Navigating Analysis Paralysis, AI, and Decision Making with William Tincup

[00:00:00] I'm Felicia Shakiba, and this is CPO PLAYBOOK, where we solve a business challenge in every episode. Today we're addressing an issue that's keeping many business leaders up at night.

[00:00:26] The growing hesitation to make bold decisions amid rapid technological shifts and economic uncertainty. With AI advancing at breakneck speed, organizations are caught in a cycle of analysis paralysis, fearful of making the wrong move, yet equally fearful of standing still. This caution has led many leaders to pause on hiring and innovation, hoping for stability that may never come.

[00:00:56] To unpack this, we have William Tincup, a thought leader in talent acquisition and a candid voice on the state of HR. He's here to offer a unique perspective on why this hesitation could be the biggest risk of all, and to share actionable insights for moving forward with confidence. William, it's wonderful to have you here. We should have recorded the pre-show. That's my only regret. I know. My only regret in life.

[00:01:25] We should have recorded the pre-show. Thanks for having me on, Felicia. I appreciate it. Oh, William, it is fantastic for you to be here. It's just an honor. You are definitely a thought leader, and I am really excited to get into it today with you. Well, the definition of a thought leader in our industry, there's a low threshold.

[00:01:48] And I have always struggled with the thought leader because most people are thought followers. Someone else will say something like Josh Bursler or Jason Atbrook or somebody, Marcus Buckingham, will say something. I'll be like, yes, I agree with that. And then they will then cut a path, right? Right. I think the, for me, the definition of a thought leader is you have to say something that people disagree with.

[00:02:17] That initially, you know, you have to say something so bold that people initially are like, yeah, that's not right. Or I don't believe that. Or I can't let, I can't think that way. And then they come around. That's, you have to actually be leading. You know, like the idea has to be so. In the unique sense. In the unique sense. It has to be ahead of where people are currently.

[00:02:45] And, and so, but in our industry, people don't think that way. They just think, oh, you, you blogged about something and, you know, you blog a lot. So you're obviously a thought leader. No, you're, you're a blogger. And that's, that's cool. Like no hate for the people that write or, or, or whatever. But like to actually be a thought leader, you have to have something that's ahead of people. And again, initially, generally speaking, people are off put by what you say.

[00:03:15] Yeah. Well, I'm going to steal that from you and, and talk about that. As you should as a thought leader. Right. So. Oh, for us, you're a great start. It's just, it is what it is. Yeah. So you're good. Yeah. Yeah. No, I, I, I love it. And, and I'm really excited to kind of get into this topic with you today, because I think that you've also met with a lot of people and you've kind of heard on your own podcast, you know, what's going on in the world.

[00:03:45] And so you had mentioned that many leaders are really waiting for stability and predictability before moving forward on their AI choices, particularly. So how did we reach a point where so many organizations feel stuck in this analysis paralysis? Like, what do you think is driving leaders to be so cautious right now, especially given the urgency of keeping up with AI and digital transformation?

[00:04:15] They want to get it right. And there's no such thing as right. And so if you do, if you use the analogy of the internet, when the internet came available, a lot of people use the internet in the mid nineties, but they use a fraction of the internet message boards and other types of things like that. When it became commercialized, people just threw stuff at it. Like, can I do this? Can I do that? Can I do this?

[00:04:44] And they took risk. And that wasn't just practitioners. That was investors and companies, startups. And so you didn't have as much of the analysis paralysis because it was just, let's experiment. Can we sell dog food over online? And is that a good idea? And so what we've learned with this thing 20 something years later is we've been marketed to for the last five years about AI. AI.

[00:05:13] We've been using AI for much longer than that, but people, especially in HR and talent acquisition, they feel like there's a right. I want to get it right. And that's what's held up this belief of, of implementing something. And you know what? If it doesn't work out, try something else. Experiment, which is what, well, if we could learn something from marketers, that's what

[00:05:40] we would learn is AB testing and being agile, which is okay. We'll try it out. Okay. Yeah. That didn't work. Okay, good. Like, and this is, well, it might even be years ago now at this point, but Amazon rolled out a hiring kind of a persona based matching bit. And, uh, it came back as, you know, let's all hire all white males. Sorry. So clearly bias and clearly a misfire.

[00:06:08] And then they came out and said, we're going to take it down and, uh, our bad. The fail for me in that is they should have said, you know what? It has our biases. Of course it says white male. Of course it does this stuff. We're going to fix it. We're going to tweak it. We're going to recalibrate it. We're going to keep tinkering with it until we get it right. And they didn't do that. They basically said, yep, it's wrong. We'll never do it again. You know, they just ran from it.

[00:06:36] And it's like, they had the opportunity to teach everyone. Yes, of course, this is wrong. Let's get it right. Let's, let's, let's, let's calibrate, recalibrate that, that recalibration process with AI. That's never ending. You're always looking at the algorithms. You're auditing your algorithms. You're making sure that it's doing what it's supposed to do. That never ends. And so the idea of a, as a leader to then think that there's going to be this magical

[00:07:05] moment where it's right. There's no such thing. So that's why we have some of the analysis paralysis. You know, when we're dealing with people, people's careers, people's lives, you know, there's a, there's a big impact in getting it wrong. Right. Yeah. From your perspective, what's the real cost of hesitation? You know, how are companies missing out by not moving forward decisively? Well, let's just use talent as an indicator.

[00:07:33] Those that interact with talent on the, let's say the internal mobility side and on recruiting. So both sides, the candidates move faster than we do and have for years. And so a lot of it comes down to their smartphones, right? When you text me, the expectation is that I text you back. Even if it's a text to say, Hey man, I'm in the weeds. Can I talk to you tomorrow? Et cetera.

[00:08:02] You know, that bit, but you expect something back. By not using AI now, you're losing those candidates. Those are candidates where there was a moment of attention. They somehow caught it and then they hit a button, did something, and then they moved on. And you didn't capture that moment of attention. And so this is also true with internal mobility.

[00:08:29] Where people are thinking about it, there's a job open and they're automatically, there's, we, Sally is excellent at that. She, let's, let's at least ask her if she would like to do it, put her name in for it or et cetera. But we've got to be able to have that matching. We've got to be able to know that, A, Sally's competent, you know, all that type stuff, performance related, but we also need to know her interest. And if, does she want to go to London?

[00:08:57] You know, does she want to open up a shop and do this bid? Like, we've got to know all that. Where do you, where is that data? If not in AI. Yeah. Where, what are her career aspirations? Right. Right. So then Sally doesn't get a note, doesn't get any of those types of things. All of a sudden someone else approaches her and say, hey, we got a job that we think you'd be great for. She leaves the company. Now we just lost that talent. So I guess, I guess you could say loss in both ways.

[00:09:25] If you're not using AI to be faster right now, you're already behind. We'll be right back. Have you ever wondered what really makes a generation tick? Who gets to pick the name and why the slang keeps changing? Don't worry. I can help. My name's Dr. Megan Grace. On hashtag Gen Z, I share the voices and experiences of Generation Z, how they're different from other generations, what moves them and why they do what they do.

[00:09:54] In each episode, we go beyond the buzzwords and the stereotypes to dive into real conversations and the insights that matter to making intergenerational collaboration a reality. You can catch hashtag Gen Z on the Work to Find podcast network and wherever you listen to podcasts. Back to the show. And the loss of, I think, attrition, the cost, you know, financially is like three times an individual's salary, right?

[00:10:22] So that's the financial cost, you know, then there's, what is it called? The like internal knowledge, right? Yep. What they know. Yep. An opportunity cost of the time in between that person leaving and a new person starting, not just starting, but getting to a place where they're competent and adding value. That quality of hire metric. I think it's a great metric.

[00:10:46] So I think quality of hire, the way that people have conceived it might be a little bit wonky, but I like the idea of connecting what happens on the candidate side to performance. I agree. I do like that. So there's some connected tissue there. I'm not, I don't think anybody's got it right yet. I think it's easy in sales, much harder in something else. Like sales, you know, okay.

[00:11:13] How long did it take for Melissa to get from hire, from source of hire all the way to her first deal closed? Right. But like, okay, well, what do you do with positions that aren't revenue facing or like that? Yeah. Onboarding is so critical. I think, I think it's a space where a lot of organizations miss because it's like the, the onboarding, right?

[00:11:34] Like the, how productive can someone be from hire, you know, to productivity or, or maximum hire, hire productivity. And it's that much time with that many candidates, like the multiplier is so big. Well, the obitrust hanging around HR's next is treating everybody the exact same.

[00:11:59] And so with candidates, uh, understanding how they like things, meeting them where they are. Okay. So like, so you can take some of that stuff that's pre-boarding and after they sign the offer letter and shoot it to them and like, Hey, whatever you want to do. If you've got questions, we got chat bot that will answer those. If you need to triage it, we'll get a person on the phone, whatever you need. You just get this out of the way before we get into work and let's make work onboarding fun. Yeah.

[00:12:28] You get them to know people and you know, like all of that stuff, even in a virtual remote environment, like you could do all the fun stuff in onboarding. And oh, by the way, onboarding is something that lasts forever. It's not what we probably grew up with. I'm older than you, but, but basically I just made you older. So, so the thing is, is like, you're always in this mode of recruiting the person that you've already recruited.

[00:12:57] Because if you're not someone else will, especially if they're talented, they're going to have options. So you're never not onboarding. Now the, the binder or the one day seminar, the, whatever the bit is that we used to do. Yeah. That's destroyed. And COVID actually did. Thankfully, it's a silver lining of COVID.

[00:13:18] COVID actually helped move that 20 years in advance where they basically said, yeah, you don't have to go to the room and watch a video and do all that stuff. Yeah. We'll just, we'll do that because some of the policies and the compliance stuff has to be done. Okay. Fair. But how can we make onboarding enjoyable for the person? And I think that where, and this is true of a lot of HR, is a cookie cutter it.

[00:13:46] So they create one and they basically say everybody has to be onboarded in this way. And they don't. Yeah. I think there's some sort of thread, standard thread, but the uniqueness of the function, the role, the manager, the team, the, you know, the region, I think all needs to be malleable. Okay. So we could talk about onboarding in a whole nother episode. Would you like to do that? We're going to tackle that. I got offboarding too. Back to AI.

[00:14:14] So you talked about the importance of making bold decisions, even in uncertainty. So what's your advice for leaders who struggle with committing to a direction without all the data, particularly in this realm of AI? So two things you want to tackle there is what is certainty? And I think some of this, it goes back, we can go back to historical events, but let's just go back to the election of 16.

[00:14:42] I think a lot of people in that election, because it was so polarizing, they just froze. Again, it didn't matter who you voted for or any of that other stuff. It's just, it was just the event was polarizing. And I think a lot of people then looked at the midterms and like two years into it, they're like, okay, now we'll have certainty. That didn't happen. Then all of a sudden the election, the 2020 election is like, we're going to have certainty. Okay.

[00:15:11] That didn't happen. And then 2022 midterms again. All right. We're going to have certainty. That didn't happen. We've just been through another election and there's no such thing as certainty. So let's just go ahead and kill a couple of myths. There's no such thing as stability. There's no such thing as certainty. Those are myths. Those are things that we constructs that we make up to make life make sense. So there is no such thing as certainty.

[00:15:40] So if anybody's waiting for certainty, you're going to be waiting for a long time. That's just, there's never a moment where you're going to be certain that this works. Secondly, all the data. Let's go ahead and kill that myth. You'll never have all the data. It's a game of making a decision with enough data that you feel comfortable.

[00:16:07] And oh, by the way, you're comfortable is getting uncomfortable with being uncomfortable. Dealing with ambiguity. You got to, you got to get weighed in, get in deep and get comfortable with that ambiguity. Get comfortable with the, I'm not going to have all the data. I'm going to make the best decision I can with the data I have. So the way I talk to leaders about it is like, listen, there's no magical moment.

[00:16:34] You know, look at the data, think about your experience, talk to your peers and then set a North Star and go. And oh, by the way, if you need to change that, you set another North Star and you go. No, it's no big deal. Yeah, yeah. You know, but the certainty, stability, I think people, they use that as a crutch.

[00:16:58] Either they use it as a crutch and that's purposeful or they're just unsure, really unsure about how it's going to play out. They don't want to make a mistake. Yeah. And both are costing in terms of innovation and in terms of making mistakes, making mistakes is how we learn. Like, like, like, again, trying to be perfect anywhere in HR, but let's just deal with AI. There's no such thing. You're going to be perfect.

[00:17:27] Whatever you think you're, you're, you're, you're going to launch, you're going to launch it. And then two months later, you're going to be like, yeah, we need to do something a little bit different with that. Okay. Well, I think like leadership being okay with, you know, not, not making repercussions will allow people to make decisions faster, learn faster. But those come at a cost of making some mistakes. Right. Right.

[00:17:53] But they're getting to the decision faster by making those mistakes and instead of waiting and hesitating. So a great example of that is when Kennedy, President Kennedy said that we're going to put a man on the moon by the end of the decade, they didn't have NASA wasn't a thing. They didn't have NASA. So they had some people that were tinkering around with in the Air Force, but they didn't have like all of that stuff.

[00:18:18] So one of the things you learned about NASA and to fulfill on that goal that was set is they had to fail fast. And so what they did is they celebrated failure. So they had champagne, they had cake in the refrigerator, already cooled. So when an engine blew up, it wasn't, that, that was like, all right, cool. Let's meet the conference area. Everybody have cake, have a little bit of champagne because now we know that that didn't work. Okay. Yeah.

[00:18:47] So that's one less thing that we don't have to try. Let's try something different. One more piece of knowledge that we can use. Right. And so if leaders looked at marketing, generally speaking, does have some latitude to fail. You know, there is like, okay, we tried this, you know, especially in B2B marketing, we tried this email campaign. It was horrible. Didn't work out. We tried another campaign. It was awesome.

[00:19:13] So they do get rewarded in some sense of trying, experiment, experimentation and failing, quite frankly. Like, it's okay. Like, I mean, I deal with a lot, I work with a lot of marketers and I'm a recovering marketer. And I just basically say, listen, the more you fail, the better. I just keep throwing it, this, you know, the spaghetti against the wall, whatever sticks, try it, put it in your A, put something on the B and then just keep rolling.

[00:19:42] Just keep rolling because the, your audience is changing. You know, you're, you're developing your tone, your voice, all this stuff, new products, like all that stuff's changing and it's okay to fail. And I think leaders in particular need to, to tell people it's okay. And maybe even make the mistakes themselves, you know, like, like that would be. Lead by example. Right. Like, oh, let me tell you about how I failed at this bit.

[00:20:11] And then let people, it allows people to then say, oh, well, if, if she's talking about it, then it's, I've got, I've got permission to talk about how I feel. And then it's not a, it's not a career ender. It's someone being confident enough to say, yeah, we are some of our, of our failures and successes too. But, but the successes aren't really successes. They're, they're failures that actually worked out.

[00:20:43] So, so again, I would, if I were talking directly to anyone in the C-suite or the board, quite frankly, I'd say you need to normalize failure. Yeah. Not feeling fast. Right. Not feeling slow. No. Or feeling slow is, you know, taking a year to decide. Right. Worse. Avoiding to make a decision, which is what you and I talked pre-show. It's like, I'm just not going to make a decision and hope it goes away. Mm-hmm.

[00:21:13] Yeah, this is, AI is not going away. So let's talk a little more about talent acquisition. You know, how has this pause affected the way the companies approach hiring? And what might they be missing or holding back? Well, you got great technologies now and matching, skills-based matching or otherwise. You got great tools, AI tools and findings of sourcing. So you can find the talent. You can match the talent in a pretty good way.

[00:21:42] There's still a moment of humanization. So the way I talk to TA leaders is your sourcers and some of your recruitment marketers, some of the, some of the position players that you had. It's not like you don't need them. You need them. You need them to be doing different things because the audience, I candidates need something different from them. And in this case, they don't need you at the beginning. They need you at the moment of when they're really serious and you're serious.

[00:22:12] And you can have a call with them and tell them a little bit more about the team, the project, kind of sell the company and the bit and, and then get to know them. And, and again, that moment of humanization, there's only so much the technology is going to give you. Getting on the phone, getting on a Zoom, getting, getting to know somebody without the, without the pressure of trying to, I don't know, be perfect. Like just, that's where I think recruiters flourish.

[00:22:41] So if you can take away a lot of the low hanging, low value stuff that they do and get them into a position where they're talking to candidates and they're just like, all right, tell me a little bit about what you're trying to achieve. What are your values? You know, tell me a little bit. And then ask me a hundred questions. What is a, what is a hiring manager like? What's the team like? What projects am I looking at? Like all that stuff to where it is. It's already been a good match technically. Right.

[00:23:10] But the human then can add on top of that, can add the spice of, okay, this is not just going to technical match. It's a good, a really, really good match for both parties. You mentioned that hiring for adaptability, I would say, is really more important. And so how can organizations shift their approach to prioritizing, you know, agility over traditional experience? Yeah.

[00:23:37] Some of that's personality and some of that is you could do it with simulations. Oh, I like that. So you can do it in scenarios and simulations and put people through those types of things where they don't have all the data and they have to make a decision. And so you can kind of test for it, if you will, but you need adaptability. You need ambiguity, need people that can be adaptable. And it's not just for the sake of those things.

[00:24:04] It's for the sake of work going forward is moving faster. And so you need people that can consume change faster. Like everyone, all human beings hate change. There's anyone out there that's like, change everything every day. I love change. I do. Every day? Every day? Like, is that ficus tree? Is that going to be on the other side next tomorrow?

[00:24:31] I mean, maybe not every single day, but I do thrive in changing environments. But I think I'm a unique animal. You know, I'm weird like that. I think that's why consulting works for me and not everybody likes that. That was a horrible one. I would agree with you. I would agree with you that that is actually the biggest challenge that I had being internal. Right. You know, and. Because you wanted to move faster. Yes. I wanted to move faster. I wanted to go to the next thing. And it was a struggle.

[00:25:01] It's definitely a struggle. I mean, it's still a struggle for most people. It's terrifying. Change is terrifying. Yeah. It's easier for consultants because you're giving advice. They don't have to take your advice. And in a lot of cases, they're paying for your advice to not take your advice. Yeah. So. Exactly. That's why I was a horrible consultant is because I would give them advice thinking that they're going to take the advice.

[00:25:30] Like you just had me go research this thing. I researched it. I put my life into it. Here's what you should do. And they're like, thank you so much. We're going to get you paid. And yeah, we're not going to do any of that. Thank you. Thank you for the. It just sent me. Why is he sucking on his thumb? I was horrible at it because I wanted him to take the advice. So yours is, yours is more, you know, your pace is quicker than the pace of corporate in a lot of corporate environments.

[00:26:00] Your pace would probably be better suited if you would ever do it to startups. Why adaptability, agility, dealing with ambiguity, all of those types of things is because the world is moving faster than it once did. So settling in and going, okay, you're going to be an account manager and this is what you're going to do. And every day is going to look the same for, you know, six months or nine months, whatever. Yeah, that's over.

[00:26:29] So you have to have somebody that now tell them that and say, okay, well, you're going to be doing this until the things, things change and you need to be retrained and deployed in a different way. Okay. So it's, it's making sure the candidate understands and making sure that the hiring manager understands team understands that, that the ground beneath you is shifting all the time. That's just basic geology. So it's always changing. This is not bad.

[00:26:58] This is, it's happening faster. And so you have to have a team of people that can move with it as, as opposed to entrench themselves in the way they used to do it. And so you need less people that are kind of, this is the way we've always done it. This is the way that we do it. You need more people. Like if we, if we weren't doing it this way, how would we do it? Right. Recalibrate, calibrate, recalibrate. Sorry, I interrupted you. No, no, no, no.

[00:27:27] I'm, I'm just, I have so many questions for you. That's all. And I'm a horrible guest, by the way. No, you're not. No, I'm a horrible guest because I'm used to be a host or co-host. So I'm used to asking questions. Right. So then now I'm on the other end of the firing line. I'm like, I don't know if I like this. Anyhow, go ahead. You better, you better like it because I'm going to ask you to come back though or get used to it. You have to change, right? You have to adapt. Yes.

[00:27:56] Speaking of. Yes. Speaking of. Speaking of. Go ahead. Using AI without getting overwhelmed. I think it's just starting. I think that's the biggest challenge is just, just start in your own life. Like, like the thing is, so.

[00:28:25] I do this bit. So first of all, Google maps, it's all AI, right? Like you use Google maps, you get in your car, you use a navigator and it tells you where the traffic is and all that stuff. That's AI. So you're, you're a consumer of AI already Alexa and Amazon, you know, the request, you know, that people bought this product also bought us all AI. So it's, you've been dealing with forms of AI forever. So like, you don't have to be terrified of it, but you need to start.

[00:28:56] And so like the university of Texas has a master's degree program for AI. It's $10,000 a year and a half. So if you like, if you want to like throw it down and go, you know what? Then you actually need to learn this stuff. You don't have to be a programmer to learn AI. So start somewhere. If not education like that, you can do things on, on YouTube, Khan Academy, just learning

[00:29:22] how algorithms work would be useful. I think how an easy access point for executives and leaders is something like ChatGPT, Gemini, those types of kind of consumer products where you learn how to ask questions. You learn how to be a prompt engineer without calling it prompt engineering. And I think that's, that's where you just start.

[00:29:51] Like the first thing I asked when ChatGPT was like the day it launched. The first thing I asked it, I was like, write William Tincup's obituary in 500 words or less. Oh, stop it. Swear to God. First thing I did. And, and it's just, it just goes through it and it's just getting all this data from public. Right. And 98% spot on. And he was, it was written like an obituary is with great sadness. No. I swear.

[00:30:20] Why couldn't you just say bio or not obituary? No. Cause I think one of the best things you can do now, this will just as a side tangent. If you, when you're really serious about a candidate, really serious. Have them write their obituary. See what they want to accomplish in their life. I got you. It's, it's a wonderful hack. It's a little dark. Totally get it. But it's a wonderful hack because you'd see them.

[00:30:47] You get to see what they've accomplished, what they feel they've accomplished. And then what they would like to accomplish and be known for legacy, family, whatever. Like you get to see into their mind. It's beautiful. You know, so it writes the obituary. I turn around and say, okay, now write that obituary in Richard Pryor's voice. And at the time it wouldn't do it because it came back and said, death's not funny. And so we can't make your obituary.

[00:31:17] You can't make the obituary funny. It's since changed by the way. So, so you can actually go and write your obituary and write it in whatever your favorite comedian is. You can write it in their voice. So tell me more about what strategies that you've seen work well in encouraging teams to really take risk and make decisions quickly. Like even when there's no clear answer.

[00:31:39] My favorite bit that I've seen is kind of a leaderboard where people discovers they're encouraged to discover new AI tools. So a new AI newsletter, a new tool. And they're encouraged, like in a team meeting, like, okay, by the time we get to Tuesday, next Tuesday, everybody come with their favorite tool. What's your favorite tool? And it's show and tell.

[00:32:05] So that you can move everyone's literacy, AI literacy up. So listening to AI podcasts, consuming AI content, you know, in all kinds of forms that you can get to it. But where I've seen it actually with teams work best is you enable everyone to say, yeah, go out and find some cool stuff. And then come back and then teach us that cool stuff so that now we know it.

[00:32:34] And now we'll just kind of keep building on that. But in practicality, how often are we doing that, right? Are we doing it once a month, once a year, once a week? Once a week. How many people are doing it? Once a week. Once a week. Once a week. 30-minute meeting within your immediate function. Show and tell. I like that. Show and tell. AI show and tell. I like that. And listen, there's no wrong. I found a thing that puts memes inside of historical paintings. All right.

[00:33:02] Like there's no wrong because at one point you might need to access that. And so you can remember back and a lot of people are recording these so that they now have a library, a content library of AI tools. Yeah. So if they can't remember that one, they can go back into their CMS or LMS or wherever they put that content, they can go and find it. And the tools are happening.

[00:33:27] That's why you don't want to do it once a month or once every six months or whatever, once a quarter, is there's probably a thousand AI apps or tools being launched a day. Yeah. I'm going to say like at least. So you go to product hunt and you can't keep up with it. No one can. So I think as a team, you know, the leader could probably say, okay, we're looking at performance. Let's just think about performance and the intersections of performance and AI.

[00:33:56] Go find anything that's relevant to that. But I think also, you know, coming from like my point of view, like how do we actually implement these things? It really should be based on the business need, right? I mean, that's great to have these ideas flourishing. Right. They're creative. They spark other ideas, you know, constantly coming in. But, you know, there's only so much we can go after as a business. Like we have to prioritize, right? Or we can't wrap our head around anything.

[00:34:21] And so having like an AI show and tell of solving a specific business problem, I think would be really cool. And making it fun. I mean, you could also do this with rewards and recognition, right? Like do it as a competition where people are trying to kind of outdo each other. Yeah. Which is fun too for certain cultures, business cultures. It would absolutely, that's probably how you would game it is you're basically like, listen, we're going to put, you know, 500 bucks on this bit. And it's, we'll all vote and you can't vote for yourself.

[00:34:52] So like, like we'll just do a bid and everyone do their presentation. Okay. Do we get five minutes of Q&A? All right. Everybody does their stuff. We vote on it and everybody wins because everyone's literacy is going up. That's what we are missing in general is we've, we're allowing everyone to just kind of go out and figure out AI on their own. And as a business, this needs to be one of the crucial critical initiatives is teaching AI to everyone.

[00:35:22] What you know and what you don't know. Like it's okay. It's like, Hey, we haven't found a tool that does this yet. And that's okay. Cause it allows people to then go, well, I'll see if I can find one. So even the stuff that's not necessarily tied to a business initiative, it could be tied to the next business initiative that's around the corner. So you still got to raise the literacy, but I think an easy, an easy application is the way that you already formulated your mind. It's like, listen, we're going to do it once a week.

[00:35:52] You're going to be tied to some business objective initiative. And if your culture warrants it or it makes sense, then I would, I would do some type of recognition, rewards and recognition around it. Yeah. And then once it's selected, you need to put a team together to actually implement. Boom. So career builder, so career builder years ago used to do this bit where it was a million dollars and you have to come up with a bit. You don't have to.

[00:36:19] First of all, if you came up with a business idea, then you submitted it to a business case competition. And this was from all career builder employees. And then you win a million dollars and then they spin that company out and they put people behind it. Sometimes a person, but sometimes other people behind it. They spin it out and they roll it into their services. That's why they had so many products.

[00:36:46] Because they had people coming up with crazy products that no one would have thought of. Innovative, creative, diverse. So how do you, I think the larger question is how do you incentivize innovation? And what we focused on is around AI is you've got to, you've got to talk about it. You've got to teach. You've got to learn. You've got to be receptive.

[00:37:08] I even think, I mean, to just push it like an extra mile further is that solve a business problem with or without AI. Which one is better? Right. That's your A-B test. Right. Right? Yeah. Because sometimes AI, especially at the stage that it's at, it's like I deal with a lot of AI chatbots that are in support, right? Help.

[00:37:37] And if they get to a certain point, like I've had them that I didn't have to talk to anybody. Like it solved my problem. And I've had some of them where I'm like, no, I need to actually talk to a human being because they're not getting the bit. Yeah. So like that's, that's true of all AI problems. Sometimes it's, it's not there yet, but it's going to be there. So it's just now you're dealing with the, when do you shift?

[00:38:05] When do you, when do you shift to AI for those specific processes and things like that within, within a business? And you're always evaluating. You're always looking at, at that to see where is this going to automate something that needs to be automated, augment something that needs to be augmented, or just completely tear it down and build it over again. Yeah, exactly. Right. William, we are out of time. What?

[00:38:32] And I feel like this, I know, I feel like our episode could be like three more episodes. Joe Rogan style? All right. I'm going to invite you back now, live on the show because I feel like this conversation is not over. We have more to do. So thank you for being here. Thank you for your presence, your knowledge that you've brought to the show. Well, thank you for having me. I can't wait to have you on again.

[00:39:00] So I've already committed you to this. I understand. Yeah. And, and, oh, by the way, we are very happy to be joining the network. That's right. Defined Network. So CPO Playbook is now on the Work Defined Network with William Tincup leading the charge. We're very happy to be part of this family with you. We're excited. We're happy to have you. Yeah. And it's, it's going to be a blast. It is going to be a blast. So thanks again.

[00:39:31] Well, thank you for joining. Thank you for having me as a guest and also thank you for joining Work Defined. We appreciate it. Our pleasure. If today's episode captured your interest, please consider sharing it with a friend and leaving a review. To learn more about how CPO Playbook can support you or a leader you know with executive coaching or organizational transformation, visit us at cpoplaybook.com.

[00:39:58] Your support as a subscriber means the world to us. So thank you for tuning in. I'm Felicia Shakiba. Let's connect on LinkedIn. See you next Wednesday.