The Inside Job - Leadership, Self-Knowledge, & Communication Styles from the Inside Out

Speaker A: Welcome back to Practical Product Management. It's a new episode in this podcast. We always talk about how there's lots of great theory about product management out there, great books, great frameworks, but we want to talk about how product management is done in context and needs to be practical. Today we have a guest, which Marilyn, I don't know, which is so much fun to talk to someone we have never really talked to before and to get to know. We, we're talking today to Ryan. And so I'm gonna turn it over to you, Ryan, to introduce yourself to us as an audience and as a group and then we'll dive in.

Speaker B: Thanks, Leah. Um, yeah, so my name is Ryan Scott. I'm the head of product for DNA Behavior. Uh, DNA Behavior is an Atlanta based personality testing company that kind of has morphed into a enterprise behavioral data analytics company. So we analyze behavior on an individual basis, but then we report on it on a whole company basis so we can generate really cool, um, C suite reports on everybody's personality inside the whole organization or individual teams. And we do a lot of work in behavioral finance. Um, so I got introduced to the company, um, whenever I was in school at Georgia Tech. Um, it's a technical engineering type school. And that's what really kind of started my drive towards getting involved in technology and product management.

Speaker C: I love it. Uh, so like, so many ways to go after that intro, like behavioral finance, omg, what is that? But let's start back at the beginning and like, let's talk about this thing, this AI thing that everybody talks about. Um, you've been involved in this in a long time. So how did you get started with AI?

Speaker B: Yeah, I, originally whenever I was at Georgia Tech, I saw, saw an ad for an internship that was available and I had a finance concentration. So originally I was thinking, okay, I'm going to get a business degree and I'm going to go work on Wall street or some flavor of that locally in Atlanta. So I saw this ad for this internship and it was using machine learning and also a little bit of finance because we were doing behavioral finance work at DNA Behavior back then. So I actually applied for the job and, and I've worked at the same company. My baby face doesn't show it, but I've worked here for 14 and a half years now. So, um, it's been a great journey. I started out as a product, uh, management intern. Um, we were processing personality tests through the faxes and people would email us PDF reports and we would have to key them into the system. Because a lot of older clients didn't have computers back in the day. And now we've iterated on the business about four different times. So a lot of the, um, tooling that we're doing is still AI and machine learning. So the company was using machine learning models back then, um, to do all the number crunching. There's 138 different, um, personality traits that we can pick up on this particular questionnaire. And machine learning has always been very good at doing all the number crunching versus, uh, for an individual, but then also for the whole population of everyone that completed the profile before them. So that was my first exposure to machine learning. Um, and I would have never guessed that, you know, we'd have this AI age where everything is gen AI and that's the buzzword. Um, we're using that quite a bit at DNA as well. So I hope we can talk a little bit more about that.

Speaker C: Yeah, for sure. I feel like, just FYI, that you and Leah are going to be best friends after, uh, this conversation because you have such a cool shared history, um, and so many shared experiences. I don't like. Leah is a recovering accountant. M. Like, there's lots of like, yeah,

Speaker A: business major and I got an MBA before I ever made this pivot. So. Yeah, yeah.

Speaker B: Our, our leader, Hugh Massey, he uh, was, uh, is a recovering accountant as well. He worked at um, Arthur Anderson back in the day. Uh, and then Leon, who's our president. He also, um, is a recovering accountant. He worked at Blockbuster and Cox Communications and led their accounting team. The behavioral space is just kind of drawing us all to it. The human behavior space.

Speaker C: Wow.

Speaker A: So what, so Marilyn asked, she hinted, uh, at it, but I'm going to ask it directly. How would you describe behavioral finance?

Speaker B: So we can pick up a lot of different insights about an individual and what motivates them to make better decisions for themselves. Um, so for instance, for me, if you were looking at my report, um, you would see that I'm a saver. Naturally. Um, if I save a bunch of money and squirrel a bunch of money, I'm not going to want to spend it on my own experiences. That's one of my little quirks that um, a financial advisor would work with me on is making sure that I'm actually using the money that I, that I've saved up. We also have some really cool insights are called behavioral biases. So things that are always going on or processing behind the scenes that are influencing your decisions. So one of mine is that I have a newness bias. So if there's shiny new toy or new thing that kind of pops up even with my investments, I will jump ship with some predefined plan that I've come up with an advisor, switch my money strategy to X, Y and Z. And then that can be, you know, at my own detriment. Um, so there's 16 different behavioral biases that we measure and there's all different types of really interesting quirks, like herd following individuals that, you know, they hear someone else did it so then they have to do it too. Or, um, you know, like the no fear individuals where they're just jumping into any investment and really don't look at any, any details, um, just because they're excited about it. There's a lot of, lot of interesting quirks with behavioral finance and what we can find out about people.

Speaker C: That sounds amazing. Like, now I need to know what my quirks are. So after this, I'm going to take a test.

Speaker A: I mean, mar. You should know, Ryan. We tease Marilyn about being a dragon sitting on a pile of, pile of gold and hoarding all of her gold, not letting anyone do her.

Speaker C: I don't know if that's an official category or official bias, but definitely I have a dragon gene.

Speaker B: Yeah. Financial advisors find it interesting because they, they'll play a game where, you know, do couples attract or do opposites attract with a couple clients, so they can put each individual on a dashboard and they can show how everybody is different or similar. Um, and then whenever they're coming up with a risk profile for a couple, it's always an interesting conversation because they ultimately have to choose a portfolio that they're both comfortable with. Uh, and oftentimes people are opposite that are together.

Speaker C: So my husband and I are exactly the same and our financial advisor has a terrible time trying to negotiate between the two of us. So I love that, um, I love that you sort of done so much with machine learning and behavior design. Um, but ultimately what you're trying to do is figure out how humans act and react. Right. And humans are like, we're wonderfully irrational and, and I guess we are predictable in our irrationality. But you know, what is something about your current work that feels especially human? Something you wouldn't think that people would expect, uh, when they hear that you work with AI and behavior platforms.

Speaker B: Yeah. So when we're designing new products, we ultimately are designing it for the business users and we will design some really cool stuff. I mean, we got excited about the AI age and we're getting all of our API credentials for the OpenAI platform. And there's all kinds of really interesting and crazy stuff that we had developed from an R and D perspective. But whenever it came down to it, our users really just wanted tools to expedite their work and save time. When we were originally launching some features, they did have an AI, um, element to them. But we actually found out that if we're less upfront of the fact that it has AI and it's just focused on saving people time, um, that has had the best result from a metrics perspective. So we actually use a lot more AI behind the scenes than even our customers even recognize. Um, and that's one thing that we've learned is that, you know, people don't always need to know how something works or what's behind the scenes. They just care about, you know, spending more time with their family at the end of the day or making sure that things are more accurate if they're working on processes or. Um, we work with a lot of business coaches that use our tool in order to have, um, people complete profile. They complete profiles and they do reporting on them. And a huge chunk of their time is chasing down individuals and having them complete the profile. And everybody's a procrastinator. So we have AI that basically will figure out the right time to ping somebody and send them an automated alert and have them complete the profile. And um, we originally were like, look at this cool AI we built. But now we just call it the Automated Email Reminder System.

Speaker C: I love it.

Speaker B: And it gets a lot more traction.

Speaker C: I love it. Wait, what? There was a, There was an article I read recently that was talking about, um, customer satisfaction. So someone like that had built, uh, an interactive tool to help perform customer service, answer, um, questions for people, answer questions for humans. And they had like this super high customer satisfaction score until they told people it was AI. And then their customer, same, same tool, same answers. And then their CSAT score like tanked because people didn't want to think they were talking to an AI. Super. I think that's super interesting and human. Thank you.

Speaker A: Yeah, yeah. So there's two things that come to my mind. I just yesterday was, um, talking to someone who's trying to write an article about, um, about gener generative AI with entrepreneurs and the lower adoption among women. And one of the data points that they're uncovering is a. There's fewer of us in, in tech just on numbers, right. So less opportunity to play with it along the way to get to comfort. And then the second one is we're not convinced. Apparently women are not convinced that the AI is not just trained by men for men. And so, so it's, it's super interesting. I was like, that's fascinating. So that's one thing sort of behaviorally that I think is showing up in what we're doing. But one of the things that came to my mind when you were talking is just this, you know, there is uh, so I work in a fractional CPO in a travel startup and you know, where I'm like, oh, where are we going to put AI? Where are we going to put AI? The truth is like I, one of the things you said was really struck strikes me and sticks out to me. Our customers don't necessarily want AI to be who they're talking to. What they're, what we're doing is connecting them with a local expert. If we can use AI to create a soft experience before they actually talk to a human, I think they wouldn't care. But if we tell them like, talk to this bot, they're going to be like, no, I'll just go somewhere else. And so I think, you know, how do we talk about using AI and to make ourselves more efficient, to make our systems more efficient, to make how we manage things more system rather than taking over the customer voice. Right. Or the customer uh, flow I think is super interesting. What you said really resonates with me about that.

Speaker B: Yeah. A lot of our work is around communication. So a lot of the insights that we use whenever we're delivering deliverables or dashboards to our clients, it's all about communication. So we know that there's four communication styles that people have just to keep it simple. So there's people that are very fast paced and goal driven and they like things in bullet points and just like the fastest answer possible. And then there's other people that like to have fun and they like graphics, maybe a video to go along with it, um, or like a voice, um, explanation because they really like talking on the phone and having that connection. There's other people that like a lot of assurances and they're, they're a little bit nervous in order to make their first step. So, so the more padding around something and the more um, security that you can wrap around it, the better. And then the last one is my style and that's information. So the more information the better. Send a research report or more details, facts, figures, that kind of thing. So, um, in our marketing we got a lot of benefit by using our own insights and then sending People distinct messages that are unique to them. Um, and then we're starting to also train our AI models to do this as well. So in about two months, whenever you're using one of our chat tools, they're going to be writing in your communication style. Um, and our initial testing is that it's so much better engagement levels, because I think that that gets into your point, Leah, about, um, while there's some people that adopt AI faster than others, it's like all these AI bots, they write in that take charge style where it's very direct and almost kind of awkward if they're just completing a particular task. So one of the things that I always do and show people to do if they, if they want to start using gen AI is that we can create a custom one for them so that all of their information that they're getting is written in their own writing style. So it's more colorful language or if it's more, you know, point blank or more facts and figures generated. And that, that seems to help with some of the adoption. I think it's pretty interesting.

Speaker C: I think that. So I did not expect this conversation to go here and I'm thrilled about it. Um, I think that for the people listening who typically are product managers at various levels, um, throughout an organization, um, you guys have just touched on something that's really key and I think something that we should sort of like really drive home as a message. As a product manager you should be grounded on communicating to your customer about how your product solves their problems. Um, and these tools and AI and all of these like cool things that people are coming up with really are, um, they're tools that help you serve your customers in ways that are more personalized. So I always call it like hyper personalized. Um, the goal is to become more and more personalized over time. So let's talk a little bit about communication styles and how you start to identify populations and how you talk to them. Um, so if you were new, um, or newer, um, and you wanted to give advice to a younger product management group, how would you tell them to start to approach this?

Speaker B: Yeah, so I think one of the things that I've really learned is, you know, it's around communication and writing user stories for a specific feature that needs to be built is that that user story is going to be read by people in leadership eventually or developer a qa. They're all going to have different communication styles. Likely the QA person, if they're good, they're probably going to be very Information style and they're going to be wanting to find mistakes. The developer is going to be probably pretty fast paced and then who knows what the leaders are going to be. Um, so you need to write to all the different styles. Um, and before we would try to do that internally, but it was really hard. I mean putting your brain into all these different users and trying to figure out how they would like to receive the information. It's a lot of work. But that's one of the tools that we use Genai for and it's amazing is, um, we actually built a custom GPT that we use internally that writes all of our user stories for us. Um, so it will create some graphics along and workflows, um, and charts and visuals and wireframes, um, to go along with the discussion. That would be for someone that's more visual. Um, it always has an executive summary at the top. So if someone's just going to read a few words anyways, you may as well put it at the top in bullet points. Um, and then at the bottom, uh, because the information style people are going to be reading the whole thing. So just put all of the details and nitty gritty details that is only pertinent to them at the bottom. Um, so we trained an AI to do this and our user stories have gotten a lot more um, useful to our team that's building. Um, and our errors have gone down dramatically. So it's been interesting.

Speaker C: I love that. Um, and as a person that loves bullet points and hates all the details, like that would be super helpful for me.

Speaker B: Yeah. And we use a tool. I love AI tools and little apps that we can use. There's a tool called Napkin AI, uh and it is very, very good. I don't know if you guys have used it, but it's very good for visuals. Um, so if you want to try something that uh, would generate a flowchart for you that is really interesting, it will come up with some very out of the box ways of displaying information and almost like an infograph. Um, but it just does it in an instant. It's really cool.

Speaker A: Oh wow. I have to, I have to try that. I have, I have a task that I need to do that I'm like, I don't want to draw this myself. Like I was just uh, I know, I know what it looks like in my brain but it's going to be so hard for me to make sense of it. So I'm like that's a, that's a good tip because you know how like Today, if you go to ChatGPT or Claude and ask it to do diagrams or anything, it sometimes, like, they, I mean, don't change anything. But this line and then it rewrites the whole thing. You're like, no. Right?

Speaker B: Yeah. And then suddenly there's a new person in there and they're in a different office. Yeah, it's. It's messy. This napkin tool, it's cool. It's like it originated. The product managers are probably very concrete because they're just like, we want this to be like if you're writing, drawing on a napkin, so they all look like little sketches out of the box, but. But it generates about 10 to 15 different sketches just for one paragraph. So you can kind of flip through which one that you want to use.

Speaker A: I love that. That's super cool. Yeah, I love that. It's funny because literally, Marilyn and I, about a year ago, we started this podcast, Buddy, a little over a year ago. And one of the early, um, recordings was about AI and what we thought AI would do. And one of the things we both said is we never need to write another user story as long as we live.

Speaker C: And.

Speaker A: And the truth is, like, I don't need to write them because I don't write them anymore. That's not my job anymore. But also, I never need to write or read another user story as long as I live. So I love the idea that that's a problem that you guys have gone in and said, okay, let's. How do we make this more robust for. In terms of making it better for communication? So, I mean, we do need them, but the question is, like, what do we need them to do and who do they serve? So I love that.

Speaker B: Yeah. Yeah. I mean, one of the, I mean, just from going back to the behavior aspect is my style. I have a very strong creative score. Um, so creative. We measure it on a continuum. So if you're completely in the middle, you're just kind of the average in the population. But if you're very strong, we call that you have a strong creativity score. And if you're very low on that factor, we call it anchored. So if someone is anchored, they're going to be more experience based, so they're going to be very good at just following the procedure, the SOP that, you know, the product leader has already set. But someone like me, I like to reinvent the wheel every time that I do something or I get very bored. So if I'm told to write a user story, it could be one way, you know, week one, and then week seven, it's going to be completely different and it's going to be all improved. But then the person that's reading it is going to get frustrated because they're reading it in different ways and whatnot. So the AI bot that we created for this, it really helps streamline them all.

Speaker C: I'm gonna guess that I'm, I'm more like you than anchored. Um, and anybody that has worked with me in the past that has read one of my monthly business updates, uh, after a while they just give up on formatting because every month I'm trying to tell a different story. And so the formatting change. So for like the first 3, 4, 5, 10 times we review a document of me telling them what's going on, people get really wrapped around the actual dot format. Like your format changed and it's like, well, I don't really care about the format.

Speaker A: You're like, it's about the content people. Right, exactly right.

Speaker C: Exactly right.

Speaker A: So one of the things. Oh, sorry, did you.

Speaker C: Go ahead.

Speaker A: I was gonna say, one of the things that, um, really struck me about your profile is it's very clear that you're very much a people first leader. You care about the team, you care about how people interact with each other and culture and all of those things. How do you strike this balance of, like, figuring out how to use gen AI and use more and more AI to, you know, make the team more efficient while still really being human first in terms of how you operate within your team?

Speaker B: Yeah, that's a really good question. I think it kind of probably comes back to my more detailed nature and my information style. So I set up little dashboards and metrics for every aspect of all of our product management. Um, so you can see how quickly we're developing features, how many bugs, um, all the different elements that we want to track. And we make it a group issue rather than an individual issue. So we have a lot of team meetings where all the metrics are in the forefront for everybody. So if one person's kind of lagging, everybody feels like it's a team problem rather than an individual problem. That seems to really help. We also are very, very, um, careful about who we add to the team, so we make sure it doesn't shake up the team dynamic. Um, we use a lot of the scenario tools that we have inside of our own application in order to see, okay, we need to bring in a new qa. We can scenario plan that with a team report tool that we have internally, um, inside of our App and figure out, okay, if we bring in a QA that's a little bit more of a driver, who is that potentially ah, going to upset in the team and who are, whose work are they going to be reviewing, things like that.

Speaker A: What's funny about you saying that is I think that that's what I do when I build teams in my, that's how I approach it. I'm putting a puzzle together. So it's interesting. I'm like, oh, that skill I acquired for 20 years is now something that can be done without me. Right.

Speaker B: Well, or you just learn how to do it intuitively. So it's um, you know, it's like a lot of, I would say our biggest competitor is people just doing, doing things. Intuition, um, you know, advisors just working with a client, um, that they meet on the street. They don't really know who they are whenever they first meet. If they had our insights they would, um, but we just help them, you know, realize the little quirks of individuals quicker and help them adapt. Um, yeah, that's probably ah, one of the interesting things about our business is that it's just, it's human connection and some people are good at it and some people are terrible.

Speaker C: Yeah, no, I mean that super resonates with me. I think one of the things I like one of my little things is I always say people build software, um, and that the interconnectivity of the team, like Leah said, is just so critical because you get, you get something going on that's not building trust and building psychological safety and it will derail your entire platform. Like it delays, timelines, it gives you like features that don't like that you, that you can't commit to or can't iterate on. Um, so let's, let's take that back and let's like, let's say that I'm a product leader, um, or a product manager and I'm struggling within a team to understand how to relate to someone or if my team is healthy. Like what are the tools that you would suggest someone looking at, uh, in that case to sort of like figure out how to get the team together.

Speaker B: Yeah, it's always interesting if you just do an after action review when something doesn't work the way that you're expecting and then just go step by step to figure out, okay, where was the breakdown? And almost all the breakdowns are going to be communication related. Um, Harvard did a whole study that's like 87% of business, um, conflict is all based on poor communication in A team. And we see that time and time again. So just understanding in these reviews that we're doing post mortem or after action reviews, whatever you call them, um, figure out where do things not work out. Right. Um, it's probably going to be communication oriented and then figure out is this something that you know, you could have done better or someone else could have changed their communication to you and then just using a tool in order to kind of bridge the gap, whether it's ours or another. Any other ones that are on the market.

Speaker C: Yeah, um, I feel like the answer usually is always humans. Right. We're using technology more and more to augment humans or to influence humans. But at the end of the day we really are talking about human behavior. Um, whether it is part of the build process or part of the relationship process. Um, I, uh, I've even worked in places where you got a full personality workup like you're talking about everybody gets their own. Right. You get your own 20 page deck. Um, and you can share it or not. Um, but people were feeding this into chat GPT and asking about communication conflicts. Like I have this problem with this person in a meeting. Help me understand how some of my biases and it's interesting because in a lot of cases um, it'll help you understand. Um, but we're still not to the point where you can actually like use it as an executive coach yet. Quite. I think. Um, um, but I don't know, maybe

Speaker A: I have a job.

Speaker B: Yeah, we find it super helpful for especially like offshore staff. It's like whenever you add, you know, communication issues in general, which everybody has, but then you're also bringing in a different cultural aspect, different power, distance ratio or just a different way of addressing communication and issues that you find that's where some of the biggest issues I think are arising. Um, if your company uses offshore development, they are going to be seeing stuff. Uh, um. And oftentimes depending on just what culture they're from, it may be very difficult for them to raise an issue or defect or any kind of problem. So um, you know, a tool that's objective and everybody's communication can really help with those types of teams.

Speaker C: Yeah, I mean I've actually experienced where I mean I had to like, I had to throw, I had to throw people from three different geographies into a single room on neutral territory to have conversations between the teams and the. Some of the biggest breakthroughs were about words.

Speaker A: Yeah.

Speaker C: So when you have like a US based team and a, a team based in Spain, but in you know, in one of the, the Catalan regions and a team in Shanghai. Sometimes the word help has a very different meaning and actually elicits almost a visceral response from people. Like, you know, for, uh, for instance, like the people in Spain didn't want help. Like they were independent. It's been part of their heritage. Like they don't, they don't need help. That word actually offends them. Whereas in, in China, it was an honor for them to be asked for help. It meant you trust them. And so like just putting people in a room and helping them work out, like we all want the same thing. You know, the words that you're, the words you're finding offensive are actually an honor. And like, so can we just sort of have empathy and understand each other?

Speaker B: Um, I do think that's a really good point. Um, the word I've learned is the word doubt. I don't know if you guys have heard this, but especially with Indian developers, it's. If they, if they find a problem with something and they're thinking that they've very critical issue that they're raising, they would say, I have a doubt. And I didn't realize what that was. So we don't really use that word very much at all in an American English. So I just felt like that was just kind of odd. And I was thinking, okay, are they like, they think I made a mistake? It almost kind of feels like that. But it's really like, okay, this is like a fundamental flaw and, and the application that we need to drop tools and figure out exactly what's going on.

Speaker A: Yeah, it's more serious than we think. Having now lived in Europe for the last almost eight years, and when I lived in Germany, you really kind of don't ever say you're ambitious because you're, that's like, that's not cool. Like you're kind of a jerk. Right. And so I was like, we're just, we're really ambitious team. And my team was like, bristle, bristle, bristle. And I was like, we are some other sort of team besides ambitious. Hold on while I figure out what other word to use. So yeah, it matters. But, you know, one of the things you just said that I think is really fascinating is I, you know, are talking about these, this kind of bringing people together. I give an assessment sometimes when I'm, when I'm doing coaching or certainly with founders, etc. And I one time had a group of founders where we uncovered that none of them have really sort of this entrepreneurial idea generation. Like, there was a team of founders and none of them were really idea creators. And so I got them in a room and we. I built them a map of all of their different personality types. And then we were looking at it and I was like, what do you see? And everybody just sort of sits quietly. And I'm like, you guys have built a startup and none of you like to generate ideas, so who's doing it? Because someone's doing it. And it was really fascinating to like, let them unpack. Who was covering that, even though it wasn't in their. Their kind of sweet spot or who else on the team are you getting ideas from that are. Is not you, and are they well thought through? And are they, you know, and so just that. That ability to sit down and say, you know, how are we interacting? Where might we be missing some opportunities and where might we be over, you know, like over indexing on something? I mean, it's, you know, I would say that there probably is a pretty good. If Marilyn and I are working on something together, we over index on some things because the two of us both like to do certain things a certain way. And then there are other places where we're both like, don't care about that. And if we neither one care about it, it's going to fall apart. Right. You know, we've had to. Excuse me. He's never come to a podcast before, by the way. It might be you, Ryan, but he's always stayed away and now I want to be in this podcast. Um, but, yeah, I just think that there's, you know, I think it's. There's an huge part of what we do as product managers that, you know, in terms of how we look at people's voices, how we understand customers, how we understand the team, how we understand stakeholders that I really try to help product people as they grow their leadership to see, like, utilize that in your leadership, because those skills are something that you've learned in order to build products, but they are also, if you consider your team, your product, how do you approach them with those same skills? And I think that this is. I think what you're describing is that there is data out there, there are tools out there that we could also use to get to that. Um, which maybe, maybe makes us faster, maybe it makes us more efficient, but also it doesn't take away the need for humanity and, and stopping and having those conversations and understanding people.

Speaker C: For sure. All right, Brian, uh, open your desk drawer and pull out your crystal ball. Um, I'm Gonna, I'm gonna ask you some projection questions and you don't have to be right. Um, because we're not gonna come back and fact check this later and nobody fact checked. Ryan, please. Um, let's look forward. Like you, you kind of have the experience that I always sort of like look for and, and, and rant about is like you've done stuff, um, you've made a difference to a business and a team. Um, where do you think this goes in the next five years? And how do you think, uh, either internal teams or people will use behavioral AI to interact with each other and other humans?

Speaker B: Yeah, it's really interesting. I think that just AI in general, I'm seeing that there's companies that are really easy at adopting it and then the bigger companies in particular are terrible, it seems, it's like. And if you, from a human behavior perspective, we know that there's only about 30% of people in a company that can adopt new technology and are really the early adopters. Um, so we're seeing that a lot of companies, if they can corral the people that are the early adopters and kind of move them away from the clunky legacy stuff and then allow them to work on things almost in an innovation hub, that, that is the only way that some of these bigger companies are able to move the needle with the AI, um, adoption. And I think that that probably just needs to happen more and more. Um, I mean there's some large insurance and financial services companies that are doing this and I think that the ones that aren't, they are in huge risk.

Speaker C: Yeah, they're going to get left behind. 30%, is that like, that's a real number? 30% of the people are able to, to. So like having worked in technology for a long time, like I viscerally feel that like, because some places it's like trying to drag a boulder wrapped in barbed wire uphill with your bare hands. Like, it's awful.

Speaker B: Yeah.

Speaker C: Um, and then there's other places, uh, one of my most recent companies that I worked with where like, because it was a mandate, everybody have to, everybody has to use it. Um, everybody must disrupt themselves. Like we have, we have this sort of whole program around. How do you make your own lot easier. It's not going to replace you. And Lee and I talk about this a lot too. Like this whole, like I'm going to bring in AI and like lay off 40 of my staff. I think that's really irresponsible. I think it doesn't, uh, I think it's like buying into headlines and it's not actually like being practical with, with technology. I think it's, I mean, I think it's stupid. Let's just call it stupid. Um, but you know, other companies that I've worked at, like in our medical, in our medical organizations, like these are the doctors and people that talk about, um, vaccines and, and viral impact and all sorts of lovely science. And we had 100% adoption of, uh, people using our tools every single month. Um, and in fact some of the earliest products came out of like our, um, compliance teams. And they were, they were using the tools to get rid of the parts of their job that they didn't like. Um, and so like monitoring inboxes.

Speaker B: They love it.

Speaker C: Yeah. Um,

Speaker A: I would just say those numbers hold in organizational development. Like when you look at, um, you know, organizational behaviors and transformation, it's about 30% early adopters, about 15 to 20. They're just straight detractors, man. They will stand in your way. And then the bulk is just everybody who's like, I guess and they're just sort of with you, they'll go with you. And they're not necessarily detracting, but they're not, they're not going to jump in if you don't tell them to. And so you need. To your point. Right. I think from my perspective, when I think about change management, I want to harness the 30 and get some wins and then get everybody else sort of excited about what's happening. And then the detractors, they either need to get on board or go. Right. Like, that's always sort of my, it's harsh, but that's how I always feel about it. But that's, I mean, even just in organizational behavior and, and organizational leadership, that's, that's those numbers hold. And I think that, that, you know, I think I've seen that play out. I usually go looking for the people who are on my side and then I'm like, I'm going to make you an ambassador. Right.

Speaker B: You know, it's, it's difficult. A lot of, A lot of companies, if they, you know, they're building some new initiative, people that are very career motivated, they want to be a part of it. So it's, it's. They need to have some sort of objective way of choosing people that would be a part of the innovation. So I think that that's where a lot of companies see where we're fitting in as we're an objective scientific way of just helping you hand pick the people that are you know, these are the people that you want to be in your innovation hub. Um, they get to be a part of it and, and they need to build something that is to your point, Leah? Uh, you were saying that you need to build something that's, you know, not just gen 1. It needs to be like 1.3 or 4 in in order for other people to see that this is exciting and can actually work. Otherwise they're just gonna poo poo on the idea and you know, grind the gears every single step of the way. So getting people to move outside of the regular day to day, help them iterate a little bit, use it seems to be that that's the future.

Speaker A: Yeah.

Speaker C: Uh, so having, having spent time in large companies, I uh, love the idea of the innovation hub. I'll tell you one of um, the challenges that I think like big financial services, big insurance, great, you have an innovation hub, but it is operating outside of the norms of the business and in a lot of ways that then you get your detractors and your people that are cemented in the past in your core technology. So like what is one way you can start breaking those boundaries? Because what will happen is you're sort of like you're building this innovation hub that's going to become more and more disconnected from the, from the stuff that runs your business today. And um, they don't want to work on the core problems. They don't want to work on the existing product with the existing customers. They want to work on the new ideas, the innovation team. And that leaves your core business just from a product perspective, right. For someone to come along and disrupt you, one of these digital innovators. And I think you kind of see that in the fintech space, you know, banks versus Fintech. Um, so I hear you and it's a great way to move people forward. But like some, at some stage you got to bring the core or you're going to get disrupted.

Speaker B: Yeah, I think a great career for people. If you're listening to this and you want, maybe you're not into an AI builder, but you like the people aspect of it. The change management aspect is going to be huge for AI age and everything in the future. Because helping people adopt all this technology and figuring out a baby step way of introducing all these different components to them is there's so much work to be done. Um, this is kind of outside of the financial services realm. It's in the medical space. But I was listening to one of the leaders at a healthcare company in Atlanta and, and she was talking about how they're going to be tackling this problem with robotics in a hospital setting. So they have an innovation hub where they have true working robots that are able to interface with the patients and walk around and deliver stuff in the hallways and whatnot. But they know that they're just. People are going to be deathly afraid of, um, these robots walking around in a health care setting. So There's a whole 10 year roadmap of how they're going to systematically introduce robots inside of hospitals. And you know, a lot of change management geniuses have had to, um, baby step that out. So I think in an organization, if we have, um, an innovation hub where we figured out how to innovate on the whole change management or the whole um, claims adjustment process for an insurance company, or um, underwriting, design it in a way so that it can be systematically delivered rather than just saying, poof, this is your whole job that's been replaced with AI. We need to develop these different products and different modules and microservices maybe so that, uh, baby step it so that each little increment a new field can pop up or a new enabler so that people just start to see, you know, the possibility without a lot of hesitation.

Speaker C: Yeah, yeah.

Speaker A: I worked in a health, I worked in a healthcare system that had an innovation hub. I worked in the innovation hub and to. And we had, you know, we had, you know, different things were successful, some things were not. And it all, uh, what was successful tended to hinge to your point, Marilyn, and to what you're saying, Ryan, on who you can get to engage. Right? Because I mean, if you can't bring the doctors, you're done, right? Good luck. Uh, they will go around you, they will find another solution, you know, or in the hospital, like using that robotics example in a hospital system. One of the biggest problems in most hospitals is actually Internet connectivity. And if you need it to, if you need the Internet to run the robot, the robot will not run because there's corners of hospitals that don't have good Internet because of the equipment. And I mean we. So I built telemedicine platforms and the telehealth machines often were. When you needed to trot it out for a stroke consult, they were dead because someone had just pushed it in a closet and not plugged it back in. Oh no. And so it'd be like, oh, all the, all the machines are down.

Speaker B: Right.

Speaker A: So we had to have, Then you had to have these analytics that were. So I think there is something to be Said for like the. The change management process. To your point, Ryan, the change management process process is huge. And making sure that there are people who understand psychology of human behavior.

Speaker C: Yes. Ah, right.

Speaker A: And how to drive that psychology in a direction.

Speaker C: And for God's sakes, it cannot be another framework like safe, which is just the same old repackaged and agile. Um, because every big company, like, they think they're agile because they've adopted safe, and it just makes me want to gouge my own eyeballs.

Speaker A: Right. Awesome. Well, how about we lightning round? Are we ready? Okay. All right. So first question you told us about, you know, napkin AI, but do you have another favorite AI tool that you're like, oh, this is one that I like to play with or like to use.

Speaker B: Yeah, there's two. Two answers. Uh, for that one is the sanebox is this inbox tool that I use to. It's like an inbox personal assistant.

Speaker C: Okay.

Speaker B: M. It's insane. So every inbound email that you get, it organizes it, it knows what content is in the email, and it puts it into the right folder. If you don't need to see that email at that particular moment, it doesn't go directly to your inbox. You never hear that little chime that drives me crazy. So that's my favorite work tool. I would say Perplexity is my favorite AI tool for doing research. Um, I mean, I'm not even reading that much, you know, on like, books or, um, white papers or things like that. Because if you can just ask a question to Perplexity and it spits out such a comprehensive answer and then gives you the additional collateral that you can click on to learn more about something, I feel like my pace of learning is up dramatically with that tool. It's amazing.

Speaker A: Yeah. Marilyn, do you have one?

Speaker C: I mean, I think the one that I. Not really a tool.

Speaker B: Right.

Speaker C: I'll. I will tell you. It is, um, uh, tldr. So just how to keep up with the volume of information and all of the things happening and figure out what's relevant and what I want to read or not? Um, so the TLDR newsletters I subscribe to, and I just get two or three in the morning, I can scan through them really quick. I can rabbit hole where I want to, but right now I'm finding just understanding what's happening in the universe of all things, um, to be one of. One of my biggest challenges. And so that helps me sort of at least keep, um, abreast of the breadth of stuff that's happening.

Speaker A: Yeah. For sure. Um, yeah, I would. I would tend to agree that I like perplexity for gathering information for information. I think that's our. We said that was our style. I agree with you. That's sort of my style. I want. I want lots of information because then I want to be the one that puts the bullets together. Right? That. That's, for me, kind of critical. Um, and then. So I think those are the. That's the big one. I think that I use a lot. Um, it's super funny. Like, I don't know. I don't know if this is true globally or not, but ChatGPT's had problems all day long. It's been up and down all day. And I. So at work, everybody was like, what do we do when this happens? I'm like, you get a subscription.

Speaker B: You have to write stuff by hand.

Speaker A: Right. Like, first of all, maybe you have to think for yourself for the day. You'll be all right.

Speaker C: Right?

Speaker A: But. But it was like, it's been struggling today, and it's been really fun to watch everybody like, what do we do? And I'm like, calm down, calm down. First. Step one, calm down. Okay, second question. So you have worked in the same place for a long time. Uh, so this may be a tricky question to ask you, but if you could. Do you have, like, if you could. If you. Not that you don't love where you're at and that you're not going to go anywhere, but if you were like, I dream of working in this place. Do you have a place that you dream about working at?

Speaker B: Yeah. For me, I love wealth management. I think it's very interesting, uh, wealth management, the insurance aspect. I feel like there's so much work that can be done to innovate those particular areas, and the company would have to have a really, really strong innovation drive. Um, so I am not at all interested in working in some legacy huge company where I'll get lost and they don't care about innovation and tech. Um, and you can kind of tell. I don't know about you guys, but if you just see the kind of computer that people have at a company, whenever you're kind of looking around, you can totally tell if they are innovative or not just from that.

Speaker A: Yeah. When I walk in a room and I'm the only one with a Mac, I'm always like, okay, yeah, yeah.

Speaker B: That's one aspect of it. I'm a PC guy, but I am very, very picky in the. In my PCs that I choose. So, like, my current one is it looks like a Mac. If you were to see it, you would think it was a Mac. Um, so it has that same, you know, glass track pad that the Macs have that I love. And um, it's very slick.

Speaker A: So, Marilyn, do you have a dream company?

Speaker C: Oh boy. Um, I don't know. I don't have a company. Um, you know, Leah, Ryan, I chase people problems. Um, and that's probably why I've ended up in some of these legacy companies with like huge, like mainframes. Um, because the problem of shifting a company like that forward is, is huge.

Speaker A: Yeah.

Speaker C: Um, and I think the potential benefit is massive. And um, you know, I, I, I like big problems. I prefer problems that are human based. Right. Like build the team. Um, you know, some of my favorite things are taking teams that haven't delivered anything in three years. Um, and then within six months getting release one and within 12 months getting five more releases out and within the next 24 getting regular release cycles out with like a vision, like visible product and uh, business outcomes moving. That's my jam.

Speaker B: Yeah, you should work for the federal government.

Speaker A: They need you.

Speaker C: People need to get stuff done. Yeah, but they all need to listen to me.

Speaker A: That's like exactly.

Speaker C: I have to be in charge and you have to listen.

Speaker A: That's the problem. I guess I don't have a dream place. I went to work for one of my dream companies and quit almost immediately. So I wasn't, it wasn't a great, wasn't a great fit. Um, but I think when I think about it now, like I love what I do. I love that I can coach part time and be a CPO part time time. That's a good for me, that's a nice fit. I think the only thing I'm gonna say this and then somebody's gonna, I'm gonna just gonna bite me in the ass because they're gonna fact check me later. I think the only thing I would go back to full time that I'm like curious about but might be terrible at is being the post founder CEO of a startup. Like founders are ready to exit or be on the board or something. They need someone to come lead. I love the idea of that challenge. So that's probably the only thing I would do full time again. I think now I've said that out loud.

Speaker C: Now I'm like, I know, I'm nervous

Speaker A: that I said it out loud. I don't usually tell anybody. So Ryan, we'll give you one more lightning round. We won't answer this one because we've answered it plenty of times. But if you were going to give advice to a young product manager, what, what's the best piece of advice you would give them?

Speaker B: Yeah, I would say just ask really good questions to the people around you. So I would say learn how to ask really powerful questions that are not just your everyday question of like, you know, why'd you do that? Or what are you thinking about this? Start to really think about ways to open people up with questions. Um, and it will be amazing what you learn from that.

Speaker A: So, yeah, it's super funny you say that because I think that's, that's ultimately when I became a coach, people were like, how did you decide to be a coach? And I'm like, I've been asking questions for 25 years. All I'm doing now is asking one person at a time. Right. Instead of asking a whole group of people. So, yeah, I think asking. That's a, that's a great point. And it leads. It kind of leans into our tendency to talk about curiosity. Like.

Speaker B: Yeah, yeah. And if you're interfacing with someone that's a curious person, um, like this conversation, I was asked some of the questions and I didn't answer it exactly the way it was meant to be answered. And it worked. You know, we had a great conversation. We learned a lot about each other and that's what it's all about. So if you're young, um, you know, just keep it open and keep it open minded and you're going to learn a lot from the people around you.

Speaker A: Absolutely. This is super fun. Ryan, thanks for coming to talk to us. This was great.

Speaker B: Absolutely. So if anybody is interested in behavioral science or your own insights. Um, so we are a B2B company, so we're not going to try and take your money. But a lot of people, they like, uh, learning about themselves. So we have built a webpage for individuals that come to podcasts and are interested about their own insights. It's dnabehavior.com start so you can take the profile as a, as a freebie. Um, and our team won't chase after you because we're really interested in enterprise deals. So, yeah, it's a fun way that everybody can kind of learn and learn about themselves, learn about their innovation drive and the world will be a better place.

Speaker A: Yeah, I love that. We'll share it on our resource page.

Speaker C: We should have done that all three of us before this podcast.

Speaker A: So we.

Speaker C: I'm gonna go do it now. And we may need a redo.

Speaker A: Cool.

Speaker B: Notes what your styles are done.

Speaker A: Exactly. That'll be great. Uh, awesome. Thanks, Ryan, for coming. We really appreciate it. And we'll see, everybody. Episode. All right.

Speaker B: Thank you for having me.