tasks. We believe that we're at an age right now where generative AI can automate many kinds of specific tasks in a job. It may not take away an entire job, but it might take away particular tasks and automate them. And I think that's a good thing. But that creates sort of the conversation of, like, well, who do you hire and how do you hire? When you're thinking about this new world of work where some tasks will be automated and some tasks will be done by humans, that's going to have profound implications for how leaders of an organization think about their human capital strategy.

Interesting. You know, it's fascinating, as I'm sitting here talking with you, I'M thinking about my own company, our 12 employees. And I was thinking about times we get together for a day to solve problems. And, you know, we're at a major transitional point right now. The pandemic really created a situation where people stopped going to live seminars. And we were a live seminar company. And, you know, we had $800,000 of deposits for live seminars on March 12th or whatever it was. And they said, nope, can't do that anymore. You know, and then two and a half, three years later, here we are and we're starting to want to do that again. And it's very difficult. A lot of people have said, well, I don't want to spend the money on airfare and hotel bills and eating out for a week to go to some seminar. And I think normally we come into a room, we sit and everyone has their ideas and we talk over each other. And I like this idea of everyone doing their own AI research, if you will, from their perspective. And, you know, we all have our different perspectives on how we interact with our clients and so forth. What we know and don't know, and big picture, small picture. I'm excited about this, quite frankly, I can't wait to actually use this. So this is fun just imagining it, let alone you doing it. So as you've been doing this and working with teams, do you have a success story you can share that would say, well, wow, that really. That's amazing what they did.

Well, it's all confidential information just because of the nature of the companies we work with. So I can't get into details of it. But I can tell you that the level of teams confidence and their abilities feel like they've actually solved problems or that they can solve problems is significantly higher when they go through this facilitated conversation and facilitated workshop of learning how to use generative AI properly. So, you know, last year, all of a sudden, you know, OpenAI drops this app ChatGPT, like basically drops it, you know, like a bomb into the universe. And everyone's like, oh my God, how do I use it? And, you know, we've had a year now of researching with it and working with it. And now we know that there are specific best practices of when and how to use it in the ideation, the problem solving, the collaboration pH. And we're excited to help teams learn that which is the focus of our research in our upcoming publication. And so, you know, I can share with you that teams feel very empowered and are excited to lean into it now. You know, one of the things that people are Concerned about is like, well, is it going to take away my job? Or, you know, it's going to kill creativity? And, you know, I want to share an example, which is a publicly sharing public example. There was an artist last year by the name of Jason Allen who created a digital image and entered it into the Colorado State Fair art competition. And all of a sudden he wins $300 in the blue ribbon in the fairest contest for emerging digital artists, making it one of the first times a AI generated piece of art has won a prize. And it just created massive, massive backlash amongst artists who basically accused him of cheating by using AI. And he had been very transparent and said when he submitted the piece, he said, I have used AI generated technologies to help me create this piece of art. So he was very transparent. And it led to this conversation philosophically, like, is art dead? Is creativity dead? Is this all outsourced? Like to the example you used earlier of using ChatGPT to write a limerick or mid journey to create an image? Well, you know, I personally think that we have. We're living in a new world of creativity. I feel like artists like Jason and yourself and others, they've created something radically new. And it's not just by drawing, but it's also about learning how to use words and descriptions and prompts to create something radically new. That to me, is creativity. And so that is threatening to a lot of people who haven't spent entire lifetime learning. And naturally, that is a very understandable feeling. I have a lot of empathy for that. But I think, you know, again, going back to my. My mindset from childhood of constantly having to move and constantly having to adapt and learn, that's. That's the reality of life. The world will never get as slow as it is today. It's just going to get faster. So how can we train ourselves, our families, our friends, our institutions to learn faster and unlearn things that no longer serve us? That's really critical, I think, into the future that we are rapidly entering in.

You know, it's interesting you mentioned the art example. I have a stepdaughter, 29, Riley, and she's a natural artist. She just, ever since she was a kid, she could paint. And recently she had a series of dreams that were very, very profound for her. And so she went to art AI that was an art program. And she said, create an image of this dream I had. And she described the dream in great detail. And then it came back with a picture of her in it, all kind of hunched over. And then she rendered the painting as an art, as a, as an oil painting. And so it was her dream, her image. But it was such a long dream, it had so many parts to it and it collapsed all that into a singular image that captured the essence of the dream. She then painted it with oil paints. It's amazing. It's an incredible piece of work. And that would never have happened without the AI part of it. So, yeah, it's like a collaborative part of your consciousness. It's fascinating.

I love that. I love that that is creativity. And I think for the first time in human history, we have technologies that are automating white collar work. Historically, technology and robotics and automation took away blue collar job. So now we have a lot of tasks that can be automated through AI. And you know, jobs, like not jobs, let's call tasks, tasks that are in legal research or accounting or many other fields. And so, you know, this has given a lot of white collar professionals some anxiety for understandable reasons. And so one of the things I've done over the last year is to actually learn how to make physical things. And so this is another way to kind of future proof myself. Obviously I'm not an artist by profession, but this is what I do for fun. I've learned how to make Neon, which is a very physical, creative piece. And I just share this as an example because AI will never automate how to make neon. It's a physically hard material or domain of creativity. And so one of the things I suggest is think about if you are somebody who is a knowledge professional, how can you learn other new skills that might not be automated with AI? And that's another form of like learning how to use your brain in a different way and challenge it so that you know, your new. Your those neuron nerves complexes are still growing and that's really what learning for life is all about. And so I love how you're. How she has used AI to download a dreamscape and then actually turn that into a physical piece of art. That to me is creative.

You, you, you've written a book called, with. With Keith, another author called Competing in the New World of Work. You mentioned you have another book where you're going to, I think, capture this research you've been doing. Talk about. Well, I first want to ask you this. You use the word future proof yourself. I never heard that word. It makes total sense when you think about it. Can you just talk about that concept for a minute? And then I'm going to ask you about the books.

Sure. So to be future proof is Actually like a oxymoron or a logical fallacy that I say because you can't be future proof. But I still use the term because it gets people's attention because the world changes consistently, right?

The only way.

Yeah, exactly. Marketing. The, the way you become future proof is by leaning into change. And so learning to love change. I think, what was that, that film Dr. Strangelove or how I Learned to Love the Bomb. And so it's like, how could you learn to love change? And by becoming what we call radically adaptable, which is the topic of our book with Keith and I that we wrote and published last year, looking at the most advanced innovations in terms of the workplace during the pandemic era. One of the things I recommend for organizations and leaders and team members of organizations as they're thinking of becoming adaptable, is you have to adapt your business model. And so let me give you a way to think about that. So zoom out 10 to 20 years in your industry, in your own field, and think about what the future might look like. You know, this is an exercise in creativity, maybe using science fiction to imagine what the future of your particular business or industry might look like 10 or 20 years out. This is called zooming out. Then zoom back in over the next 12 to 18 months and see if you can run a series of experiments that will allow you to move closer to that longer term vision. And so the more experiments you run in the short term, you'll learn and you'll be able to pivot and move in different directions to ultimately get out, hopefully to that vision that you defined longer term. This is all about becoming adaptable. And so, you know, I and my business have run 10 experiments just this year alone, just 10 experiments. And you know, I want to ask you, Jack, how many experiments have you run at, you know, the, the Canfield Institute or your listeners? How many experiments have you run this year to incorporate new technologies and get out to this further vision of what you think the future might look like. This is how we become adaptable. And future proof, a term that doesn't really make sense, but you have to learn to love change and a lot of people don't. But if you reorient your mindset and, and think of change as an opportunity rather than a threat, this is how you'll thrive in, in this, in this future, which is shifting so rapidly.

No, I agree. I. One of the chapters in my book, the success principles, is called Embrace Change. And the idea is it, it, it's the only thing that doesn't change is change. It's always happening. And I remember, you know, some. I grew up thinking that, you know, the evolution was the law of the, you know, the whoever was the strongest one, you know. And now we realize it's not that it's the, not the survival of the fittest, but the survival of the fitness. Which means what? Who can adapt the fastest to the changing environment, Whether it's ecological. As we're seeing now with all the emerging heat problems we're having and the wine industry is moving north, pretty soon our best wines will be being grown in Alaska. It's funny how it's all happening, but you got to be willing to do that. I totally agree. When you just think, talk about your book, Competing in the New World of Work, in addition to this AI generativity, can you give me like one or two other things you talk about in that book that's critical for the future world of work?

So I would say two things. One is in our research for the previous book that came out last year, Competing in the World of Work, we interviewed over 300 teams from across the world, thousands of individuals, to understand what were some of the behaviors that they deployed to become adaptable. And so there is a team level answer and there's an organizational level answer. The team level answer of how your team is going to become adaptable is a four step process. Number one is foresight. So again, how do you look into the future? So ask your team to think one year out or two years out and identify the specific variables that might impact your, your, your business, whether you know it's ecological or environmental, economic, technological, competitive, and have a method, method, methodical process of understanding how the future might change. So this is the practice of foresight. The second one is now that you've got this practice, we want you to be agile. So this, this idea of how do you run experiments very, very quickly to be able to move towards that idea of where the future is. The third one is that, well, when you run experiments, it's exhausting. You can't be in like full sprint mode all the time. You have to have resilience. And resilience is the ability to rest and bounce back from experiments and bounce forward. So measure your team's level of energy to make sure that they're capable of actually constantly running experiments. And then the final one is this level collaboration, which is a buzzword, but it ultimately means how do you come together to identify the best ideas within a team and then together be able to move forward to the goal where you're going, this is an iterative loop. So we found teams that were really successful in crises or in terms of adapting with change. They were really good at foresight, very good at being agile, very good at having team resilience, and very good at being collaborative to solve problems. That's at the team level. Now, if you're looking at the organizational level, how do you become future proof? We talk a little bit about the idea of zooming out and, and identifying technologies that are going to disrupt your future and then zooming back in and running experiments. The second thing I'd say for an organization is how do you think about structuring your workforce? Who do you hire? In many ways, this remote work revolution has opened up the vast majority of the world to being able to be on your team to solve problems. So at the X Prize, when I was there for many years, we would run global competitions to solve for X and people from everywhere in the world would participate. And so we were crowdsourcing the world's genius to solve a grand challenge. Moonshot. And now you can do that for any kind of problem at any organization because of the technologies that we have as a result of the pandemic, like zoom and remote work, etc. So this goes to the next question, like, well, okay, who should you hire to do the work that you need to do? And we suggest that you should have a methodology called a Lego block workforce. So if you think about like Legos, you put them together and you build new things and there are different kinds of Lego blocks and you can create new different things. And we think your workforce should be like a Lego block. You have your full time employees, they might be within specific physical region that you're located. You might have remote employee employees, you might have some hybrid employees, you might have some contractors, you might have some third party vendors who partner with you. And you probably should also have some AI who's working with you. Remember, in the bionic age, we work with AI. So responsibility of a leader in terms of building a future proof organization is also stepping back and thinking, who should be part of my team and my organization? How do I build this Lego block workforce that is maybe human, maybe AI and, and maybe local, maybe not local, maybe remote. And so all of these are opportunities for us to be able to use the technology to really accomplish things that we previously never were able to do. And so this is a blessing. But of course it has challenges in terms of, you know, managing humans. It's always the hard part. The soft part is always the Hard part.

You know, it's fascinating. When I first met you, you were talking about moonshots and this moonshot technology and so on and so forth, moonshot strategies. And what I'm reminded of is at the beginning of. And it's reaching out, you know, to this worldwide set of resources that you could draw upon. And when the pandemic first happened and we had to cancel all our live seminars, there was about 30 of us that got on the phone, people we normally compete with each other, and we were all going like, what are you doing? What are you doing? How are you going to handle this? What are you going to do? And we generated tons of ideas for how to help each other, what to do for our clients, how to co. You know, work together for certain things, cooperate, collaborate, et cetera. It was like, you know, do that or don't survive. So again, it's like, okay, this is. The environment's changed, we've got to change. And this thought about the moonshot was like, let's stay in business. Let's solve. How do we get to the moon? Or, you know, the bigger things that the X Prize was focused on. So I'm, I'm curious, you know, everything is changing rapidly, and you're out there working with companies, and I know you've got tremendous team that you've worked with. Talk about, like, if you were to come into a group. I know a lot of people listening to this are small entrepreneurs. Some are people working in the C suites of big countries, companies and so forth. What. Describe how I'm a client, Tell me what you would do with me, how would you come in and help me?

So we do two things. We help train organization and leadership teams on how to use new technologies like generative AI to ultimately solve a problem. And that involves two things. One is a strategy piece of, like, okay, what's the problem we're solving for? And the second part is, okay, how do we actually implement that? Because again, that requires humans. And that. That part is the hard part. So let's, let's tackle them individually. The first part is, like, the strategy of, like, how do we solve a problem or this particular problem? That is a series of workshops where we identify together with teams what's the main problem they're trying to solve for. And part of that is facilitated. Part of that is using AI. Part of that is relying on the expertise of the company or the leadership team to help us identify what the main problems are. So part of it is, like, we are trying to narrow down the focus of what the problem we're trying to solve for, what the company needs to solve for to be able to thrive five years out, 10 years out. So there's a strategy piece that we do ideation and facilitation and strategy development for. Then once we have buy in and say, like, for example, the leadership team says, yes, this is exactly where we need to go. Now, we then need to run a series of experiments, right? So the experiments are run by the teams within the companies. We serve as an external coach to help facilitate the behavior change that is required for those experiments to be successful. And part of that requires individual change, part of that requires team behavior change. And part of that is like leadership coaching. And so once we identify where the end goal is for the particular problem that we want to solve for, we've identified a variety of challenges or opportunities of getting there. We say, okay, now let's run experiments. The company is running the experiments. We serve as external coaches to help them along that transformation process, to tweak their behaviors, to make sure. Okay, are we making sure that we're being collaborative, that we are being inclusive, that we are running experiments quickly, that we are measuring our team's energy levels to make sure that they are not burning out, and then are we rapidly doing this again? And so our work is unique depending on which kind of problem we're trying to solve. For sometimes we're trying to solve moonshots, and, you know, moonshots are really radical solutions to really complex problems. And sometimes we are trying to just make sales 10% better. And again, that's a very important problem. That's not a mundane problem, but it requires a different form of lens in terms of how we approach solving that problem. We're in the business of solving problems, democratizing problem solving by training teams and individuals to adopt these kinds of technologies and then coaching them on that spectrum so that they actually stick to their word and do it. And then it becomes human habit. Eventually you go to the gym, and then the gym becomes part of your life. And so you need to go to the innovation gym and the agility gym. And all of that requires coaching.

Have you or people you know about been using this to take on using AI and the problem solving techniques you talked about for what we might call bigger global problems, like the ecology, like the cultural divide, like global warming, you know, racism, things like that? Nonprofit governmental issues?

Yeah, sure. So at the xprize, I'm still a big fan and part of the family, even though I'm not formally there anymore. They've Launched several competitions over the last two, three years. One is in the world of forest preservation and so being able to identify the different kinds of species that we still don't even know yet that exist in, in the forest. And this competition is not like you go into the forest and actually count species you've never seen before. It's actually using AI and data to be able to identify those kinds of species so that we can preserve it for the future. So this is now using this technology to a particular use case, which is, how can you identify species that we don't even know that exist and then be able to actually then save the forest? So, super important, another one that the XY is looking at, for example, how can you use generative AI and AI technologies to be able to predict when a fire might happen? We know that climate change is happening. Some parts of the world are experiencing unusual weather changes, like we are in California, like we saw in Hawaii with the devastating fires in Lahaina. And so our ability to predict when a fire might break out can save lives, tremendous amount of lives. And so this new competition is deployed to be able to better predict when a fire might erupt, maybe using satellite imagery and being able to see when it happens quickly, and then being able to deploy, for example, a drone, a robotic drone to go and put out the fire before it even becomes bigger than a small bushfire. And so all of these technologies have the capability to be deployed across a multitude of problems, whether it is a grand moonshot that's benefiting humanity, like bio preservation or fire prediction, or towards business problems that are still very important to individuals all across the world. And so the responsibility falls to us. And my moonshot is like, how can we democratize this problem solving? And I think AI is a beautiful tool to help us give everybody the opportunity to use these tools to democratize what previously was expertise within experts. And now you can have this really at the fingertips wherever you are in the world. And then the next step is, okay, how can we now teach people to use it, A, ethically and. And then B, really put it into practice to solve the problems rather than them just thinking, oh, I tried generative AI, it just drew me a picture. It didn't really do anything for me. But that's really our goal, is to help people learn about how to use these technologies for the better of their organization and for the world.

So two last questions. What advice do you have? There are people out there watching this and listening to this. They're saying, okay, I'm interested. I Want to get more involved with this, what would you advise them in terms of staying up on what's going on in the field of AI and especially in relationship to this human creativity, problem solving. For someone who's just getting into or listening today, are there some resources, places to go, things to read, websites to pay attention to?

So I would say first of all, read very widely beyond what you might find in a mainstream press. I think the press and the news does a great job of covering events and sometimes they are interested in getting more eyeballs to their, their periodicals because they get advertising revenue out of it. So they have a tendency to hype things up. And so, and oftentimes those really hype stories are not really valuable in my opinion. I think they serve to actually do detrimental impact to how we can use these technologies for the better. And so sure, read those, but then also read other publications. So for example, the MIT Technology Review has really good technical but easy to understand periodicals on keeping up with it. Harvard Business Review, which, where I published in the past, also takes a lot of the technology and translates that into business capabilities. You're always welcome to look at my website geolab.com which where we publish our research available to everybody to to see. And so I would just really recommend reading very, very widely and then experiment go and actually download some of these apps like ChatGPT on your phone or Dolly or Mid Journey. And here's the one thing I say I think would be really powerful way of learning how to have a conversation with a generative AI model. Instead of asking it like a business question, which is, you know, very useful to do, I want you to engage with it like you're having an emotional conversation with it and think of the most important or like one important emotional thing that's top of your mind for you right now and use that as a prompt by having a conversation with the generative AI about that emotional topic. Because I want you to think about this as a co pilot in helping you solve problems and think about not just the, the technical answer that might solve the problem, but rather how can we address some of the human behavior issues, the human emotional issues that we need to address, the soft skills that we need to address in order to really use these technologies well. So download ChatGPT on your phone as an app and have a conversation with it about something that's emotionally important to you and see how it, what it gives you. And then I think that'll help rewire how you might approach using it for problem solving in the future. Because It'll give you, you know, the right technical answer, maybe, but then it'll give you different soft answers of, like, things you still need to think about in order to put some of those concrete answers into. Into reality.

Great. I think you already answered my last question. I was going to ask how people can reach out and find more information about you and your work. You mentioned geolab.com. anything else we should tell people about?

I'm very active on LinkedIn. You're welcome to follow me on LinkedIn. I post a lot of content and research there as well.

Okay, great. Well, this has been fascinating. I'm sitting here, my, my wheels are spinning. I'm excited. I'm thinking about several ways of beginning to explore, experiment with this in my own company, in my own life. So thanks again, Kean. I really appreciate the work you're doing. I appreciate you being here with us today and sharing everything you share with us.

Thank you so much, Jack. It's been a privilege and an honor and I really deeply value your friendship. It's been a pure joy to share time with you.

Me, me too, my friend. And to all of you listening today, thank you for joining us. Make sure to tune in again next week for our next podcast where I'll be sharing a breakthrough idea or a tool with you, or engaging in a conversation with a leading thought leader. And one of the many areas of human transformation, such as breakthrough principles and techniques of creating more personal and professional success, greater levels of happiness, fun and joy, increased self esteem and self confidence, greater health and business, fulfilling relationships, spiritual growth, etc. So until then, download some of these. ChatGPT. I use the word I've used dolly really successfully. And experiment. It's beginning. The first time we ever had a computer, you know, you felt like you're 3 years old. But we learn by doing. Learn to play the piano by playing the piano. I think the same thing's true here. So thanks for joining us. See you again next time.