Holding Complexity Under Pressure: What Your Meeting Transcripts Actually Reveal

00:19 Warren Kucker: Hey there and welcome to another episode of Selling AI. Today we're diving into what is possibly one of the more contrarian applications of AI I've come across, using it to measure leadership capacity from the pe way people actually talk at work. I have Logan Yanovak on the show today, and Logan is the co-founder and CEO of Readiness Engineed, an AI-informed diagnostic platform, runs signal analysis on your real work transcripts, all hands, board meetings, internal calls. To really surface whether a leader can actually what she calls hold complexity under pressure, which we'll dive much deeper into. In today's episode, Logan and I talk about what linguistic patterns reveal about leadership readiness, why most AI sales coaching tools are scoring the wrong things, and how she sells an AI diagnostic tool into a market that's drowning in AI pitches. We'll also get into the uncomfortable question of whether AI is not exposing the managers who maybe weren't ever really coaching their teams. All right, let's get into it. Logan, thank you so much for being on the show.

01:20 Logan Yonavjak: Thank you, Warren. What a great intro.

01:23 Warren Kucker: Great, great, great, great. I'm glad. It's always a little bit of pressure to try to intro someone and not be not be under lofty for the work someone's doing. So cool. Well listen, you spent twenty years just really broadly in what we called impact finance. tell us about your journey and how did that lead you to build like an AI diagnostic platform for leadership readiness? What was the pattern you kept on seeing that just made it so you had to build this?

01:47 Logan Yonavjak: Yeah, absolutely. Well, I spent so a fair amount of my intellectual brain power on understanding how capital flows and understanding portfolio dynamics and specifically really interested in alternatives investing and real assets. So I was pretty specifically going in a direction of working to deploy capital for good. And in that c in in my case it meant improving environmental outcomes, improving social outcomes. Community outcomes, so widening the aperture of what people think of in terms of investment returns alongside financial returns. And so I really dove into the capital markets and understanding the investment landscape. I I have an MBA from Yale and Asset Management and also a master's in forestry. So What I was trying to accomplish there was again understanding how capital is deployed. And then what I realized over time or spent more time thinking about was the leadership behind excuse me, behind the deployment of capital. So the family off especially in the alternative space, you have family offices, you have s sovereign wealth funds, pension funds, this whole kind of like ecosystem of players. And behind them are people making decisions. And especially in the alternative space, and as you get earlier and earlier in company in investment deployment, you're investing in people more than you're investing in the actual technology or product in some cases. So I got really interested in like how decisions were being made about who was going to lead a company, how the team was constructed, and also the leaders behind it, like realizing that there were limitations I was seeing in managers, in leaders. And I'd had sort of this thread of interest all along since high school in Jungian analysis. Yung Jung was like the founder of of psychotherapy in this country in many ways. not in this country, he's he's from Europe, but he really influenced modern psychotherapy. And so I was interested in like the Myers-Briggs personality tests and some of these other leadership assessments. And I just got to thinking like how are these being used in investment decision making? And it seemed like there was a lack of kind of deployment of these, especially in early stage investing. And so that's where the business idea came from for Readiness Engine.

04:17 Warren Kucker: Interesting. Let's peel into just a little bit further before we actually dive into Readiness Engine. it's a really good point. Let's I'll stay in my realm, which is venture capital, right? Especially early stage venture capital. The who matters a lot to investors, right? Who are they investing in, both from a an expertise standpoint? Do they know the space that they're trying to build into and sell into? but secondarily, do they have the aptitude to be a founder or an early stage someone who could build an early stage company? In my perspective, most of that's done by gut feel or like maybe a couple of check boxes, right? Like we're like, listen, I'm looking for these three personality traits. It seems like that's really underweight. It's overweighted, it's weighted well in the ideology space. They think about this, but it's not weighted in the actual evaluation piece. Would you say that

05:04 Logan Yonavjak: Exactly.

05:04 Warren Kucker: you found the same? Is there like a mix that you feel like is there that should be like are you trying to tilt that mix somewhat?

05:12 Logan Yonavjak: Yeah, I think that's really well characterized. So basically a lot of a lot of weight is given to the the people and investing in the people and the team, but the actual like rigor of that is mixed. And I would say just like with any other skill, there are some investors who do have a knack for finding talent. their gut feeling and pattern recognition is is superior to others. But everyone thinks they're that person. And so there's sort of this like this bias people have of of overagranizing their skill set. And this happens in all sorts of ways with human beings. People tend to not want to be average in something. So it's not like it's just specific to VC investors, but that's what so what I've run across in talking to about 150 VCs about this topic is that most of them have this perspective and the the way that they go. about making decisions is based on gut feel, warm intros, you know, kind of like the halo effect it's in some cases. So there's kind of a variety of different ways decisions are being made. Very few of them, I'd say less than 10%, are using any sort of like people analytics that's standardized. I have heard a few recently. Andreasen Horowitz, there's a few kind of big names that are starting to get into this. So I don't want to dismiss that it's that's not happening. But just by and large, that's just not the way things are right now. So we set out to create a product and and and platform that could really service this this segment. And so that's where we started our we've since pivoted to a large degree, but that's where we started.

06:57 Warren Kucker: Yeah, it's interesting. it's probably the only area within an investment thesis where it's okay that I have a knack for something. Like nowhere else would like be like great, like, you know, the when you do the modeling, the financial returns don't come back to like be positive in a way that I should make the investment. But you know what? I have a knack for understanding that returns are better than like the the analysis will say. No one would take that approach, but in this space, a knack for people is perfectly acceptable, right? Like and you're absolutely right. It's a good example.

07:23 Logan Yonavjak: It's so fascinating. It's it's it's it's a conundrum. I and it's a it's a paradox. And I think in some cases I just want to be clear, like I I'm not coming in with the attitude of, you know, better analytics will solve problems. it or will solve all of our problems. This is not a silver bullet like getting analytics on on the people. Gut feeling can be very valid and patterns and biases can be positive and negative. So it's not like we should just totally take over human decision making. We should augment it, i in my opinion, just like we have with many other analytical tools with pattern recognition approaches like AI, like what we've built. And so I I try to just be clear with people. It's not like we're saying, Hey, don't use your gut. we're just saying why not have this why not have this third party

08:14 Warren Kucker: No, it should supplement it. Right. Exactly right.

08:17 Logan Yonavjak: evaluation. Yeah.

08:19 Warren Kucker: Awesome. Cool. Well listen, let's dive into Readiness Engine. Let's go with let's start with the broad idea, what you broadly are trying to achieve. And then I want to dive into like the inputs and the outputs. So in particular for Readiness Engine, when you're going in, you're trying to solve a problem for an organization. What is that problem that you're trying to solve and how do you solve it?

08:37 Logan Yonavjak: Yeah, so in ta in talking about business outcomes, things like reducing execution risk. So when there's goals, you know, quarterly goals or annual goals, you wanna be able to meet those goals. And most of the times that those don't get met, it has to do with people's decision-making process. And, you know, if you drill down, that could be anywhere from team friction, it could be related to someone's emotional resilience, you know, they get overwhelmed and stressed. So there's all these sorts of Of human factors that can prevent or accelerate decision making in positive or negative ways. And so also employee retention, you know, keeping people happy at work, making sure that they feel motivated, those figures are declining, and we can see kind of broad patterns in that in corporate America. And so, how do we come in and how does our tool relate to that? Well. We're evaluating a number of different what we call constructs within individuals and teams. And we are trying to illuminate patterns that will help prevent some things like execution risk, help improve employer retention and employee satisfaction, improve promotion and succession decisions so that people don't flame out in 18 months, things like that.

10:03 Warren Kucker: Excellent. Yeah. When you talked about this, from a process perspective, what kind of data are you analyzing? And then what kind of types of outputs are you looking for to kind of like dive into execu reducing execution risk, improving employee satisfaction?

10:21 Logan Yonavjak: Yeah, so what we're looking at is there's two pathways. Someone can sit down and take an assessment. So that's a one-way video interview where they're asked open-ended and scenario-based questions. many of us might have been through cases or, you know, any unstructured interview, you're you're basically doing the same thing. you're talking about yourself for a period of time. We're capturing that transcript data. So then we can also just capture transcript data. You we can take call notes, we can take meeting notes, transcripts. From presentations, and we can do the same analysis. So once we get the transcript data, we're looking at things like how it's it's called a bot, it's the the approach is called quantitative linguistics. So you're breaking down paragraph and sentence structure, and you're looking for patterns in things like how someone makes reference to experiences they've had, how many perspectives they're bringing in, what ways they've built systems around themselves to augment stress. Like there's just a variety of different things we're we're drawing out so that we can create a measurement factor around things like how does this person handle stress? how good good are they at juggling complexity and juggling a bunch of balls at once objectively? How coachable is this person, like in terms of fixed or or flexible identity? Like those are all things that we can tease out from the transcript data.

11:49 Warren Kucker: Interesting. And you're using live transcript data as well, like you'd be ingesting calls that teams are having internal to themselves, right? If I'm the mm VP of sales, you're taking the transcript from my weekly all hands with the entire sales team, for example, and seeing how everyone's interacting on that call.

12:03 Logan Yonavjak: Yes, and then there's the team piece, which is like we can track kind of team development over time. like if you isolated the the sort of like aspect of relational intelligence, just how people are relating to one another and the psychological safety and the motivation of that team and how it's changing and tracking over time. And again, that leads that can lead or not to decision drag or friction points that could come up and we can predict those more accurately if we have this data. So

12:40 Warren Kucker: Interesting. The parallel I I took from it and to use a belabored sports metaphor here, it's like in let's say both the MBA and NFL, there's all kinds of like physical tracking devices on athletes. How fast are they running? Are they fatiguing?

12:55 Logan Yonavjak: Yeah.

12:55 Warren Kucker: To try to prevent injury and and reduce practice loads or increase practice loads or reduce strain or an increase strain when necessary, right? In this scenario, like most of what's happening at work is you're meeting with your colleagues, right? So like and most decisions are made in a group setting in a meeting as well. So like you're kind of using that, this is the live physical data that athletes have, but in the the workplace. we've had this, there's been this technology around for like 15 years. I remember at Apple about a decade ago, we talked to a vendor who was we were gonna wear a card that acted as a microphone so we could see this type of data in the meeting. And and most of it was superficial data. How often did you talk? They're looking for things like psychological safety and those types of things in the meeting. How is the leader interacting with you? I imagine there's a big leap with LLMs, but describe it for me. What's possible now? What's the leap that AI and and generative AI can give us now that we process this data that we didn't have three, four, five years ago?

13:54 Logan Yonavjak: Well the way that this field of psychology that underpins our assessment was was tracked before ai was human scoring. So one of the reasons it stayed in academia for so long is that you would have people speak and then they would take the transcripts and someone would have to hand score and they would have to be trained to do that. And so that's one of the kind of big leaps and now we can do ingest, you know, obviously higher volumes of data at a lower cost more rapidly. And so there's just an opportunity to democratize and disseminate this type of information into workplaces at a rate that has never been available before without these academic specialists. And so and we're we're partnering with a lot of these organizations too to continue to up our game and and so it's an exciting time to see the field kind of coming alive with AI. But yeah, I mean there's also once you get bigger bigger pools of data, I mean some of our counterparts Who've been in vertical development have amassed like huge volumes, hu huge data sets, which can now be analyzed for outcomes and look at looking more at predictive analytics. And one of my favorite areas that I think once we have the data, you know, the piles of data that we can start analyzing trends, we can look at functionally where do people at different levels are they best situated and with what team members are they gonna be most effective. And so we can start looking at breaking this down by industry. and job function. And that just gets really exciting in my opinion because it's not like anyone is is bad or good or that it's better or worse to be at different developmental levels. It just means that you might be suited for certain jobs rather than others. like for instance, I think there's a lot of hype around being a startup founder and it's actually requires a lot of A lot of these s skills like are not skills, but it requires the ability to hold complexity, don't really hold pressure, to be able to pivot quickly. Like not everyone really has that. And so would they really want to be the CEO or be suited for that? And maybe there's a different role on the team, or maybe there's just a side, you know, I'm gonna do something a little bit more within a big big team or organization where I have my lane that's a bit more structured for now. yeah.

16:19 Warren Kucker: That's awesome. before we dive, I want to dive into specifically maybe like a sales leadership angle. what I want to kind of pull into is this. Bring up a good example with there is a there is a cachet around being a startup founder that people think has been around forever. It's really only been around since the mid 2010s, 2015 when Wine Carbonator started to grow. It No,

16:37 Logan Yonavjak: nice. That's good. I've never heard anyone pinpoint it before.

16:41 Warren Kucker: it's really it wasn't that cool to be a startup founder. A startup founder, I I actually kind of attributed it to two things. You had Y Combinator Grow. And they started putting out some courses around what it takes to be build a startup. And then you have the startup podcast, which was in like 2014, 2015, where I was walking through the journey of building their own startup, which included the podcast, right? Before that, there is a technology, like a Steve Jobs level cachet, not a Sam Altman and the thousands of founders that have come out of this kind of Y combinator generation here. So like it's

17:12 Logan Yonavjak: Yeah.

17:13 Warren Kucker: relatively new and it's increasing and it's great because I I think smaller smaller segments of entrepreneurial businesses servicing and niche market is a really good market transition. So so that's wonderful. Th w what I'd take it to though is I've often thought I am a pretty structured corporate guy. I was UPS is almost like a militaristic kind of view of like hierarchy. You never talk to your boss's boss about what something that your boss doesn't know about, right? Like

17:39 Logan Yonavjak: gotcha. Okay, yeah.

17:40 Warren Kucker: so very structured. Apple was different. It was very broad and I actually struggled a little bit to Apple. You don't know who to go to for what. Like you build relationships To mesh yourself through the organization. But I'm a corporate fellow. I like process, I like these things. Those are actually kind of counterintuitive net to being a good startup founder. I actually find I have to push myself out of a box of like what's the alternative viewpoints that I should be taking in order to build really what is like an alternative business, right? Like it's an anti-corporate organization. And I personally struggle with that. My point being with this, what's like a counterintuitive data point? that you would find that would lead to a kind of counterintuitive result for leadership, right? Like, hey, we find that leaders who talk like X, Y, and Z either thrive or struggle in this type of environment that you found along the

18:26 Logan Yonavjak: Yeah, I mean, actually I think some people with higher developmental levels tend to be slower movers in decision making. So, one example is we we have one of our early founders that took the assessment, the formal assessment, he came out as having like a high level of relational intelligence. So we have six measures that we look at, and and one of them is relational intelligence. We call it like relational and emotional IQ. And so he came out really high there. He he really likes getting feedback from his team and he's very open, he's very coachable in the sense that like his identity is flexible, he's not like defensive about taking feedback. So he he'll he'll really kind of hold court with people and and take in feedback all day. And then when it comes to making a hard decision or a pivot, he struggles there. So that was illuminated in the results. and that dynamic we see a lot with especially impact oriented or like mission-oriented founders where they've kind of reached this level of strategic complexity and vision in their in their lives. They've done enough interdisciplinary thinking and self-work that they're sort of seeing the system in a new way and seeing possibilities to fix the system. But they are almost paralyzed by all of the possibilities of how to solve the system. And then if they if you bring in like a higher relational intelligence There's even more information coming in. So people can kind of get deer in the headlights a bit. And people at a slightly lower level of development tend to be very fast movers and pivot able to pivot more quickly. So there's this kind of counterintuitive thing there where like you want to pair a visionary founder with someone who's almost like a better pivoter and more directionally focused. And so that's one of the patterns that I've have found most fascinating.

20:24 Warren Kucker: That is interesting. If you think about it, I mean see if I could simplify that, right? Like so move fast and break stuff, meta ethos is dumb in most scenarios, right? Like that's just dumb ethos, right? Like that's in some scenarios in meta it worked, right? Like high like these moonshot businesses works,

20:38 Logan Yonavjak: Yeah.

20:38 Warren Kucker: right? On the flip, like sourcing a wide variety of opinions before making a decision isn't a is a good thing, right? In a lot of scenarios. But like in a and there are a decent amount, especially in the entrepreneurial sense, right, of like startups where a stubbornness in decision making and moving fast and pivoting is what's needed for the business. And you might that like that's not holding court and sourcing a whole bunch of opinions and that's not moving slow and making the right decision. It's like kind of breaking both those like normal like normal seats, right? Like where like you you should you have to kind of be stubborn in your opinion making and to make some of these really challenging decisions not be kind of like frozen by the indecision. That's really interesting. Yeah.

21:21 Logan Yonavjak: Yeah, exactly. Mm-hmm.

21:22 Warren Kucker: That's a g that's a good counter take 'cause that's not how most people would want to behave at work. And I actually find that there's a i again, not to overemphasize on the founder piece. Founders were bad at corporate life. And that's why they're founders, right? They weren't like and that's why they're good at founders, right? Like and so you have to kind of like when I when people go work at a startup for a first time, I'm like, you should be prepared for this. It's going to be unorganized, it's not gonna be run well, right? Like bad decision you think it's bad decisions are gonna be made because these people like it's not corporate. Corporate is where everyone comes to slow decisions together, right? Like and coalesces around a safe choice. That's not a startup, right? So like your founders aren't built like that. I still have to wonder, I'm still on on the fence ten years in if I'm on what side of the fence I'm in there. So that's

22:04 Logan Yonavjak: I know. It can be complicated. Sometimes, you know, the grass is always greener too. so

22:08 Warren Kucker: Yeah. You know what that I'll I'll go one more question before we go into the sales side. So you're a founder. Like you talk about self reflection and this type of development. How does like how do you how does your analysis push you towards specific directions as you kind of build this journey with with Readiness Engine?

22:26 Logan Yonavjak: Well what came out in my assessment was that I needed to be more transparent on how I was making like the frameworks and dimensions of the system I was working with to make decisions and voicing that in real time with people because I think I am leading I'm I'm dealing with a lot of complexity but I tend to keep it internal. and especially when things need to move quickly, I don't necessarily take the time to explain like all the pieces I'm working with. So that came up. and then also naming uncomfortable emotions in real time. So like there's this edge of picking up on, you know, maybe there's like a disturbance in the force, you know, to use some Star Wars language, but you don't necessarily you just kind of move on because you need to move quickly. And instead, like again, voicing that so that people feel seen and heard in the uncomfortable emotion. And that sort of sets the scene for more psychological safety and and people feeling like they can show up and be seen at at work. So those are kind of two areas that like I personally that showed up in my results as like growth edges for me. So I'm I'm much more aware of them. Yeah. It's been a good

23:43 Warren Kucker: That's awesome. Yeah.

23:46 Logan Yonavjak: good thing to get things out of my head and like out towards, you know, the the shared space.

23:54 Warren Kucker: Great. that's really fascinating. Growth edges is a good way to put that as well. so cool. Okay. I'd like to dive into some maybe let's say tactical items from the leadership side. And I think being that this is a sales podcast, we'll stick we'll try to put this into a sales bucket. So let's put ourselves in the in the scenario of a VP of sales. I think there's two key areas that you've talked about that I find interesting here. Let's talk about hiring. You talk about how like certain people work well with certain other types of personalities, right? is there anything assessment wise that you kind of gear towards like how you hire, how you build your team, both that they work well with you but also cover up some of your blind spots? How do you kind of like analyze data, figure out what those blind spots might be and and gear push someone towards the right team building?

24:44 Logan Yonavjak: Well, we can either have them both take the assessment or unassessment, or we can analyze team, you know, transcripts where they're both involved, or we can analyze multiple transcripts of both of them and then run the same analysis. So it's it's really just a factor of We've found that the main thing is translating the results in a way that people can digest. So it's taking the same results we have, but then talking about patterns in between, like, you know, that example I I can I mentioned about the founder who was really high in his relational intelligence, but then he had trouble kind of pivoting. So we would try to find someone with that higher degree of like pivoting quickly, and then try to support him. in finding that person or looking for some if it's a larger company looking for someone who could support him, keep him accountable. So I think it's just a matter of like you need both sets of data and then you have to look at the overlaps and the gaps and risk factors. And that's just a an exercise we've, yeah, that's available.

25:53 Warren Kucker: Okay, great. Let's talk about the maybe the team dynamics. I'm a VP of sales, I have eight sales directors that report to me, maybe a VP of maybe a RevOps person and a sales enablement person, and that's my team. what kind of like patterns are you looking for in let's say how that leader interacts with their teams? How do you create psychological safety? What kind of like indicators do you see of a pro and a con of those types of things? what what tactically can we take away from this as a leadership question?

26:21 Logan Yonavjak: Yeah, I think the team the psychological safety and creating, we call it like presence. It's it's almost like people are are looking to match the nervous system of who they're working with. And so, I mean, there's a couple of different ways I could answer that. One is, how like three sixties are a really good way to actually augment what we're doing and we are able to kind of expedite the three sixty process by taking in transcript data and sort of adding it to the mix. But like how people respond to a leader and how they describe their experience with that leader is just that's gold because you can't really have the person only describing how they think their team is responding. So you kind of have to get that input from the team. But like foundationally you want p there to be a flow state between people and the the folks they work with or manage in that those people can show up with a relaxed nervous system which opens up the the cognitive and executive functioning of the brain, which helps us solve problems and motivate ourselves. If we're in a fear state or in a f in a state of anxiety around someone, it's gonna shut down some of our creative thinking and some of our ability to execute and perform tasks. We're gonna be resentful. So so much flows from that dynamic of being able to hold space in a safe way. And some of that's communication skills, like you can pinpoint how someone is speaking to other people in transcripts, like whether they're using short, curt sentences, whether they're saying things like, you know, what I heard from you, like acknowledging people's responses. So there's a lot of like sentence structures you can pull out to to analyze from there.

28:09 Warren Kucker: Interesting.

28:10 Logan Yonavjak: But I just guess I wanted to say foundationally, you're just looking to create a flow state where your team can show up with where their nervous system is ready to perform rather than shut down because they're afraid you're gonna yell at them or retaliate or something.

28:28 Warren Kucker: Yeah, there's again, there's a common theme that I might talk about every episode now is that a lot of what you're talking about are tried and true kind of leadership, let's say enterprise analysis kind of like tactics, right? A 360 we've been doing for 30, 40 years, right? When you take a survey and you ask people how they feel about you. but I think with the technology now, again, I think the simplest way is you take it from a human scoring mechanism to an AI scoring mechanism that's much better at pattern matching. than we were before and actually gives you an ability to get much more robust as to what patterns you're looking for because you have more data to analyze, right? rather than a human just using a subjective kind of approach to what they know. And again, the knack for the scenario there, right? I would say that would you say that this is a right or wrong statement? I think most leaderships have leaders have no idea whether they're creating an environment of like psychological safety that fostered creativity, right? Like I

29:20 Logan Yonavjak: I would agree with you. I mean yeah.

29:23 Warren Kucker: have no clue. Everyone would probably it's probably some more in the investment conversation. Everyone would assume they do and they have no idea how they're doing it, or if it's true that they do. Does that that seem like the right kind of like paradigm there?

29:32 Logan Yonavjak: I mean Yeah, I think there's a couple dimensions that I think we all have a felt sense sense of what's going on with someone if we really pay attention. But I think people have different priorities in their work and different skill sets that they intrinsically and then for whatever cultural or, you know, familial reasons have honed in on. So it's just a it's a broad mix of of skill level, you know, with that. And so and then there's the tiredness factor, there's the lack of paying attention factor because. because you're looking at your phone. There's all sorts of reasons to have more standardized data to draw from. Like if nothing else, there's this great study that was done of, and I I need to s get this correct citation, but basically I was looking at judges making determinations throughout a day. And they in the beginning of the day they were more lenient on people and after lunch they were harder on on people. And basically more people were convicted. These were like I think petty crimes that they were studying. But What I found interesting about it was that even someone whose job it is to perform like a judgment on a case, they can get tired or grumpy or just irritated, you know, for even at different points in the day. And so you're not necessarily getting the same person consistently. And so I think we need to think about those risk factors, even with the people who are great at reading other people and great at holding teams. so those, I don't know, these all I've been thinking about this stuff a lot and like, nothing's really convinced me that it's not good to have this information.

31:16 Warren Kucker: No, you're absolutely correct. I use that example. So that's a Daniel Kahneman study, Thinking Fast, Slow, that talks about that judge use case. I think about this all the time. Yes. because it's one of the better better arguments for the

31:24 Logan Yonavjak: it was? Okay, okay. Thank you. I heard it on the radio and I met it. Yeah.

31:30 Warren Kucker: whole book is chock full of just those, right? Judgments being more skewed than you think. I use this argument all the time for AI augmenting human relationships, because you there's a lot more that goes into play. So for example, most sales leaders have coaching calls once a week with all their AEs. I have a coaching call with Jim at eight, Sally at nine, Tom at ten, and Jill at one. and Tom is probably getting a much worse coaching call because I am hungry at ten o'clock and I've done three in a row than than the other AEs and structurally that'll just always be the same. and honestly it's probably a huge impact, 30, 40% of the way. So what should be happening is when we make a case for AI That's the case for it. That should supplement. I should still be the coach, but I should be looking for these patterns in the conversation I'm having, using something that doesn't get hungry at ten o'clock in the afternoon. I have three podcasts scheduled in a row. I am petrified that my third podcast, I'll seem a lot less interested. So I'll have to overly focus on this. So you're absolutely right. The having the data, having the conversation data. Yeah.

32:31 Logan Yonavjak: But I got you early in the day I got you early in the day.

32:33 Warren Kucker: You did, you got the first one. So you you got me at my my brightest here. that's excellent. So maybe one last question before we dive into one last question. when you look at when you're selling this product, who is most interested in this type of like analysis? Are you going to HR? Is this an HR problem? Is it a senior leadership, CEO, founder? Who do you go to and say, Hey, I I have this technology to help better execute decisions by solving the interpersonal problems that you're having in the organization?

33:06 Logan Yonavjak: Mostly senior leadership and L and D professionals have been kind of the the sweet spot. Our we've honed in on our ICP being growth stage companies that are in some sort of high growth, high high innovation industry that really need to solve like either manage their team growing quickly, solve for complexities, solve for execution risk, look at promotion. I've look the senior leadership at a few boards have been interested in succession planning. So yeah, I would say definitely the senior leaders and the the the L and D professionals have mostly been our kind of our our focus area.

33:51 Warren Kucker: Cool. That's great. okay, last question. So with generative AI, we're obviously in the early stages of leveraging this technology, improving the models, although I think we've hit a relatively leveled off point here. What do you think might be possible three years from now that you're thinking about, but we aren't quite there yet from a technology standpoint that we can implement?

34:12 Logan Yonavjak: Well, one of the things that gets me most excited is the idea of having everyone having an angel on their shoulder. And what I mean by that is our second brains all being developmentally trained through a coach, like an AI coach or agent, to be at our top level of development and know us. Because of the data we've fed it and the patterns in our own individuality, and then be able to inform decisions and do role playing with other people's highest selves to fix problems and to get through difficult conversations that may have really run into roadblocks because of the egos involved with other, you know,

34:54 Warren Kucker: Yeah.

34:55 Logan Yonavjak: the the real people. I think there's just a really exciting opportunity as we develop second brains and as we develop develop ourselves more systematically using some of these developmental tools. That's like a future that I think is possible and just not quite here yet.

35:14 Warren Kucker: Yeah. it's a really good example because the like generative AI technology is there for that at this point. What the I think the two things to solve is like structuring how that data flows in and out of your day. And so like how do you provide that right context into just the trust level of how you manage conversations, right? We're still trying to figure this out on our end, right? In theory, what should happen is every conversation that happens across the whole organization should be shared in an organizational database that everyone has access to. and is able to be analyzed and you trusted in a way that no one's coming for your job or scolding you for something that like came up in a meeting or a decision that you made along the way. Breaking that trust barrier, finding the right way to share information, step down from that, because not everybody in the org should have every conversation. it'd be the interesting part of how to solve this kind of like enterprise conversation bucket here, which I'm really fascinated to see how it gets solved. cool. Great. Logan, if people wanted to find you, where can they find you? Do you post on LinkedIn? Do you have anywhere that you write or you go to in any events?

36:20 Logan Yonavjak: Definitely on LinkedIn and then readinessengine.io is our our website. You can contact us through the contact form. That pretty much goes to me. And yeah, I do go to conferences, but there's they're varied. I feel like I don't have like a s stable set of conferences yet. We're sort of still figuring out the the right mix, but definitely LinkedIn or our website. Yeah.

36:44 Warren Kucker: Awesome. That's great. Well, I'm really looking forward to seeing where you take the technology. It's it really touches on a key theme that we talk about all the time. AI seem we're worried about AI breaking human relationships. I think there's an opportunity where it really elevates them.

36:57 Logan Yonavjak: Yeah, well said.

36:59 Warren Kucker: Awesome. Logan, thank you so much for being on the show today.

37:02 Logan Yonavjak: Appreciate it.