EP 387: Why Companies Must Embrace AI to Survive the Talent Crisis

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Something that we don't talk a ton about on this show is we're going to be losing a lot of workers, right? As later generations hit the retirement age, there's figures out there up to maybe 500,000 workers a year could be retiring. And do we have enough young workers to replace them? Maybe, maybe not. And how can artificial intelligence actually help? And how can companies embrace AI to kind of survive this talent crisis? That's what we're going to be talking about today. And a lot more on everyday AI. What's going on, y'? All? My name is Jordan Wilson. I'm the host and welcome to Everyday AI. Before we get started, though, I have to give a quick shout out to the Work Lab Podcast. So the Work Lab Podcast from Microsoft is made for leaders who want to understand the future of work. It offers expert insights on everything from how to approach digital transformation to how AI can help unlock more value from your company's data. That's W O R K L A B, no spaces available wherever you get your podcasts. All right, another place you can get your podcasts here with me at your everyday AI.com. so if you are joining on the podcast, thank you. As always, make sure to check your show notes for a link to our website as well as other resources. And to all of our crew joining live, thank you. As always, Tara, Brian, Michael, Alice and some others, thank you for tuning in. Get your questions in now if you have any for today's guest, who I'm excited to talk to in a minute after we get to the AI news. So a lot going on today, as always. So first, Hugging Face has launched Hugs, a potential game changer for businesses and AI model implementation. So Hugging Face, in collaboration with Amazon, Google and other partners, has introduced a new open source software called Hugs, which aims to simplify the integration of AI models into applications. So Hugs stands for Hugging Face for Generative AI Services automates the adoption of open source AI models such as Meta's Llama for use on Nvidia and AMD chips. So the software can be developed both in the cloud and in private data centers, offering flexibility for businesses. Right now the service is priced at just $1 per hour. Wow. Okay. Making it an accessible option for companies looking to harness AI's capabilities. All right, our next piece of AI news. An AI company faces a lawsuit over a teen's death death linked to a chatbot interaction. So a Florida mother has filed a lawsuit against Character AI and Google, alleging the Character AI chatbot played a role in her son's suicide. So this tragic case raises concerns about the influence of AI on vulnerable individuals, especially teenagers. So Megan Garcia, the Florida mom, claims her 14 year old son Su Setzer, developed a virtual emotional and sexual relationship with the chatbot Danny from Character AI, which she discovered only after his death. So Garcia alleges the Character AI intentionally designed its chat bot to be hypersexualized and marketed to minors, potentially endangering young users. Character AI expressed condolences to the family, emphasizing its commitment to user safety, while Google clarified that it was not involved in the chatbot's development, despite a licensing agreements with Character AI for its machine learning technologies. All right, last but not least, Apple has unveiled new AI features, including its Chat GPT integration rolling out now in beta. So now There is the 18.2 iOS beta that Apple has just now released, which includes its Chat GPT integration. So key features in the beta version of 18.2 for developers include the ability to customize text rewrites, the introduction of Genmoji Yay, you said no one ever for new emojis, and two AI image generators, Image Playground and Image One. Additionally, the Mac Sequoia 15.2 beta also includes these new Apple intelligence tools. So notably, the integration with OpenAI's ChatGPT allows Siri to refer users to ChatGPT for more complex queries, enhancing the user experience without requiring an OpenAI account. So yeah, that's. That's fun. That's normally how I use Siri and Alex. Anyways, I ask them, it doesn't work. And then I use Chat gbt. So the initial rollout of Apple intelligence features includes text rewriting, the updated Siri interface in notification summaries that condense multiple notifications into concise updates. All right, that was a lot. And we have a lot more. So make sure if you haven't already, please go to your everydayai.com Sign up for the free daily newsletter. We'll be recapping those stories and a ton more, but I'm excited now to talk about how companies can embrace AI to meet this talent crisis head on. It's something we don't talk. We obviously talk about AI every day here, but we don't talk about what happens when the workforce starts to shrink. All right, so I'm excited for today's show, so please help me welcome to the show Carrie Sullivan, the CEO and founder of Culminate Strategy Group. Carrie, thank you so much for joining the Everyday AI show.

You got it, my friend. I'm so glad to be back.

Yeah, Carrie was actually we've been doing this thing for almost two years. Carrie was one of our first guests, so it's great to have her back. And another Chicago native. So Carrie, thanks for joining again. But can you tell everyone maybe who wasn't around for the first few episodes of Everyday AI a little bit about Culminate Strategy Group?

Yeah, we focus on at this point it's AI adoption. We're not really going to do much else over the next 12 to 24 months because that I think is the big problem to solve. We started out as a transformation consulting firm doing lots of post merger integrations and corporate transformation. But now it's all about how we close that gap from a talent perspective, using AI in really productive ways.

And even before we get into that. But I kind of want to know from you personally, like why or what made you go through that pivot? Because Carrie, I think that's actually important. We have a lot of business leaders listening to the show and they're probably thinking through some of the same thoughts and it seems that you've made a pretty significant pivot here. What caused you to say, hey, AI adoption as a strategy group. That's what we're focusing on from here on out?

Yeah. A couple of things. You and I, when we, when we first started talking, chatted about the technology that we actually we use to make all this stuff happen. So we use AI to map language from LinkedIn profiles to personality traits and developmental psychology. So within a couple of hours I can find the most resilient people in pretty much any organization. The only thing we can't really do well is frontline workers, obviously. And the reason I started the company originally was that I'm really good at change. I'm great at launching new things, changing big things. I'm one of those people. And what I've learned through this process is that it's only about 7% of the population that runs into that fire of early adoption and embracing change very early. So I've changed my and pivoted my model in part because I we, we figured out how to turn the AI ROI problem into something that's not a problem anymore. And I think that's a big enough problem solve. So I'm going to focus on it and it's consumable. Right. It's you say a word like transformation and people are like, right. But when you say AI adoption and more specifically looking at tools like ChatGPT or Copilot or Gemini. And those tools have made AI adoption not frictionless, but they've taken a lot of the friction out of adoption because it's a license. You turn it on and it just works.

So let's go to kind of the big topic for the day, Carrie, and this kind of demographic. Winter, can, can you explain, you know, for, for those of us out there that aren't really sure, like what is happening with this workforce and this potentially, you know, huge loss of workers that we're going to start seeing.

Yeah. So this all happened, this all has to do with post war baby boomers. Right. And everybody knows, we've known for a long time that as baby boomers retire, we're going to have some challenges. What most people don't quite understand is how big those challenges actually are. In the US alone, we're going to lose 500,000 working age people per year. And that's because it's not just the boomers. It's because we don't have enough Gen Z to backfill them. So we've got 11,000 boomers retiring per day and we only have 8,000 Gen Z that can backfill, you know, coming into working age that can backfill those workers. So when we, when I say 500,000 people that we're losing, we're losing 500,000 working age people, workers per year. And the bigger problem is that because the biggest resources that baby boomers had to solve problems was people, when they had problems, they threw people at it. They didn't do a great job of investing in infrastructure. They didn't do a great job of investing in data. They didn't do a great job of really paying close attention to processes and things like that as they were building and growing their margins. So there are a lot of companies that just are not prepared from a technology and a data perspective to adopt more sophisticated AI programs and solutions. But they can turn on something that looks like Copilot and Gemini.

I want to take this in a slightly different direction because, you know, this is something we, we help companies with this all the time. And sometimes I, I chuckle right when, when, when companies talk about, you know, getting an ROI on generative AI because, you know, $30 a month. And I like, I get it too. I get it because we've talked to companies with, you know, literally hundreds of thousands of employees and I, you know, at that point it's a huge cost. But you know, for medium sized companies, $30 a month for a license of something that can instantly do a huge percentage of manual knowledge, work much faster than humans. Mind boggling. Sorry, I just had to get that out of the way because I'm like this, like people who care so much about that. I think as soon as you turn that license on, even if you don't really know what you're doing, you're going to get a decent roi. But anyways, so let's talk about that.

Only for the people who, who will actually use it if you go in. And so part of the challenge right now is that copilot. So let's use Copilot as an example. It's being sold to Office365 administrators. And the push is usually, well, just buy it for everybody at the company. They'll use it. And that's not the case. It's maybe 25 to 30% of the population that is actually wired to experiment and generate productivity out of those licenses. And they're, they're at varying degrees. So you're only going to get ROI out of about 25% of your, your employee population. The other part of the population, it needs a lot of, of prep and care and feeding and psychology to get them to, to do what you need them to do and actually change their behavior because they're stuck. They just, they, that's not how they're wired. And that's okay. We can, we can close that gap.

How can for, you know, business leaders who are listening in right now and they probably see that, right? I think, you know, your early kind of gen AI champs naturally rise up, right. And then you do have that other group that's maybe more stuck in their ways and they're like, hey, I've been, I've been fine for, you know, a decade or three of working, doing things my old way. How can companies go about addressing that 70%? I know there's no blanket answer because it's, it's, you know, highly specific to the type of work that people do. But maybe there is some of those best practices that you've seen.

You got it. There is. So the trick is to generate momentum with that 25%. So the way that we advocate with companies is that only by only buy licenses for 10% of the population and we will tell you who to, who to give those licenses to because what we're looking for in the psychology or as we segment those employees is that top 7% that can immediately generate productivity out of the license. And usually what we're seeing right now is that they're getting about two and a half hours to four hours per week of additional productivity out of the license, and that's almost immediately. So it takes them a couple of weeks to get used to it. But we asked that team to, to tell us, for their function, what is the most productive use of Copilot or Gemini for. For your function, for your, you know, like marketing or sales or whoever. Right. So we ask them, what's the most productive use? Then we go to the next group, and we call that group Resilient. So the first group is called Results Drivers, and the second group is called Resilient. And these look like subject matter experts. They're not necessarily the first ones in, but they can fast follow. And this crew looks, they like to learn, they're intellectually curious, but they also like to teach, and that's key. So they are a little afraid of failure. So they want somebody else to work out all the bugs first. But if we give them a little bit of framework and some instruction, they can pick it up and start to run with it and get nearly as much productivity as that, that first 7% that likes to experiment. So these two groups are key. Once we get the proof of concepts done by vertical or by function, then we're into assessing ROI and prioritizing the things that we need to do for the 70%. And what that starts to look like is a combination of custom playbooks, so detailed process maps and playbooks and bots. Bots and automation. So you need to take more of the friction out for the 70% with some automation so that they have a more intuitive experience with the, with the AI, you're kind of. You're not taking them out of the process, but you need. We need to make it more intuitive for them.

Yeah. And, you know, I do want to ask you here in a second about some use cases, because I think people can really start to apply this in their own kind of domain once they hear some success stories. But real quick, before we do that, I have to first go ahead and again shout out our partners at Microsoft. So the Work Lab podcast from Microsoft is for leaders who want to understand how work is changing. Effective leaders adapt. They stay on top of trends. They embrace any edge they can get. They learn from the ways technology is transforming other fields and how it's enabling organizations to work more efficiently and productively. So for real world lessons and actionable insights to help you stay ahead, check out the Worklab podcast. That's Work Lab. No spaces available. Wherever you get your podcasts all right, so yeah, new one just dropped too, FYI. Go listen to it. So Carrie, so let's go maybe go through use cases because I think you kind of just laid out for the US there. You know, hey, you have your results drivers, you have your kind of resilient group, but maybe can you take us through, through a use case where this actually worked, you know, AI adoption and then we can talk a little bit more about how, you know, the talent gap.

Absolutely. So when we start to think about the use cases, the biggest use cases that we end up with in those mid market and larger companies is usually somewhere around project management, account management, and sometimes recruiting, even depending on how, how complex your recruiting is. Whenever you've got complicated meetings with multiple people that you are running, that's, that's usually the biggest, most time saving, ROI generating use case. And it's, it's pretty easy stuff to start out. It's, you know, you transcribe the meeting and then you turn it into meeting notes and then you turn it into next steps from the meeting automatically. And then as we start to add bots and additional automation, then we're doing things like how do we make sure that we for the project manager automatically schedule meetings with, with the, the next step in, in their process or their project. How do we automate a PowerPoint update or, or presentation from the meeting and meeting notes so that it takes that, that content generation off of the project manager's plate. So we're doing that with bots and streamlining that process to make that easier for them. And what it does is it makes, it makes that process easier and intuitive for the folks that aren't necessarily wired for prompting. And that's, that's kind of what we're doing. So with the 70%, they get stuck in uncertainty. 70% of the population freezes. And because they're uncomfortable with uncertainty, what we are hacking psychologically is their need to fit in. So by working with the first two groups results in resilience first and driving momentum. And then we work with our C suite and VP leaders and folks to point to and put on a pedestal that those 25% or so of early adopters, so that the message going to the 70% is this is the behavior we expect, this is the performance we're looking for. This is the kind of bandwidth and capacity creation that we need to create in our company. So that once we start to roll out those detailed playbooks and use cases and bots to make it more intuitive for the 70%, it's ready to go for them. And it's, but it's not just training our communication that we're relying on to, to get them to change the behavior.

Yeah, I think that that one use case, Carrie, is so important. Right. I, I was just having a conversation with someone the, the other night at dinner and she's like, hey, we, I was in a meeting with 60 people. Right. And, and like I.

Exactly.

Even how we meet, it needs to change. Right. And you need, I think you need to be able to create business value out of that meeting. Even if, quote, unquote, nothing happens. But you know, you know, guarantee that.

Only five people in that, in that meeting of 60 were actually talking. Y other 55 were just looky loos who were afraid of missing out. It's fomo. FOMO is a huge motivator for people not only to attend a meeting and sit there doing nothing necessarily but listening or multitasking, but also to adopt new technologies like, like an LLM.

Yeah. And you know, Carrie, can you maybe share any other insights that you've gained? Because I'd say most companies, you know, are either kind of on step two or beyond kind of of what you laid out and maybe, you know, step two kind of takes a while, right? So finding your results drivers and those early adopters, that naturally happens. Right. And then going through kind of that resilient group. But can you maybe share a little bit more insights or, or use cases on kind of that other 75 and some common trends? So you said, hey, you really gotta highlight, you know, the accomplishments and you know, of the first two groups. But maybe how can you get that other 70% and maybe do you have any examples of successful strategies that have helped, you know, start to convert or help those, those other people that aren't as quick to adopt?

It's, it's counterintuitive, but ultimately you're, you're creating fomo. So when you withhold that license or there's a little bit of carrot and stick, right? So with the 70%, what I'm essentially saying to them is if you want the license, if you want to fit in with the, with our early adopters, I need you to promise that you are going to follow this detailed process that we give you and these, and these automations and use cases that we've given you. So things like how do we, how do we do a good job of automating email or customizing email to go to a customer? How do we do a good job of creating consistency in our PowerPoint presentations? Or other things like that. And we're starting simply, we're not asking them to do anything complicated. It's get them used to doing a little bit of prompting and get them used to weaving the tools into their process. So keep it simple is usually trick number one. The other use case that's really kind of emerging is often with people leaders and they're using it to coach their people. They're essentially saying to the LLM, this is the profile of my employee, this is the situation I'm going through right now. Please act like a performance coach and help me coach this individual. That kind of prompting has worked really well for some of those leaders who are just trying to level up their game from a management perspective that's been one of the more interesting ones that's popped up recently.

Yeah, that's a great call out care because I think in 2023 when we're talking about generative AI or co pilot or Gemini or whatever, people thought of just low hanging fruit, right? Which is fine. But if you want adoption out of those people in middle management, senior leaders, sometimes you have to look at the other benefits of AI, which I think what you called out there is a great one. You know, using it as a thought partner, brainstormer, etc. But I want to kind of get back to this, this whole concept of hey, we're losing so many workers, right? I think you said 11,000 a day of kind of the boomer generation, but only replacing with 8k from the younger generation. So you know, losing up to 500,000 workers every single year. Because another problem, see is those younger workers aren't always learning AI skills in colleges because they're still banning it. So you know, it seems like a couple of problems. So for organizations that are have already recognized this kind of demographic winter, you know, what is your best advice or strategies for also getting these new people in and trying to help them close the gap, you know, that is growing larger and larger.

So first off, I'm so hopeful about Gen Z. I've got two Gen Z kids, they're both in college and frankly I love them partially because when you think about resilience and complex problem solving, it's usually created and found by an individual through adversity. Whether it's little bits of adversity or friction in their life every day or it's big adversities in their life that they've had to figure out how to process and get over. If you think about Gen Z, their generation has gone through some of the worst and most impactful adversity that any generation since the World War I and World War II has ever been through in the pandemic. So they lived through a pandemic and were schooled at home and things like that, socially held back during their formative years. So as a generation, as long as they're looking at a screen, they're pretty darn resilient. When you put them in front of humans, they have a tougher time. So. And I think that's partly what we're finding in the workplace is that Gen Z, if they're on a screen and they're on a virtual meeting or if they're on a phone or, you know, using technology, they're just fine. They're picking it up fast. You can train them, you can teach they. But they need to understand work process and output and the basics of working. And they need some training around personal interaction. They need to figure out what corporate culture looks like and what's expected of them and what it means to interact with people. So in part, the use case around Copilot or other AI tools is giving those Gen Zs a little bit more time to practice actually being a human.

It's so weird, but it's so true, right? Like, yes, AI can help you be more human, right? Like, that's a great example. Like, like, yeah, wild, wild world we're living in, right? You know, one thing, Carrie, that, that I always tell organizations is sometimes you have to unlearn things to, to really learn Gen AI, right? It's all these, you know, buzzwords they're throwing out there. Upskill, reskill. And I'm like, no, you have to unlearn good skills, right? And I, I think what you also mentioned earlier, you talked about change, and that brings me to a great point. A great question here from Cecilia. So we just got Chicago representing everywhere, another Chicago person here. So she's asking, you know, getting people to adopt change has so much to do with the organization's culture. How do you understand an organization's culture up front and then use it to accelerate change? I think that's a great question.

So great question.

I've seen you do that.

So like I said, I use AI to do it. So within a couple of hours, I can tell you how resilient overall a culture is. I can tell you how resilient their leaders are. And I can tell you the path, essentially a roadmap to success in change. Most change management really falls down because what we start to look for is or spend a lot of time on is, is fixing it and fixing the issues or figuring out where the resistance is going to come from. So the counterintuitive approach really is ignoring the resistance and giving them a bit of a carrot and stick to fit in with. So with the resistance and the fence sitters, the reluctant folks, our goal is fomo and that's how you drive change. It's social pressure inside an organization. So that's how you drive change. The other thing that I do is because I'm surfing, surfacing, that super productive early adopter, 7% of the population. I'm actually also uncovering usually some talent hoarding and things like that. So a lot of the folks that get promoted in big companies especially tend to tend toward command and control leadership styles. And that's because they're kind of stuck in the safety. If you remember Maslow from high school psychology, they get stuck in safety and security mode and nobody expects them to grow beyond it or expects them to level up their mindset. I personally suggest that every leader in every company get either a therapist or a coach or both, because it's in that expectation and facilitation that we learn how to process emotion. That's, that's what creates resilience and that's what creates the ability to change or transform or innovate in any company. So that's what I'm looking for. And when I find those people either in the middle management of a company, in the C suite in, or whether it's, it's the closest to the work in your individual contributors, I'm looking for those people specifically so that as we pull them out of the organization and start to give them chewy problems that they really love to solve that creates energy inside the organization, it creates momentum. And it's that momentum of making the company feel a little bit more like almost more like a startup in those instances, that really creates and sparks the change for the rest of the company.

Carrie, we've talked about a lot in today's show. We've covered, you know, some of these challenges of adoption. You, you brought us kind of how you work with companies and, you know, how you kind of segment groups and you know, amongst early adopters and those may be more resilient. But, you know, as we wrap up here, what would you say is the one most important piece of advice that you have out there for companies as they're, you know, looking to better use AI, but to also, also at the same time address this forthcoming kind of talent crisis?

Focus on a couple of things. Number one, is surfacing those early adopters and doing it early and embracing them and giving them complex and chewy problems to solve. Focus on momentum and creating it and focus on roi. If you do those three things, you, you almost can't help but be successful. The, the challenge for larger, you know, mid market and larger companies is that it's hard. You start to lose track of who's in your company. Right. So that's essentially the problem that we solve. Smaller companies that you should know, you know who your resilient people are, go find them and, and embrace them and work with them and, and give them the, the work of helping you change.

Such great advice. This one was a literal playbook, y'.

All.

I don't know if you realize it, but I think Carrie just gave us all the playbook on how to better implement generative AI as we are facing, you know, a pretty big talent drop off here in the coming years. So Carrie, thank you so much for joining the Everyday AI Show. We really appreciate your time.

Thank you, Jordan. Appreciate it.

And hey, as a reminder, y', all, yeah, that was a lot. Maybe you were at the gym listening to this podcast or walking your dog and you weren't able to pick up every single great nugget of wisdom that Carrie just shared with us. Don't worry, you can do that on our website, your everyday AI.com. we'll have the complete episode there as well as in our newsletter. Today we're going to be recapping everything that we talked about. So make sure if you haven't already, go to your Everyday AI dot com. Sign up for the free daily newsletter. You should also be joining us tomorrow as we talk with someone at Microsoft and going through some strategies for using copilot. So great 1, 1, 2 combination here. So thank you for tuning in and we'll see you back tomorrow for more Everyday AI. Thanks y'.

All.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going for a little more AI magic. Visit your everydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.