- Megan Alexa Watson: [00:00:00] ...being able to weigh the impacts of an outcome of a decision against some sort of moral boundary...

- Carl B. Watson, Ph.D.: Right.

- Megan Alexa Watson: ...and having that sense I do think currently is uniquely human. It's the ick.

- Carl B. Watson, Ph.D.: Mm-hmm, mm.

- Megan Alexa Watson: It's the ick of a choice.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: When you make a choice and you're like, "I can't really explain why this feels ick, but it feels deeply ick, and so it feels wrong, and so I'm not gonna do it."

AI doesn't have the ick.

- Carl B. Watson, Ph.D.: It does not.

- Megan Alexa Watson: I don't think we can teach it the ick.

 This is a podcast for leaders who want to drive change in their organization without losing sight of the people behind it, the purpose that fuels it, or the principles guiding it. I'm Megan Watson, joined by Carl Watson, and this is The Watson Standard.

All right, friends, we all know this question well. Today we are gonna definitively answer, is [00:01:00] expertise dead?

- Carl B. Watson, Ph.D.: Yes. After officially completing my Ph.D. I can declare that expertise is officially dead.

- Megan Alexa Watson: You made it past the finish line, and now the race is over for everyone else.

- Carl B. Watson, Ph.D.: Right.

That's right.

- Megan Alexa Watson: I love it. I love it. So for real, how do we think about this? Especially for those of us, two people who might be sitting in this room,

have thrived through our expertise throughout our careers and our experiences.

- Carl B. Watson, Ph.D.: So it's interesting to me. You know I always do this historical thing, right?

And so as human knowledge has become more available, going from learning language to writing that language down on tablets, to papyrus, to the Gutenberg press, the internet, all these things have made knowledge more accessible, more widely accessible. None of those things have killed expertise.

And so I don't think this is going to be different. What I do think is gonna be different [00:02:00] is that the ability to quickly gain knowledge will be different. That's been the trajectory, right? The speed of access and the ability to get that information in a way that you can consume as a non-expert.

That's the trajectory. And so is expertise dead? No. Is expertise always changing? That is also true. Question is "how?"

- Megan Alexa Watson: Yeah. How and in what areas? There may be types of expertise that are considered less expert as they become more generally available-

- Carl B. Watson, Ph.D.: Mm-hmm

- Megan Alexa Watson: ...it becomes easier to access them and leverage them.

And then there may become types of expertise, honestly, that become harder to access if we continue to decrease human interaction through our technology, there [00:03:00] are certain skills and behaviors that we lose our muscle strength on.

And so all of a sudden, do we end up with a bunch of experts in actually talking to another human being?

- Carl B. Watson, Ph.D.: You know, it's not where I thought you were, like, going with that.

- Megan Alexa Watson: Yeah, I know. I, that's not where I thought I was going either. But let's run with it ...and see what happens.

- Carl B. Watson, Ph.D.: But it's interesting, so here's where I thought you were going

and then we'll figure this out, right?

- Megan Alexa Watson: Fine. We can bring it back to the topic. We'll bring it back another episode, as I like to say. Another episode.

- Carl B. Watson, Ph.D.: So, where I thought you were going is that prior to the Industrial Revolution essentially most households took care of a wide variety of the needs for the family.

And so there were times when people would make their own clothes, they would produce the tools that they needed to farm the land, they would butcher the cows.

They would grow and process their vegetables and the food that they eat and that's obviously no longer the case, right?

And so that expertise, that broad expertise that was available in every [00:04:00] household essentially or most households it's gone. It's where I thought you were trying to go. Sort of a different conversation.

- Megan Alexa Watson: That, that might've been where I was trying to go and I just didn't get there. But yes, I think the reality is, like, friends, if you can find me somebody who knows how to sew these days they are a freaking hero.

Because of how specialized that was.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: Where to your point, it used to be an absolute required capability in every single household.

- Carl B. Watson, Ph.D.: And so the question is, is that a problem?

I think that what shapes the skills that people keep are the challenges that we continue to face.

- Megan Alexa Watson: Mm-hmm.

- Carl B. Watson, Ph.D.: And so to the extent that those challenges are continually being presented to us, we will maintain those skills, and when those challenges aren't necessary, then the skills will go fallow.

- Megan Alexa Watson: Yeah.

- Carl B. Watson, Ph.D.: I think that's the real flip on the problem, which takes the problem out of it and says humans are adapting [00:05:00] to the conditions that they're facing. The issue is if those conditions radically change and you need to bring back an old skill in this radically changed environment, then that becomes a harder problem.

- Megan Alexa Watson: Yeah. It to me, connects to this idea of frictionless ...that everybody talks about. When new technology takes a significant amount of friction and toil out of a system, and what is left over after that?

There is a version of this world where AI at its pinnacle will remove all of the friction and toil in the human experience, and with no challenge, to your point, becomes no need to develop specialized expertise, skill, capability, et cetera.

But I might rein us in a little bit because you and I will definitely philosophize on this on the grand scale of humanity. I am gonna bring us to organizations, [00:06:00] businesses, systems. In the context of expertise within an organization, what are you seeing as you look at the research and think about the organizations that are at the top of their game right now?

- Carl B. Watson, Ph.D.: I think that the areas in which AI is a very strong tool are areas in which I don't think the expertise goes away, it creates an entry point. It raises both the ceiling and the floor for people who are engaged in that expertise. And so it's easier for me to get to something that is good or an acceptable quality as a non-expert, so the floor gets raised. And then what we aren't particularly aware of is that the ceiling can raise as well.

Those who have that expertise can create more powerful work, more creative outcomes based on that raised floor, and that raises the ceiling.

- Megan Alexa Watson: Yeah. It's connecting me to a perspective that I heard [00:07:00] from

Dan Shapiro of Glowforge, who was talking about organizations who are leveraging AI not necessarily to cut back on team and talent and make their organization smaller

- Carl B. Watson, Ph.D.: Yeah.

- Megan Alexa Watson: But instead are thinking of AI as a way to set bigger goals.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: And so the, the redeployment of talent in their organization is about

moving talent to something that in the past would've been completely inconceivable.

- Carl B. Watson, Ph.D.: Yes. Yes.

- Megan Alexa Watson: And so what does it mean to do 10X of everything and make that the new opportunity?

- Carl B. Watson, Ph.D.: 100%. Higher productivity, 10X, but also higher quality, 10X ...20X quality.

- Megan Alexa Watson: Yes. Quality and productivity.

- Carl B. Watson, Ph.D.: 'cause the efficiency focus feels like a red herring.

- Megan Alexa Watson: Yes, it does.

- Carl B. Watson, Ph.D.: Particularly because anything that is available to everybody, [00:08:00] every firm that you're competing with, is standard

- Megan Alexa Watson: it's no longer a differentiator.

- Carl B. Watson, Ph.D.: It's not a differentiator, right?

Yeah. It's standard, right? And that's not a

basis for competition. You don't wanna compete for a commodity asset, whatever that asset is or how powerful the asset is. If it's a commodity asset and everybody has access to it, then that's not a basis for competition. It's not a basis for differentiation of creating unique value.

And so the real question is not around efficiency. The real question is, what can we do now that we couldn't do before?

- Megan Alexa Watson: Yes. I love that, and it's part of what I think we're struggling with, to your point of how far ahead are we looking or how far ahead are we speculating, is we're doing the thing of "there are going to be new things that we do, things we haven't conceived of."

And I think for most people, myself included, it's really hard to get motivated by [00:09:00] some random thing that I can't possibly conceive of, but "I promise you I'm gonna love it," I'm gonna be able to do in the future because of AI.

- Carl B. Watson, Ph.D.: Yeah.

- Megan Alexa Watson: I can get motivated by this thing that I am super awesome at already. It is going to become more efficient, more effective, and higher quality.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: I'll be able to innovate in new ways. I will be able to make the wobbly ideas in my head more real and more tangible, more viable.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: You know, viability will shift in what I do because of that. That I can get excited on. That I can get motivated by.

- Carl B. Watson, Ph.D.: And for me, that's the same idea, just putting it, bringing it down to the ground.

- Megan Alexa Watson: To the individual.

- Carl B. Watson, Ph.D.: Right. And to the extent that we focus on AI replacing humans and squeezing out that talent and reducing the size of organizations, you end up with people having the same time constraints, the same focus on how do I do all the things that are important for my work and I don't have enough time or [00:10:00] capacity to get that done.

If AI increases our capacity, then that increased capacity can add to the value creation of our system, and that's actually part of the problem, right? Is that we're trying to add AI in areas where the value creation is wobbly, right?

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: We don't understand it yet in a way that the cost of AI is worth the actual contribution it's making to your work. And so, the challenge we're facing right now is really integrating the tool that AI is into our process, our ways of working and making sure that it can play a part across the kinds of work we do but in ways that can unlock more of our brilliance, right?

To support not just our judgment, but our [00:11:00] creativity and our ability to connect and deal with harder problems, our ability to see problems that we couldn't see before, therefore, we can solve for those in ways that prevent systemic failures or prevent foreseeable failures that are now foreseeable, that weren't foreseeable in the past 'cause AI can stitch across information sources in a way that humans struggle to do.

Those are the real opportunity and that I think the efficiency play misses.

- Megan Alexa Watson: Yeah. Yeah. What you're poking on here connects me to something that we've chatted a bit about around the maturity of the technology or the tool to be able to deliver on the aspiration that we have for it

- Carl B. Watson, Ph.D.: Yeah

- Megan Alexa Watson: In order to make the leveraging of it worth it.

So a concept that you've talked about that I'd love for you to take a minute [00:12:00] to articulate here is this idea of the tool chain, and how that impacts how organizations and systems get stuff done.

Can you add more on that?

- Carl B. Watson, Ph.D.: Yeah, yeah. And so organizations are a combination between humans, their tools, and the process that integrates humans and tools. The canonical example that everybody likes to talk about are assembly lines because the tool chain and humans' interaction in a tool chain is so explicit, right?

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: You can watch an assembly line and understand exactly how the tools integrate with the humans, and it's paced. It's entrained by the speed of the line, right? And so much of how the tool chain and the system works together is explicit in an assembly line that in other kinds of processes are less clear.

And so our challenge right now is that AI is not well integrated [00:13:00] into a lot of the processes as a tool in part of that chain, a tool chain in the process in certain areas, right? And that, that becomes a challenge. That's when the maturity of AI will increase dramatically, is when we're able to say, "Here are the points in the process and the moments where AI and human judgment are separate, AI and human judgment come together, where human judgment is separate from AI and not together at all."

And this flow of work actually producing deliverables, judgment when decisions are made, and review to understand where you are, right? In terms of it can be review about the quality of the process, it can be review about the connection between stakeholders, whether it be customers or whatever the corporate stakeholders are.[00:14:00]

But those are, the basic kinds of tasks: You produce things, you make decisions and choices about what you are going to do and how you're going to do them, and you have to process the information that's coming into the organization, like fundamentally. AI can help with all those things, but there are differences in how it is integrated into our tool chain, and that's the maturity problem that we're facing in many of the ways we're trying to use AI.

It's sort of random and you're allowing people to do it however they see fit. That experimentation can be extraordinarily useful, but again, AI is insanely expensive. And so that will bankrupt us if we try to do it that way everywhere and so that's for me, that's the fundamental challenge is that integration of AI across what we produce, what choices we make, and how we [00:15:00] process information or review what's going on.

- Megan Alexa Watson: But I wanna pull us back to expertise, specialization-

- Carl B. Watson, Ph.D.: Sure

- Megan Alexa Watson: ...and how that ties to this human judgment piece, 'cause I fully agree, that's a-

- Carl B. Watson, Ph.D.: Right

- Megan Alexa Watson: ...a critical nuance.

- Carl B. Watson, Ph.D.: Right.

- Megan Alexa Watson: So as we think about what are the ways in which the power of the technology that is AI starts to play with the edges of professional expertise in an organization

- Carl B. Watson, Ph.D.: Mm-hmm

- Megan Alexa Watson: ...thinking through what are the impacts of that, especially as you think about leaders at the top of an organization or deep subject matter specialists, experts,

- Carl B. Watson, Ph.D.: Yep.

- Megan Alexa Watson: ...what starts to show up in a system as the edges of that expertise are played with?

- Carl B. Watson, Ph.D.: So let's first give a quick description of expertise. So that we know what we're working with. An expertise is knowledge and experience that has been coded over time in an individual that [00:16:00] allows them to understand both complex and difficult-to-see things

so that they can incorporate with little information a judgment about what something needs or what something is doing. Because of that accumulated experience and knowledge.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: And so that expertise is applied in a way that you can actually produce work in a way that becomes higher quality.

And so our challenge though is this, right? What AI does is it can aggregate information. It can take codified experience and incorporate that into what it presents to someone, if it has access to it, right? Not all experience is codified, and not all [00:17:00] experience is easily codified.

- Megan Alexa Watson: And not all experience is explicit.

- Carl B. Watson, Ph.D.: Right. And so there's a gap there to the extent that AI relies on codified experience and the extent that some of that codification is not as good because it's difficult to codify. And then you have a whole set of experience that we have not been able to codify.

That's insufficient, that's incomplete. And so a- again, it's this idea that the combination of this tool and the expert human or humans, I think becomes the most valuable pivot. That's where that judgment and that ability to do bigger and better and more things comes in, [00:18:00] is in the combination.

And so identifying where that combination is most valuable is part of the struggle.

- Megan Alexa Watson: So since we are in the vein of how to make sure we define the terms, the narrative on combine human judgment with AI is the unlock, I agree with, and it still feels really freaking hand wavy on what is it that the human is actually bringing to the table.

And maybe we dig in on the idea of judgment

- Carl B. Watson, Ph.D.: Mm-hmm

- Megan Alexa Watson: ...a bit more.

- Carl B. Watson, Ph.D.: Yeah.

- Megan Alexa Watson: So you defined expertise. Let's define judgment.

- Carl B. Watson, Ph.D.: Yeah. That's, judgment is what's the right word? It's a noisy word ...in that it has a variety of different kinds of definitions. But let's talk about in terms of good judgment

- Megan Alexa Watson: Okay

- Carl B. Watson, Ph.D.: ...which is what we typically mean

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: ...when we talk about judgment.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: We don't...

- Megan Alexa Watson: there, there is poor judgment

- Carl B. Watson, Ph.D.: There is.

- Megan Alexa Watson: So we can also talk about that

- Carl B. Watson, Ph.D.: ...there is [00:19:00] poor judgment. But when we talk about good judgment, it is an effective weighing of a variety of factors to come to a good choice that is healthy for the goals you have.

- Megan Alexa Watson: All right. Speech and debate nerd Megan is about to come out. Yeah. Define good.

- Carl B. Watson, Ph.D.: It accrues to the goals that you have essentially.

- Megan Alexa Watson: Mm.

- Carl B. Watson, Ph.D.: Right? And there are ...there are no organizations with singular goals.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: There are no teams with singular goals.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: There are no individuals with singular goals.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: And some of those goals are conflicting and overlapping and all this other stuff, and so the world is messy.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: That's all I'm saying right there, right?

- Megan Alexa Watson: Accurate.

- Carl B. Watson, Ph.D.: But that's also part of good judgment, is to understand the goal hierarchy, to understand when that hierarchy shifts from one goal to another, when I should make primary one goal in the hierarchy versus another goal in the hierarchy.

That's part of good [00:20:00] judgment, and that is actually quite difficult. It's quite complicated. It's part of what we mentor people on.

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: Right? And whether AI is going to be really good at that in some point in the future, I don't know. I don't think it's good at it right now.

- Megan Alexa Watson: Yeah. I would agree. I would agree. And I think you, you landed on it in a way that cracks useful things open for me, which is this idea of being able to be as fluid and adaptable about a goal hierarchy as possible, to me, as you said, still feels uniquely human.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: So we love to talk about what is uniquely human

- Carl B. Watson, Ph.D.: Yeah.

- Megan Alexa Watson: ...what can AI do.

I feel like the ability to take in real time new data

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: ...inputs from the world and say, "Yep, four seconds ago this might have been the goal hierarchy, but I now have new data which shifts the [00:21:00] goal hierarchy." None of that is coded in.

- Carl B. Watson, Ph.D.: Right.

- Megan Alexa Watson: That is something that we as humans do real time as we interact with the world.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: And the ability to do that. That to me as the unique identifier of judgment in this moment feels... I can hold onto that. That's something I can grab that doesn't feel hand-wavy to me. I also think the piece that you called out around it being about whether or not the choices and decisions achieve goals, maybe that's part of our problem, 'cause we keep talking about good judgment.

Maybe we should be talking about successful judgment.

- Carl B. Watson, Ph.D.: Hmm.

- Megan Alexa Watson: There is a goal you are trying to reach, taking out the moral implications of it taking out the right or wrong. It is truly about, does this decision reach the goal, a success state? It feels like more is the core of what we're trying to get [00:22:00] to.

 I wonder if conflating with the use of good some sort of value that is potentially tied to morality is actually confusing the conversation in a way that's not useful.

- Carl B. Watson, Ph.D.: I don't know. And so again, I think that's part of the uniquely human thing, is that, that morality comes into play-

- Megan Alexa Watson: Oh, certainly

- Carl B. Watson, Ph.D.: ...in our judgments. Whether something is goal accretive, useful, but not acceptable for how our principles and our values of the organization of ourselves play out, that's meaningful, that I think that we have to provide AI with those boundary sets. Those boundary sets are both internal and organizationally defined for us.

And so, as AIs will operate against the given boundary set [00:23:00] for the most part. A human will say, "those boundaries are not in line with my values. I'm out." AI won't do that. Outside of very, very big egregious things that are programmed at the base level in AI

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: ...and it's still breachable, right?

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: It's-

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: ...it has continued to be breached to this day-

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: ...in terms of the boundaries that others have set-

- Megan Alexa Watson: Yes

- Carl B. Watson, Ph.D.: ...for the AI. And so I can't take morality out of that picture because that's a real critical, uniquely human thing that has to be brought into the picture.

- Megan Alexa Watson: I actually think that's what I'm poking on, is I am not sure that we're being explicit enough-

- Carl B. Watson, Ph.D.: Ah, okay.

- Megan Alexa Watson: ...that morality is a standalone individual component

- Carl B. Watson, Ph.D.: Yeah

- Megan Alexa Watson: ...to judgment-

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: ...that is different from success or failure state of achieving a goal. And if we don't call it out, we forget how critical it [00:24:00] is, and we lose it.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: And so my push of that is to say we should be clear when we are talking about successful judgment and moral good within boundary judgment to make sure we always have those conditions in play when we're applying this. 'Cause otherwise it becomes really easy to say, "Yep, AI's got good judgment" because we forget how critical that boundary state is.

- Carl B. Watson, Ph.D.: Right.

- Megan Alexa Watson: And so the two concepts that I feel like we're surfacing that I wanna restate for clarity is this idea of being able to quickly adapt and flex an understanding of goal hierarchy and being able to weigh the impacts of an outcome of a decision against some sort of moral boundary.

- Carl B. Watson, Ph.D.: Right.

- Megan Alexa Watson: And recognizing when something crosses that threshold or does not.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: And having that sense that I do think currently is uniquely human. And I'm gonna get [00:25:00] really, really basic with y'all, but it's the ick.

- Carl B. Watson, Ph.D.: Mm-hmm, mm.

- Megan Alexa Watson: It's the ick of a choice.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: When you make a choice and you're like, "I can't really explain why this feels ick, but it feels deeply ick, and so it feels wrong, and so I'm not gonna do it."

AI doesn't have the ick.

- Carl B. Watson, Ph.D.: It does not.

- Megan Alexa Watson: I don't think we can teach it the ick.

- Carl B. Watson, Ph.D.: I don't think so.

- Megan Alexa Watson: But that's, that to me feels core to the question.

- Carl B. Watson, Ph.D.: 100%. And so, another aspect

- Megan Alexa Watson: Yeah.

- Carl B. Watson, Ph.D.: ...of uniquely human.

- Megan Alexa Watson: Love it.

- Carl B. Watson, Ph.D.: And we talked about this in a prior episode as well, and that is problem finding.

- Megan Alexa Watson: Ooh, yes.

- Carl B. Watson, Ph.D.: Defining what problems are important enough to solve. And we do this for AI by creating the prompt.

The prompt

- Megan Alexa Watson: The prompt, the skill, the, yeah

- Carl B. Watson, Ph.D.: ...is laying out the problem and the required response to how do I understand or how do I produce something for this problem?

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: And so we are doing this prior to engaging AI. And when we are evaluating the results of what AI produced, we are continuing to do, "Oh, AI went [00:26:00] there. That's not important. That's not part of the problem I need focused on." And so we redirect it and we produce something else. And so this problem finding is another uniquely human aspect of ...AI has not been able to do that for us at this point.

- Megan Alexa Watson: Yes.

So I wanna frame that again in the context that we're talking. This idea of there is goal hierarchy once you have identified a problem, but there is also problem identification, and probably problem hierarchy

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: ...in the sense of like

- Carl B. Watson, Ph.D.: That's exactly it, yes.

- Megan Alexa Watson: ...your ability to move through space and decide the most critical problem for me to solve right now is whether or not it is safe for me to cross the street. There is a whole bunch of other problems I could take on. What's the weather like? What's that bird sound that I am hearing? Do I wanna actually go down the street and get a coffee,

right? There's plenty of other problems that someone could be solving at any given moment.

Humans have a [00:27:00] ridiculous capability to, in the moment, make calls on what is the most critical problem to solve,

to be able to identify it in some form that we then make a choice.

- Carl B. Watson, Ph.D.: Right. And the heuristics that we use to make those judgments in real time are super powerful. And the challenge that a lot of organizations face is that the problems that are pressing, 'cause there are problems that are pressing that are still not important enough to solve-

- Megan Alexa Watson: Yes.

- Carl B. Watson, Ph.D.: ...at least not yet.

- Megan Alexa Watson: They're distractions.

- Carl B. Watson, Ph.D.: Right, they're distractions. And so, that's a clear task of leadership, and it's an important aspect of expert judgment, 'cause an expert can look at a set of problems and say, "Hey, there's an order of operations to solving these problems, and there are a set of problems that will take care of [00:28:00] themselves if we solve these other problems."

- Megan Alexa Watson: I love that. The idea of expertise allows you to filter out the problem distractions.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: And this goes back to your prior point about what's really expensive, either in dollars, resources, human time, whatever the case may be. Experts allow you to make better use of the resources that get applied to problems because either, one, they're already super clear on, "Oh, we actually have answers to all of these."

So, it's really easy for a non-expert to put a bunch of questions into AI that are really basic questions that an expert would know the answers to without even trying.

- Carl B. Watson, Ph.D.: Right. Right.

- Megan Alexa Watson: So you wouldn't have to cash in the resources, the tokens-

- Carl B. Watson, Ph.D.: Right. Right

- Megan Alexa Watson: ...to even spend time on that.

- Carl B. Watson, Ph.D.: Yes.

- Megan Alexa Watson: Because an expert knows, "I already know the answer to that. I don't need to waste time on that."

- Carl B. Watson, Ph.D.: Exactly.

- Megan Alexa Watson: And so the ability to say, "Here are the remaining unanswered [00:29:00] questions, the unsolved problems. Let's put AI to that-

- Carl B. Watson, Ph.D.: Yeah.

- Megan Alexa Watson: ...and save resource for the system or for the organization," that's an expertise value that you can't currently replicate.

- Carl B. Watson, Ph.D.: Right. 100%. I love that framing because it, it also gets to the efficiency play. You're both more efficient and more effective at the same time, and I love, love, love when those two things come together, and anything that does both of those things is super, super genius. It's brilliant.

- Megan Alexa Watson: And that's it for this episode of The Watson Standard. We've shared some things to think about, talk about, and put into practice. Follow the series and join us next time as we continue redefining leadership and redefining organizations.