The Democratization of Understanding| Mahan Tavakoli | 529

Peter Winick: People who practice thought leadership spend a lot of their time making a good idea into a great one. But what if your audience really only needs an idea that's good enough to solve their problem today? I want to explore potential ways that generative AI might change how people seek out and consume thought leadership. I've been having this conversation with my friend Mohan Tavakoli, who is the CEO of Strategic Leadership Ventures. In this conversation, we'll be spanning the past 100 or so years of how people have consumed thought leadership from the early days with Dale Carnegie into today, where someone can type a complex question into generative AI and get a pretty good or good enough answer. I'm Bill Sherman, and you're listening to Leveraging Thought Leadership. Where is thought leadership going? Let's explore together. Welcome to the show, Mahan.

Bill, I am thrilled to be joining you.

As am I. You and I have been having conversations on this topic for a while, and I'm really excited to be able to share this with our audience. One of the places that you and I got into a very interesting conversation on thought leadership is The difference between ideas that are good enough and those that are cutting edge. And you said something about good enough that I think really sets the stage for this entire conversation. So do we need great ideas, or is good enough good enough?

Good enough is not good enough, Bill. And one of the interesting things about AI and generative AI is the fact that it does an outstanding job reflecting our past to us. So for everyone who's been studying and somewhat familiar with generative AI, it's fed on all of the information that's been there on the internet, all the writing, all the blog posts. So it gives good enough answers, in many instances better than average, to most people, which is why good enough is no longer good enough.

Absolutely. If you're in the practice of thought leadership. So where I wanna emphasize on that is you talk about generative AI reflecting back to us our past. And I would say, and sometimes that's a true reflection and sometimes it's more like a funhouse mirror where things get distorted. But if we're trying to reach our audience, if we were naturally looking in a mirror, we could tell if that's our reflection or if it's distorted. Our audience can't see the distortion between good enough and great the same way an expert will. How do you bridge that problem?

Bill, you made a great point in the conversation you had with Spencer Ante about the importance of questions and poking and prodding. So some of what you mentioned in terms of what people sometimes call the hallucinations that AI produces, it depends on the types of questions we are asking. So the differentiator in thought leadership becomes the ability to ask the right questions in order to get the right answers. That becomes a big differentiator as people try to be unique and different rather than repeating patterns of the past.

Building on that, I listened to a podcast from The Economist recently where they were looking at AI in scientific research. And what they said is they were able to feed several years of research into the AI library and then ask the following question, what will be the next advances in research and who will be the authors who publish them? And I found that that was an interesting thing when they took studies from, say, 2018 through 2021, and then they were able to look at 2022 and 2023's publication data. It was getting it right a surprising amount of the time. And so that ability to ask the insightful question is really one of the differentiators that we can do.

And it is a challenge for all of us, Bill, to start thinking about the information we have access to very differently. So to me, you and Peter are the thought leaders' thought leaders. And I know a lot of your listeners are thought leaders themselves or people who lead thought leadership for their organizations. We are all sitting on a lot of information. In every one of the organizations I deal with, whether it is customer interactions that they've had in the past or employee input information that they've gotten. There's lots of information that now because of AI can be accessed. The point is understanding there is value in all of that information and asking it questions. So whether you're an individual thought leader, someone who does a podcast or has written blog posts for years or decades, or an organization, it's now you can ask all kinds of questions to this information that you have produced in the past or has been produced in the organization. That's where the insights will be.

So you used a term that was new to me. I don't know if it's one that you've coined, but the democratization of understanding. And I think that is something that really we have to start wrapping our heads around in thought leadership. That was one of the, the aha moments in a recent conversation you and I had that I said, whoa, you've made me think in a new way and I now need to think about it. So what is it that is this democratization of understanding? Let's start there.

So there are a couple of things that I want to combine, and I wish I could take credit for all of these. None of them are mind, I think part of thought leadership is marrying insights of other people and seeing patterns in them.

Absolutely, absolutely, right?

There are a couple of people I've learned a lot from, Bill, myself. I started my AI journey first about 5 years ago reading A.J. Agrawal's book, Prediction Machines, outstanding book on artificial intelligence.. And AJ talks about how AI reduces the cost of prediction and increases the accuracy of prediction. So you can predict a lot better. Married to that, Paul Daugherty also wrote a book about 5 years ago, Human and Machine, talking about augmentation of how we need to think about AI as augmenting our thinking. So going to your question about the difference between information and understanding, there is lots and lots of information. AI moves us from the need for access to information to access to understanding. A simple way to think about it is we've been able to Google for the past 15, 20 years. And in many instances we find information, but it doesn't help us with understanding. Understanding is the insights that we can act on. That's what AI does. And when ChatGPT, Claude, any of these tools are used well, we have access to that. So that's the transformation where we are going from access to just information to understanding that can be acted on.

And I think if you look at even going further back than the launch of Google, you could go back to the Library of Alexandria. And even there, the collecting of the resources gives you access to information, but the individual had to spend time to achieve understanding. They may have had to seek out a teacher, a tutor, or a mentor. That role, which often has been bridged by people who are practicing thought leadership now gets redefined, I think, because of this democratization. I can have a chat back and forth with generative AI about my questions and receive a whole lot of information, and they can check for— or it can check for understanding. I just personified a generative AI there.

And one of the interesting things I've been thinking a lot about, Bill, is that this also changes relationships in organizations drastically as well. If you think about it, in many organizations over the development of organizations, information has flowed to the top and people higher up in the organization have had the greatest access to information, therefore made decisions based on that information.

And their role was sensemaking. Of the information.

Yes. And now that is shifting drastically to the need for understanding. And anyone that asks the right questions to the right dataset can reach the understanding. So it shifts the dynamics of how organizations will operate as well.

Even pushing that forward, I think the role of expertise gets challenged as well, right? So often we talk about thought leadership as being something that someone with deep expertise shares with someone who has less expertise. When we talk about having the learner sort of develop a mental model similar to the expert so that their understanding of the world is similar. I think in the world of thought leadership, it's often hard for the expert to go back to beginner's mind and say, how does someone who is fresh to this topic look at the world? And this is something that we have to do.

It is. I love the perspective of that growth mindset. I know a mutual friend of ours, Eduardo Briceño, has written an outstanding book Performance Paradox on the growth mindset. And what Professor Ed Hess also talks about, the need for learning, unlearning, and relearning at a faster rate. So actually, in some instances, the experts are handcuffed. They're hampered because when you believe you know, then you don't ask the simple questions the fundamental questions that can unveil new insights. So that's why that learning mindset and that new approach becomes even more important because now the power— I go back to a brilliant point that you made, Bill— the power is in the questions and the questions that challenge past beliefs and paradigms rather than questions that follow through based on past experiences.

And that's the differentiator in some ways, right? Where you talk about generative AI reflecting our past back to us. If generative AI is used well, then we're seeing things in our past, our present, and our potential future which we didn't see. If it's used inefficiently, all we're seeing is the past repeated. And so we wind up getting caught in a cycle of we know what we know because it's what we've known, right?

Yeah. And that's one of the challenges that I see with some organizations, some thought leaders in using generative AI, including there are now AI tools that can generate hundreds of blog posts. Literally with a click. So it's a lot of mediocre, middle-of-the-road content that is going to overflow and is going to overtake, whether it is in books, blogs, online, wherever you go. That's why the ability to differentiate and the ability to have that cutting-edge thinking becomes even more important. And additionally, one other thought, that's why I do believe the current way we are thinking about the internet is going to drastically shift because of this massive amounts of content that is being created in many instances, not value added, but just content for the sake of generating content.

It's more straw for the haystack and fewer needles, right?

Which therefore requires people to find tools and ways to find the needles. That again becomes the reliance on AI agents and AI tools to find those needles. And those AI agents and tools sift through lots of data, lots of information to find the needles. And therefore the question to the thought leaders and to the organizational thought leaders is how can we produce the needles? Not how can we produce a thousand new blog posts to put out there?

Right, right. You're more effective on the quality of getting one good insight to your right audience today or over the next few months than spamming every 3 hours opposed to LinkedIn Twitter X and who knows what, right? If you're enjoying this episode of Leveraging Thought Leadership, please make sure to subscribe. If you'd like to help spread the word about our podcast, please leave a 5-star review at ratethispodcast.com/LTL and share it with your friends. We're available on Apple Podcasts and on all major listening apps as well as thoughtleadershipleverage.com/podcast. So I want to shift the conversation a little bit. And this fits with the conversation of the past. And the question of, is there a new idea under the sun? Right. But it also fits with one of the early 20th century landmarks in thought leadership, which is Dale Carnegie's How to Win Friends and Influence People. And I know that's a world that you have been involved in. And so how do you keep an idea that's quite literally over 100 years old in that format fresh, relevant, and useful a century later?

It is a challenge. The fact that the idea, when it came out, had its own uniqueness has helped. So a couple of thoughts. I love the book The Creative Curve by Alan Gannett, and he talks about how there is a balance between creativity, the known and the unknown. Somewhere in the middle there, that's what we seek. Therefore, you are absolutely right. A lot of content, including what Dale Carnegie wrote in 1936 in How to Win Friends and Influence People and 1938 in How to Stop Worrying and Start Living, both of which are repeatedly bestsellers on Amazon to this day, is the fact that he took wisdom, in some respects Stoic wisdom, in other respects wisdom that had been shared from the founding of this country in the US, and repackaged them in a new way. So there is that combination of the newness and what people had been exposed to before, which made the ideas last. And what the organization has had to do, Bill, over the years has been continually the same thing. How do you find the balance between new and known and combine that? Because that's where the creative curve is. That's what appeals most to people.

I like the phrasing of new and known. The term that I've used sometimes is evergreen thought leadership in that regard, and the ever being very, very long shelf life. I mean, a book should have a shelf life of at least 5 to 7 years for the author, let alone be relevant long past the author's lifetime, as Dale Carnegie's has been, right? And so in some ways, when you reach the 100-plus-year club for a book, you know you've done a lot of things right to reach an audience, and you've tapped into a deep need. But as you said, you can go back to the Stoics, and you can see several of the pieces come forward there. And so some of it is really understanding and tapping into a vein that you can find your audience and speak to them in a clear and specific way. And the magic of that book is that it does sustain for much longer than the 1930s.

And that evergreen that you mentioned, Bill, has to be packaged and put in a way where there is some newness to it.

Mm-hmm.

So a lot of people talk about Simon Sinek's why and the purpose of the organization. There have been different versions and iterations of that again for decades, but the way Simon Sinek packaged it and communicated it made that concept that was evergreen new, and then therefore Simon Sinek owned it. And to this day, he is known and associated with that. So it's in part relying on those time-tested evergreen principles, but being able to communicate and package it in a way so people see new value and new perspectives in those ideas.

Well, and this comes to one of the questions on newness and generative AI., right? So whether you're working with a dataset that is specific and custom collected, or you're looking at one of the large generative AI datasets as a whole, you can start asking questions to bridge what is known with what is needed today. And I think one of the things that we need to think about in the practice of thought leadership is not how do we fear the spamming of content, but how do we use these tools to create better insights that are more relevant to our audience?

I love that, Bill, and I will circle back to the mindset and therefore the questions that we ask in that, for example, with generative AI, we can ask the right questions to be able to localize and make more granular thought leadership specific to audiences and specific to groups. Or we can identify white spaces in thought leadership.

Mm-hmm.

So there are a lot of opportunities, but the opportunities don't come from just generating content. They come from asking the right questions.

Absolutely. Absolutely. And being willing to narrow those questions down and get more and more precise and more and more specific. Because the paradox on thought leadership is the more specific you are, the more deeply relevant you are and the more you will be heard, right? So I know, for example, in your work, in your practice, you made a choice around geography, for example, around your hometown and the Washington, DC metro. Right?

Absolutely. In addition to the fact that this is the nation's capital, it is the world's capital, Bill, and it is the center of the world, not just to me, to everyone. So part of the reason I focus on Washington, D.C. region is because of my love and pride in this community. But you're absolutely right. One of the things that I know a lot of my colleagues and friends in thought leadership have a challenge with is that because they can focus on a lot, they focus on a lot. And while that might have worked, mildly worked before AI, AI forces us to go down, double down on niches even more. So rather than mass reach and mass market, which is a lot of this content generation tools that are coming out, as I said, hundreds of blog posts a day are trying to maximize right now. It's looking for differentiated positioning and doubling down on that niche even further. So there is an element of geography to my focus that helps with that, but that's not it. I continually try to pigeonhole myself. The more I pigeonhole myself, The more likely it will be, I will be that pin in the big stack rather than just another part of all the noise that crosses people.

Absolutely. And I know from my own work, it's not just the thought leadership is my lane, but it's the frameworks, the ways of thinking about thought leadership, the structure and the tools, which is really what I spend my time not only trying to create, but then also to put out into the world. Because that's the gap that I've seen again and again, is that people are trying to put good ideas out there, but they don't know how to set up the guardrails. They don't know how to reach their audience.

And it is a challenge for all of us, Bill. So it has been a challenge for me. And I think, again, with a lot of whether organizational thought leaders that I've seen or people on the individual level, we all come up with rationale on why our thought leadership is focused enough and is of value. It doesn't matter if we think that. The question is whether the market thinks it and whether we can test it and it works that way as well.

Well, Mohan, I'll give you the flip of that too, because I work with many people who practice thought leadership. And if I ask them many the question, Who is this for? A surprising amount of the time I hear the following answer. Well, you know, almost anybody could benefit from this. And that's the point where, and you can't see on podcast right now, I just put my head in my hands and I'm shaking my head and going, if it's for everyone, it's for no one.

But that's a major challenge for us and it is an ongoing process and an ongoing challenge. But part of what I hope that people get out of this conversation, Bill, is a couple of things. One, the need for us to really be focused and niche down through our questioning, that becomes really important. So the value of questioning based on the datasets, based on the information, the more the organization, for example, has—

Case studies, examples, anything that you know look at it to find better questions.

Yeah. And even, even right here in DC with, uh, some clients, for example, I work with some large nonprofits and associations. They have lots of data from past interactions, past funders, past organizations they've supported. All of that data is Great forethought leadership insights that no one else would have, only they would have because only they have that data.

Right, right. The proprietary information. That's the great stuff.

So having that and asking questions around it to bring out unique perspectives, unique ideas, and the uniqueness of it won't be determined by you thinking it's unique. It will be determined by others. But this is another place where I think generative AI and tools like this come in handy. Rather than necessarily always needing to research, you can interact and ask great questions to see if there is uniqueness to what you are looking to share or not.

Right.

So again, it's having a buddy or a partner, a cognitive partner as Paul Doherty calls it, where that partner can challenge you and can say, no, I already know this, or there are already other people who have shared these thoughts.

Well, and one of the things that you can send an AI tool of your choice on is to do a literature review. And for those of us who in college or grad school read articles and had to summarize them, we're like, never again will I need this. But if your question is, who is already talking on this topic? What are they saying and what are their key points. That allows you to lay side by side and compare what's being said, what's not being said, and where the conversation is going. Absolutely.

And a couple of, couple of thoughts. First of all, there are specific tools that can help you with this. But for people who use ChatGPT or Claude, first of all, you can adjust whether you want it to be more creative. Or you want it to be more factual. So people who complain about some of the creative responses ChatGPT comes up with, you can modify that. Secondarily, again, it goes back to what are the questions you are asking, and you can then ask it to check itself. So when it gives you responses, any of these AI tools, you can say, "Now cross-reference this and tell me what parts of it are questionable, what parts of it are factual." So you have this powerful tool, but it requires constant questioning. It's not you ask one question, you get your answer, you run with it.

Absolutely. I'm thinking back earlier this year in 2023 when, uh, there were a couple of attorneys that filed a brief citing cases in their brief that didn't exist. And the judge said, can you provide the summary of these cases? They went back to GPT, provided the summaries, and it was all AI hallucination. And then they had a difficulty having to explain to the judge why the AI had done their homework, right? And so you have to have a degree of skepticism even when you get an answer, even if you've asked the right question.

Absolutely. And when you do that, here's one other thought, Bill, in terms of thought leadership. Now you have access to lots of insights that lots of people over the decades have shared on the internet, which therefore means what? You can test your initiatives with AI to see what would be more viable positionings for what groups what makes more sense and doesn't. So it doesn't always need to be tested. Eventually you can test, but you have data from across the globe that you can test it against. So you can check your thought leadership and see whether it has likelihood of getting traction or not, and what you need to strengthen in order for it to get traction.

I love that. So as we begin to wrap up, Mohan, I have a couple closing questions. The first one is you've been practicing thought leadership, not only your own thought leadership, but also you've been spreading the thought leadership of others going back to Dale Carnegie. What do you wish you knew earlier in your thought leadership practitioner career that you now know? What do you want to tell your younger self?

First and foremost is one of the things that we talked about, Bill, which is that the power is in the differentiation and in the niches rather than a belief that you can and should serve a broader group. This also actually plays a role in Dale Carnegie. My last role for the organization, in addition to running international operations, I was chief strategy officer and we had a constant struggle and I we made mistakes in trying to expand the focus of the organization rather than niching down. So that's one of the things that I have learned over and over again, that niching down is powerful even for the largest brands in the world. Now with the democratization that AI brings, it becomes essential and it becomes part of survival.

That's a really good point. It doesn't matter how big your brand is or how small it is. You have to develop a niche or otherwise it will disappear from you.

And it is, it's very tempting because one of the things is we start seeing relevance, whether it was at Carnegie, we started seeing relevance of the human relations principles and the communication. To all aspects, which is true, but that's not what, from an organizational strategy, you should do. And then from a thought leadership perspective, the things we write, the things I write, could relate to lots of different leaders across industries, across geographies. However, that is a temptation we need to stay away from. If in a world full of noise we want to stand out. And I think there are a lot of lessons to be learned from many people, including the Taylor Swifts of the world. Taylor Swift is not trying to appeal to everyone. She knows exactly who her target audience is, and she's making billions doing it. She is.

Yeah.

However, Taylor Swift is not saying Now I'm going to sing for a different type of audience because my music can appeal to them. She continually doubles down on her niche and that gives her even more broad power. I think that's one major lesson that I've learned in thought leadership and something that with the democratization of AI will become even more important.

So final question. You've already name-checked a number of books, but I want to ask you this question. Whose work, current or past, do you think more people should be reading?

Personally, one of the reasons I got involved in Dale Carnegie is the fact that my father had read the books and had it on in his study. So, I would see them, and that's my, that was my first exposure. So I love those books. And I tell you, Bill, as I represented the organization, whether it was down in Brazil when they talked about how a general, Brazilian general and a Paraguayan general came together and de-escalated a conflict because they both loved Dale Carnegie. To being in China, in Shenzhen, where people love Dale Carnegie. In many instances, the books that they had were knockoff books, but they still love Dale Carnegie. And in Bangalore, India. So I think there is a power to the principles that Dale Carnegie captured and to the beautiful storytelling that he had that I find for myself reading and rereading the books is of tremendous value. So one of the recommendations I typically make to people is if you read an outstanding book, and to me Dale Carnegie's books are outstanding, I love Good to Great by Jim Collins. So when you read an outstanding book, read it, reread it, reread it, rather than reading lots of new books.

I love that recommendation and I will share one of my experiences. So what I wind up doing on that is when I realize this is a book that I really want to digest and keep close to my heart, I wind up going back into grad school mode. I'll take out a pencil and a ruler and I'll start taking notes in the margins, sometimes agreeing, sometimes disagreeing. But I can pull a number of books on my shelf that are behind me off the shelf, and I have notes from the first time that I've read them, the second time or the fifth time. That I've read them and I've seen my thinking evolve over the decades.

I think that's a great point, Bill, and that's a thought I want to connect back to the AI in that AI does help us with understanding. One of the things that I have found though is that over time, because of time constraints, we are looking for summaries of summaries of summaries. Give me the 5 bullet points of this book. And that minimizes the deep understanding we can have of concepts, which can get us to think differently and therefore come up with our own new ideas. So I highly encourage people, whatever book is recommended to them, whatever book they wanna pick up, to read the book and soak it in. Summaries don't do any good. And in this instance, asking generative AI to summarize a book doesn't do you any good. It gives you the highlights that are good enough for a book report, but not good enough for the depth of understanding it takes to have your own unique insights come out of it.

And there we have looped back to the beginning on the difference between good enough and cutting edge. Mahan, thank you for joining us today and for a fantastic conversation.

Bill, what an incredible joy. Thank you for having me.

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