316: Be Sports Minded About AI with Dr. Dafna Aaronson

[00:00:00] [SPEAKER_00] So in sports, the idea is a coach, a GM, they are mini data scientists. They have so much experience that they're basically processing like a machine learning algorithm. And that is not to be discounted. They're looking sometimes just for the validation from AI. And other times it is the idea of, does AI give you a moment to pause and check your gut before you make the decision?

[00:00:26] [SPEAKER_01] Hey, everybody. I'm Lori Rudiman. Welcome to Punk Rock HR. My guest this week is Dafna Aaronson. She is a PhD, big time AI thinker who also has a background in sports psychology, just a really brilliant mind. And I brought her on the podcast today to talk about human performance, human potential, AI, and sports, none of which I'm really proficient in talking about, but she's great.

[00:01:01] [SPEAKER_01] And is very patient with me and answered questions on human performance, human potential, the role of AI, how companies are different than sports teams, and just overall, how we should be thinking about AI in this new world of work. Dafna is awesome. I want her to be one of my new best friends. And I think you can hear my eagerness in this conversation.

[00:01:25] [SPEAKER_01] So if you're interested in all those topics and more, sit back and enjoy this conversation with Dafna Aaronson on this week's Punk Rock HR. Hey, Dafna. Welcome to the podcast. Hey, Lori. Good to see you again. You too. It's been forever. I looked in my inbox and the last time we connected was 2019. That's pre-pandemic. Can you believe it? Right at the cusp. Yep.

[00:01:48] [SPEAKER_01] Yep. Ridiculous. We were introduced through a friend of ours and also a former podcast guest, my dear friend, Ryan Arnold. So what a strange, small, fun world.

[00:02:00] [SPEAKER_00] Yes. It is kind of apropos that we're back together talking about what we're talking about again.

[00:02:05] [SPEAKER_01] I know. Well, before we get started with all of that, why don't you tell everybody who you are and what you're all about?

[00:02:10] [SPEAKER_00] Sure. So Dafna Aaronson, I have a PhD in AI machine learning, a master's in sports psychology, background in engineering and management.

[00:02:21] [SPEAKER_00] So there's a lot that goes into it, but what it boils down to is I work at the intersection of human performance and technology and really understanding how we can ethically improve both to work together to get people to realize more about themselves, their potential, productivity and organizational excellence.

[00:02:45] [SPEAKER_01] Well, there's so much there. I mean, really, I just, I just want to talk about like music and fun things, but I feel compelled, compelled to talk about this topic. And I reached out because I keep hearing two stories about AI at work. One story says that it will help people do more than they thought they could. Right. And the other story says that it will box people in and make old biases worse.

[00:03:08] [SPEAKER_01] So you build these tools, you've worked on mental performance and pro sports, and I want your help in figuring out which story is true, or maybe it's a little bit of both. So as we get started, let's set the stage. When you say AI and human potential, what do you mean by those words? And how is potential different from performance?

[00:03:29] [SPEAKER_00] Yeah, that's a great place to start. So really getting clear on what the definitions are between us, right? So level setting, people might have other definitions, that's totally fair. But really AI and machine learning are, it's a type of learning.

[00:03:49] [SPEAKER_00] And it's really this science and art of learning from data to gain insights on complex problems that traditional methods really can't address. Or there's just no known algorithms that are currently able to process the complexity along with the data.

[00:04:12] [SPEAKER_00] So with that being said, it's understood that this is something that's probabilistic versus what I think people really sort of gravitate towards in the deterministic sort of predictive way. I mean, probabilities can lead to predictions, but we're looking at the likelihood of something.

[00:04:32] [SPEAKER_01] I'm listening to you talk, and I'm listening to the very delicate and careful way you're putting this all together. And you've got a PhD in this shit.

[00:04:40] Yeah.

[00:04:41] [SPEAKER_01] Really fascinating to me. So if we're like an HR lady or a marketing director, and we hear a lot about the performance and the potential of AI and of humanity, I just wonder, there are a lot of assumptions being made right now that AI is helping organizations and people right now. And I wonder, is that true? Is there an example from sports or the workplace where AI is really making a difference?

[00:05:09] [SPEAKER_00] Yeah, I think there's a lot of examples. I think, you know, and also to go back and answer, like, how does this help with human performance, right? That's where it gets a little bit touchy because we're not looking at something that isn't a kind environment. Like, is the test result positive or negative, right? You're looking at human as a complex adaptive system that is reacting to internal and external stimuli.

[00:05:35] [SPEAKER_00] So there's naturally a lot of relational variables going into play. And we're asking AI to say, take the data we're giving you and give me a prediction of what you think about this person's, and you said performance and potential. So like for me, performance is more on the outcome. It's about execution and executing on details. So define details by the organization, by the team, the plan, the play, et cetera.

[00:06:04] [SPEAKER_00] So it's generally going to have measurable metrics. So it can seem more objective on a high level. The potential is really the projection, right? So that's where I think a little bit of the AI is trying to be used a little bit. It's seemingly objective because you're looking for data correlations when it's really more subjective after you get out of like the top 1% where things can seem pretty clear.

[00:06:32] [SPEAKER_00] So in terms of where it's being used effectively right now, when we think about talent assessment and talent identification, there's ways in which that it can, based on the definitions that an organization sets for this is what we define as a high performer, a high achiever, someone that is successful in this role. These are the metrics we're looking for.

[00:06:59] [SPEAKER_00] It can do a great job on narrowing down who the pool is. So maybe you get, even for sports, like you got 1,200 athletes to look at. If it cuts out 30% of them, right, the highs and the lows, that's really effective for the scouts and for front office and operations to be able to understand like who do we really need to focus on and where do we need to place our resources?

[00:07:24] [SPEAKER_00] And on in that pipeline, it's able to say, okay, if we're going to make things as consistent as possible, ask the same questions and not have everything be a little bit too unstructured.

[00:07:39] [SPEAKER_00] If we can create some structure around that, AI can be helpful in saying, based on what we're hearing from the person today, but also based on the past results that we say are either above a benchmark or below a benchmark. What's the probability of this person being, being able to thrive, right?

[00:08:02] [SPEAKER_00] So it works both for the organization and individual in this environment, given these circumstances, not just are they a good player, but are they good in our culture, in our workspace, the way that we have our, you know, our coaches set up and our system set up, et cetera. And then it's the third is, okay, now we're still, we're in the selection phase.

[00:08:24] [SPEAKER_00] And that's where, again, like you, it's not about replacing, it's all about enhancing the human's ability to make decisions smarter, faster, better. Okay, we're going to choose someone. So now AI can be enlisted to say, what sort of resources can we be prescriptive about including in their world, in their environment, so that we can actually create, we can potentiate all of these things that we've seen and we want to see from them.

[00:08:53] [SPEAKER_00] So those are ways in which it's being used currently. I think the message is, if we agree, it's never 100%.

[00:09:02] [SPEAKER_00] And there's just a way to engage with it, that there's a lot of really good things to take away and improve our own ability to be self-aware about the way in which we make our decisions, but also really improve the efficiency and the efficacy of our decision making for the collective.

[00:09:24] [SPEAKER_01] Yeah, I am. I'm tracking with you. And yet, the way we have seen AI implemented in many organizations is not as an enhancement tool, but it's a rip and replace, a rip and replace for human judgment, a rip and replace for the labor budget. And I don't know, maybe I'm naive, but I'm also a child of the 80s, right? Do we trust it? It's a fair question.

[00:09:53] [SPEAKER_01] That's like my very basic question. Daphna, are we cool? Can we trust this stuff?

[00:09:58] [SPEAKER_00] I think the trust comes in the transparency behind how something has been trained. So, for example, when I worked with a company on bringing in a new employee listening platform, it was very important for me to understand how this platform validated its questions, validated its results, what the process was, and how it determined its metrics. Based on the results.

[00:10:24] [SPEAKER_00] And really to then infuse a lot of what was important and external from this platform, but was important to the culture of the company into the questions. So, it is really, the answer is, it kind of depends. Like how much do you really want to know about this platform and what you're bringing in?

[00:10:47] [SPEAKER_00] And then it's your decision on the assumptions it's making and the way that it's processing data if you're willing to accept them. Hmm.

[00:10:57] [SPEAKER_01] You know, one of the things I was thinking is that maybe we should, just for fun, go back and talk about what an employee listening platform is. What is that?

[00:11:06] [SPEAKER_00] Sure. So, employee listening is basically the pulse surveys, the employee engagement surveys, the way in which an organization reaches out to its employees to assess how things are going on a broad level of different areas of the culture, productivity, the departments.

[00:11:28] [SPEAKER_00] A lot of these platforms come with, you know, their version of what's, you know, science and evidence-based metrics. And I'd say like different drivers and levers that lead to productivity. So, everyone wants to validate and show why they're different and how they can sort of then present the data in a way that can lead to positive conversations between managers and employees.

[00:11:56] [SPEAKER_00] And, you know, a lot of times some of that is sound, but to your point, can we trust it? It's also what happens after you get the results too. So, let's say you say, yeah, we buy into this. This sounds good. You've done a little bit of the due diligence that's important to you versus seeing how many of your competitors are using it. Also recognizing like where the benchmarks come from and accepting like that nothing's perfect. It's kind of like your whoop or aura ring, right?

[00:12:24] [SPEAKER_00] It's like, you know that there's a margin of error. And if you're willing to go with it, it's then, okay, how are we going to implement this in a way that still takes that and gives grace to that error? And I think that's sort of where you were, you know, touching on also like not just trust, but the biases.

[00:12:42] [SPEAKER_01] As you were talking about what a listening platform is, I was just thinking that these pulse surveys, these questions came about because organizations realized that there was broken communication, not only between an employee and a manager, but between an employee and senior leadership or even between managers and senior leaders.

[00:13:02] [SPEAKER_01] And it's interesting to me how this like HR tech industrial complex decided that a solution is the answer to this instead of like the core human conflict that we've had for a very long time, which is it's very difficult to listen to one another. So it's a very difficult to take feedback. And it's also very difficult to implement change at scale.

[00:13:25] [SPEAKER_01] And so the answer in the 90s, the 2000s, the 2010s has been to buy technology, but the underlying problems of managing human beings and making people feel safe and included and heard has not really been disrupted or evolved upon in this whole period of time. Like people are doing employee surveys, they're buying these listening platforms. And so what are we doing now?

[00:13:53] [SPEAKER_01] We're surveilling the organization in a lot of ways, but we're not really doing anything with what's being said. And I think that's my problem with a lot of the use of AI and organizations. It's perpetuating problems instead of going back and really looking at the core root problem with just working with people. What do you think about that?

[00:14:14] [SPEAKER_00] I think that's a really excellent point. You know, in my experience in sports, it's different because it is about execution and it's about getting there quickly. You're playing games every night. The feedback loop is a lot shorter. It's not the same in corporate. And you're also looking at the scale, right? So the scale of a global organization, even something. So if we stay on the corporate side for a second, I agree with you that it can often become another layer.

[00:14:43] [SPEAKER_00] And it's not often a layer that flows and allows information to flow with integrity in both directions. So the way in which I like to work with organizations, again, is to say, if this is a direction you're willing to go in, sometimes it's not about implementing one solution across an entire organization.

[00:15:08] [SPEAKER_00] And can an organization be flexible enough to say this might work and how can for one department, for one unit, for one region, right?

[00:15:23] [SPEAKER_00] And understand how they can make it more customizable so that it can allow the flow of information to flow with integrity relative to the people and the goals within the different silos of an organization. Then you can have it streamlined back up together to give an overall view and perspective.

[00:15:46] [SPEAKER_00] But to your point, it is so important to be able to meet the organization where it's at versus trying to sort of force it through something that might create even a more of a bottleneck than an extra lane on the highway.

[00:16:01] [SPEAKER_01] Yeah. Really well said. You know, as you were talking, I'm a Bears fan. I think you're in Chicago, correct? Yeah.

[00:16:08] [SPEAKER_00] I'm in Park City now, but yeah, born and raised in Chicago.

[00:16:12] [SPEAKER_01] Born and raised in Chicago, right? I was born and raised in Albany Park, right? So I'm Northwest Side of Chicago girl. And we're coming off the heels of a Bears win on Monday night without Caleb Williams. And we had the third string quarterback come in and he's been around for a while, right? He has all this institutional knowledge about the NFL, but he hasn't played and he hasn't started in several years. So would AI say that putting in case the third string quarterback was a good decision?

[00:16:41] [SPEAKER_01] Or would it have said go with the second string who didn't practice all week, but at least had organizational knowledge and understanding of play calling and had a little bit more and more recent exposure to the team? I mean, I'd like that AI can inform a decision. Ultimately, right now, it's still a human decision, especially in sports.

[00:17:00] [SPEAKER_01] But would AI have missed this really beautiful story that unloaded on Monday night where this guy, this 38-year-old comes off the bench and just leads the team to a victory? I don't know if we're there yet with machine learning. So what do you take on that?

[00:17:15] [SPEAKER_00] Yeah, so maybe this is an unpopular answer, but my answer would be it depends. It depends on how AI is being implemented and how it's being used in the organization. You know, there's different ways. Some use AI during the game. And so what are you asking it to do? When are you asking it to do it? A lot of AI can be used to preview.

[00:17:41] [SPEAKER_00] So my hunch is if you know that there's a potential for an injury, a switch or something, right? Or let's just say, you know, there's a lot of if-then scenarios, decision trees that have to be understood. They're running that through AI to get probabilities.

[00:18:00] [SPEAKER_01] So major sports teams are using AI. And I mean, I see those Amazon commercials, right? You know, it's like, oh, AI is being used to calculate like the flow of the ball and this ball speed. But I just wonder, it's being used, but also how much is still, it's still a human decision, right? Still Ben Johnson's decision?

[00:18:17] [SPEAKER_00] A hundred percent it is. And that gets to the nuance of what AI doesn't capture, what's left out. And it's very real and it's very critical, especially in something like sports where, again, things are happening in real time versus the flow of a day throughout an organization. So in sports, right, the idea is a coach, a GM, they are mini data scientists. They have so much experience that they are basically processing like a machine learning algorithm.

[00:18:47] [SPEAKER_00] And that is not to be discounted. They're looking sometimes just for the validation from AI. And other times it is the idea of, does AI give you a moment to pause and check your gut before you make the decision? Now, sometimes you can't and that's, you don't have the moment. You don't have that natural break. And that's to your point where it's like, that's why they're getting paid when they're getting paid and they're in that position.

[00:19:13] [SPEAKER_00] AI is there to just sometimes validate, inform and give them a preview. But yeah, no, they'll make that decision on their own unless there's, I'd say something that is so unique out of what they're, you know, between them and their brain trust of people that they are like, well, wait a second. Let's we need a tiebreaker here.

[00:19:34] [SPEAKER_01] I like that. You know, as you were talking, I was thinking about when I was coming up in the business world, there was nothing we enjoyed more than a competitor rolling out like a new version of an HR tool and then it failed. We're like, better you go first, right? You know, like, oh, we heard about your loss in implementation. That sucks too bad, you know? So I find it to be a little different now in the world of work because people are excited about these tools, right?

[00:20:01] [SPEAKER_01] They're excited about the potentiality of it and they're becoming first to market roadkill. And I'm like, where's the executive discernment? Where's the consulting discernment to go hold up the cost versus the promised gains that you're going to get? The math isn't mapping here. So when you're consulting, I wonder, are you trying to inject discernment? Do you find that your clients are excited about these tools? Like, what's it like for you out there right now?

[00:20:29] [SPEAKER_00] Yeah, it's interesting. I get asked a lot about what's everyone else doing? Is everyone feeling the same way we're feeling? So a couple of things. One is, I think there's a lot of confusion and mixed in with the curiosity because the technology is rapidly changing. So there is this, do we need to build, buy, hybrid? What's right?

[00:20:58] [SPEAKER_00] I think the first thing is really being able to answer, do we want AI? Versus feeling forced to have to go figure it out and find, you know, search for the answers. Yes. A company or an organization or a leader that can say, we value AI. We know it has a place here. We want it here. We're saying yes to it. That's really important because that really sets the tone of the commitment.

[00:21:26] [SPEAKER_00] And then it's, okay, well, what is the problem that we're trying, that we need to solve internally? Is that a good use case for AI? What do we have here that works already? What's not working? I think where people get confused is they feel like it's just this like great big, you know, savior of efficiency. And that didn't work. So this should make it work.

[00:21:52] [SPEAKER_00] And now you've got a lot of things, whether it's a CRM that offers a chat agent versus do we build it using a wrapper on Claude or OpenAI? And then it's, but the models are changing. Oh, but is our data safe? And so it becomes very, very overwhelming.

[00:22:14] [SPEAKER_00] And the first step is really to say like, are we even an organization that can absorb this? Can we bring this on? And then it's understanding, okay, we want it. Where do we actually need to implement it first? So when they come to me, it's like, they want to say yes.

[00:22:35] [SPEAKER_00] They have all these little fun conversations about, you know, the, I'd say the vision, the North Star, the waving a magic wand. And they, then when you start to step it back into what it takes to get there, it's like, oh, okay. This isn't just like a snap of the fingers. And it goes back to a lot of the same problems that non-AI tools have, which is how is your data integrity? Right?

[00:23:03] [SPEAKER_00] What does your data warehouse look like? How are things, your current workflows and current processes? All of those things are going to have to be fixed, fixed, let's just say, or nurtured in order for any tool to be in place. But especially for something like an AI driven tool.

[00:23:22] [SPEAKER_01] You have said a few times in this conversation that the business world and the sports world are similar but different. They have points of intersection, but there are points where they diverge, right? And so I just wonder when we talk about AI, whether it's the use or the implementation, where is it similar and where is it different? I'm sorry I didn't ask you this sooner because I'm really super curious about that.

[00:23:46] [SPEAKER_00] So it's similar in that you're dealing with humans, right? And I'd say like the theme in the conversation was looking at human performance and human productivity. So from that perspective, they can be very similar because you're looking at an individual within a collective, within an organization. And so those relational variables can be very similar.

[00:24:11] [SPEAKER_00] You're also looking at, you know, this bridge of how do we go from point A to point B, improving an individual's ability to thrive. I use the word thrive versus looking at potential or excellence because thriving implies that they are growing not just within the organization, for the organization for the organization's productivity, but for themselves as well.

[00:24:35] [SPEAKER_00] It's about being able to do it in one shot versus having to create like whatever the organization's doing is going to help promote that individual's thriving, but also for the collective. So a lot of that is where the similarities are like, hey, you know, when we thought about back in the day, Angela Duckworth's grit and how it came into play, you know, from an organizational IO perspective, but then was applied to sports.

[00:25:01] [SPEAKER_00] And, you know, even she has come back to say, you know, these assessments, these tools were not meant to be taken so literally where we forgot the human in the loop to have the conversation on these other factors that influence how a person reads the questions, interprets the questions, you know, creates the definitions in their head and answers it, right?

[00:25:25] [SPEAKER_00] So that is, that's where a lot of the common ground is, is on the human level. Again, with how do you make a team better? How do you make the organization better? How do we do, how do we get an ROI in sports with wins is like, how do you get the ROI on a balance sheet with EBITDA, right?

[00:25:47] [SPEAKER_00] And that it all, everyone has come to the agreement that it more or less comes down to the people and the resources you provide them to allow them to actually be productive.

[00:25:57] [SPEAKER_01] So how are they different? What's the difference? I mean, it's, it could be both obvious and non-obvious. I think there's so many executives who love to use sports analogies and sports metaphors. But I just wonder with the implementation of AI, I wonder if you can go faster on a sports team or in a sports organization versus a publicly traded or a private organization. Like how is it different in both the obvious and non-obvious ways?

[00:26:22] [SPEAKER_00] Yeah. What's interesting now with private equity firms getting involved in sports, it's all becoming kind of public investors, investment opportunities. Um, so the one is the scale, right? One is the scale and one is the schedule. So the scale of a company. So let's just take, you know, a midsize company. Let's forget about like the obvious, like fortune 500 big companies. They're like, that's just going to be too great.

[00:26:47] [SPEAKER_00] But one is, so that scale is really a huge differentiator because it's how do you take care of all of these people versus this core group that pretty much stays the same. There's trades and there's flow between, you know, the, the, a national league or the major league professional league and, and their, um, their minor league systems. But it's, it's definitely a core group that doesn't change as much.

[00:27:16] [SPEAKER_00] And even on the operations level, those people have been there for a while. A lot of times they play the sport. So a different sort of, that's one difference. Um, the schedule is a big difference. So you have your in season out of season and some organizations and some industries have seasonality. Um, but the idea of having that compressed set of months where you are performing every day or every other day, and then you have your playoffs, right?

[00:27:45] [SPEAKER_00] Like that is very different. Um, and it's very different for the athlete as well. But I do think that's why people in corporate like to take the mindset and, and that idea of training and resilience and all of those like nice big words and see how they can start to apply that to their own organization.

[00:28:04] [SPEAKER_00] Um, when really maybe it's more valuable to see about how do you apply to a C-suite, um, because there's probably a little bit more flexibility in some of those differences that can be transferred into that area of the organization versus, you know, I'd say the earlier stages of someone's career.

[00:28:23] [SPEAKER_01] I feel you. I joined an HR department when I was very young and it was led by a very over eager man who was maybe a little disappointed that he had just a bunch of HR ladies reporting to him. I'm not quite sure, but very early in my tenure there, he brought all the ladies and it was all ladies into a room and showed them a video on Lou Holtz. Oh, that's great. There you go.

[00:28:52] [SPEAKER_01] And from that moment on, I've been on this professional journey to, even though I'm an athlete myself and love sports to decouple sports in the business world, because I think your points around the schedule, the relentlessness, the role of the individual contributor, even among a team is a little bit different, right? And, uh, and the pace is different. So when someone starts to bring in the metaphors and the analogies, I'm like, that's cute. Get your Lou Holtz ass on here.

[00:29:22] [SPEAKER_00] And by the way, bring me a Mike Ditka one if we're going to talk.

[00:29:25] [SPEAKER_01] Yeah, yeah, yeah. I know. Notre Dame in Chicago was like a terrible choice for that man. So, and anyway, I'm like always suspicious of it. Yet this conversation is really interesting because performance, like really good performance is beautiful, both at work and on the field, right? Or in the rink or wherever on the track, like beautiful performance is gorgeous. It's awesome. It's really fun to watch.

[00:29:50] [SPEAKER_01] And so I don't know, do you, these similarities between the worlds, are they exciting to you? They must be, they must be interesting, right?

[00:29:57] [SPEAKER_00] Two things. One is the answer is yes. And I think that goes into this sense of, you know, the, the word that's often used is a flow state and this idea of when things are really flowing and happening, happening between yourself, your work and your teammates. It's a really great feeling. And there's a lot of psychology that goes into it. That again, is why people kind of look to sports to often say, how can we, how can we mirror that a little bit and at least maybe adopt some of those.

[00:30:25] [SPEAKER_00] But to your point on, on that idea of, yeah, I think it's really great. There's longer stretches of, you know, can we produce something in the workspace where you really don't have all of that? I mean, you can sort of say we're building throughout the season to get to the playoffs, but you still have to have certain results in order to get there.

[00:30:46] [SPEAKER_00] Um, I think in corporate, it's like, how do you learn to balance that every day isn't going to be a win, that there are going to be setbacks, whether it's with your, someone that you're reporting to or someone that's reporting to you. And it is really finding that sense of, um, it's not always about flow, but it's about having the amplitudes and ways not create so much discourse where all of a sudden it's just, this isn't good or this is the best.

[00:31:16] [SPEAKER_00] And you just kind of can land in the middle and allow things to resonate without too much disruption. And I think AI, unfortunately, is creating a lot of those amplitudes now where it's like, you can't really stay in this like center space. And so how do we get back to that with something that is ambiguous to a lot of people?

[00:31:39] [SPEAKER_01] Really well said. As we wrap up the conversation, you know, I'm a noob at really talking out loud about performance, potential, and AI. It's like stuff that I've been thinking about for a while. So I hope I did the conversation a little bit of justice. But I also recognize that I might be thinking too small about performance and potential and AI. So what are your observations on that? And what's your advice?

[00:32:04] [SPEAKER_00] I think that AI can be really awesome. I think if we think of it as another person in the room, let's call it, versus the one that is the decider or has to tell us or direct us.

[00:32:21] [SPEAKER_00] But if it is literally like next to you versus across from you, engaging with it in a way where it can allow an organization, a team to see the forest from the trees and be more strategic. And in a way where I think a lot of times it's very difficult to carve out that time to do that.

[00:32:48] [SPEAKER_00] But if you really see it as an opportunity and you look at it with that sense of what else can you show me that I may not see? Then it's not creating this me versus you, us versus them sort of a thing.

[00:33:07] [SPEAKER_00] So I look at it as the opportunity is, let's see what it can show us from the more projection and the future and the trends. While the people that are maybe making the decisions every day can continue to do what they do best. And then there's another way that they can employ AI without really having to feel like everything has to change or get upended or there's these new workflows.

[00:33:36] [SPEAKER_00] So let's be honest, like the models are changing. So the way in which that a person can say, work with me, help me be better. And I do think the hybrid of a build internally versus just let me go get an enterprise cloud is really going to help people even become more self-aware about themselves. And I think that's one of the biggest things that might be overlooked with AI is like

[00:34:05] [SPEAKER_00] you get to build your own self-awareness about yourself. We should be looking at it from a self-actualization perspective. Even if we are feeling fear towards it, even if we are feeling a little like insecure about it, like that's good to know. But in the implementation of it, like that opportunity is not lost. So I think this is a really optimistic time.

[00:34:30] [SPEAKER_00] Um, we do have things we have to be considered about with data, cyber security, all of that. But we have, we have ways to deal with that.

[00:34:41] [SPEAKER_01] Well, I like the idea that, um, AI can help us self-actualize. Um, interesting and also provocative. And I think, you know, for companies, I don't know that I want a company to self-actualize, but I want a company to operate at its best and operate according to its mission and its values and to do good work in the world and solve problems. So if AI can help with that, Daphna, I'm on board.

[00:35:07] [SPEAKER_00] Well, the self-actualizing for companies is like, are they really working with integrity? And then it's, do they have the courage to change or do they have the capacity to, right? So really bringing that to light and saying, are we really living up to what we said we were going to do?

[00:35:24] [SPEAKER_01] Well, that's a really, that's a really great way to end. And I just want everybody to follow your really interesting, awesome brain and to learn more about you and to learn how they can work with you, um, on the corporate side, if they work in HR or marketing or leadership, but also to like learn about your awesome world of sports, like where do they go?

[00:35:43] [SPEAKER_00] Thank you. Well, I just have a website, DaphnaAaronson.com. Um, I, I will say I'm not a huge social media. So the best ways to email me and we can engage directly. I love the personal conversations. I really prefer to have those engagements. Um, so, but yeah, DaphnaAaronson.com. Email me at DaphnaAaronson at gmail.com.

[00:36:09] [SPEAKER_00] Um, and I look forward to seeing you in the future, having these conversations connect in the future. And I appreciate the opportunity. Thank you so much.

[00:36:20] [SPEAKER_01] Well, it's my pleasure. We'll make sure your contact information is in the show notes. And I like that we're ending with you asking for a personal connection because that means you're a normal human being. Nobody should be out there just talking to themselves on the internet, including me. I don't know what's wrong with me, but that's for another day. Daphna, thanks again for being a guest. Thanks, Lori. Appreciate you. Thank you. Hey, everybody. I hope you enjoyed that conversation with Dafna Aaronson. I loved it. I'm still buzzing about it.

[00:36:49] [SPEAKER_01] I learned a lot of important things in today's discussion. And here are three that stuck with me. Number one, business and sports are intertwined and analogies and metaphors are helpful until they're not. There is a way that we can think about performance and teamwork that applies to both worlds, but sometimes it's a little overstated. The second thing I'm thinking about is the use of AI in both worlds.

[00:37:14] [SPEAKER_01] There's a business case for AI thinking about performance and potential in both small, medium and large size organizations. But Daphna asked a really great question like, is it even appropriate for AI to come in your organization? And I just love thinking about that. Like, do we really need AI at work? I want to talk about that more in upcoming Punk Rock HR episodes.

[00:37:40] [SPEAKER_01] And then the third thing is I just think it's really cool to think about having a colleague, a person in my network who's implemented AI projects at large scale organizations. Like, for example, she talked about an employee listening program, but also sees this from a different perspective. She's actually built machines and she's actually worked in the world of sports. And that kind of diversity and her approach to thinking about AI is just so cool. I'm still thinking about it.

[00:38:10] [SPEAKER_01] And hopefully you'll connect with Daphna and learn more about her background. And hey, maybe just have a really great person in your network who could be helpful in your business job, but maybe just helpful in like your personal life. I don't know. I think that's the beauty of Punk Rock HR. I just introduced you to someone cool. So if you're interested in learning more about Daphna, her contact information and all the good stuff will be in the show notes.