111: Using AI in the Workplace for Smarter Hiring and Pay Decisions with Cary Sparrow

Speaker A: Half of the hours in a day are spent working with AI.

Speaker B: Now, uh, how much of what you're seeing now at that level is indicating the type of AI literacy that those roles are looking for? Are you seeing that in the job descriptions?

Speaker A: Yeah, there's no question it comes across in different ways. Yeah, but there's absolutely a movement and it's amazing how fast it's happened.

Speaker B: People that are doing the hiring, in your experience and in your gut, do they know what they're looking for?

Speaker A: Well, I think they're looking for a degree of AI literacy in a number of cases where the organization has pushed into using AI, uh, even if the organization doesn't know all the benefits they're going to get.

Speaker B: If you're good at your role and you know AI, you're probably pretty safe. Would you say that was accurate?

Speaker A: Well, even there, maybe, maybe not. It depends on what kind of organization you're working in. But you're definitely being more productive. You're definitely able to be, you know, more, um, directed in terms of where you're putting your energy and your cycle. Times are going way down. Yes, those I would say are the hallmarks of somebody who's integrated AI into the way that they work.

Speaker B: Carrie Sparrow is the founder and CEO of of Wagescape, a leader in real time pay intelligence and labor market data. A former US Navy submarine officer and global business executive, he helps companies understand the fast changing talent market and compete for the people they need most. Welcome to Using AI at Work. I'm, um, your host, Chris Daigle. Each week we'll be learning how today's business owners, entrepreneurs and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Right now, every business leader is asking the same question. What are we going to do about AI? If this is you, chiefaiofficer.com has the answer. We give you a simple path forward where we provide executive and team training so your people know exactly how to safely use generative AI in their day to day. We also manage the deployment and implementation to make sure tools actually get adopted and deliver results. And we'll also guide company wide transformation so AI becomes part of your operating system, not just another shiny object. The companies that act now will increase productivity, cut costs and grow faster than their competitors. Those that wait will get left behind. So if you want to make AI work in your business, visit chiefaiofficer.com and see how we're helping companies of all sizes finally get results from AI. Okay everybody, welcome to the latest episode of Using AI at Work. My name is Chris Daigle. I'm the host of the show. And today we're joined by Carrie Sparrow. Kerry's the founder and CEO of Wagescape, which is a labor market data company. Uh, think about labor market intelligence, pay transparency, hiring trends, um, things like that, competition for talent. And Kerry is the perfect person to be talking to us about this because he's bringing 35 years of leadership experience across engineering, military, consulting, operations, HR, IT technology, and the list goes on. And we just had a fantastic conversation about a, um, mutual vacation spot that we're both going to be taking care of. So why does this matter to you? Business leaders today are under pressure to make better hiring, compensation and workforce planning decisions with a lot less guesswork. Right. And Kerry's going to be able to help, um, us with that because he's going to address a major executive pain point of how to make pay and hiring and talent strategy decisions in this fast changing labor market with all of the AI angles that are happening and advancements that are occurring and demand for talent that's AI enabled, you having access to better intelligence when it comes to making those decisions is only going to support your company becoming AI emergent much faster. So, Carrie, uh, you want to take a second and just, uh, address maybe any, uh, elements that I might have, uh, missed when it comes to, um, who you are and what you've been up to.

Speaker A: Sure. And Chris, thanks for having me on the show. I'm glad to be here today. Uh, we've had some great conversations. I'm looking forward to continuing those great. Uh, you know, as you said, I've had a career that's been kind of unusual. Uh, uh, I got trained in computer engineering way back when computer personal computers were brand new. And then, uh, when in the military, I was a submarine officer, nuclear submarines. And, uh, did that for. I was in the Navy for like 12 years. And, uh, got out and did consulting and helped companies figure out, you know, really interesting complicated problems and, you know, implement solutions and sat on the corporate side in IT and HR. And then about 11 years ago, I founded Waitscape. And Waitscape was designed to help bring more transparency to the labor market because companies and individuals spend a huge amount of effort working with really, really quite crappy information about what's going on with jobs and pay and hiring and skills and demand for all the stuff you said. Right. Um, and I thought that there had to be a better way. I had an idea on what the better way could be. Founded the company along the way picked up a couple patents in AI. It's an area that I did academic research in back in the 90s and have continued that, uh, we use AI all the time in my business. In fact, I was telling my wife, uh, I took a step back and evaluated every once in a while I look at very deliberately how am I spending my time time as the CEO of the company. It's not a huge company. Um, and is that appropriate? And that's usually a pretty good barometer of, you know, are we running things the right way? And, and the last time I did that, like literally half of the hours in a day are spent working with AI now. Half of them, roughly half of them are spent facing either clients or, or prospective clients. Um, and a, uh, portion is spent with my team and all the product development and all the sales enablement and all that is all being done with AI now. And it's about half my time. So it's really changed. It's changed a lot over the last even six months. Um, and I think it's changed it for a lot of folks. A lot of folks listening to this are looking at, you know, how do we get the most out of AI? Some folks who aren't really using AI are like, well, what's going on with that? And uh, you know, is it real or is not? Um, you know, and is it going to affect me? I'm scared by it, or I'm not scared at all by it. Neither of those are really good answers, by the way. Um, and uh, in my opinion, uh, so yeah, that's, that's kind of my world now. The business itself, all the stuff that you said is exactly right. I mean we, we have, you know, a data platform that tracks all around the world, every country in the world, every single day. Who's hiring what, what skills are they looking for, how much they're going to pay them, um, and how easy or tough is it to get the talent they need? And that data is extremely valuable to lots of folks who want to know things that range all the way from economic analysis down to career decisions, like what skills should I invest in? Or is there a company that will give me a promotion faster than my company? Or is there a location where they pay better for the kinds of jobs I want to have? So all the way from the really esoteric down to the very, very tactical of our business. Most of our clients are businesses that are trying to set pay policy or hire more effectively, um, or are trying to use the labor market, uh, data to evaluate, uh, Investment opportunities and uh, who's doing a good job and who's positioned well for the future. We're seeing AI drive demand for our data just through the roof. Um, AI models need high quality data and they need it continuously. And that's what we provide when it

Speaker B: comes to be in. Yeah, for sure. So you mentioned that, um, a better way. What, what does the, the not better way of doing all this look like for a typical company?

Speaker A: Well, the not better way involves really limited data that people make really big, you know, exaggerated assumptions about. So government data is designed primarily to be consumed by economists. Um, it's designed for long term trend analysis. It's not designed for practical, you know, business process applications. So it, it does what it's set up to do quite well for. You know, I mean some of the best labor economists in the world work for the Bureau of Labor Statistics. And uh, but it's uh, that data tends to get overstretched. You know, it's, it's updated relatively infrequently. It's not localized. In other words, you know, it's, it's not the same to say that unemployment is X at the national level or the state level. It doesn't really help you if you're in Des Moines, Iowa, you know, or, or Cedar Rapids or Zagwa or, you know, I mean you got to know what's going on in your area because, because hiring and pay is, is entirely local. And so that's it. Uh, like the government. And then when you want to talk about pay, then the people that dominate the pay are the people that come up with salary surveys and those things that have uh, their own use, but they have serious limitations, serious limitations that they're out of date. It's the same thing. They're out of date. They're not localized, they're not precise enough. Also if you lay three salary surveys for exactly the same role and exactly the same geographic coverage next to each other, you're going to get three different, totally different answers. So we took, you know, that that's, those are the limitations with the data that's out there. It's fundamentally not, not designed for all the different things that require insights to what, what's going on in, in the labor market that individuals and especially businesses deal with every day. And so they get, they get stretched way beyond the bounds of reasonableness. And, and we looked at that and said now I'm going to flip it around and say, well, what's a better way? Well, there's a ton of data that's getting upd single day that's available about who's hiring and what do they expect to pay, what skills are, uh, and, and they're called job ads. Now some people have a negative opinion about the quality of job ads, but having dealt with this now for 11 years, I will tell you that it's the most richly documented source of intelligence about what's going on in any given business. It's completely transparent. You don't have to hide you know who's hiring what because the companies themselves are putting it right out there in the public. Um, the pay data is proving to be extremely reliable leading indicators because there's so many of. And they're so localized. You can get really big sample sizes in really small areas and really tightly defined roles. So it's enormously powerful data asset. And then when you bring it in with those other sources, which we don't just limit ourselves to job ads, but then we bring in the government sources and the salary survey sources and we help folks triangulate on a bigger picture of the truth. Um, that's entirely real time, entirely localized, totally transparent, very, very granular. Brings together, you know, hiring demand, job demand, pay skills, competitive analysis, financial outlook, overall economic outlook. All those things are available all starting from one data asset. And that's what we do.

Speaker B: So seems pretty straightforward. However, compiling all of that information at a, like, that's where the real magic is. I guess that's where the AI comes in to be able to assist because

Speaker A: like, yeah, all those data points you're talking about, right? We, so we, we collect data from uh, about 800,000 websites every single day.

Speaker B: Okay.

Speaker A: Every one of those has their own technical architecture. Okay. So we know how to overcome that. Um, and every one of them has their own way of describing jobs. A, ah, marketing manager in, in a franchise, quick. Uh, service restaurant, fast food restaurant is completely different than a marketing manager for, you know, a big grocery chain, for example, um, or a marketing manager for uh, you know, an IT start. Those are totally different jobs even though they have the same label. And so being able to discern that and then package the information about those jobs in a way that lets people analyze. Recognizing those differences is part of what we do. And that's, you're right, that's kind of a special capability. Um, and then there's other things. I mean you can just pull the thread and say, okay, well you know, how do you know what industry the companies are in or how big the companies are and how do you extract the skills that people need and how do you go beyond what's written in a job description? How do you figure out pay? We do it extremely well. We're able to figure out what companies expect to pay, uh, for their open positions about 95% of the time, uh, more than anyone else, by far, even with wage transparency. But that's a very specialized skill. So we developed all those things. And that's, uh, how AI helps in a few of those. We didn't need AI to develop a few of those also. But we started this back in. I got my first patent in 2017, and it helped. It was, you know, helping with things like this that we're talking about. And, um, you know, so AI helps, but it's for us, it's integral to the business process. I mean, you can think of us as like a factory for data. Right? We got raw data coming in from hundreds of thousands of places and lots of different forms coming in the back end of the factory. And then we do all this magic. And then data that's packaged in ways that lots of different types of folks can use it come out the front end. And, um, you know, AI helps with, with the machinery in the middle. Um, that's one way we use AI. The other. The other way, like I said, is we use it every single day. Every one of my team uses AI. I didn't prescribe. You know, I think it's kind of interesting. Some companies are, are saying, well, we're going to measure how much you're using AI because we want you to adopt it. Uh, I didn't have to do that. I just tried it out. You know, I tried one of the, one of the models, uh, we use, uh, within our company, we use three models and we're open to using more. Um, there's one that we lean into pretty heavily. And when I tried it, I said, this is great. Um, and I did some things that made my life a lot easier. And then I bought licenses for my entire company, uh, and said, you guys figure out how to use this best. And your team just got bigger. Your team's just got a lot bigger. Interesting by doing this. So do you want to share what

Speaker B: that, what the preferred model is for Wagescape?

Speaker A: No, because it varies. I mean, uh, some of my team members, uh, like I was literally, you know, right before this call, uh, and, and I should, we should anchor the discussion. So now we're talking in June of, of 2026. Right. And so, yeah, I was just on the phone with someone who prefers Gemini and, but uses Quad and sometimes uses ChatGPT. I personally prefer Quad, but I use ChatGPT, uh, to validate a lot of what we do is we red team the AIs, right. Yeah. So if I have Claude come up with a plan or, uh, like we're getting ready to go bring a, uh, really interesting product to market, and to pressure test the go to market plan, I flipped it over to ChatGPT and said, okay, critique this whole thing and, uh, you know, using a totally different Persona, uh, and came back with just a ton of things that were total blind spots for us. And so I flipped that back over to Claude and, uh, you know, Claude incorporated it, and then sometimes it'll work the other way too. So lots of different possibilities that we're all figuring out. To be honest with you, every time I get together with other CEOs, you know, because founders are kind of an interesting group, uh, we, you know, it's pretty lonely group, so we like to hang out with other founders because other people, um, can discuss. You can relate to this. Right? You know, and, uh, uh, so every time we get together now, you know, half the discussion is about how we're using AI, and it's not, ah, you know, airy fairy, general stuff. It's very specific things like what I just said, like red team, which models?

Speaker B: Yes.

Speaker A: You know, and, uh, but I think that, you know, the. The different models are, you know, they have strengths and weaknesses on the margins, but for the most part, you know, the bigger thing is just to lean into the opportunity, regardless of the model that you're. You're using. Yeah.

Speaker B: Uh, so I think that that's great, uh, for the listeners, if you don't have paid account, not free accounts, paid accounts for a couple of different models. Exactly. What we're talking about here is how you should be thinking about using AI. Okay, great. I got my answer. Well, wait a minute, let me see what a different model using a completely different Persona, how it would evaluate the output I just got from this other model. Now, look, if you're not doing that, doesn't mean you're doing it wrong. But I can certainly say that that is an easy way to really level up how you think about using AI, but also the quality of the results that you're getting from AI. Fantastic suggestion, Carrie. Um, one of the things, like, what is a decision that a company could make differently if it had access to your information, if it had better wage or better talent data?

Speaker A: Oh, well, our clients will speak volumes to that. Uh, we have a number of clients that set pay policy, uh, using our data, and they're able to get down to the zip code level to figure out who the competitors for their talent are, um, and how they need to create targeted strategies to recruit and retain, uh, that talent. When you've got, you know, especially like in an hourly workforce where, you know, people have to put food on the table and they will literally walk to walk out the door for an extra 50 cents an hour, you've got to know who the competitors are. Interesting that you're, uh, you're competing against. And by the way, the competition is what people read. Right? And so that's why you've got to be paying attention to what's, what's being advertised, because that's what your own employees and that's what your prospective employees are paying attention to. And so lots of folks, especially with big hourly workforces, are paying really close attention to that. But in order to do it, you know, people are resorting to building their own kind of scrapers and that's just not efficient at all. And not only that, it's fraught, it's frankly fraught with lots of quality issues, uh, around a lot of things we talked about, like how do you combine information from different, you know, different companies and make sure that you're seeing things through, uh, an appropriate lens. And that's all stuff we take care of. And so, um, you know, but that's, that's one of the ways is to get really local about your, your pay practices and understand where you're starting to get out of bounds. And the other is they catch trends way faster. Like when the pandemic happened, it was really easy to see which markets were going way out of bounds. And, and we're starting to, to throw money at the problem of finding the workers that they could no longer find. And, and wages went up dramatically for a couple years and then they stopped, they started to plateau. And then over the last year they've moved into this kind of hunkered down approach. Now these are things that the economic data and the survey data will not tell you until it's well after the fact. And it will not, uh, they will not tell you in a way that's actionable. And they will also not tell you whether the market that you're in or the jobs that you care about are um, in the same category as the headlines. Right. I don't, I mean, I think that, yes, now halfway through the year, we know that 20, 26, a lot of companies are still advertising that they're hiring, but they're not Doing a whole lot of hiring and a lot of, a lot of employees that were, you know, actively looking for jobs before are not really actively looking for jobs anymore because there's more uncertainty about what, what the future will hold. And so they're less willing to take a risk to move down the street. Now that's stuff that we know now, but frankly it started happening nine months ago and with the right data you could, could catch that, but with the wrong kind of traditional data. You hear about it in, you know, in the press and, and it's really scrambled to react. Right? Yeah, right. Well, and also, how do you react to the, you know, a headline like that? Well, does that, you know, does that matter to me in Flagstaff, Arizona? Yeah. You know, or up here where I am, I'm sitting here talking to you from Minneapolis. And I can tell you the dynamics in Minneapolis are totally different than the dynamics in Flagstaff, Arizona.

Speaker B: Uh, so, so you, you've mentioned this concept of pay transparency. Just for the listener. Explain what that, how you define pay transparency.

Speaker A: Well, so there's several different ways to think about it, but the one that's talked about the most is the idea that governments are now mandating that companies expose the pay range of a job when they advertise it. And that's in the U.S. what we call pay transparency. And that's also very relevant. Europe, uh, is implementing laws around pay transparency. The most significant are in the uk Right right now, um, that are demanding that every position, you know, be, you know, the pay be exposed. Exposed for. That's totally different than the past. Companies guarded that information very, very tightly. Lots of companies are still very reluctant to do that. The compliance with pay transparency laws, I think there's like over a dozen right now states that, that require that and, and the compliance is really low, like 30%. Um, and so, um, but that's what we're talking about with pay transparency. But even when companies are transparent about pay, uh, they'll give you a range and the average of that range is about plus or minus 15 to 20% depending on which kinds of jobs and what market you're in. Well, plus or minus 15 to 20%, that's a 30 to 40% range in pay. And you can hide a lot of sins in 30 to 40%. Right. And people thought, well, this is going to be great for equity. But no, it's not. I mean, you still have huge inequities. This will be great for helping, you know, folks, uh, you know, find better, higher, higher paying jobs. Well, not really. Because you know, what are you going to, which part of the range are you going to pick? And lots of companies are still, they're, they're beginning to coalesce around a standard way of choosing the range that they publish. At least here in the States. Yeah, so that's, so you've talked, you've

Speaker B: talked about this idea, um, on, on the like the fast food side or the, the hour where the hourly rate matters. What about when in the professional services environment? Um, is the compensation the number one thing that, that as an executive I should be focused on? Is it or is there more than that at the, at that kind of like professional um, job hunt once you

Speaker A: start to get to mid level salary and above or really high demand, high skilled entry level like professional services, management consultants or you know, otherwise, um, AI you're looking at, you're. Yeah, well AI is its own category kind of now, so we'll. Okay, but, but really you're looking at the whole package obviously. Um, so even pay transparency laws only require you to publish base ranges. But if you're not emphasizing what the benefits uh, and the work work environment is, if you're not building in other kinds of, of remuneration uh for really high demand, uh, uh, roles and talents, then you're going to have a really tough time competing. Uh, a lot of you know, AI, it's, I would say it's pre pronounced uh with AI. But AI talent can mean a lot of different things. I mean to be honest with you, my whole team is AI talent. We're all, we're all self taught except me. Um, and even most of what I, I do with AI was self taught. Um, there's, you can't, you know, you can't turn on a video without getting an ad for some sort of AI certification. These days if you're over 50, you know, and you know you're not learning AI then blah blah blah, call us to get you know, this certification. Well, you know, it's, you know, it's commoditized at that point. So, so you know, the kinds of folks where the demand is, is like stratospheric are the ones that can build the you know, next generation models. And that's a very, very rarefied small group of folks.

Speaker B: Yeah. And they pay those guys tens of millions of bucks.

Speaker A: Right. If you're, I mean this is, is an ongoing evolution of any kind of project, product, product process and technology manager is building AI into, into the way that that work happens. And so it's not, it's not so much a differentiator up, uh, from a pay standpoint, unless you're in that really, you know, really specialized group, um, it's much more of a, uh, what's becoming a boilerplate requirement for a lot of jobs that involve process management.

Speaker B: So let's, let's talk about this audience in particular. I would imagine that, um, the bulk of the listeners that we have for our show are career professional. They're not necessarily at the hourly level, although some may be, um, what does. And they're investing their time to listen to a podcast. They're probably using it after hours, or maybe they're already using it in their role with, with the, the job requirements, uh, that are being put out there. How much of what you're seeing now at that level is indicating, like, the type of AI literacy that those roles are looking for? Are you seeing that in the job descriptions?

Speaker A: Yeah, there's no question.

Speaker B: Okay.

Speaker A: There's no question now. Um, it comes across in different ways. Yeah, but there's enough, absolutely a movement, um, and it's amazing how fast it's happened. Right, but tell me more. Well, I mean, who was talking about, you know, large language models or AI in general is affecting kind of, um, you know, most ordinary people's lives, let alone jobs, uh, even 12 months ago.

Speaker B: Yeah, yeah.

Speaker A: Um, it was this like, kind of curiosity from. For most people. I mean, we started adopting it really early, uh, just because that's how we are. But for most people, it was out there now. Now it's, it's kind of interesting because there's this like, generational issue, issue with AI. So for the, the audience that you just talked about, so mid career salaried, looking at what do I need to be doing? There's lots of ways you can build AI into your everyday work. Um, and some companies have really high guardrails around the use of AI, others have no guardrails around the use of AI. So you got to match, you know, but you can even, you can build it into your own life. Like if you're going on a vacation, uh, you can plan amazing trips way easier now, uh, by using AI agents than you could just on your own. Unless you're just a really seasoned travel planner. So, um, there's ways that you can absolutely build it in. But what's interesting is if you go and talk to your kids who are probably just entering the workforce, if you're like our age, um, you know, you're in my age, uh, they've got a totally different view of AI. Uh, they, you Know, it's, it's, it's very tainted view in a lot of cases associated with data centers, which environmentally have very tainted views.

Speaker B: Yeah.

Speaker A: And, um, you know, so like, my daughter and I. My daughter's 25. She and I have learned that we don't really get into deep discussions about AI at the, you know, when we're out, out together. And, uh, but we know kind of where each of our cohorts stands. Yeah, I have a really, you know, so that's, that's kind of an interesting view. I think that there's a lot of things that are, that are, you know, getting sorted out about the societal implications of, of A.I. um, some of which are pretty big open issues, others of which are like, well, what's the, you know, what's the societal implication of the Internet? If you were looking ahead in the late 90s?

Speaker B: Sure.

Speaker A: Um, you know, you probably had a similar set of emotions about the prospects of the Internet as folks have now about the prospects of AI. And very reasonably quickly, those things just kind of washed away. And it's just kind of how you do things. And more and more aspects of our lives started centering around that now. I mean, most people use AI in their everyday life and they don't even realize it. You open your phone, you open up an app application. Well, that's AI you, you know, the search algorithm for Netflix. Well, that's AI. I mean, there's. Yeah. I challenge anybody to, to point to an app on their phone where there's A.I. uh, in it. You know, I want to bring this

Speaker B: back to, I want to bring this back to the, to the listener that, that is investing their, their time and their, their treasure to learn more about AI. Um, where's. You say we're seeing a lot more language in the, the job postings themselves, uh, soliciting people who have AI skill sets. Do the companies that are doing the hiring, do they. So, like, there's this meme that I've seen. It's like a four panel meme, and it's like it says panel one. What do we want? It's like this group and they say, AI say, when do we want it? Now. What do we want it for? We don't know. Right.

Speaker A: We don't know. Right.

Speaker B: Yeah. Is that, what, is that where you think most businesses are now? Are they savvy enough to be able to tell if I walk in the door versus somebody who's using it to, you know, to, uh, plan a vacation? Like our skill sets when it comes to. We're both using AI, we might both be using it regularly. Because one of the things that I see with executives especially is I'll talk to them and I'll say, oh, I use AI all the time. I'm like, that's awesome. What are you doing with it? Oh, I use it to write emails and summarize documents. There's this comparison between frequency of use and proficiency, and that's not the same thing. Do the people that are doing the hiring, in your experience and in your gut, do they know what they're looking for?

Speaker A: Well, I think they're looking for a degree of AI literacy in a number of cases where the organization has pretty pushed into using AI, uh, even if the organization doesn't know all the benefits they're going to get. And there's a lot of, I mean, so again, the middle of 2026, uh, we've, you know, the last six months, a lot of organizations have pushed more heavily into equipping their workforce with AI models and tools and access. Um, not everyone. Lots of folks still aren't, aren't, aren't doing that, but lots of folks are wild. Uh, and now we're hearing that, you know, they're actually kind of pulling back from that because it's expensive. It's more expensive than they thought. Uh, so the compute costs are pretty high because the more you have it, the more you want to use and the more you rack up tokens and the cost is going to go up. And so folks are starting to throttle it back a little bit in certain cases. But the general trend is still very much towards adoption. And the implication in terms of what the industry that supports AI, uh, is facing is the subject of many, uh, other episodes of podcasts and news articles, lots of other things. So we don't need to get into it here. But when you were talking about, do they really know what they're doing with it? And I would say that that falls into a few categories. I mean, so there's like, what are you doing with it individually within your job? Right. Um, and in that case, it seems like folks are kind of doing what my company's doing, which is equip the workforce, and they'll figure out how to do their jobs a lot more efficiently and, and effectively, and you'll create scale around that. Then there's the question of, okay, well, what is the workforce doing with their jobs? Um, and that's a little bit less clear. You know, it's the kind, uh, of if you build it, they will come approach, I would say is dominated the first half of this year. Uh, and that's starting to get a little bit more, more nuanced. But, but, and then there's like the CEO, the board, the CFO perspective, which is, okay, where can you make a game changing, uh, change in your operations by adopting AI? And I would say that there absolutely are very clear opportunities that have already been identified there that are already showing up on the doors of their vendors and their workforce. So the tech industry has taken it on the chin. They were one of the first that were hit where it's like, hey, AI can do coding. So yeah, that puts, you know, our coding population at risk. AI can do process management. That puts all of our, you know, folks like Rev Ops, HR Finance, all those folks, uh, at risk. But the other thing that AI tools allow folks to do is your customers can now create the solution that you're providing them with, you know, a lot easier. That barrier to replicate that has just fallen down. And so, you know, when I talk to other CEOs, they're like, I've got, I'm, you know, spending a huge amount on given SaaS platform platforms, for example, and I want at least 50% of that spend to go away.

Speaker B: Yeah.

Speaker A: And that's big, big dollars, board level dollars that they're, they're looking at. And other CFOs are like, you know, if I get, if I can get 10% productivity improvement, I can get 50% productivity improvement from an individual. They're going to spend the 50% to find new things to do and be more effective. But that's, you know, whether or not we can implement any of that, we don't know. But if I can take a team of 100 people and give 50%, then I, I'm pretty confident I can cut 30 of those folks and still come out ahead.

Speaker B: Yeah.

Speaker A: And that's more of the mentality. So I think that we're going to see more workforce implications that go outside of the tech sector as we move into the second half of this year and into the first half of next year. Um, if I were to kind of put my prognosticated add on.

Speaker B: Well, I'm interested in that because I want to talk about that. You know, uh, one of the stories that I've referenced a lot on this podcast because I'm pretty interested in what they did was Jack Dorsey from Block, um, at the end of February, fired 4,000 employees in a day of their 10,000 person workforce. Right. And I thought, oh, it's, they, they brought in AI and they did exactly what you're talking about. They got more efficiency, ended up that, that wasn't the case. But you know, at least the people that we work with, with our consulting side, it tends to be lower middle market. They might have, I don't know, 50 to 500 employees, let's say. And at that level, look, uh, Jack Dorsey didn't know those 4,000 people.

Speaker A: Right.

Speaker B: But in an organization where you've got 50 employees or even 500, you may not know them all that well, but they look familiar if you see them in the office. Like, like there's, there's a little more association with that person than there is. It's just a, you know, a work force quote unquote. It's an individual. Are you um, hearing anything? And from, from Wagescape's perspective, are you hearing that even those smaller companies are looking to uh, uh, downsize the staff and have agents take over or where, where's that threshold to where it's more likely you're at risk of losing your job to an agent than not? Do you have any, any perspective on that?

Speaker A: Yeah, I mean I have a point of view on this, but this definitely falls into Kerry's opinion category. Right. Uh, and uh, so my experience, because I talked to lots of kind of small business owners in addition to lots of big enterprise owners. Owners, um, the small business owners are all about growth. That's their reason for being. And AI is a way that they can grow without growing their team. They can hit the gas. And we're experiencing it ourselves. I mean our product pipeline, our sales pipeline has never been more powerful than it is today. And it's driven entirely by the leverage that we get from AI agents. Uh, and so, so if you're in a small business, this is really exciting time I think. I mean you should be diving in head first to say, I can do all these other things. You still, as a CEO of a small business have to say, okay, well what can we actually execute on? All these ideas are great. The fact that we can, you know, basically give somebody the power to work as 10 people instead of one, um, is great. But do we have the power to actually get things out into the market? You got to be, you know, there's a different set of leadership decisions there. But in small business it's not about, or even in a medium business, it's not mostly about cutting heads. It's about growth and innovation. If you're in a bigger business though, you should be watching really closely I think at ah, what are the management priorities of the business from two lenses, like I said. One is what's the exposure of the business, uh, on the supply chain side and on the sales side to disruption from AI. So are your customers going to not need your products as much anymore? In which case you're going to have very serious cost pressures, uh, in the business. And then the other is if you have a general philosophy where you're a margin based business that uh, your cost directly impacts your profit and that's embedded in the culture, then you should be paying very, very close attention there too. Uh, because companies are investing a lot in AI, expect a measurable return on that. And if the primary way they get return is from headcount, then you need to know that, you need to be very aware of that.

Speaker B: Yeah. So as, as an employee, but it's

Speaker A: two, two different audiences from this standpoint. I mean AI is allowing the small guys to act like big guys. And you know, for me being a, a small guy, relatively small guy, thank uh, God for that. Right. And then the big guys are going, you know, sorry, the, it works. You know, our customers are starting to eat our lunch and they're saying they don't need us as much anymore. Um, by the way. Pardon?

Speaker B: Or uh, do you think that the uh, the, that we're at the point now to where AI has had enough penetration into use that companies are starting to recognize, oh, somebody who wasn't a competitor before is now actually somebody we need to pay attention to because they're using AI, I would say.

Speaker A: I mean the way that I would characterize it is your own customers are becoming your competitors.

Speaker B: Ah.

Speaker A: So because they don't want to spend

Speaker B: a half a million dollars on a license, they want to build it themselves.

Speaker A: Right. So if you're in the tech space or if you're in the business services that are primarily tech enabled, well, the barriers to replicate your service just fell through the floor. Right?

Speaker B: Yeah.

Speaker A: If you're in the business of hard goods or um, construction, something like that, customer experiences totally different situation. AI brings a totally different means to that. But people are still pushing the boundaries. I mean, you know, go into a restaurant and a lot of times you're dealing with some form of AI that's going to take your order and schedule the, you know, the food and do everything, you know, there. So people still push the boundaries on service oriented businesses. But uh, m. Yeah, if you're in hard goods especially, uh, there's, you know, it's a little more resilient I think, um, even there.

Speaker B: Yeah, I get it on the industry side. What about as an individual? Are there certain roles that your wagescape environment, uh, is starting to identify? These roles in particular are, um, this role plus AI means this person has a, a solid economic viability, not necessarily the threat of AI replacing.

Speaker A: I was, I was thinking more from a threat standpoint as you were talking and then you said economic viability. And I don't think that picture is sorted out yet, to be honest with you.

Speaker B: Okay.

Speaker A: Um, I, you know, it's probably pretty

Speaker B: universal if you know AI. It's probably pretty universal that at this point if you're good at your role and you know AI, you're probably pretty safe. Would you say that was accurate?

Speaker A: Well, even there, maybe, maybe not. It depends on what kind of organization you're working in. Right back to my earlier point. But you're definitely being more productive. You're definitely able to be more, um, directed in terms of where you're putting your energy. Um, and your cycle times are going way down. Yes, those, I would say are the hallmarks of somebody who's integrated AI into the way that they work. Their cycle times are going way down, their productivity has gone way up. They can do the work, you know, 10, not 10 times necessarily, but several examples of what they were able to do.

Speaker B: Yeah.

Speaker A: You know, without AI. Um, you know, and so those are, you know, those are, are some of the benefits. Are, you know, are you not at risk anymore? Maybe.

Speaker B: Here's what I tell people. I tell people that if you're, if, you know, if you're an A B player even, and you know, I, you're not going to be the first one they fire. Right. They're going to keep you around.

Speaker A: I was going to say that's a way to think about it too, is if you're part of a group of folks that, you know, maybe you're spread across different divisions or different businesses or you know, different parts of processes and you're leaning into AI and others are taking more of a skeptical or commodity, you know, approached AI and they're being noisy about it. I would say you're in a safer place among that group. Uh, if you're, if you're leaning into

Speaker B: AI, even if you're not, I would

Speaker A: agree with it a whole lot.

Speaker B: Now let me ask you this.

Speaker A: It's going to show up with your, your work performance.

Speaker B: Yeah. So now, okay, so what we've talked about generally, I would say qualifies as a staff level role. What about this impact on the executives? The like is the expectation that the CEO should be using AI or the entire C suite or executive leadership team is the expectation now that are they, I'm not going to say immune, but should they be like leading in this or should they be adopting at the same kind of pace or percentage that anybody in the organization should or what's the expectation at that level?

Speaker A: So I think um, as a top leader, like a C level, you know, position or a board level position, the need to bring AI into your daily activities may not be quite as pronounced as a number of the other other

Speaker B: positions because you're not producing widgets. It's not like a, I produced five

Speaker A: knowledge widgets or you know, you're not.

Speaker B: Yeah.

Speaker A: But the need to understand AI, uh, its implications on the business and how to direct the organization.

Speaker B: Yes.

Speaker A: In the midst of this enormous disruptive trend that's moving way faster than any other trend, uh, has in the past is way more pronounced. The need to be able to stay ahead of it and provide direction to the organization and stay focused on the things that count, put the energy and the resources into things that are going to be important going forward and very quickly move away from the things that are no longer going to serve you. Well is more pronounced, I think than it ever has been. And that's just speaking as small business CEO, uh, and a former global, I

Speaker B: guess that kind of maps to just traditionally, if you're like, here's what I would see. Well, here is how I would interpret what you just said. As an executive, I need to understand strategically what AI means to my business and my industry, whereas my staff needs to understand AI tactically. But, and the same thing would apply to, I mean just their roles in general. Staff level is probably doing more tactical, C suite's doing more strategic longer term thinking. Um, and I can tell you anecdotally, but my experience has been, and we work with, you know, we go on site with companies, we spend a day with their teams, we get them all tuned up and the executives are in there. And in a lot of cases, uh, different layers of the organization are all present. And the CEOs who are like, okay, this is, um, I'm just now using it. They're, they're, they were thinking about it, you know, strategically enough to say, hey, let's bring in some outside resources to accelerate the upskilling of our team. But the CEOs who have done that and their tactical users of the tools, what I see them doing, super impressive. The CEOs that are, uh, that are doing it. So tell me your perspective on that.

Speaker A: Yeah, I was going to say is I, uh, I'm inclined to believe what you just. What you just attested to that because things are changing so fast. I mean, where do. Where do CEOs traditionally go to kind of, you know, understand disruptive trends or potentially just. They go to the consultants. Well, the consultants are figuring this out just like everybody else, I hate to say it, you know, um, and the ones who are figuring it out fastest are the individuals who just start using it. And so to really kind of appreciate the opportunities, not just the threats, but especially the opportunities, you kind of have to take it on yourself. And to your point, the ones that have become their own practitioners, um, who aren't afraid to go out and start creating tasks and doing things differently with AI in the middle of it and enabled by AI, have a much, I gotta believe, have a much deeper appreciation of the opportunities and also set a different standard. Interesting. Leading, uh, from the front in the organization about what, what's expected. Because, you know, frankly, there's not a CEO that, that wants their team, you know, chasing the next shiny thing if there's not a business payoff and they don't have the time themselves to be spending any time, you know, learning something. That's just kind of an oddity. That's kind of cool, right?

Speaker B: Yeah.

Speaker A: Uh, there's got to be a payoff, uh, in terms of their own productivity. And so they're going to bring that perspective. Perspective too, which is, you know, I don't want you sitting there playing games with this thing or, you know, going off and coming up with side projects that don't have any relevance. You know, but here's how it can work in terms of really driving your performance within your. Within your role. And here's how I use it, and here's my expectations for the way that. That we, you know, embrace it, but also manage the risks associated with it. Right. And they're also in a position then to have a much more informed discussion about what are those opportunities and what are those risks, and bring the team in, especially their leadership team in to bring a much more robust understanding of, okay, how are we going to deal with things? And, and it's like going from, you know, a drive down a country road, which is the way that most, you know, most companies run, frankly, as bigger companies run to, you know, Class 4, Class 5 Whitewater Rapids, which is. You're adapting every second that you're in that boat.

Speaker B: Yes.

Speaker A: And so if anyone, anyone's ever been, you know, in some serious whitewater, you know that you're not thinking, what's three miles? You're locked in like, yeah. What's 10ft in front of the boat? Right. Yeah, yeah. So it's a great way to think about it.

Speaker B: Yeah. And that's because it's. If you're engaged with AI and it's like you're enthusiastic and you're using it, you're paying attention, you are making changes almost daily depending on what tool, what, what's the new release or whatever the case might be. So, yeah, uh, so.

Speaker A: And you're constantly going deep and pulling up. Right? Going deep and. Yes, ah, finding new ways to do it and then pulling up and say, okay, is this something we can scale for our business? Right.

Speaker B: Uh, yes.

Speaker A: Um, so, yeah, I think the requirements on top leadership are, are probably the most demanding, um, because it requires that strategic tactical balance and, and it will really, it compresses, uh, the leadership cycle as well. You've got to adapt, you know, engender a culture and a team dynamic that is much more responsive to new information, um, than you have been in the budget cycles don't work anymore. You're not working off budget cycles. That's how lots of companies plan is. What's, yeah, you know, what are we going to be planning for next year? And they start that 18 months into it. That's not, it's not what you're doing because no, you know, your customers aren't doing it, your suppliers aren't doing it. Your suppliers can move through you. By the way, we talked about the impact of the, of the customers, but frankly, the suppliers can move right through you too. I mean, entire parts of a supply chain can be obviated, uh, if with folks that put the right energy into imagining how to bypass their own customers to get to an end user. Uh, and you can work backwards too. You can, you can cut out a lot in terms of your own suppliers. So, you know, lots of those opportunities and they don't move at budget cycles. Those are happening in real time.

Speaker B: Crazy landscape. So, Gary, as we're getting to the top of the hour, here's what I want you to do. I want you to give, um, some advice to the listeners who are at the executive level. What you. Based on what, what wage scape is seeing in the marketplace on trends, what they should be doing when it comes to AI at an individual level, not necessarily because this is about their career. Right. And then give me your perspective on what you think the staff level should be doing for their career. Specific to understanding, using generative AI.

Speaker A: So I Think at the executive level, it would be the same advice for, frankly, anyone, which is, this is a trend that is, um, is so. I hate to use the term transformative, but I'm going to use it that you really got to get to know it, right? And you can't just take the headlines, uh, you got to get into it the headlines. There are agendas behind every headline and every headline is obsolete the moment that you hear it, right, this is changing fast enough that you got to get into it and you got to spend a dedicated part of your time, your personal time, paying attention to what's going on and what it, what it means to you. And, and the way that you do that is going to depend on what your position is and what your interests are and um, you know, what the needs of, of kind of your teams and, and you, you know, your life is, is. But, but you gotta, you gotta spend, spend time, even if you think it's, it's way far away from me. The other thing I would say is for those organizations that are like, oh, we're too risky. I was on the phone with, with, uh, you know, somebody, uh, I was at a doctor's appointment yesterday and I was like, well, can you just. I wanted them to email me my pictures of an X ray that they took of me. And they're like, no, we can't do that because. And uh, I'm like, I got talking about AI and they're like, yeah, we can't. You know, we've got real restrictions on how we use AI because of HIPAA requirements in the states and there's regulations about information sharing. And I'm like, so even if you're in that kind of organization, don't think AI is not coming to, you know, not going to show up on your doorstep. It absolutely is in lots of different ways. And so, you know, even when you've got, you know, your organization, like my wife works in government and they have really strong requirements about the use of AI. Like, even if you're in those kinds of organizations, you got to start knowing it and you got to dedicate part of your time to understanding it and you got to be talking to other people as well. I mean, that's the other thing is you're, you're not going to. The consultants are figuring out just as fast as you are, the stuff that's on YouTube by the time you consume it is not, you know, is obsolete. The people that sit there and debate which model is better than the other are having a useless discussion because it Changes week to week tomorrow. Uh, right, exactly. And oh, by the way, there's others that are out there too that you know, you haven't heard about yet. And uh, you know, so spend, you gotta, you gotta spend some time. Uh, otherwise you're good. You are either with the AI trend or the AI trend is going to, you know, or you're going to be reacting to it way too late, uh, is what I would say. So that's, that's my advice. You know, at any levels, but especially the, the executive level. In terms of your individual career, I would say now is the best time ever to reconceptualize what you want out of the intersection of work and life. And because I know a ton of people that, you know, have gotten off the, the corporate train and are now doing their own thing and there's a bunch of, it's really tough for college kids to get, get jobs, um, for some of the reasons that we, we talk about. But it is super easy for them to start their own business now and to scale it in a way that doesn't require the same capital that's traditionally been required. You know, and if you're, if you're like a mid or late career person, you know, there's an explosion of fractional, uh, fractional work that's really well adopted, uh, in corporations now of all sizes. And there's never been a better time to, you know, to hang out your own shingle from that standpoint. Also, if you want, you know, a much different, you know, balance between work and life, AI is a huge leverage, you know, source, so you can get as much done while working, you know, less if you can get the right job and the right people to work with, um, that'll still pay you the same. Uh, so you can, you know, like I said, now is the best time you're ever going to have to recon, reconceptualize what you want out of work and life.

Speaker B: Fantastic advice. Yeah, for sure. Dude. Thank you so much for, for sharing this perspective. Like, like the, the access to what's happening in the labor markets globally that you have is completely different than anything that I would get from any, any headline, for sure. That's clear. Um, and the, the advice that you've given and the perspective that you've given on how both staff level and executive level should be thinking about AI. Hopefully, regardless of where you are listener, um, I think that the advice here is exactly what I would have given you had I been asked this question. So I think there's Consensus from the AI experts over here.

Speaker A: So one of us is redundant then

Speaker B: slow down. Um, hey, so for people that are, especially those who are listening, that want to dig in more on, um, understanding the intersection of AI when it comes to, or just making better decisions when it comes to the, their, their labor choices, pay, uh, transparency in environments like that. Where should they go to get more information from you guys?

Speaker A: Oh, from us. If you want to know what's going on in the labor market, just reach out to us@wagescape.com our website. It's the easiest way. Or you can reach out to me at, ah, LinkedIn if you want to know what's going on with AI. Get really curious. Is my advice there?

Speaker B: 100%.

Speaker A: There's lots of video assets that are available, but the biggest thing is just go out and ask people. You interact with all kinds of people in all kinds of organizations, in all kinds of walks of life every single day and just make a point to ask them, you know, what do you think of AI? How's it impacting you? What are you doing with it? And you learn so much by doing that.

Speaker B: I think that's fantastic advice. So we'll have the links to the, their website and uh, car's LinkedIn in the show notes and lots to think about. Carrie, thanks again for taking the time out to share this with the audience and dear listeners. Yeah, we'll be back next week with another exciting episode of Using AI at Work. Thanks, everybody.

Speaker A: All right, thank you.

Speaker B: Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at Work. And a special thank you to our sponsor, Chief AI Officer for Empowering Businesses with AI Education and Training. Visit their website for a free AI Readiness Assessment and AI Strategy Guide to help you get started using AI at Work. That's www.chiefai officer.com. follow us on Twitter at the handle Using AI at Work and visit www.usingaiatwork.com for free resources to help you harness AI in your role.