Ep 138: Reframing Leadership and Accountability for the AI Era with Dr. Adrian Wolfberg

[00:00:09] [SPEAKER_02] Hey everyone, welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, responsible innovation, and the future of work. In this episode, I sit down with Dr. Adrian Wolfberg. He's an organizational scientist leading organizational insight consulting and the author of the new book, Who Leads When AI Thinks? Adrian spent decades in national security from flying Navy reconnaissance missions, teaching at the War College,

[00:00:38] [SPEAKER_02] many years at the Defense Intelligence Agency and a research fellowship in the office of the Director of National Intelligence. Adrian argues that as AI takes on more of our work, the leader's job is not to have all the answers. Instead, it is to slow down long enough to truly understand the problem, bring every perspective into the room, and only then decide how much to lean on the machine. Adrian's job is to have all the answers, and also learn how much to lean on the machine.

[00:01:10] [SPEAKER_02] Adrian's job is to have all the answers to the questions that you can ask for the questions that you can ask for. And then, let's go talk to Dr. Wolfberg. Hey, everyone. Welcome back to another episode of Elevate Your AIQ. I'm your host, Bob Pulver, and with me today, I have Dr. Adrian Wolfberg. How are you today, Adrian?

[00:01:32] [SPEAKER_00] Great. Thank you, Bob. Thank you for inviting me to your podcast.

[00:01:36] [SPEAKER_02] Yeah, my pleasure. Yeah, we've got a lot of things that I'm really anxious to dig into. Before we do that, I certainly want to give you an opportunity to just introduce yourself to my listeners, talk about your somewhat unconventional career path, but a lot of really interesting experiences and all sort of culminating in some of the work you're doing now in the new book. So I'll let you do your own intro. I was a naval officer flying reconnaissance

[00:02:04] [SPEAKER_00] aircraft in the Middle East. After I did that for a few years, it was a seven-person crew. I worked in the Pentagon, still in the Navy, providing warning to the chairman of the Joint Chiefs of Staff. And then I joined the Defense Intelligence Agency and spent about 30 years

[00:02:28] [SPEAKER_00] or so. So I've been in, as a practitioner, I've been in the world of national security for about 40 years having some very diverse experiences, both in terms of knowledge production and in terms of organizing, in terms of kind of internal consulting. And during that same time period, I also got

[00:02:53] [SPEAKER_00] interested in doing a doctorate on how knowledge is created, how it's communicated, and how it's been explored by people who make decisions, which came from my, originally my Navy experience. And I've been doing research for the last 20 years and what people in organizations, how they make decisions, not only in intelligence, but national security, law enforcement, business, elite sports,

[00:03:21] [SPEAKER_00] and medicine. And so I'm now kind of, you know, at the end of this kind of working career, putting everything together and coming out with some books. And the latest book is about, it's a leadership book about how to think about working with AI, who leads when AI thinks. So that's it in a nutshell,

[00:03:45] [SPEAKER_02] very quickly. Well, there's a lot there, certainly. And so, you know, as we go through this discussion, I think we'll probably, you know, ping pong around to a couple of those experiences because, well, I know you probably can't share all the stories from inside the Pentagon or defense intelligence and things like that. But certainly some of the concepts, I think, are of utmost importance today

[00:04:13] [SPEAKER_02] as we think about how to put the right minds in the room. And nowadays, of course, that collective intelligence and that cognitive diversity can include some different, you know, AI solutions. So we've got to think about, you know, what perspectives might be missing and what can, how can AI sort of augment the collective and not just an individual's judgment and knowledge.

[00:04:41] [SPEAKER_02] And so, yeah, so, I mean, even that, going down that thread, there's probably a bunch of sub-threads when it comes to judgment and, you know, who can exhibit true, you know, leadership, which doesn't necessarily mean you have all the expertise and all the knowledge, but who do you know that you can trust and who has information that is relevant to a particular decision, right?

[00:05:08] [SPEAKER_02] Right. So, so I think, you know, well, I would imagine, I guess it's digging into the book a little bit, but, but how you think about how all that information sort of gets synthesized, right? Across different perspectives and disciplines and, and how that, you know, may reveal itself over the course of, you know, enterprise decision-making. Maybe you could just unpack a little bit more about,

[00:05:36] [SPEAKER_02] you know, but what's, what's in the book and some of the, some of the lessons and some of the key components of, of the book, I guess, not chapter by chapter, but however you want to sort of unpack it a

[00:05:47] [SPEAKER_00] little bit. You know, the, there, there is a benefit, I think a recognized benefit, I don't think this is controversial, that not, that no one knows everything. And so there is a need for multiple perspectives to make the, you know, best attempt to fill in so that we have a good understanding about the completeness of an issue. And, you know, there are some, this is a non-AI particular, but I think as I discussed the AI, one could

[00:06:17] [SPEAKER_00] see how I transferring this into that context as well. But this is, this, what I'm going to talk about here, I think everybody can see anywhere in their experience, you know, in their work, career experience. And, and that is that, you know, structurally we develop expertise in the areas that we're educated, we're trained, we're tasked with responsibilities within an organization.

[00:06:47] [SPEAKER_00] And one of the kind of structural issues with trying, with wanting to achieve the benefit of multiple perspectives, again, that's the goal. Now, you know, what, what are kind of the barriers to that? And one of the main barriers is the, the language and how we framed the content that we have expertise

[00:07:13] [SPEAKER_00] in and the language associated with it. So this makes for kind of a double whammy of a challenge for absorbing, for gathering and absorbing and synthesizing multiple perspectives, which is, which should be the goal, which I think usually is the goal. And so just at a very practical level, do the words mean the same thing from different organizations? And do we even care about the

[00:07:42] [SPEAKER_00] same things I'm talking about within an organization, silo A, silo B, silo D? You know, there was a famous, I go back to this famous study done in 1958 by management scholars giving CEOs and their direct reports, VPs of, you know, silos, VPs of engineering, marketing, manufacturing, and so forth. And they

[00:08:07] [SPEAKER_00] were from different organizations. And the research gave them a scenario about some challenge that was out there that was, you know, threatening or, or, you know, challenging the organization's success. And it was only the CEOs, the head of the organization who had a global view of their organization to provide the best assessment of how to approach the problem in this scenario they were giving.

[00:08:37] [SPEAKER_00] And everybody else could only see a contribution to the problem, which was, you know, a global problem in a sense that it covered the entire organization. They could only see it from where they sat. So the, the manufacturing could only see their contribution to resolving it from a manufacturing perspective and each other as well. So there is this kind of structural dilemma of not only this,

[00:09:04] [SPEAKER_00] where you sit determines what you see, but also the language itself, when you get into the actual operational aspects of having people in the room communicating with each other. And it is very real to see the paralysis or the miscommunication, the, the signals of, you know, anger or of,

[00:09:28] [SPEAKER_00] of resentment or the challenge of using the same words or that mean different things or using words that don't exist in, that only exist in one person's organization, but don't exist in another. So advocating something with those words means nothing to the person in the other organization. So there's this, you know, structural and linguistic aspect of actually accomplishing this goal of achieving a

[00:09:56] [SPEAKER_00] shared understanding, kind of a more global picture about what's going on to comprehensively understand the problem. Because, you know, who doesn't want, who wants to go half, you know, half baked into addressing a problem and then finding out on the back end that, gee, if we'd only considered this, if we'd only considered that, you know, wasting of resources and restarting, wasting of time and

[00:10:23] [SPEAKER_00] then restarting and stuff. So that's kind of there all the time whenever people are involved. So when you introduce AI into the scenario, the same challenges exist, but there's an additional dimension that adds complexity to pursuing this shared understanding, this global understanding of

[00:10:48] [SPEAKER_00] what the problem is, so that down the line, your solution is effective and efficient and so forth. And that is that, you know, contrary to pre-AI technology, every tool, and I'm including technology and language, any tool that has been created, humans have created for the purpose of accomplishing a goal

[00:11:12] [SPEAKER_00] for the task that they're working on. Those tools have been designed and manufactured with well-understood inputs, with well-understood outputs, with well-understood design of the process getting from the inputs to the outputs. And that is transparent to how that's actually happened and can be, you know, retroactively digested or even in real time understood what's actually happening. But that's, none of that, for the most part, is true in the world of AI.

[00:11:42] [SPEAKER_00] So, this additional dimension is, how do you deal with a thing in which none of these, these understanding of a tool is now available? And in fact, that it does things that humans cannot do. I'm not saying it does everything, but it does things that are well-recognized that it can do. It can process information quickly. It can identify patterns based on the data that you're giving

[00:12:09] [SPEAKER_00] very quickly. It can evaluate scenarios, even posit scenarios. So, there's a lot of useful things that it can do. But there are some key things that it can't do. And so, this is where this additional dimension comes into play, that in the age of AI, the humans have to stop this built-in pattern that we've had since beginning of humankind, and especially since the origin of the Industrial Revolution,

[00:12:39] [SPEAKER_00] of reacting first to, give me that tool, get me that tool. I just need to get me that tool so I can be successful. Which has always been true, because in the past, because of the reasons I just identified. So, now with this intelligent being, so to speak, where we're really, we're in a,

[00:13:04] [SPEAKER_00] the latest transformation of humankind, when we're in a, in a totally new kind of transformation of shared consciousness between human consciousness or human intelligence, and to the degree that one can define machine intelligence. And of course, you know, that's constantly being talked about and thought about in the public sphere as well. So, what, through my research and thinking about it,

[00:13:31] [SPEAKER_00] what this book talks about is that we can't compete with these aspects of AI, and nor do we want to default everything to these aspects of AI. So, what is it that humans need to do that leadership really needs to be responsible for? And, and that is to ensure that there is time taken at the front end of

[00:13:55] [SPEAKER_00] when facing an issue, that there is a intentional, with intention, time to truly understand the kind of diversity from all the different perspectives of what the nature of the problem is. Now, that latter part, as I mentioned previously, has always been the case of the ideal that should be done. Some, you know,

[00:14:21] [SPEAKER_00] a lot of times it's not done because a lot of reasons. Time, there's time pressure, there's agenda pressure, there's competition pressure, there's all these other kind of pressures which collapses time and prevents a full vetting of what the nature of the problem is. Well, what I'm advocating is that in the age of AI, that's, that compressing of these pressures is going to remove us, was going to make

[00:14:50] [SPEAKER_00] us less successful in achieving the outcomes that we actually want to achieve. And so, the role of humans becomes important in this nature frame, the nature of the problem framing activity. And so, why is that important? Well, for one reason, it's important even before AI so that you don't

[00:15:12] [SPEAKER_00] have to rework and suffer the loss of effectiveness and redo time and reuse resources. But the other reason is to optimize in a sense, I hate using the word optimize, but to do the best one can to match the right kind of AI to the right kind of human involvement. And so, you think about this as a,

[00:15:37] [SPEAKER_00] really a spectrum from A to Z or from one to 100, whatever, from one end to the other, where on one end, there's maybe 99% AI, 1% human involvement. On the other end, it's 99% human involvement, 1% AI. In other words, it's always going to be both involved, but there'll be different levels of, different degrees of combinations. And so, what the human is responsible for is ensuring that the best of

[00:16:04] [SPEAKER_00] and the needed aspects of, and I can talk about that later if you want, the needed aspects of human involvement are as best as possible matched to the nature of the problem as we understand the pieces of the problem. It's not like, one, we can encapsulate the nature of a problem in one sentence.

[00:16:28] [SPEAKER_00] No problem manifests itself in categories that we've identified either in organizational language or in just in language that we use based on our training, our education, our experience, etc. So, it's like a Rubik's Cube in a sense that there's multiple pieces and they have to align.

[00:16:50] [SPEAKER_00] And this alignment is done through this intentional human focus on understanding the pieces, figuring out what part of human and AI and how they go together. And not only that, what's different now, well, what AI has accentuated is that the difference between

[00:17:15] [SPEAKER_00] pre-AI and AI integration with human intelligence is that monitoring over time now becomes critically important because this management, so to speak, of the human AI intelligence, this joint consciousness, this shared consciousness cannot just be decided at time zero and just say, go forth and do this because

[00:17:43] [SPEAKER_00] reality changes. AI is going to be sucking in data, is going to be constantly processing information. The world is changing. Context is changing. These are things that humans understand. They're the ones who potentially can understand all of it. We do the best we can. We're not always perfect.

[00:18:01] [SPEAKER_00] So, there's this human AI combination at many levels that occurs at time zero when the problem is for certain time and has to be maintained and monitored over time. So, the leader's responsibility is not anymore, I know the answer, here's the solution, go forth, implement it via the resources that we have

[00:18:28] [SPEAKER_00] available to us. It's more now going to be setting the conditions so that the awareness of what's going on by everybody in the organization, which that in and of itself is a leadership challenge, can identify and reprocess or reframe as necessary as time moves up.

[00:18:50] [SPEAKER_02] So, Adrian, just going back to some of the core premises you were alluding to at the beginning, as far as the silos and that management research done way back, was that 60, almost 70 years ago now, if you're an organizational leader and you didn't already have enough evidence of why you need to break down

[00:19:17] [SPEAKER_02] those silos. We're seeing examples of this every single day. And I think as you think about the culture and the way that knowledge and expertise is shared, the way responsibilities around decision-making might be not egalitarian, of course, but not necessarily just sort of command and control, realizing that

[00:19:43] [SPEAKER_02] there's a reason you have, you know, a leadership pipeline. So you have people that you can trust in decision-making roles and things like that. But it also seems like, you know, we're trying to address some of the human biases that may exist within these silos as well. And so, you know, just through my own research on collective intelligence, you know, methodologies and collaborative

[00:20:11] [SPEAKER_02] decision-making, you know, you've got to have the cognitive diversity, if for no other reason than to balance out some of those biases. It's not just the aggregation of, you know, domain knowledge, it's additional context, it's biases that might sort of, you know, balance each other out. And then when you bring, I think the other important point, well, you had a lot of important points,

[00:20:38] [SPEAKER_02] but one of the other ones was around, you know, when and how and what AI to bring in to that decision-making process, you know, not all AIs are created equal. And so as you train, you know, AI on proprietary, you know, data sources and specific knowledge, you know, it can do a better job to

[00:21:04] [SPEAKER_02] augment either the, you know, the individual or the collective intelligence of a decision-making body, because it's not just bringing in perhaps a missing perspective or angle. It's also the ability to call us out as humans where we are exhibiting, you know, human bias. And so I think AI,

[00:21:30] [SPEAKER_02] for people just really sort of early in their AI, you know, journey, trying to understand, you know, what it does, where the benefits are, and where those benefits outweigh the risks, you've got to understand that this is not, I kind of describe it as, it's not a tool. It's at a minimum, a tool kit or, you know, a workbench with lots of tools. And you've got to pick the right tool

[00:21:58] [SPEAKER_02] for the job. But that's, but that's part of how you determine the, I guess the, I don't know if ratio is the right way to put it, but how do you balance, you were talking about 99 to 1, 1 to 99, or some variable in the middle. How do you, for a given decision, for a given set of variables, for a given context or whatever,

[00:22:23] [SPEAKER_02] how much of a role does AI play? And I think that's also a big, you know, workforce transformation challenge these days. So what, what should be done by humans? What can we offload to AI? How do they, how and where do they sort of meld together? But ultimately, of course, having humans with the responsibility and the accountability for those decisions being made.

[00:22:53] [SPEAKER_00] Yeah. I mean, all that, everything you said is right on. What's going to be key, which I just kind of glossed over in my kind of introductory statement just a few minutes ago, is that, you know, how do you figure out what that ratio is? That's your term ratio. I don't know. That's probably a good enough. The balance. Yeah. The balance. Yeah. So it goes to, how do you, how do you understand what,

[00:23:19] [SPEAKER_00] what the issue is that you're facing? And so you have to have some kind of framework. And so I'm, I'm positing that there's a framework that you can think about that consists of where on the spectrum between things that you've seen before and things that you've never seen before is this,

[00:23:39] [SPEAKER_00] you know, how new or how old is the well-understood problem? Another is how simple to how complex is this problem? The other is to what degree is it wicked or not? So there's a distinction between complexity and wickedness. So anytime that we're dealing with, with humans, which is the kind of

[00:24:03] [SPEAKER_00] issues that I'm, I'm thinking about, thinking about things that occur within an organization or outside of an organization that involves people in the process and or people who are the beneficiary of whatever the outcome is, as opposed to thinking about how an oil refinery works or how the International Space Station

[00:24:31] [SPEAKER_00] works or how nuclear facilities work, those are kind of what would be called closed systems. There's no, there's very little human or external interaction there. They just kind of go on themselves, they self-monitor and, you know, except for some kind of catechismic, you know, issue, they're just going to be contained and just continue forever and forever. But anything else,

[00:24:58] [SPEAKER_00] it's all bets are off and everything changes all the time. So this idea of wickedness, which is a term that just refers to, you know, in just simple terms, how value-laden is the issue? Do people even agree on what the problem is? Do people even agree or is there even an end state in mind can be envisioned?

[00:25:27] [SPEAKER_00] Is there an agreement on what that end state is? You know, these are things like, how do we address the healthcare problem and the healthcare issue in America? Or you could say, how do we address generative AI or large language model use in public schools and universities? You know, these are the kinds of things that are an additional dimension to complexity, whereas complexity is just,

[00:25:55] [SPEAKER_00] you know, kind of something that's very complex has got massive amounts of interdependencies, whereas something simple is, you know, none or, you know, no interdependencies. So, you know, air traffic control systems are highly complex because there's a lot of interacting factors, aircraft crew, aircraft themselves, fuel systems, aircraft, airport logistics, all, you know,

[00:26:22] [SPEAKER_00] all sorts of things. That's a very complex kind of an issue. And then the degree which things are new or not. And so, as you get to the outer edges of these three dimensions, which are part of the framework, things that are novel, highly complex, and wicked, you get into problems like, how do we fix healthcare?

[00:26:48] [SPEAKER_00] You know, important problems that can be not only at the society level, they can be at the international level, they can be at the sector level. And those are going to be much more, have much more emphasis of human involvement in. Why? Because the wickedness entails issues of values and ethics. That's not where,

[00:27:16] [SPEAKER_00] maybe someday, I don't know if AI could ever, ever have that kind of human awareness. The issue of complexity would depend on, you know, the nature of the problem and whether an AI would even understand all the different facets that are involved. Certainly in a closed system, that would be much less of a problem. But in any kind of open system with humans, that would be, that would be an issue.

[00:27:43] [SPEAKER_00] And then the issue of novelty, you know, AI is just pretty well bounded by what we know today, not by what's tomorrow.

[00:27:50] [SPEAKER_02] I think the novelty concept of the framework is important because this is about the, it's like the, you know, known unknowns. Like, do we know what we don't know? And then you have unknown unknowns, right? Like, we don't, we're kind of, this is outside our sphere of knowledge and understanding and, and I don't even know what I'm overlooking or I don't even know what I'm missing.

[00:28:15] [SPEAKER_02] And so it's just another potential sort of valuable contribution. If you have an AI that could basically fill in the blanks, what you're, what you're missing is, is this, and have you thought about these other consequences? And I think as, as the world has gotten more interdependent and, and complex, you know, there's more potential risk that, you know, there are some unknown unknowns that

[00:28:42] [SPEAKER_02] we haven't thought of. And so we want to do the best we can to sort of structure these things from, from the beginning and make sure that we've got, that we're designing these things as appropriately and as best we can based on all potential information that we can gather. And I think that's part of, you know, synthesizing all this information. I also think it ties into one of the concepts within

[00:29:07] [SPEAKER_02] responsible AI, responsible with a capital R, is sort of a category of sort of attributes that we need to look out for, whether that's, you know, privacy and security or the explainability and observability of these, of these models. But I feel like being responsible by design, you know, I've been framing that as, let's be responsible as we design the AI systems themselves.

[00:29:35] [SPEAKER_02] But you've given me another sort of way to frame that, which is as leaders, we need to be responsible by design and how we're structuring, you know, decision-making systems and how we're, we're framing the problem.

[00:29:51] [SPEAKER_00] Yeah, exactly. And, you know, we're just at the beginning, you know, obviously of, of this new era and we're not prepared for this yet. I mean, any major transformation takes time to understand because you don't have, you know, just, just kind of, you know, at the practical level, you don't have the experience, the knowledge, the language to talk about what these issues are.

[00:30:19] [SPEAKER_00] So any way that we can move the conversation to start thinking about this, which is any way that I feel like I can contribute to that conversation, you know, will, will help along with others who were thinking about this, to moving the ball down the lane so that, you know, over time, when we do have enough experience and we start developing a shared language for how to understand

[00:30:49] [SPEAKER_00] what's actually happening, we can start better designing, I would say, both on the, on both sides, on the technology side and the leadership slash organizational design, design sides. So they're, they're both important. I mainly focus on the, the leadership and organizational aspect because I think there's plenty of people who are currently focused just on, on the technology side, but both, both sides have to be thought about.

[00:31:19] [SPEAKER_02] Yeah, for sure. One of the things that I'm curious about is your perspective on the, what we've been calling the, the, the human skills, the durable sort of human skills that everyone says are becoming, you know, more valuable in, in the age of, of AI. And I think a lot of those hit on, you know, some of what your, your research has, has focused on in terms of critical thinking,

[00:31:48] [SPEAKER_02] creativity, judgment, again, there still needs to be a human that's ultimately making decisions and is applying their, their judgment and the context and all of that, hopefully with all the input from all those folks in the, in the room. And so, so how do you, how do you think about those, those risks of us sort of offloading, doing too much sort of cognitive offloading to, to an AI system or potentially

[00:32:17] [SPEAKER_02] multiple AI systems just as it builds up a better track record? Right now it, it still hallucinates a lot and we've got to check its work, but I could imagine the time, you know, maybe a decade or whatever it is in the, in the future where, you know, hallucinations are, are minimized. We've got better alignment between humans and AI so that it is actually working and, and, you know, some of

[00:32:41] [SPEAKER_02] those Asimov core principles about, you know, do no harm to, to humans. But, but if we give, if we look at the trajectory of how AI is, is advancing and we assume for the moment that it's doing so in service of, of, you know, humanity, what happens? I mean, do we, do we just start relinquishing

[00:33:08] [SPEAKER_02] more and more control to these systems? I mean, what are some, what are some of the dangers that

[00:33:13] [SPEAKER_00] you've thought about? Yeah. Yeah. Well, it's important to think about what these, what are innately human strengths and these strengths are not just, are just, are not just a list of things that people are picking up or I'm picking up. They're actually related to the, in the ingredients to, that are necessary to understand and solve problems. So that's why these things are important. So I'm going

[00:33:41] [SPEAKER_00] to go through this, I'm going to go through the list that I use. And some of this is just going to build off of what I've been talking about, but critical to this whole thing. From my perspective, perspective, it's, I'm talking about what we need as capacity building, capability building for humans.

[00:34:05] [SPEAKER_00] That's what I'm talking about in this kind of sport or this game where we have the shared consciousness between machine and human intelligence. And just like a football game, every moment there's different things going on and you have to monitor everything that's going on. And there's different use of resources and stuff like that. Unlike a game though, there's no rules that are being abided by,

[00:34:31] [SPEAKER_00] there's no boundaries that we have to abide by. But so here are some, here are some of the things that are uniquely human, that are uniquely important to, to problem solving problem, to dealing with, to achieving goals through eliminating barriers and achieving what, whatever we want to do. You know, one goes into, to understand the nature of the problem, we first need to frame

[00:35:01] [SPEAKER_00] what, what the issue is. And so framing, I don't know how widely that term is, is understood, understood. But the, the, once it's explained, you know, it's an everyday thing that people understand. It's taking the context, the situation that you are in and figuring out of all the universe that's

[00:35:23] [SPEAKER_00] going on, what things are important to understand as best one can within the, within the limits and capabilities of oneself, one's team, one's organization to kind of put a box around this issue, to be able to name it and categorize it and understand it and to discuss it and to be able

[00:35:48] [SPEAKER_00] to articulate what resources might be appropriate or usable, including, including AI, where and when it would come into play and to what capacity. So this framing aspect is uniquely human because only we have, you know, a, like a God's eye view, ideally in the reality of human existence. And here,

[00:36:13] [SPEAKER_00] I'm talking about what people are thinking and doing, what their agendas are, both in, in teams, in organizations, in sectors, in society, and internationally. So understanding that context and putting it together is just, is all about human interactions and human feelings and human needs, human intentions.

[00:36:39] [SPEAKER_00] That is something that's not disclosed in, not in real time in any kind of data stream. So that's something that's innately human, this ability to frame by virtue of crafting the contents, that's in the context that's important to us that we're dealing with now. Then the ability to reframe as

[00:37:01] [SPEAKER_00] reality changes is, for the same reason, is innately human capability. And why is that important? Well, because reality changes. So many things change that we constantly have to monitor what's going on to determine if there needs to be some adjustments or tweaking. And of course, this has always occurred even before AI, but it becomes more important with, because of the speed of AI, that we adjust as

[00:37:31] [SPEAKER_00] necessary this ratio, so to speak, as multidimensional Rubik's Cube ratio of human and AI contribution, intelligence to an issue. So reframing and reframing, uniquely human, but, you know, very important to ensure that we're being effective in allocating resources to achieve the objective that we want

[00:37:58] [SPEAKER_00] to achieve. So those are just two. So another one is empathy. And here I'm talking about empathy on the back end. So once a decision has been reached based on some kind of combination of AI and a human contribution, where there's always going to be some AI contribution, I think from now on that will always be the case. It is the human, as you said, the leader who makes the decision, the go-no-go,

[00:38:28] [SPEAKER_00] so to speak, of what needs to be done. And this thing that needs to be done is going to affect people. Now, whether it's in a closed system or an open system, which 99% of what we're living in is an open system, it's ultimately going to affect people. So it's, whereas in pre-AI society,

[00:38:56] [SPEAKER_00] there, hypothetically, there should be a well-understood vision and understanding of the impact of our decision that we're doing. Obviously, there are decisions that are made that don't have that, and that's sad. But theoretically, that's possible. When you get into the shared consciousness of machine and human AI, human intelligence, that becomes less so because of the AI portion.

[00:39:24] [SPEAKER_00] So having this empathetic mindset of imagining what this decision could have to fill in the blank, people in my organization, business partners, societal members who are using this output, the international system, having that empathy will be more important now, especially for,

[00:39:52] [SPEAKER_00] you know, purposes that are business-related or organizational-related reputation and efficiency in terms of having to rework and redo something that's broken, so forth. So that empathy is important. The idea behind control, which really has been amplified by the Injustice Revolution,

[00:40:20] [SPEAKER_00] but has always been the case since tribal organization has existed, there's always been this control, this hierarchical and controlled mechanism. So no longer, so the leader, kind of the model of the leader as we move into this shared consciousness space, has to move away from a control mechanism to

[00:40:44] [SPEAKER_00] setting the conditions where people in the organization have eyes open to what's going on around the world, to be able to contribute to those observations, to participate in sense-making about what's going on, so that as changes occur, and even at the beginning time zero, this ratio of human and machine intelligence can be

[00:41:13] [SPEAKER_00] not wasted and can be optimized as best as we can. And so that shift from control to setting conditions, which has been that kind of idea of leadership is not necessarily new, but in the age of AI, it becomes a critical competency to have. It's no longer a nice to have. Another one is the sense of accountability. So

[00:41:45] [SPEAKER_00] we don't want to just default accountability to AI. I don't think humans will allow that to happen. Well, some humans may want it to happen, but other humans are going to be upset that that's going to happen. So there needs to be this kind of norms, this cultural norm that no matter how sophisticated the AI is, that humans are still going to be accountable. And so there's all sorts of implications

[00:42:14] [SPEAKER_00] for that in terms of one's reputation, one's integrity, one's career, one's ability to learn more, and so forth. So I think I've hit maybe about five. Well, I'll hit one more, this notion of critical and creative thinking. And so where does that come into play? Well, you might say, well,

[00:42:38] [SPEAKER_00] why is that important? You know, if AI can do this and do that, setting aside whether they can actually do it or not, if it can, you know, do that, then what's the big deal about losing it? Well, because in this kind of calculation of this ratio, you know, with understanding the problem itself, going back to that problem identification and the decompensation of the problem,

[00:43:07] [SPEAKER_00] that's where this innate ability of, well, this human ability of critical and creative thinking is going to come about to figure out what is the decomposition of the problem and where can this combination or this ratio of AI even involve? Yeah, I think those are all like really important.

[00:43:30] [SPEAKER_02] I just, you know, as you're talking, I'm kind of sort of envision, you know, where some of these things could go awry, right? Like it is part of not relinquishing your own critical thinking now is really flexing the muscles and building the habit so that when AI gets more capable, you're not as likely to just sort of let it. Oh man, that is so important, what you just said.

[00:43:59] [SPEAKER_02] Yeah. Otherwise you, I mean, I'm just thinking, you know, my daughter just started undergrad and she's, she's in an AI court, her, literally her first assignment is talking about like, you know, writing about creativity and, and AI and why stay, you know, creative and critically and curious and things like that. And so I don't want to, you know, put too much help in her, in her head,

[00:44:27] [SPEAKER_02] but because I've got my own opinions, I want to see what, what she comes up with and then I'm happy to look at it. But, but, you know, these, these, this future workforce needs to understand this as much, possibly more than, you know, current organizational leaders, but I think it spans the gamut, right? It's, it's current and future workforce. It could be people already, you know, Gen Z or

[00:44:52] [SPEAKER_02] millennials already in the workforce with aspirations to, you know, move up that, that ladder and take on leadership roles and decision-making capacity. I think all these things that you're calling out are, are critically important, even if you're an individual contributor, because individual contributors are still going to be managing agents, right? So you may not want to call,

[00:45:16] [SPEAKER_02] call them, you know, workers or employees, and don't have to go down that rabbit hole. But fact is you are going, even as an individual contributor, you're going to have oversight over entities, you know, who or what doing work on your behalf. And then, so you've got to know when to sort of draw the line, where, where that balance point is just, you know, referring back to

[00:45:44] [SPEAKER_02] what we were talking about earlier, but you're also highlighting about, you know, as context changes, as new information comes in, you don't just have, you know, with AI, part of the reason you need to continuously monitor it and audit it is because it, and it's always learning, right? It's, it's learning new information, it's adjusting, it's, you know, algorithm and, and things like that. And, you know,

[00:46:12] [SPEAKER_02] the human brain is in some way, I mean, the same thing, you could have this, this sort of drift, where someone is either clinging to their preconceived notions or clinging to a bias and not giving proper weight to that new information, because they might think it's a sign of weakness that you're changing

[00:46:36] [SPEAKER_02] your mind or have some other ulterior motive for not, you know, reaching consensus on a given challenge or what have you. So all these things, all these things matter now. And I agree with you. I think it's, it's going to matter even more as we go forward. And AI seems to be more capable. I mean,

[00:47:01] [SPEAKER_02] we're already seeing parallels with, you know, sci-fi movies. And I'm just thinking, you know, the smarter and more capable and potentially more accurate some of these systems are at, you know, sort of prediction, we're going to feel more and more comfortable starting with self-driving.

[00:47:22] [SPEAKER_00] I want to talk about it from the perspective of, you know, what we can do to maintain, you know, human, the benefit of humans on this. And so one of the things that I'm concerned about now, and that we see this in some of the school systems, is how do you develop creative thinking? How do you develop critical thinking? And, you know, with these decisions recently by, was it the New York school

[00:47:50] [SPEAKER_00] system and to prohibit smartphones until you get into high school and another school system is, some other kind of effort to reduce, and I bring up smartphone because now AI is embedded in smartphone. So it's, it's, but that's kind of, there's this pattern of behavior that began with

[00:48:13] [SPEAKER_00] smartphone usage that has kind of accelerated the problem AI. So one of the, and so I think one of the benefits of some of these decisions, like the New York school system, is to go back to reading. And, you know, I've heard and talked with and read about professors in college who are receiving first-year students who've never read a book, who may never have read a chapter. And I was just

[00:48:41] [SPEAKER_00] speaking to someone today who, who the student said, why do I have to read the page? I can just do a query on a chat GPT and get what you're trying to have me understand. So we have to go back to reading. And so this goes back to how do, how do you create creative thinking? You don't just say,

[00:49:03] [SPEAKER_00] be creative. And I, so, yeah. So one nice thing about reading, whether it's nonfiction or fiction, is that, excuse me, reading offers the mind an opportunity to wander as one is thinking about these things. And wandering allows one to achieve different perspectives on what may be happening. You know, maybe it's a couple of perspectives, maybe it's 10 perspectives, whatever, whatever it is,

[00:49:30] [SPEAKER_00] the mind is now being exposed to looking at things differently and, and through doing that, coming up with different insights and about what's going on. That's one important way of developing creativity. Obviously the arts, but it would be another way, you know, painting and so forth. But in terms of something that would be done by everybody, reading would be important. So the other, besides this kind of perspective of creative thinking, there's also this critical thinking aspect

[00:49:59] [SPEAKER_00] that is built from reading. It's not just from reading, you know, mathematics and any of the sciences, you know, you can also build critical thinking as well. But let's just look at mathematics as kind of the central core of physical sciences, whether it's mathematics or management book you're reading, or whether it's a novel that you're reading, the opportunity to continue to, you know, of this idea of repetition of

[00:50:28] [SPEAKER_00] seeing things again and again and seeing these patterns and evaluating these patterns provides you the opportunity to dig deep into what may be going on. That helps train you to think critically as well. So this combination of critical and creative thinking is trained with reading and with mathematics and other things that are built upon mathematics. So that's why I'm really happy with the New York School

[00:50:55] [SPEAKER_00] system decision. And that, and if we don't move in that direction, and I'm not going to be an advocate for any particular method that's being used now, because I'm not an expert in, you know, education and how one would do things. But preserving critical and creative thinking, that would be the worst thing to give to a machine and

[00:51:22] [SPEAKER_00] offload it, you know, from human existence. So anyway, that's just my two cents on that.

[00:51:29] [SPEAKER_02] Yeah. I mean, I think that smart, well, I don't want to go too far off on a tangent at the tail end of the conversation, Adrian, but I do, the smartphone thing I totally get and I agree with. The AI decision, I have mixed feelings about, particularly for the middle schoolers, because I'm part of an AI task force in my own school district. And I've seen very encouraging results from, from a survey about

[00:51:57] [SPEAKER_02] usage, not just like time spent using tools, but, but the actual use cases and they're quite constructive. Right. And so, so to sort of rip that, rip that out and not pair them for what will probably, I hope at that, by the time they get to high school, will probably be part of the curriculum depending on the, on the course and things in the school. But I just, this is about, you know,

[00:52:25] [SPEAKER_02] sort of preparation. Right. And so we've got a, AI is not going away and we've got to learn to assimilate ourselves with it. So that's, that's my only concern on the middle school,

[00:52:35] [SPEAKER_00] right? You know, the, the tweens. Yeah. Well, certainly these empirically based use cases are important to understand and benefit from. I'm just thinking about building creativity and critical thinking. And, and if there's other ways to do besides reading, that's great too. Yeah, for sure.

[00:52:55] [SPEAKER_02] Adrian, thank you so much for spending some of your valuable time with me. This is really fascinating discussion. I'm really excited to check out the book and unpack even more of, of your, your research and your writing. So, so thank you. Where can people find our information about your, your book and

[00:53:14] [SPEAKER_00] your, your work? Well, LinkedIn is, is, is one way to, you know, find out about me doing a Google search. You can find out about me and what I've done, or you can do a chat GPT or Claude, Claude search or whatever on me. I also have my website, Organizational Insight Consulting, which talks about, has things that I've written, talks about me, what I, what I think about, has a

[00:53:37] [SPEAKER_00] discussion board on the, the book, who leads when I think. And of course, Amazon, one can go directly to Amazon to get the book as well. Great. I'll, I'll put all those links in the show notes so people

[00:53:50] [SPEAKER_02] can get to that easily. So that's great. Adrian, any, any other final thoughts before we wrap up?

[00:53:59] [SPEAKER_00] Yeah, I was just thinking about this. I've been thinking about this more and more and that's time. And that's how we, we think about time as a resource, but really from a negative perspective of how to not waste time. And I think we need to start thinking about time from the perspective of using time to ensure that those innately human assets, the facilities that we've been talking

[00:54:26] [SPEAKER_00] about are, are optimized to the greatest extent, greatest extent possible. Even though we have such a history about not wasting time, so this idea of what we've been living with is compressing this pressure into the situation to make decisions as quickly as possible and to not waste time.

[00:54:50] [SPEAKER_00] And, and that has to change on the front end. We need to spend time to do that. And so that's going to be a shift in something again, that, that leadership is going to have to model. And a lot of these new behaviors, leaders are going to have to develop and model so that everybody else can actually see what, what would benefit the entire organization and society as well.

[00:55:18] [SPEAKER_02] Yeah, I totally agree. All right, Adrian, we will leave it there. Thank you so much again for your time. Thank you everyone for listening. We will see you next time.