← Coaching for Leaders
Coaching for Leaders
710: Becoming an AI-Savvy Leader, with David De Cremer
00:37:41
David De Cremer: The AI-Savvy Leader David De Cremer is the Dunton Family Dean of the D'Amore-McKim School of Business and professor of management and technology at Northeastern University. He's also an affiliated faculty member at the Institute for E...
Loading summary
Transcript23 lines
- [00:01]
Dave Stahoviak
We've all heard the warnings that AI is going to take our jobs. That's certainly a possibility in the long term. But the story emerging, at least for now, is looking a little different in this episode, How Leaders can Use AI to Augment, not Replace, Human Intelligence. This is Coaching for Leaders, episode 710, produced by Innovate Learning, Maximizing Human Potential. Greetings to you from Orange County, California. This is Coaching for Leaders, and I'm your host, Dave Stahoviak. Leaders aren't born, they're made, and this weekly show helps you discover leadership wisdom through insightful conversations. A conversation we have been having and will continue to have on this podcast is about AI, of course. It is a technology that is transforming so much about how we think it will transform how we work. And of course it is coming with lots of assumptions, some of them not correct, about how we can utilize AI in the best possible way and how we can lead with it. Today, I'm so glad to welcome an expert. It's going to help us with some perspective on taking the next step on being more savvy with using AI, but also leading with AI. I'm very pleased to welcome David de Kremer to the show. He is the Dutton family Dean of the Damore McKim School of Business and professor of Management and Technology at Northeastern. He's also an affiliated faculty member at the Institute for Experiential AI at Northeastern University and an affiliated researcher at the center for Collective Intelligence at mit. His newest book is titled the AI Savvy Leader Nine Ways to Take Back Control and Make AI Work. David, what a pleasure to have you on.
- [01:52]
David de Kremer
Thank you, Dave. It's a pleasure being here.
- [01:54]
Dave Stahoviak
There is so much in this book. We are just going to scratch the surface in this conversation, I'm sure. I was so struck by two things you mentioned from your research that come out really prominently. Two findings. You say that AI is substantially different from previous digital transformations and also AI adoption efforts are failing at alarming rates. And I'd love to ask you about both of those, but let's start with the first one, how this is substantially different. I think we all have the sense that this is like a really different thing than we have seen in the recent past as far as technology. But but I'm curious from you because you've been in the trenches and helping organizations do this. What is showing up for you as so different about this technology?
- [02:44]
David de Kremer
That's a very good question because as you just mentioned, people have a gut feeling that this one may be different because we've seen so many new challenges arise, crisis, new innovations. But this one seems to be affecting everything and everyone. And it's also being emphasized by literally every company in government that it's going to change everything. So if you just think about those words, it's going to change everything that leaves an impact. So I see two ways to illustrate why I say it's a different animal is first of all, from a more philosophical point of view, if you think about innovations and technology in the past, what we've outsourced to machines, to technology, has usually been our physical capabilities, our body. When there's a. When we had the steam engine, when there was the car, we don't have to carry our luggage anymore. So we outsource basically our body. But now it seems like we're outsourcing our cognition, our mind, our brain as well. Because you see so on social media, you see so many pictures of human brains and then a robot saying, oh, this is where it all started. We are outsourcing our brain as well, our mind as well. So if you've outsourced our body and we've outsourced our mind, what is left? What is left of being a human?
- [04:06]
Dave Stahoviak
Yeah.
- [04:07]
David de Kremer
From a philosophical point of view, I can imagine that people are thinking like, wow, this is such a game changer. Because as you probably are also aware, our society, the way we do business, the way we live, we live very much in our head. A lot of things are cognitive. And you see the pushback there that we see in society. People want to go back to their emotions because we live so much in our head. Head. So, of course, AI then seems to be something that may take over whatever humans are, what humanity is, and that installs anxiety. So if you look at the recent surveys, still a lot of people are afraid of AI. And there's even a general sense of anxiety, which means what is AI going to mean in our lives and in our futures? So that is something that we haven't seen, I think, before, that level of anxiety that it's so high. The less philosophical approach would be looking at is what do we expect from this technology? So if you look at surveys, business leaders today expect so much more of AI than they expected in the 90s from the Internet. And we know that the Internet basically changed the way we look up information, the way we communicate, the way we create content and wisdom. And we expect even more from AI now, especially when it comes down to making humans efficient. So this anxiety taps into again, like, oh, we expect to be efficient across the board, because AI is going to make us think differently, walk differently, talk differently, decide differently. So that is something that leaves a big imprint. And that imprint, I can see, is very responsible for people not understanding AI. It seems too big to understand. It seems hocus pocus to many people, and it is to some extent, and I can elaborate on that, but it feels completely different because of these reasons. Now, what is hype and what is reality? That's, of course, the big question here.
- [06:10]
Dave Stahoviak
Yeah, indeed. And speaking of the hype, we see examples in the popular media all the time of all the cool things that AI is doing. All the tech companies are touting the cool features that are going to be part of software very soon, if they're not already. And yet you have example after example in the book and in your research of AI adoption efforts failing at alarming rates in many organizations. What is it that's going wrong?
- [06:39]
David de Kremer
Well, it's that observation that you're just pointing out that a lot of the AI adoption projects, especially in business, but it's also the government in society, they're failing. And by fail, let's first define failure. They don't fail in buying the AI. The AI is being developed. They buy it, they bring it into the organization. But they fail in creating value. And we can define value as efficiency, more productivity. Well, we don't see that level of productivity really going up. And actually, this is interesting because if you look in history, every time it comes down to adoption of technology, it takes always longer and productivity doesn't go up right away. And this is a mistake that a lot of businesses make because they're not AI savvy enough, hence the title of the book, the AI Savvy Leader. To really understand that the real value of AI is a J curve. Basically, first you adopt, you get the AI. You have to get used to it, you have to train it. You have to train the AI. You have to train the human working with it. So basically, initially, you don't perform better. You may even this, you may even perform worse than the status quo. But then after a while, once you integrate it, you know how to do it. You can upscale. And with the upscaling, that's where the real value comes. And you also want to create value across the board. You want to have value in business for all your stakeholders. So that means you want to use it in responsible ways, that it doesn't reveal discrimination on the market, that people are treated fair, that it's used in ways that benefits all these stakeholders. So if you don't understand what it is that AI can provide and what it cannot provide. So we call this demystifying AI. As a business leader, it's very difficult to work with because they think, oh, we just buy it. I delegate the responsibility to my tech experts, we plug it in, and immediate results, well, that's not the reality. So I observed this while when I was thinking about this book and the, the push towards writing the book was, I see that there's these failures. We can't upscale, we can't reveal value across the board. So what is the reason? And one of the reasons that I saw was what I already mentioned is business leaders were never involved. What I see in businesses today is that adopting AI is treated as an engineering exercise. It's mechanical, it's tech driven. That's why I call it in my book, the Myth of Technology Driving Technology Transformation. People literally believe, and that's the hocus pocus that I referred to earlier. You need to be a tech expert. You really need to understand AI very well if you want to make it work and create value across the board. That's why business leaders, what I see is they step back right away. They delegate it right away to the tech experts. But we're using AI here for business. Are tech experts trained to be business experts to ask the right business questions? No, they're not. Because everything starts with a business question. If you bring AI into business, the first thing to do is asking the right business question and then see where you can use AI and where you can't use AI. Once you've decided where you can use AI, you know why you're using it, then you can delegate to tech experts. But that requires that business leaders have a level of AI savviness. They need to have that AI comprehension so that they can align it with the organizational purpose, with the strategy of the purpose, and with the strategy of the company, with the goals and the strengths of the organization so that they can make that much better. And that's something that we don't see. Most business leaders are not trained to be AI savvy. They got their education when AI was not on the curriculum. Sustainability was not on the curriculum. So where do we start that lifelong journey for those leaders? How do we do this? Because using AI without having the right question, you will never create value. And recent surveys actually showed this. So the most recent survey shows that up to 80%, 80% of AI adoption projects fail to upscale so to create value. And this is twice as much as any other digital transformation project where no AI is involved.
- [10:58]
Dave Stahoviak
It's fascinating, and it's one of the reasons I wanted to talk to you. Because as I studied your work, one of the key messages you have is leaders aren't leading. They're being deferential to the technology, to the engineering, to the data people. And what you just said really illustrates that, that because this is so new, we're forgetting a lot of us the value that leadership provides and actually needs in order to make this work, aren't we?
- [11:31]
David de Kremer
Definitely. That's why I always say AI creates great opportunities. We can see that. But those are, in theory, we need to get there. So that's why I see AI as the next great leadership challenge in business. Because as I said, AI adoption is a J curve. It doesn't reveal itself right away. It's also not an engineering exercise only. It's more of a behavioral exercise, I always say, because you need to have your people adopted. Because at the end of the day today and in the next 10, 20 years, it's still humans who are doing the job, who are delivering the final product, who are at the end stage of any workflow process. It's humans. So they have to be willing to accept it, don't resist change. They have to be trusting that technology. They have to feel that they're still in control, the human in the loop, because their job, they want to see it to be meaningful. I mean, I think all of us at least want to understand why you do a job, what it serves, which purpose does it serve? Now, if it's such a behavioral exercise where people need to buy in, when AI enters the organization, you see why it becomes a leadership challenge. What is leadership about? Leadership is about pointing a direction, being visionary, and empowering people to buy in with that vision and at the same time creating the right conditions for them to perform better, to do better, and to be motivated. Well, that's a behavioral exercise. So that's why leadership is so important. And as I said earlier, when I looked at all the examples in the. In the companies that I interviewed or that I was part of as a consultant, one characteristic was always very clear. When an AI adoption project failed, business leaders were nowhere to be found. The wrong questions were being asked, or no questions were asked that were of relevance, and it was basically without any direction.
- [13:26]
Dave Stahoviak
Well, and this highlights so strongly, probably the thing that I read in the book that just keeps landing with me, is you make a distinction of two different perspectives that people tend to bring to AI, especially thinking about it in the context of organizations, and you say Perspective one is AI is an increasingly cheap way to replace people and achieve new levels of productivity and efficiency. Perspective two is that it's a powerful tool to augment but not replace human intelligence and unlock more innovation and creativity in workers. And you do strongly have a feeling on where we should be heading as leaders and organizations on this perspective?
- [14:16]
David de Kremer
Yes, I've chosen a direction there myself, leading myself. Yes. I think these two perspectives make it very clear to people in what we're seeing today when it comes down to discussing the impact of AI and how our jobs in the future will look like. As you said, perspective one is very much focused on cost effectiveness. And of course we're in the business world, of course we're looking at costs and we want to reduce costs. So that's why the focus whenever we talk about AI is very much on enhancing efficiency, hence productivity. And that's justified because yes, it can make us more efficient, we can do hopefully more in less time, we're being more productive. But that should not be the end in itself. It's a mean, it's a mean to achieve. So AI is not. If you only adopt perspective one, you see AI as an end in itself. AI is basically there to enhance productivity. And if artificial intelligence would ever reach artificial general level intelligence, AGI, which is human like, then from a cost effective perspective, why would we use humans? Because humans, we have to pay a salary, they may be sick, they retire, there's all kind of costs there. But if we have AI who can actually do the same thing, why have any human? So that's the fear of replacement. And that's a very rational point of view. It's solely focused on cutting costs. But what it's missing is what's the end game? Where is the purpose, what is the long term vision? Because the end game here would be that humans are replaced entirely. Do we innovate to eliminate ourselves? That's an important question here and I don't think that's what we should be doing. So the unfortunate thing, however is even today, if you would listen to any interview or read magazines or go online, when they interview business leaders, most of them are still stuck in perspective one because of their lack of knowledge about AI, they're still focusing on are you going to keep your job? How is the job going to look like? How much of your job can you still do? They treat it in what I call a zero sum game. It's either you or the AI. And in a way we see this also in the tech industry. We're getting closer to artificial general intelligence, are humans still needed? Of course, it's a very flashy story and it's what the media probably likes to focus on. But it's not a perspective that's really going to help us as humans because ultimately it may eliminate humanity. And it's a competition between us and AI and that's completely wrong. People need to understand and that's where AI sevenness starts. Human intelligence, artificial intelligence is like comparing apples and oranges. It's not the same, but together they'll produce so much more and we have a future, hence perspective too. And that's also why I'm endorsing it so much AI at the end of the day. And that's why it's not an apple, but it's an orange. Human intelligence is an apple. AI is a tool. It's a tool that we need to learn to use, we need to learn to work with and we need to learn to say this is where we use AI because it enhances what we're good at Already. If you're a company and you know this is your competitive advantage, simply adopting AI is not going to make you more competitive. But adopting AI and using it to enhance what you're really good at, that's going to make you really competitive. And that's augmentation. That is what we need to strive for.
- [18:00]
Dave Stahoviak
And it's the story of how this has gone for decades and decades on technological transformations anyway, right? There was all these stories, you know, 50, 60 years ago with computers would come up, like we'd all be working three hour weeks and then calculators were in classrooms and folks thought that would destroy math education. And it's certainly changed how we work. It certainly changed how we taught, of course. And yet the best technology advances have done exactly that, augmented that. And so like who knows what the future is. But boy, it sure seems like the opportunity would be for us to look for the ways that we can augment and that we can utilize the tool and it be a both and versus an either or.
- [18:45]
David de Kremer
Yes, exactly. It's not that zero sum game that I just said and the past has shown this. It's about augmenting now. Like I said, the biggest challenge for companies is now. Okay, they may accept this perspective too and say we're buying AI, we're adopting it. But yeah, how? And this is again where I say business leaders with their expertise are needed because they ask the right, they're trained to ask business questions, the questions that are relevant to the purpose and the goals of your organization. And that's where tech experts don't have that expertise and they need to have that communication. So as a business leader, you need to be AI savvy because you need to have a narrative and develop a narrative where you can say, these are the relevant questions to my organization and explain that to a tech expert so that they understand, ah, this is what the technology has to help us with. And that's a completely different ball game. And this is the point where leadership changes today compared to the past. We're not reinventing leadership, but today as a leader, you're going to be evaluated in how you can make sense of AI, in what you need to achieve as a business leader and work with it.
- [19:59]
Dave Stahoviak
You write that your expertise is exactly what your organization needs to deploy AI successfully. And I was thinking about that and just what you just said and how often we do what you talked about earlier, which is we defer to the data analytics folks, the engineering folks on like, you know, starting up the AI engine service, whatever it is, and sort of letting them follow the path. But I'm curious for the organizations that you see that really don't forget about their own expertise and bring that in in useful and valuable ways, what is it that they're doing that's different, that the organizations who aren't doing that are missing?
- [20:46]
David de Kremer
Well, there's a few things. So first of all, AI savvy leaders, they understand the benefits of an AI, but also the limitations of an AI. I always say in understanding the limitations of AI you will recognize what the true potential of humans are. The unique human abilities, so you can marry those, because the unique human abilities, you want to make sure that they stay alive and that the jobs context is still there, that they can train what is uniquely human. But then with AI in support of that. So an AI civil leader understands that because they understand what AI is about, what it is not. So that's one important thing. The second thing is once you have that understanding that AI sevenness, communication becomes so much better. Because leaders today, AI savvy leaders are narrators who bring together tech expert and business experts and make them work together. So we need to eliminate more silos. Communication is key here, but it needs to be AI savvy in the context of business. So they're very good at that. They understand, do we have these talents? We don't have them, we need to hire them. They also understand not to use their tech budgets just in buying the AI because the most expensive, what is most expensive is not necessarily buying the tech, but Implementing it so everyone understands it and that you can integrate it across the board in your organization to create that value that requires which infrastructure, which talents do we need? Training of people, empowering people, creating the right conditions, restructuring the jobs on the go to say so. Because you need to leave room there to experiment, to learn. Look, if you bring in AI, any model is not perfect or optimized from day one. It learns. AI models learn and they do so because we provide feedback, we reinforce them. So in a similar way, you need to bring AI into the job in a similar way where there's feedback and communication, so that allows for a room of experimentation. You give trust to people, you discuss failures and you learn from it along the way. And that's also how you build the jobs of the future. Not simply by saying, oh, these are the skills we need and we create these new jobs. Jobs are actually created as part of changing the workflow when AI enters. Which brings me to the third point. It's a collective lifelong learning lesson, AI adoption. And I'm saying lifelong learning by means of a. When something changes in the AI that we can use in terms of innovations, we need to stay updated as leaders. But that doesn't mean that every time AI changes or that a new level is achieved or a new large language model hits the market, that we have to use it right away. No, you can be updated. You don't need to be a computer scientist, but that you are aware of these changes when you talk to tech experts, but you do it collectively so that everyone is on board, so that managers of different departments are on board, that the, that employees, that teams understand it. That's why you need your sevenness as well, because you need to make it meaningful to people and at the same time reassure them that AI is not an end in itself, but that it's a mean to an end. And that end is achieving the purpose and the values of the organization.
- [24:07]
Dave Stahoviak
You write on this on becoming an AI savvy leader, that you do not do this by becoming a coder or asking a very sophisticated calculator what the best answer is and following it blindly. You do this by applying all the core leadership skills you already know to this new challenge. Communication, emotional intelligence, vision and mission. All those things you already know how to do can be applied to this new challenge. I love that paragraph because I think sometimes we see this and we see anytime, like the AI term gets thrown at something and we think to what you said earlier. I never got training on this. Of course, none of us have, right? But this wasn't around when I was going through school. I didn't get my technical training in this. I don't have the savviness that the, the technology folks have. And yet we don't need to be experts, but we do need to start to have a little bit more context. And my sense is, is that there's, you've seen some people do this of like get to the place where they've got a bit more context, of knowing enough so they're savvy. The people who are doing that, what are the practices they're doing that get them to that place where they're not the coders, but they understand enough to be able to find the intersection between the business, the leadership conversation and the technology.
- [25:29]
David de Kremer
Yeah, so it starts with maybe defining as well what AI savviness means. So first of all, as you said, you don't have to be a coder. And that's also not relevant today anymore because as you know, AI can code, but also everything that is AI today is bottom up, which means AI learns from data. It's all about data. It sees patterns, it learns from it. We reinforce it by means of our prompts in large language models, for example, so it's all bottom up. That's the kind of level that you need to be aware of. So you need to have the right kind of data and then you understand, oh, but what are the right kind of data? Well, it depends on the question. Oh, wait a minute, I need to be involved. Yes, you need to ask the question. You need to have the discussion about what makes sense from a business point of view. So that's one thing. So you see already right away here. Oh yeah, I see parallels in how it's being processed. Statistics is important. AI civil leaders understand statistics because you know why that's important. They see the difference between correlation and causation. What is causal, what causes what, or what is simply correlated. Because another thing AI civil leaders know is that we can develop the most sophisticated models today in the lab, but the one that you see in all the demos of OpenAI or any other tech company is not the AI that you're going to use right away in your company. And the reason why is in a lab, there's not many stakeholders when you develop, so you can't harm too many people, too many stakeholders, interest. But once you bring it into a business context, you have stakeholders. So AI adoption becomes a risk management issue and the risk goes up exponentially. So that's why most of the AI that we use when it involves your stakeholders in business decisions is still supervised because you want to know what's in the black box. You can't make it. The large language models that we know today, even the most sophisticated computer scientists don't know what's happening in the black box because of risk management. You're never going to use that in your company. So that's why I always say to people as well, I mean, I know a lot of boards and we're saying like, okay, we have the fear of missing out and at the same time, the development of AI accelerates and keeps accelerating. How can we, how can we stay abreast? Well, you don't need to because you're not a computer scientist, you're a company. You don't need to know in three years. This is the large language model. You only need to understand what, what a large language model is. And once you use that new one, you'll bring it in. That's lifelong learning. You get updated, but you need to have the basics because you're never going to use that right away. So if you bring all that knowledge together, then you start seeing what I said earlier. AI is a tool. Is this what it means? This is how I can use it. And this is what we already have. And this is what we really are strong about. How can we bring those together? And that's when what Deloitte calls H with the H with AI. So AI is not the end game. Humans are. AI serves human intelligence. So how do you make sense of that in your company? And if you follow that logic, you arrive at a very powerful conclusion. I always say there's a difference between companies who say we have an AI driven company or an AI enabled company. An AI driven company means who drives it? AI. So AI leads it. It's the mindset simply of AI and if it gets better, will ultimately replace us. AI enabled means it's an organization that knows it's a tool and we work with it. It enables us to do even more things now. And that's a big difference in the narrative. I've noticed that.
- [29:13]
Dave Stahoviak
David, it's fascinating. The distinctions you've painted in this conversation, I think are just such an important entry point for how we think. And as you've mentioned, so much opportunity for us to, if we just spend a little bit of time beginning to understand the fundamentals, to do such a better job of leading our organizations to utilize this technology. Well, and let me just say a great starting point is your book. Reading through the book and looking at the key points. I mean, what a wonderful foundation and starting point. The book's gotten a ton of traction. It's been on the Next Big Idea Club. It was an Amazon bestseller and one of the lists when it came out. I mean, you've really helped to advance this conversation. So thank you so much for your time and your work on this and I'd love to ask you one last question. You've obviously been in the midst of this, you've been studying this for a long time. You are consulting to organizations to help them to do a better job at this. As you put the book together and have been sharing these ideas with leaders in the last year or two, especially as AI has come into the conversation so much more broadly. I'm curious, what if anything, have you changed your mind on?
- [30:22]
David de Kremer
It's a very good question. Well, what is interesting is no matter how much you study AI, you're always surprised about it to some extent that yes, it does impress. And everyone says this, if you use ChatGPT, it comes across as confident. It provides the answer. One thing I've learned is we're just as much responsible for the hype of AI as AI is. And let me explain this more clearly. We see it can do all these things, but the fact that we feel AI is confident and it impresses us and it's overwhelming is usually self attributed. It's our perceptions and we provide meaning to it. So that's something that I've really learned is that every time when there's something big, and in this case AI, yes, it follows the same yes, it's immense, it can have a big impact, but we put our own fears usually out there, we put our own interpretations out there. And in any crisis or any new innovation, it always takes a while to realize that and then put it in the right proportion. So you'll see with any crisis in the last hundred years or innovation, we will probably have gone through the same process of wow, there's a hype because we have huge expectations ourselves and then the reality kicks in after a while and then there's a little bit of a dip. But we're actually responsible for that dip ourselves because we set these expectations so high because we would like to see it. And that's something I've learned by interviewing a lot of business leaders because they want to see certain things. And that was a big eye opener. And I'm tooling around now, I'm toying around now with that idea a little bit more in making that clearer to society of how it influences how we influence our own society and in a way there's a lot of self fulfilling prophecies there. So that's something that has attracted my attention a lot.
- [32:19]
Dave Stahoviak
David decremer is the author of the AI Savvy Leader Nine Ways to Take Back Control and Make AI Work. David, thank you so much for your work.
- [32:29]
David de Kremer
Thank you Dave for having me here. I enjoyed.
- [32:38]
Dave Stahoviak
If this conversation was helpful for you three related episodes I'd recommend one of them is episode 612, how to solve the Toughest Problems. Wendy Smith was my guest on that episode. I think about Wendy's work all the time and her invitation for us to move past just the either or which is how we tend to frame problems and situations and decisions and invites us to think about the both. And you hear the spirit of that in this conversation today about AI. Not just thinking about the it's AI or it's us, but the both end of how we are working together with technology. So many wonderful invitations in episode 612 of How we shift our thinking in so many of the problems and situations we find ourselves in each day to look at the both and the complexity and the opportunity. Also recommended is episode 649, how to begin Leading through Continuous Change. David Rogers was my guest on that episode. He's an expert in digital transformation, of course, AI, one of the digital transformations that so many organizations are thinking about. And in the midst of right now we talked about his expertise and some of the patterns he sees that are helpful for all of us to lead better in the change process which is ongoing for so many of us on all kinds of projects. AI, certainly a key example of that. Episode 649 on how to approach that in a very effective way. And then finally, I'd recommend episode 674 principles for using AI at work. Ethan Malik was my guest on that episode, one of the leading thinkers and researchers on AI and we talked about some of the, I don't think I would say timeless principles of AI, but as timeless as you can get. We looked at the short to medium term on what are four things that are key principles for how you utilize AI at work. I think about those four principles all the time in my own work. I continue to reflect back to them. They're just as relevant today as they were when we recorded that content conversation, and they will be for a while yet. By the way, if you're not following Ethan's work, LinkedIn is a great place to do it. Also his substack keeping you in the know on what's happening with AI. Again, that's episode 674. All of those episodes you can find on the coaching4leaders.com website. And if you haven't set up your free membership, I'd invite you to do so by going over to coaching4leaders.com and setting up your free membership. It's going to give you access to be able to get into the Episode library and find exactly what you need. One of the categories we have inside the Episode library is AI. Had many conversations already. Of course more to come on AI as we all navigate this new technology. Also, this conversation is filed under Strategy Today. Many strategy conversations in the past and whatever you're looking for right now. Maybe it is resources on how to handle a difficult conversation, or maybe it's productivity, or maybe it's a bit of work life integration. All of those topics and so many more inside of the free membership. Just when you set up your free membership, go over to the Episode Library, you'll be able to find exactly what you're looking for. And one of the other resources inside of the free membership is access to my book and interview notes. I recorded tons of notes from David's book, highlighted many of the things that we didn't even get to in today's conversation. They're all inside of my interview notes for almost every conversation that we have here on the podcast. Also, one of the benefits of your free membership when you log in, just go over to Book and Interview Notes. You'll find that for all of the episodes in recent years for the books and resources with a lot more. And if you are looking for more, I hope that you will take a moment when you're online to visit Coaching for Leaders Plus. By going to Coaching4Leaders Plus, I am writing a journal entry every single week that is helpful to you in some way of moving you forward in your leadership. And just this week I responded to the question of how do you keep track of it all? When you're having conversations with employees about their development or stakeholders or customers, how do you actually take notes and keep track of those? In this most recent journal entry, I detailed what are the three principles, both mindset and tactics that I use to capture notes about people and how I utilize that and make it work for me? It's one of the recent entries, as are many other entries inside of coaching for leaders plus for details of all the benefits part of coaching for leaders plus, just go over to coaching4leaders plus. Coaching for leaders is edited by Andrew Kroger. Production support is provided by Sierra Priest. Next Monday, I'm glad to welcome Karthik Ramana to the show. He's going to be joining me to discuss what leaders can do to turn down the temperature on outrage. An important conversation for all of us right now. Join me for that with Karthik. Have a great week and see you back on Monday.