Karrie Sullivan: Results, Resilient, Reluctant, Readiness | Work 20XX podcast with Jeff Frick Ep51

Karrie Sullivan: Results, Resilient, Reluctant, Readiness | Work 20XX podcast with Jeff Frick Ep51

English Transcript

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Cold Open:
All right.
So I will count us down
and we'll go
in three, two, one.

Jeff Frick:
Hey welcome back everybody. Jeff Frick here coming to you from the home studio for another episode of Work 20XX. And I think you know when I started this podcast a couple of years ago, the focus of the future of work was all around distributed teams and hybrid work and everyone trying to figure out how to react to kind of the Covid emergency and urgency. But as we're like 3 or 4 years down the road or five years, I guess, since Covid, hard to believe it's really focusing on AI and AI adoption. And how do we get people to use AI? And everybody's scared of AI. And what is it exactly and how do we get it in our workflow? So I think that's really kind of come to the forefront. And so I'm really excited to have this next guest. She's an expert at it not only in AI specifically, but helping organizations assess how they can increase the probability of success in trying to get things going inside the organization. So joining us all the way from Chicago, the Windy City, she’s Karrie Sullivan, Founder and CEO of Culminate Strategy Group. Karrie, great to see you this morning.

Karrie Sullivan:
Thank you. It's so great to be here I appreciate it Jeff.

Jeff Frick:
Oh my pleasure. So I'm just curious, were you always in this space or was it one of these classic cases where the market just came to you over the last several years?

Karrie Sullivan:
It's more like that. Yeah. I started out as a marketer, oddly, and turned into a database marketer during the, you know, dot com boom. I was an early stage employee at Cars.com. I found my space, my niche in digital. And that's really I've been doing a lot of transformation into digital for the last 20, 25 years.

Jeff Frick:
All right. Great. So you are very active on LinkedIn. You're a great follow. If people don't follow you, check out Karrie's feed on LinkedIn. That's where I kind of came across her. And you've got this interesting process where you help people do an assessment of their people to then figure out who are the best people to start their kind of AI journey or to have success. So before we get into some of the details I wonder if you could share where that came from and how do you got started on this? On this process?

Karrie Sullivan:
One of the things that I was trying to figure out as I was working on scale for my own business was what's our superpower, right? It's a good research question. You got to ask it. And the answer was my weird EQ. I didn't realize it was weird at the time. But it was a little weird. I have this, apparently I have this ability to read the room really fast and compile teams together really fast and specifically teams that do a really good job of change and adapting to ambiguity, adapting to uncertainty—things like that. That's kind of what I look for in humans, when I recruit them. That was kind of my ‘Oh, crap! What if?’ moment. Oh, crap. My EQ doesn't scale to every project that we're going to be on. But then what if—what if my EQ could scale to all the projects that would be on? And what would I do with that? And I reached out to a colleague of mine. His name is Christopher Skinner, also a good follow. And he actually is a—he's a data scientist. Computational linguistics wizard guru. And he invented this model. He maps language to developmental psychology and essentially we can get a bead on individuals in any company. Frictionlessly in a couple of hours. I know the EQ of most people.

Jeff Frick:
So one of the things that Brian Elliot talks about—another great kind of voice on the future of work—is that in terms of management, capabilities and management propensities, that it's the same kind of characteristics of good managers that we're able to kind of deal with the ambiguity around Covid and changing rules and, which is kind of the same attributes of helping organizations implement AI, which in a lot of ways is the same thing. It's kind of ambiguous. Nobody knows really what to do or how to start. So what are some of those characteristics in, I guess both the managers as well as the individuals who are going to have a higher propensity to be successful with new things?

Karrie Sullivan:
I can give you percentages, how’s that? About 7% of any company population is made up of people who are already really good. So do you remember Maslow's Hierarchy from high school? Right, so like the 5% to 7% have gotten kind of close to the top of Maslow's hierarchy. Kind of that self actualization-ish. Or cognitive. And what you're actually doing is dropping the emotional baggage that people are learning to get past over the course of their lives. And earlier in earlier stages at part of what we have to learn is comfort with ambiguity or comfort with uncertainty. And, you know, 80% of the population is deeply uncomfortable with uncertainty. And that's really the biggest challenge with any change. I don't care if it's future work or AI or a big pandemic or even just implementing sales for a certain other system inside your organization or M\&A, right? The fact that people don't like change and uncertainty is universal amongst about 80% of the population. The other 20%, gets into varying degrees of that you know, the middle layers of, you know, esteem and belonging, in on Maslow and in the upper layers of cognitive and self-actualized. And that's essentially what we're looking for. We're looking for grit and levels of grit on kind of a continuum. In terms of success of the project.

Jeff Frick:
If the 7% are successful I mean, obviously those other 90% are not going to change their fundamental kind of psychological makeup. Is it just because it gets beyond the uncertainty that then that's what drives the adoption beyond that first seven and it's less crazy? Is that why it then works with those other people? Because they don't really change?

Karrie Sullivan:
Well, we're triggering a couple of things. So they’re not going to change their behavior just because you tell them to or just because you train them to. Right. What we're doing is it's a kind of boil the frog scenario, right? So you take the 7% and those are the folks who jump straight to experimentation on the change curve. I call this hacking the change curve. And very, very few people can jump straight over that trough of the change curve into experimentation and just acceptance and moving forward. And that's our 7%, right? Those are the earliest adopters. They are super comfortable with experimentation. They automate themselves out of a job. They're super comfortable with ambiguity. Their innovation mindsets even, but they're also incredibly good at creating leverage. They know how to generate a lot of result for very little or as little effort as possible. And that's kind of what you're looking for in any optimization of a population. So we get them to do the experimentation with us and tell us what the most productive use cases are in an organization because that's problem number two with AI, right? Is coming with the right use cases for your organization. So they came up—they come up with the use cases. And then group two is about 15% of the population. And they are smart. They are intellectually curious. They like to learn. They like to teach. And it's that learn and teach tipping point that is really important here because they're the translators between the two groups. So if the first group does our experimentation and they're running off on innovation, you're not going to just hand those innovation projects to the 80%, right? So we work with that 15%. And they’re a natural kind of crossover between a proof of concept group and our change champion group that’s there—essentially the same thing psychologically. And that's really—that's essentially what we're doing. So if you want to map this to Kotter or Prosci, that's kind of that second group that you want. And what they're doing is proving out the concept, proving out the use cases, and then giving us the detailed process maps, the playbooks, the checklists, the detail that everybody else needs so that with the 80% what we're doing is taking the ambiguity out. So we're creating prescriptive—here's how you adopt for your function. And we're triggering their need to belong or fit in. They won’t change their behavior because you tell them to. They will change their behavior to fit in. So if you give them people to fit in with and you give them a prescriptive way to fit in, they’ll do it. They will absolutely do it. Especially if their executives are saying, this is our expectation. This is the behavior we want. These are the kinds of things we need to do. This is who we are now.

Jeff Frick:
And then what about the difference between the managers versus the people within that 7%? Because it's one thing to have somebody who's, you know, always looking for high leverage. Not lazy, but looking for ways to be much more efficient. With that comes experimentation. And with experimentation comes failure. Just as a percentage. And if you're not failing on some things, you're not really trying hard enough to get out on the edge. So it's one thing for the person to do that. It's a different thing for the culture or the management to be okay with some failure along the way, knowing that with aggressive experimentation and with trial and with kind of charting new paths, that not everything is going to work out and there's going to be a couple hiccups on the road. So are the profiles of the managers and the people within those early adopter teams the same? Or is there a slightly different twist between managers and peeps?

Karrie Sullivan:
They're the same. The biggest challenge that we run across is that outside of the tech industry, leadership teams are a bit of a mixed bag, and middle management tends to be not quite so top of Maslow. That we see a lot of safety and security in middle managers. And therein lies your problem. What's essentially happening is that you've got a gap between the high resilient individual contributors that may be sitting at the bottom of the organization or they're buried, and those resilient, experimenting, transformation or growth or innovation-oriented leaders—they’re at the top of the organization. And then the middle management layer, they're not bad per se, it’s just that what is typical in organizations outside of the tech industry is that people are promoted for IQ, not EQ. So they are promoted for being detail oriented, methodical, process oriented and things like that. Again, there's nothing wrong with that. The challenge is that as you're going through change or transformation or growth or big velocity kinds of things, those folks are not wired to handle that easily. And that's a lot of the challenge that you see in future of work and remote and Covid and things like that but also with AI. So it’s about finding, surgically finding that 7% wherever they sit in the organization. And to your point, no. They're not really fundamentally different psychologically. They're probably just a little bit different when it comes to experience or maturity level.

Jeff Frick:
Yeah. Interesting. Okay. So you did the test. You did the test on me. So one of the interesting things about your test is you don't necessarily, I don't have to sit down and fill out like a Myers-Briggs or, and as you've said in some of your other episodes, other things I've seen getting ready for this, you know, psychological assessment is not new to HR, and trying to figure out people's strengths and weaknesses is not new to HR. And there's been lots of different ways that people have tried to figure that out. Your approach is different because you just basically mine, scratch, scrape, I don't know what’s the right verb, existing content that people have already published on the web, whether it's LinkedIn or other social media, or maybe you can explain kind of, where do you get the data? Where’s the data come from?

Karrie Sullivan:
So the data that we get is through the API that LinkedIn or other organizations would send to the search engine. Okay. So you know, you show up on Google, on your LinkedIn profile, that's essentially what we're getting. So it's not a scrape or anything. It's just pure, pure data. And then what we do is map it into the model that gets, you know, 60 or so columns of traits—personality traits—and scores in those and then maps that to a summary of developmental psychology, like the one I sent to you.

Jeff Frick:

Right? Right. Okay, so I'm going to read the categories. I'm going to be the guinea pig here. Which is really interesting. So I think there's about 6 or 7—opportunity, disciplined, expert, results, empathy, systematic, and holistic. And my range, my scores range anywhere from 2, which is not great, to 15. And the one that when we shared this, you know, before today, I was like, oh my gosh, my empathy score is only like seven. That feels horrible. I thought I was more empathetic than that. I try to be empathetic. So I wonder if you can explain a little bit behind these characteristics and where do they fall and how are they important for people in terms of what you're trying to measure, which is, you know, propensity for success with adopting AI or other new things.

Karrie Sullivan:
Absolutely. So if I remember yours actually, I'm going to pull you up here. Okay. One sec.

Jeff Frick:
This should be called empathetic. The fact that we're doing my psychological breakdown here on my own podcast is crazy.

Karrie Sullivan:
Well, it's that results score is where your EQ comes from. Okay. So you are, you are a lovely individual. That was one of the things that actually drew me to you because of your language. People recognize one another through language. Okay, so one of the things that I recognized in you was that results score that you have and that makes you a really solid CEO, right? That discipline score that I showed you in your results score and then your expert score kind of those three things clustered together. Okay. It's a really nice profile for a CEO. So for you opportunistic was an 11, discipline was a 15, expert 9, results 13.

Jeff Frick:
So what's the range? What's the top end of the score? What's the most you can score on the category?

Karrie Sullivan:
You have your own scores. So, if you add them all together, that's essentially what we call capacity. Okay. So your total score is kind of loosely related to IQ. But it’s really how much capacity you have to grow your EQ and to evolve yourself—and yours is pretty high. You've got a fairly high score and you clearly are good at evolving yourself. So you've got—you're also analytical. We know that, so

Jeff Frick:
Flattery will get you everywhere, keep going.

Karrie Sullivan:
So, and you've got enough empathy to be introspective too, so we kind of look for that. Kind of a high-ish score. We look for little bits of empathy or enough empathy or introspection, and we look for analytical, and what we're looking for is that ability or that capacity or aptitude to keep evolving yourself and doing the work on yourself. And it's usually the work that comes along with a therapist or a coach or a guru or whoever it is that you might tend to work with, or you just went to the school of hard knocks and had plenty of adversity in your life, and or perhaps a little bit neurodiverse. I don't know if you are or not, but neurodiversity is a bit of a superpower. Steve Jobs was dyslexic, Henry Ford was dyslexic. Neurodiversity does tend to push or seem to push up emotional psychological development a little bit earlier in some folks. But for you, you've got a nice high score. And so your main score is discipline. Your secondary score is results, and in that what you will have is this person who is able to create a very efficient machine around pretty much anything that you do. So you probably have a bit of a playbook. You probably have a bit of a method for doing things. It's probably pretty repeatable and you’ve turned it into something incredibly efficient. I imagine that the editorial process behind your podcast is down to a science and incredibly efficient. And almost—I wouldn't say identical every time—but probably continuously improving every time. Right. You're improving upon that playbook. And that's kind of how you're wired. And then that results score—again we call that kind of the CEO mindset. It's, that's about leverage. That's the one. And it's over an eight. So anything over an eight on the scoreboard, if you will, is impactful to your behavior. Your empathy is a little bit slightly different. Empathy is right on that borderline of impactful. You've got enough to be introspective and read the room, right. You may still use some rules in your head to read the room a little bit now and again, but for the most part you're probably reading the room pretty fast. That results is where your EQ comes from, though, and that's where your complex problem solving comes from. So as you see those complex problems emerging in the market in technology and things like that, you're easily able to understand them and go and run after folks that can help you find answers for those problems.

Jeff Frick:
So then where are red flags? What? Where are red flags that people are just not—not necessarily me—but what are scores or categories on this thing that jump out? This is not the person that you want to lead your first AI project.

Karrie Sullivan:
So typically what we're going to do—so what I do with these scores is actually segment the employee base into three groups. We call them results, resilient, and reluctant. So results is going to be literally results. You know they're going to be high results plus empathy plus systems. Maybe some expert. So you would be what we call resilient because you have plenty of results. And you've got a discipline score that's almost equal to results and your expert score is pretty high up there. So I call you resilient. And so you'd be in that 15% group. And then the reluctance would look a little bit more like heavier discipline, heavier opportunistic. And those folks that are just not comfortable with uncertainty. And most people are pretty darn self-aware. They know who they are. So when we start to break down the employee base like this, nobody tends to be super offended.

Jeff Frick:
Mis-categorized?

Karrie Sullivan:
If you will by that, right? So there's no good or bad. It's just where you are right now. And what you're comfortable with. And I kind of call those like our transform and grow team and our sturdy and stable team. You always want in any transformation, whether it's return to office or whether it's AI adoption or just a business transformation, you kind of want to have an eye on who's in those two teams, because you need a team that's going to do a really great job of driving the ship and making sure it's steady and stable and that we're maintaining revenue and we're not losing anything or losing any customers. And you also need that group that you identify that is comfortable with ambiguity and comfortable with uncertainty. And they're going to run after the change or the optimization, but they're going to also do a pretty good job of translating and normalizing all that for the rest of the team.

Jeff Frick:
And if you start an engagement, do you do a more direct assessment to validate against what you've gotten off the API in creating something like this or is this accurate enough that you can move forward? Or do you have to do—something a little bit more direct, like the old Myers-Briggs?

Karrie Sullivan:
If I'm doing recruiting or something like that, and I often get asks for recruiting or organization design or things like that. If I see any red flags or if I don't have enough data for somebody or if it's a really critical role, I’d staff—I have psychologists—and we will interview and confirm that our scoring is right. I can also do the same thing with spoken word and transcript if we've got some recordings. I can ask simple questions on a meeting and get basically the same thing as a confirmation of somebody. Right. So if precision is required, we can do that. It takes an extra step. But when we're talking about adoption or change management or things like that, it’s horseshoes and hand grenades. It doesn't have to be perfect. We're looking for big broad strokes of groups of employees and how we think they're going to behave and what they're going to be comfortable with. And if we find that there are some differences as we go, we can make those adjustments.

Jeff Frick:
And then as part of your engagement, do you help suggest actual applications or use cases? Or you work together in terms of the project? I just want to share a post, I don't know if you saw it. It’s great, it was shared by Henrik Jarleskog—I’m probably messing up his name—he's another good follow. He's like, this is my team. And it's a dozen. His dirty dozen AI Staff. And he calls them staff and he even gives them all names. So he's like—I love that—ChatGPT is my Chief Strategy and Innovation Officer. Claude is my Executive Editor-in-Residence. Perplexity is my VP of Research and Insights. Gemini, my Director of Real-Time Verification. Midjourney—I haven’t heard of a lot of these—Creative Director of Visual Production. Canva, Head of Rapid Design. Eleven Labs, Senior Manager of Voice Experience. Notebook LM, Chief Knowledge Curator. Gamma AI, Director of Presentation Development. Otter AI, Chief Meeting Historian. Ambient AI, Chief Workflow Orchestrator. And Veed, Director of Video Content Creation. I mean, I just think it is such a great post. I reached out to him, I’m like, you gotta share this because it just shows you—if you change your mindset. He's operating like this huge staff of people that are helping him. And he, you know, he spent the time to figure out which apps do what.

Karrie Sullivan:
That’s right.

Jeff Frick:
But it's pretty interesting now that, you know, we should be to the point where there's enough examples that you can point to to help people start to figure out where they can start to see some real results.

Karrie Sullivan:
And usually that's kind of the big ‘aha’ moment. As we get further into—I’d say three or four weeks into any implementation or adoption cycle. It's the moment for leaders that’s—oh, we’re not optimized for this. My team is not optimized for this. I'm not organized for this or ooo, now that we have digital workers that we're relying on for certain things, what does that mean to my human workers? How do they work together? How do I organize them? What kind of capacity does that create? Or what does that do to spans and layers inside my org design? So those questions really start to come up pretty quickly. And we start to answer them in a bit of a hey, it's an iterative process. Right, right. There's no single answer. And it kind of depends on what cycle you're in as far as whether you're trying to grow or if you are just trying to maintain stability and survive in an economic downturn or things like that.

Jeff Frick:
Yeah. I had an interview with this guy Charles Corley. He's a great guy and he's in real estate in Singapore. But what's interesting is he's just a curious guy. But he's been playing with ChatGPT every day since it was announced in November two years ago. And so he's doing all this stuff and he's like, Jeff, I just think of it really as a thought partner. And I run through and you know, we do all the stuff. And I was like well Charles what about hallucinations? What about hallucinations? And he's like you know, you treat it like a really good junior associate. If you're going to actually do something, check their work you know, don't necessarily take it at face value but do take it for direction and do take it for thought process. And the other thing that I thought that he said that was really profound, which I think a lot of people blow, is they're looking for this like question and answer, almost like a search and return and get the result. You got the ‘Can AI Generate Results?’ over your shoulder. But his thing is like no, it's not like this one time thing. It's this iterative process. Both you and the machine over time exploring things conversationally. You probably see these posts, you get these ridiculous prompt engineering charts to put up on the wall that have a thousand lines of, you know, nine point font that you can't read when you're our age. But he's like, no, no, no, no, no. You know don't try to just be conversational and work the tool and just talk to it. The tool will start to return information back to you.

Karrie Sullivan:
I call it a good drunken intern. It's like the perfect name for it. Because that is kind of what it is. And an LLM—LMS aren't a panacea. They're not where we're going to end up. The way I look at the large language model tools is that they're a really good first step because they're low friction. Anybody can push a button on a screen and get started with it and get to understand how to use it, how to optimize their day with it, if they're wired to do that and they're wired to experiment. But what we're really doing is conditioning organizations around something that most, you know, office environments aren't used to. Manufacturing has been practicing Six Sigma for decades. Right. But what white collar jobs are practicing Six Sigma and continuous improvement? So can we start to introduce concepts, from other functions or other industries, that make sense into this process? And you start to get people used to the idea of creating virtuous cycles around productivity, improvement, quality. Value creation, you know. How do you take a 5x employee and turn them into a 10x employee? So how do you start to challenge yourself to do some of those things? The current tools aren't always going to do that. There are tons of AI tools and models out there that can be baked into lots of different systems. The problem is that they're higher friction and higher expense to implement inside most organizations. Not to mention the fact that most organizations don't have the data governance and organization to make a lot of these tools work. So the way I see it is let's get some of these easier base hit kind of things right. Let’s condition the organization and the people. All right. So I will say that there are a few things that we’re doing early in this process that I think are working really well and that is helping people understand what AI is, what it isn't, what it does well, what it doesn't do well, that it's not going to think for them, that they still need to use critical thinking, that they're going to get out what they put in, and all of those kinds of things. Helping them think about new ways of working and giving them that freedom and latitude and showing them the value to them personally and how this is going to impact their work, their team, their company, their customers, all of that, helping them reframe their mindset for how they need to think about change, think about AI, understand that it's not just going to take their job or consume what they do. And then start to really do a good job of coming up with the high value things, the value creation things that other AI models and tools can do for the organization. So that if we're really successful early in some of these pilots and we get ROI and we get lots of adoption, then the CIO or CTO can walk into the CFO's office and say, hey, this is successful, we know exactly where we need to go, and they get the green light for the investment. I don't know any CFO who's going to greenlight an investment in any AI tools or projects that have 20% adoption rates. So it's kind of breaking that cycle of innovation projects, getting low adoption and then just being shelved. And creating higher success or quick wins early so that they can get yeses on the next investment. Yeah. That's what we're going after. Yeah, the data governance and the data process to feed these things is certainly a super important part.

Jeff Frick:
I've got some recency bias. I was recently at the Atlassian ‘Team 25’ show and they just jumped in with both feet because they're in this unique position where they've got a lot of

Karrie Sullivan:
What a great organization. Great minds at Atlassian, they’re so good.

Jeff Frick:
They've got all this great data. And so they're attacking kind of the knowledge discovery process. And I forget the numbers but like, you know, a third of our time is spent searching for things, searching for information, right. Because you can’t remember is it a text, an IM, an email, whatever. And so that's how they're really going after the opportunity is to use their internal—they just released—it's called ‘Rovo’ to work within this huge amount of data in the system already to start returning, you know, better results and helping people get their work done. And the other thing they've done I thought was pretty interesting and I’m curious is they think you know you got to have this ‘top down’ to go with the ‘bottom up.’ So you have to have, you know, senior support and not only support, but actually modeling the behavior as we know all the time for senior leaders to model the behavior that they want their people to do. I'm just curious, do you think in the not too distant future we'll have like a Chief AI Officer? Because I mean just like the list of tools I read down just trying to keep up on what is happening. You mean you definitely need an AI tools friend who can help turn you on to what's—Is just changing too fast to keep up with,

Karrie Sullivan:
Yeah, is changing too fast,

Jeff Frick:
So how do you—How do you see it evolving? As you know your early adopters have some success? How does it start to proliferate through your organization? and what will happen at the top levels in terms of driving it from the senior positions?

Karrie Sullivan:
Yeah. Atlassian is just such a good example across the board. They've got a great leadership team, they've got great mindsets, you know, heading up the company. and you can see why they've been successful and been able to grow and evolve with the market as they have. If I remember right weren’t they one of the few that would promise especially digital, digital creators or people with digital IP that they wouldn't use their code to update the models and and stuff like that so that it would be proprietary to them. They recognize things quickly with early adopters, things quickly with early adopters and they respond very quickly and that's exactly what you want to see in leadership teams. So what I think is going to happen longer term is that you're going to see a bit of a differential start to happen, and it'll be kind of like your, Go take a look at AutoZone's stock price. It's just kind of a perfect example that, I don’t know how much AI they use or they don't but William Rhodes \[AutoZone Chair] is so high in his results score—he is, he—has been able to turn a brick and mortar auto retailer into a company that has a $3,000 plus stock price. Like, it's unheard of but that's the power of what leadership mindset is actually able to drive inside an organization because they're able to attract similar talent. Right. So you're going to start to see a lot of that talent collecting. It's going to look kind of like a weird people cluster analysis. Where you've got talent that can adopt and adapt to new stuff clustering together in organizations under leaders. And you're going to see those organizations pulling faster ahead of the pack. You're seeing a little bit of that with Shopify. They don't want to see any asked for headcount unless you can prove that AI can't do the job. You're going to see some of those leaders starting to cluster together and pull ahead of competition or you're also going to see some mid-market or smaller organizations that are growing that are able to grow faster. Right. Because the cost to build and the cost to grow is sinking like a stone. That's the other thing that AI is going to do and is doing right now. The cost to build code or syndicate or create leverage is sinking fast. And those who are able to leverage it are going to be able to grow and pull ahead of their competitive set very, very, very quickly.

Jeff Frick:
Yeah. And it's also about creativity. I mean, again, recency bias Andrew Boyagi at that show who's in charge of their developer experience evangelism talked about actually for coding. He's like, you know most of the coders are pretty good. AI is not going to replace the coding. But there's a lot of tools. There’s a lot of steps in the process that AI can help. Exactly. So like run a basic review before you send it out to your peer review. One of the things I thought he said was really creative is you know, a lot of the non English—for a lot of developers English is not their first language. So being able to write nice summaries about what they did for everybody else for the documentation like, wow, that's genius. You know, that is something they struggle with. Exactly. Not the coding piece. So even all that kind of periphery. But I'm curious in terms of the adoption let's just go straight at it. You just said you know people aren't going to hire if they can't prove AI can't do the job. How does that get resolved when you're trying to do this? You know, it's not taking your job. It's an assistant. Versus, I'm afraid it’s taking my job. That's all I'm reading about.

Karrie Sullivan:
See, I think there's going to be the technology industry and everybody else. Okay. Really when it comes to that. Because so much of the tech industry is developer led, code, you know, code oriented, that's what a lot of these models are kind of optimized for is writing code. So, yeah, they're going to be able to automate quite a bit. They're not going to automate their 10x developers or even probably their 5x developers. They'll make their 10x developers 20x developers and their 5x, 10x. So I think that'll happen a lot in the tech industry. And you're already seeing it almost across the board where they are rethinking their staffing and their you know, taking out some of those that where they can optimize and you're seeing—I mean last year we saw more developers on the street. But they're fairly quickly going to find a seat in other industries, I think. Yeah. Because other industries don't have that data science, data architecture, database, and any of those foundational things most companies just don't have, because they haven't invested in it. Yeah. They're, that's not who they are. So those developers will find other homes in different spots in different industries that are now starting to invest in it. But most of them—if you're in manufacturing, you're creating actual physical things. So, you know, are you going to swap out your entire back office tomorrow? Probably not. Will you in five years? Maybe. It's possible. But here's the way I really look at it, Jeff. The fact is that our baby boomers are retiring at 11,000 people per day. So we—and we only have about 8,000 Gen Z's coming of age to backfill them each day. So it’s a delta of about 3,000 humans a day, right? If we've got a delta of about 3,000 humans today that’s about 5 million humans by 2035. So what that does to different industries—and we're already seeing it in manufacturing, in health care, in senior health—as baby boomers continue to age and they move into senior living homes. Right. We're going to have lots of big shifts in where we need talent to go. And it's going to be a lot of skilled labor. So those people who maybe either aging out of a white collar job or things like that, you know, does it need a Gen Z to backfill that? Maybe. Maybe not. But do we need Gen Z or do we need other people to reskill themselves into home health care or other roles? Absolutely. I think that's a lot of what's going to happen from a human perspective. We’re going to see a lot of humans with AI doing their jobs, and you’re going to see a lot of humans reskilling either into different industries or upskilling into their existing organizations.

Jeff Frick:
Yeah. And it's not only developers too, right, it's creative writers and you know—a lot of the artistic stuff and—Totally—you know it's got to be just wreaking havoc on the poor Fiverr subs that are all over the developed world for all those just quick little—those quick little things. I want to shift gears a little bit, because I know most of your clients are big organizations, big companies. You're running a smaller organization. And I know you think a lot about, you know, kind of this step function that smaller organizations in terms of numbers of people can have using this technology to grow a really sizable, important and significant business. And, you know, I look no further than that list that I just read from Henrik with his giant, you know, his cabinet of 12 people that I don’t know what he's paying for all those apps, but it's not hundreds of thousands of dollars per year, that is for sure. So I wonder if you can share as—cause I know you think about this—how this is going to change the opportunity for smaller businesses to really find opportunities or grow in ways that maybe they didn't think were possible at all before.

Karrie Sullivan:
Yeah, actually I do. I work with some smaller organizations. I didn't actually think I was going to, but I've ended up acquiring a few smaller clients recently even and what I'm finding is that the scope is just a little bit different because they want to jump a little faster into org design and optimization of the team. So that especially if they've already got a big chunk of their team already using tools like ChatGPT or Perplexity or, you know, Canva, other things, and they're really just starting to jump into how do I optimize? But they're trying to grow and their goal is to grow without overtaxing the team that they have but then without actually growing headcount in ways that are that are onerous to the bottom line either, so that optimization is absolutely happening right now. And it just—it happens in varying degrees depending on the leaders and founders inside the organization. I do think we've already got companies out there that are at, you know, \$20 million in it in a couple of years with nominal amounts of VC investment and things like that. Because those founders are able to leverage the tools that are available and the cost of those tools is not big. So we will absolutely see smaller, you know, 10 person, 15 people organizations with \$1 billion in revenue probably in the next 2 to 3 years. Yeah, I'm pretty sure that's going to happen. I will probably never have much of a back office at all.

Jeff Frick:
Right, I heard that in one of your other podcasts you said basically you have no back office and you chose in terms of a focus on automation, you know, those—the stuff that nobody likes to have to deal with like accounting and finance. And you've automated a lot of that.

Karrie Sullivan:
Why would I hire? Why would I hire for that? Yeah, I started with RPA years ago. So

Jeff Frick:
Is this RPA? No one ever talks about RPA anywhere. RPA was supposed to be—I know, nobody’s talking about RPA—supposed to be our great digital assistant. I used to go cover the Automation Anywhere show a number of years and those were our little peeps that were supposed to be our digital employees, as they were sold, our digital assistants. Totally, they’re perfect little

Karrie Sullivan:
They’re perfect little swivel chair employees. If you’re going to take data from one spot and put it into another, it's awesome. You know, now I'm moving on to other technologies that are a little bit more modern as my processes change and as we grow. But the same principle applies. I would rather hire a really great CFO that can think strategically about the money and think strategically about growth than I would an army of bookkeepers to keep track of all of the, you know, invoices from, you know, contractors and things like that. My team is 100% virtual. I have—it's all 1099. I train them, I invest in them. But it's all 1099. So it's a very cost and overhead light organization. And that's on purpose. Right. Because I don't want to—because we're a services organization—I don't want to throw a bunch of overhead costs at client projects and have them consume that. So that's just the way that I approach problem solving. I'm not special necessarily, but that's just how I think. And if I think that way, there are other people thinking in similar ways that they're going to be able to grow companies even faster than I can. Yeah. So it's going to happen. It's going to be really interesting to see. Mid market, I'm actually really optimistic about, too, because we're going to see a lot of PE over the next ten years I think. We're going to see regulatory environment changing over the next 2 or 3 years. And a lot of those boomers that are running companies right now are going to want to get out. And as they do that, we're going to see a lot of consolidation. We're going to see a lot of companies being bought and then technology being applied to generate the value in that private equity thesis that they just haven't necessarily done as much of before—their cut and burn kind of approach to things.

Jeff Frick:
I was going to say, I don’t know what private equity folks you're talking to but

Karrie Sullivan:
it isn’t going to work anymore. It’s going to

Jeff Frick:
more about the harvesting and the milking the cow than investing in growth.

Karrie Sullivan:
It’s going to give birth to—it, this is going to give birth to a new generation of private equity. Yeah. I'm quite certain and that’s—we're going to see that in the next couple of years too.

Jeff Frick:
I hope so. I've seen them destroy too many—too many industries from—my favorite is the old rental car, you know. Those were all divisions, you know, to keep the factories. It's a great illustration of, you know, what are you optimizing for? Or, you know, what they've done, you know, kind of in the radio business.

Karrie Sullivan:
They won’t, they just, they won't be able to follow their old playbook anymore. And that’s exactly the point, because most of the folks in capital markets tend to be kind of that more safety and security oriented mindset that needs, that likes that playbook and adheres to that playbook kind of religiously. They love the spreadsheet, right? So they're going to end up breaking out of the spreadsheet or somebody else is going to go in and attract that LP money because the thesis is going to be much more focused on leverage. But that also means that they're not going to be able to hire the same way anymore either. You know, it's not going to be we’ll just go hire my friend from Wharton or Harvard or whatever. It's you're going to find those people that look more like that kind of scrappy version of a Steve Jobs or a, you know, Jeff Bezos.

Jeff Frick:
Right. Interesting. All right, well Karrie, we're getting towards the end of our time. And, you know short of calling 1-800-Karrie-Help-Me, what are some kind of words of advice that you have for leaders as well as, you know, mid-level managers and even, I suppose, frontline workers in terms of how they should, you know, kind of approach the challenge, how they should think about the opportunity, and really start getting their feet wet if they haven't already. And if they haven't or if they have, how do they help their compadres get into it? And if they haven’t, what should people seek even within their own organization to try to get some help without necessarily raising your hand and saying, I'm scared to death of this thing?

Karrie Sullivan:
Great question. I will give the same advice that I give to my college kids. Every week I ask them, what risks did you take this past week? Tell me what risks you took.

Jeff Frick:
That's a great, great question.

Karrie Sullivan:
What I’m doing with that is getting them to be mindful about the risks they take and their comfort level with uncertainty, and just getting them to be aware of their relationship with uncertainty. And if you keep doing that over and over again, eventually you kind of find that you're dealing with some of that emotional baggage that might have been in your past. Aside from that, get a coach. I love my coach. I've got a great coach who used to be a therapist. Therapists are great. You know, it's not about what you learn in school necessarily. Learning is awesome. But learning and earning level of resilience and comfort with ambiguity is absolutely what it's going to take to be 10x in the next generation of talent.

Jeff Frick:
I'm just curious. This group of people, the young people, COVID was five years ago, which is hard to believe, have lived with a lot of uncertainty. And there’s been a lot of ambiguity just in the greater global environment. Things that have been stable for 80 years are not necessarily stable that way. So I wonder if their, you know, is that, is that uplifting for them in terms of, okay, we're comfortable with the uncertainty. We can deal with it, we've been dealing with it. Or is it almost like, screw it. You know, this is such a crazy, bizarre thing and is changing so fast. You know, I'm not necessarily excited about investing when things are going, you know, what's going to change tomorrow. I'm curious because they've been, they've lived in a very different world than, say, you and I when we were 20 and you just kind of followed the, you know, the bouncing ball on the script. You went to school, you went to college, you got a job at a training program. I went to a great training program, boom.

Karrie Sullivan:
Education was a manufacturing plant.

Jeff Frick:
Yeah. It was great. So different. So I'm curious, your take on their perception of living in a world of ambiguity, having, you know, got through COVID and everything else.

Karrie Sullivan:
We're already seeing the difference in mindset. So we can see mindset differences kind of in aggregate. And do some of that analysis. So we see weirdly we can see like migratory patterns of people in their mindsets going from company to company or industry to industry. But we also can see some generational things too. And Gen Z already has more in their savings account than most of their millennial peers. Gen Z is unique in the adversity that they experienced in their formative years. And that adversity is a great teacher. It sucked for them for sure. Right. But it really was a great teacher. And they are all we're already seeing it with them at work too. There's a reason they keep kind of turning over in their jobs kind of quickly. It's because, remember what we talked about with middle managers and those middle managers being pretty focused on playbooks and routine and process and detail orientation. Some of those Gen Zs have already kind of outgrown their managers, get frustrated and leave. So when that more safety and security kind of mindset tries to put the more resilient or a resilient problem solver into a smaller problem solving box, they get frustrated, it creates friction and they leave. Yeah. But we're already seeing it. And there's a lot of frustration with Gen Z. There's a lot of Gen Z hate. I love them. I hire them all the time. I think they're great. And when I score them, they're fabulous. Like, I clearly find them a little bit easier. But when I score them they're fabulous. So if I'm you, I put Gen Z into some of those projects that are new, that do have a bit of ambiguity around them, that aren't just—they do have to pay their dues for sure. They need to learn how to operate inside an organization and deal with, you know, the politics and the culture and all the things and really, you know, get some domain expertise. They can't just skip ahead. But to keep them interested we need to give them some interesting things to do and give them some agency because they already have agency themselves. You got to give them some agency over improving their jobs and improving the work culture that they sit in.

Jeff Frick:
Yeah. It's interesting as you're saying that I'm thinking of my grandfather. You know, the Greatest Generation fought in World War II, went to Korea for a little bit. And just the—in terms of the uncertainty that they faced in the 30s and the 40s, in terms of building really strong resilient people. Now, what's kind of odd. Then they all went to the job market at that point, you know, through the 50s that was just rocking and rolling, everything up and to the right and pretty steady and constant. And you got that job at AT\&T like my grandfather did. And you could have it for 50 years or 40 years with lifetime employment.

Karrie Sullivan:
I think they also, they also grew up in that great influenza pandemic or epidemic in the, you know, teens. Yeah, yeah. Yeah. They dealt with lots of adversity. So Gen Z is the first generation since the greatest to have that kind of adversity in their lives. And I see kind of big things or similarly big things for them.

Jeff Frick:
Yeah that's great. Well that's good because we need as you said the demographic trend, we need it. Is the biggest trend of all and, you know, especially in developed countries, you know, there just aren't enough there aren't enough people. So let’s lean heavily on our young people. And I'm glad to hear that you've got such great enthusiasm for their contributions. Well Karrie, this has been great.

Karrie Sullivan:
Thank you.

Jeff Frick:
Thank you for taking the time today. Thanks for trying to make a difference in the world of AI and AI adoption. It was really refreshing at this Atlassian event because they're so into it. You know, they actually have like on their internal thing, every morning it starts, you know, start your day with AI. I mean just like this constant encouragement.

Karrie Sullivan:
That’s awesome

Jeff Frick:
to help people get over this hump. And then you end up with people like Henrik who’ve got, you know, 12 little AI assistants helping him get through his day every day. So, really appreciate the time.

Karrie Sullivan:
Well, absolutely. And next time you need a bag carrier at an Atlassian event, you just let me know, and I'll be there.

Jeff Frick:
Okay, I'll let you know. All right. Well, thanks again.

Karrie Sullivan:
You got it.

Jeff Frick:
She’s Karrie, I'm Jeff. She's in Chicago. I’m back in Palo Alto. Thanks for watching Work 20XX. Thanks for listening on the podcast. We'll catch you next time.

Cold Close:
Thank you.
Excellent.
Thank you guys.

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Karrie Sullivan: Results, Resilient, Reluctant, Readiness | Work 20XX podcast with Jeff Frick Ep51

English Transcript

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