#167 - Your Company's Biggest AI Advantage Is Already Sitting in Your Database: Scott Snyder

Speaker A: Welcome to the Outthinkers podcast. Plug into fascinating minds and breakthrough ideas that are transforming industries and the world. I'm, um, your host, Kion Krippendorf, founder of Outthinker, a global ecosystem comprised of strategy and transformation officers who are shaping the future of business. If this describes you, join us@outthinker.com this episode is sponsored by LHH. A beautiful working world starts with leaders who inspire and elevate others. LHH Executive Solutions partners with boards and senior teams to identify, develop and support executives who drive meaningful transformation. Because when leadership thrives, organizations thrive. Learn more@lhh.com LHH A, uh, beautiful working world. Now let's dive in to this week's episode with Scott Snyder.

Speaker B: AI path to a better career and multiplying the impact you can have in your job or in your community. And until people discover that, I call it your AI moment. Where the flash goes off and you're like, wow, I realize I can do 10x more. I can do, I can have 5 to 10x more impact as an entrepreneur or as a lawyer or as a manager. And I think until that happens, you know, telling people or laying objectives on people when they don't have a true understanding and appreciation and excitement for what they can do with AI, I think eventually backfires. So I think the key is to go the other way. Is like, how do we pave the ground to allow people to get excited and accountable and see themselves in a successful AI future three years from now and, uh, work backwards from that and say, like, okay, you know, where are the steps we can help you with to get there?

Speaker A: What if the reason most companies are failing to get value from AI has nothing to do with the technology and everything to do with the same mistakes they made with mobile, with E commerce, and with every major technology wave before it. Our guest today has lived through all of them and he spotted the pattern. Scott Snyder is a senior fellow at Wharton. He's the co author of Goliath's Revenge and one of the most experienced voices at the intersection of technology transformation and business strategy. He co founded Mobiquity at the dawn of the enterprise mobile era, has advised some of the world's largest companies through digital reinvention, and now brings that same hard won perspective to the AI moment. Most recently through his new book, your AI Life. It explores how individuals and organizations can find their own AI superpower. In this conversation, we dig into why incumbents keep falling into the same traps when new technology arrives. What large companies actually get wrong about their own Advantages. Why those advantages are bigger than most leaders realize, and how to move your people from fear and resistance all the way to genuine excitement about working alongside AI if you lead people through change, this episode will give you a sharper lens for what's actually holding your organization back and a practical place to start.

Speaker C: Scott, uh, thank you for being here. I know you're busy, and I really appreciate you taking some time to sit down with us and share your work and ideas.

Speaker B: Thanks so much.

Speaker C: Where are you joining us from?

Speaker B: Outside of Philadelphia. Garden spot of Berwyn, Pennsylvania.

Speaker C: Berwyn. Uh, okay, great. I was born in Philly and have family there, and my father's a professor at your university for a long time as well. So I have so much to cover with you, and we probably won't get to all of it, so I'm going to kind of jump in. But I want to open with two questions that I always ask all of our guests. The first is just for us to get to know you a little bit more personally. Could you please complete the sentence for me? If you really know me, you know

Speaker B: that that's a really great question. But I guess at its root, I'm a fan of people in terms of just realizing that every AND has hidden potential. So how do you unlock it? And, uh, whether it's an entrepreneur, whether it's a student, whether it's a colleague, whether it's a customer. And so I'm always looking for opportunities like, how could we elevate the, uh, potential of everybody? And I think AI is kind of bringing that all to light. So. So, yeah, I'm a big fan of that. And I think. I think there's so much hidden potential. Just we don't have to look very far in our communities and our surroundings.

Speaker C: We could make this. We could make the whole episode just about that. You know, what is the potential of human in the world of AI Maybe we'll get to that. Second question. What's your definition of strategy?

Speaker B: Yeah, I mean, you know, there's the textbook definition of, like, a choice among alternatives. To me, strategy is about finding your unique pathway to creating value for customers and your business. Right. And when what's unique about it has to be. It has to leverage what's core to your company, your values, your unique advantages and assets. Whether it's data, whether it's how you do, how you perform work, whether it's your culture, how do you magnify that through the strategy to go, you know, uh, both kind of capture and expand and grow value and. And it's all about customers in the end. So work that for your customers. So.

Speaker C: So I didn't hear anything in that would necessarily change in a world of AI. Does that definition change in the world of AI?

Speaker B: No, no. And I don't think it changed in mobile or digital and AI, which is clearly more amped up, moving faster. But at the end of the day, every business should have a business strategy to deliver value and grow. And AI should amplify that, should accelerate it, not replace it. That's why I say, when somebody says, what's your AI strategist? First tell me your business strategy and then how you're going to use AI to help drive that.

Speaker C: Right, that's great. We'll get into that. Now, you have worked through and experienced and written about multiple waves of technological, I'm not going to call it disruption, I'm going to call it, what do you want to call it? Evolution or stages or waves of adoption. Wireless, digital and now AI. And so let's start off with what do incumbents usually get wrong when a new technology hits?

Speaker B: Yeah, I think it's an awesome question. And I lived through it with one of my startups, mobiquity. We co founded mobiquity right at the beginning of the enterprise mobile wave, when every company's like, we got to have an app, right? Because the iPhone and the App Store was starting to really gain momentum and people could see the world was going to change. But company's initial reaction was, we need an app. They didn't necessarily know why or what they were solving, but they're like, we gotta have an app. And that was the first mistake is like looking through the lens of the technology and failing to realize it's really about, uh, changing your organization, how you operate and the mindset of your people to deliver your value in a different way. In this case, you know, back then, it was about now, all of a sudden, I kind of say the inmates were running the asylum. Now people with smartphones have all this capability in their hand, right? They could shop, they could, you know, see their surroundings, they could look up products while they're in a store. And that was a different capability. They could report an electrical outage to the power company. So it was really about instead of looking through the lens of the technology, it was more looking through the lens of your customers and your employees about what's possible now that wasn't before. And then how do you then challenge the way you deliver value, the way you operate? And so I think once again, don't over Index on the technology is one. And I see the same thing happening in AI. Everybody's obsessed with the model and being able to generate content out of an LLM. But fundamentally what does that change in how you deliver value in your business to your customers? And then yeah, the second thing people get wrong in these waves is they fail to realize that at first everybody kind of overestimates how big the impact is and then they miss when the real transformation happens. And I think this happened with E commerce. E Commerce, everybody forgets Amazon was founded in I think 1994. So here we are 30 plus years later and E commerce is still 30% of overall retail, still transformational. But it took a while, right? And so I think they kind of overreact sometimes but then they miss the longer term change. And I think that happened in E commerce, it happened a little bit in mobile that companies started to maybe missed the mark in the early days and then they underestimate the big transformation and they don't prepare their organization to run a different race. And then that comes back to the third point which is like how do you run um, your current business which you have to deliver today because that's what everybody's expecting and make that a little bit better but also then start to plant the seeds for the future version of your business. I call that a two speed business model. And that's really hard, right, because they kind of fight each other. You know, the people running the today's business view that as a distraction. But yet if you don't do that three to five years from now, you might be behind the curve or irrelevant. And so I think every one of these waves comes, it should cause you to start to think about what could our business look like three to five years from now, whether it's as a mobile first or an AI first company and start to put some of that thinking about what new opportunities would that offer up to a company like ours and how do we pursue those. And that's usually a different model than the one we have today.

Speaker C: So there's the overestimating and then the under. And I'm thinking, I'm thinking of three reasons but this probably something else. One is timing. You know like the technology adopts but people's behavior adopts more slowly. And that's kind of like how fast it. There's the, the misunderstanding of the secondary effects. Like maybe E Commerce is only 30% ever, but there are like, like, like secondary effects and then there's just like the pressure of stakeholders, investors want to this now or. And it's hard to like manage the managed stakeholders. Is there any of those? The main. A main reason or something else?

Speaker B: Yeah, I think you hit on a couple of them. But a lot of it has to do with human behavior. So it's easy for company to companies to run pilots and kind of experiment with things and see what's possible. But to actually have it deeply affect the way they operate or the way they make money, that challenges the very core. Especially when you have a bunch of executives that grew up in the current model and were successful in that model presumably. And now you're asking them to change that like from a, you know, whatever. Maybe it's a product sales model to a subscription model or you know, to a, you know, kind of asset sharing model which is totally different and blows their mind. But uh, that might be the model that customers want in the future. So I think there's a little bit of that built in entrenched mindset mental models from the past. There's getting your employees to kind of realize that they need to reskill themselves to be ready to run this next race. Whether it's in mobile E commerce. Like back in the day, companies had a physical retail store and an E commerce online store and they actually competed against each other. Now we just have an omnichannel experience and the best companies don't even think about that anymore. It's like, what does the customer want? They want to come in the store and then they don't find what they want, they order it online and that experience is now stitched together. But before those employees actually were incented to fight that kind of experience. Right. So the incentives weren't aligned their knowledge of like, you know, what does it mean to be an omnichannel marketer and why do I need that skill set? Like, so there weren't clear pathways to what's in it for me for the employee. I think we have the same thing in the AI world. I just wrote an article on incentives and AI and how important they are. And then the last piece is really, I think it's just fundamentally change is really hard in these organizations and some of it requires moving some of your resources and capabilities around and that's super hard. And yeah, you can hire McKinsey or BCG to tell you how to do that, but to actually unaffect it and drive the change really hard. And so I think so while you can experiment and see if the technology can drive value or not early on and sometimes you get frustrated and realize it's going to take longer. The real underlying change in your company is much harder work and that requires all hands on deck, uh, hr, technology, operations, business lines to work together to move all those functions to the new model. I think it's not some chief Digital Officer, chief AI Officer that's going to wave a wand and change the company. It takes a long time. And most transformations, I hate to say it, that I've seen that are successful take five to seven years. They aren't like six months.

Speaker C: Right, Right, Yes. But I guess it sort of makes sense that you want to appreciate that. I really liked Goliath's Revenge. That's sort of a thing that I. You speak to something that I'm personally committed to, passionate about. Most people that I know work inside companies and the idea that, you know, it's always the disruptor entrepreneur doesn't play out. But, uh, what really one thing I liked about the book was how you laid out kind of the key advantages. Because it seems to me like you shift the mindset, you know, you're willing to put the time in to do the transformation, but you got to understand, like, what are those capabilities that you have as a large company that you can leverage that the smaller companies don't have? What are those?

Speaker B: Yeah. And one of the questions I always start some of my exec ed sessions with, which are usually with execs from large companies. I'm like, so what's the batting average in venture capital? And you know, maybe a few might raise their hand in the room that know about it. And the reality is venture capital is 1 out of 10, right? 1 out of 10 of those venture investments turn out to be really successes. 2 out of 10 might pay their money back. 7 out of 10 fail. And then I asked the next question. I'm like, would your leadership team accept that as a batting average on your innovations and your big investments? And they'd say, no way. Right. And then I pushed back and said, well, you shouldn't have to. Right. Those startups don't have the data, the customer bases, the expertise, the knowledge, the regulatory access your companies have. You have such a big starting point advantage over them. So why can't you be the disruptor? Why can't you run the race faster, given you've got all these advantages that you're gifted with that startups would kill to have, and it's mostly they can't get out of their own way? That gets back to the six rules in Goliath's Revenge, which is if you really want to Disrupt and create this second speed that's really the next version of yourself, which is kind of incubating a startup within an established company. It's like one, you have to be willing to think boldly, or 10x, which is what Silicon Valley does, not just 1x. And dust off last year's plan and say we'll do 10% better. But you got to think, how would we take a leap in how we deliver value to our customers? Number two, how do we balance the continual innovation that we need today? I call that little I to steal a term from George Day, who's awesome and big I innovation, which is that breakthrough, bold stuff that's going to really plant the seeds for reinvention. And it's not all going to work. And we need to give that stuff air cover because it's going to drive tension with the core business. Number three, one of the biggest advantages to incumbent businesses and in the AI world, I think it's the biggest advantage is data. You know, it turns out 50% of the unique data in the world lives inside enterprises. And the LLM companies would love to get their hands on it, but they can't a lot of times because it's really hard to get to and it's buried in systems. But if companies could figure out how to leverage that unique data and content that they have about how their business runs, what their customer preferences are, how their product gets developed, then they have a unique advantage that the outside companies can't get to and they could build their own AI solutions that leverage that, number four, is about you can't do it alone. So the faster you can build an ecosystem of partners that can innovate on top of your company and alongside of you, it's like accelerating your R and D department. And that's a major advantage, but it fights the command and control of most companies I've seen, especially in regulated industries. Number five is the talent piece and valuing talent over technology. And how do you bring your talent along? And it's not just about the key AI hires or digital hires or data scientists. It's about really, how do I rethink every role in my company in an AI first future? Right, there's still going to be financial analysts, they're going to be doing different things. How do we help them rewrite those roles and then prepare themselves for that? They're still going to be HR associates, they're still going to be procurement engineers, they're going to be just doing a different set of tasks. And I think if we enable uh, our people to help rewrite those roles and then build their own development and training path to get there. I think we could transform faster, but companies aren't asking that question. What's your role going to look like three years from now? And then the last one is purpose. And the only reason I think that's so important is I think with the noise level rising and so many lookalike Me too plays out there via AI, it's going to be more important than ever that people understand what do you stand for, why do you exist? And they're going to want to interact with companies that are truly human and truly have core values that they associate with. And so I think that's going to be a key part that persists through all this.

Speaker C: Mhm.

Speaker B: Yeah.

Speaker C: And I can see how many of them become barriers to whatever the technology adoption is. You've uh, done a lot of work recently on AI adoption. You talk about this gap between people using AI, but it's not really impacting their work. Just talk to us a little bit about, you know, what are the problem with AI adoption not being as fluid as it could be should be inside an enterprise and what are some of the reasons you know, talk about skill and will. And you mentioned earlier, what's in it for me?

Speaker B: Yeah, it's a great question. And yeah, you could pick up any report, they're all over the place. But there's plenty of reports that say what you just talked about, which is like the adoption in theory is seems high like 70, 80% among knowledge workers, maybe a little lower among mass market workers like 40, 50%. But coming out the other end, maybe 5 to 10% of all employees say they're truly transforming their role. So where's the grinding gear there? And I think it's a couple things. One is it's what you talked about like for an employee to raise their hand and say I found a way to do my market research 30% faster and cheaper. What's in it for me? If I tell the company that are ah you they're going to say thank you very much, do more with less. We'll take that gain for the company versus you know what, that's awesome. We're going to let you keep some of that, gain that 30% and you can decide to reinvest it in your own M training or you can take time to go after the next innovation project. And there are a couple of companies experimenting with this concept of credits or tokens which is like you find something cool. We're going to let you decide how you want to reinvest some of that savings instead of the company taking it all. And it kind of creates this perpetual interest and energy around wanting to find the next cool solution for AI. And same thing you could do with teams. But ultimately then right now leaders, very few leaders have incentives or the flip side have objectives in their performance reviews around their teams and also their business performance or function line performance around adopting AI. And I think that's going to change in the future because you got to give people a chance to figure out what can I actually drive and not drive. But then ultimately you got to say like, you have to start to adopt this for your business and drive, show real value for it. So the incentives aren't there or the objectives aren't there at the leader level and incentives at the junior level. And then fundamentally this is all about changing people's behavior. You talked about skill and will. I think there's been so much energy put on the technology side and cutting partnerships and getting tech in place, uh, and training people on basic things like prompting and agent building. The thing that's missing is like, how do you change people's mindsets? How do you get them excited and accountable for the AI, uh, solutions they build? Because if that doesn't happen, you're never going to get widespread adoption and transformation. So we talked about incentives. The other part is just messaging that it's okay to experiment and fail, it's okay to go build an agent and maybe it doesn't produce the benefit you want, but that's part of your learning experience. So instead of just watching a video, you're going to go build something and learn. And it's may or may not work, but that's part of learning, that failure is part and parcel to learning. It's a full contact sport and something like AI, uh, and then I think the last piece is leadership messaging. Like how many times are leaders getting on town halls and talking about success cases with AI or even where we failed and learned something, and calling out employees and teams that are adopting things successfully and in every function, not just the tech group, but in finance, accounting and you know, in hr. And I think that starts to get people woken up to say, wow, my peers are doing this, I can do it. And then who do I turn to help? Give me some coaching and mentoring to go to, kind of elevate my own path. But I think it's all those things combined that are creating that gap right now. And I think that's the thing, it's the People side, it's not right now. It's not the tech side that's holding us back.

Speaker A: And that's exactly the challenge. The technology is ready, but the people side of transformation is where the real work happens. Which makes this a perfect moment to thank the sponsor of today's episode, LHH. LHH. Executive Solutions partners with boards, CEOs and senior teams to shape that kind of leadership, helping organizations identify developers develop and strengthen the executives who drive meaningful transformation.

Speaker C: Now, let's get back to the conversation. I heard someone on CNBC kind of saying, you know, that they expect AI, uh, adoption in China to be, it is more rapid than in the US and because of the kind of social contract or the expectation of, you know, whether I'll be taken care of. Does that make sense? And like, do you think that companies that have like a strong people social contract are going to.

Speaker B: Yeah, I think there's a little bit of that command and control approach can backfire. Uh, and that's why I think if you just tell employees you will use AI or not or you don't have a job, and I think there are some companies going that route, I actually don't think that's the right route. I don't think that's sustainable because that's fear not helping you see that AI is your path to a better career. And multiplying the impact you can have in your job or in your community. And until people discover that, I call it your AI moment, where the flash goes off and you're like, wow, I realized I can do 10x more. I can have 5 to 10x more impact as an entrepreneur or as a lawyer or as a manager. And I think until that happens, telling people or laying objectives on people when they don't have a true understanding and appreciation and excitement for what they can do with AI, I think eventually backfires. So I think the key is to go the other way is how do we pave the ground to allow people to get excited and accountable and see themselves in a successful AI future three years from now. And uh, work backwards from that and say like, okay, you know, what are the steps we can help you with to get there? And I think if you ask most employees how much of your job is drudgery today, I bet they would say up to 40 to 50% of the tasks they do are drudgery. And what are the things you don't get to do today like calling on that marginal customer or thinking of a new product feature idea that you just don't have time for? At the end of the week because you've basically consumed yourself with the drudgery. I think most people would be refreshed if you ask them to reallocate their time to a set of tasks that are more meaningful and offload some stuff to AI, as long as they, they have an appreciation for how to do that responsibly. So I think the opportunities there. We're just not asking our employees to participate in redesigning their roles yet. And we need to do that.

Speaker C: Yeah, we don't have, we haven't created a vision for them to step into. I gotcha. But it does seem that we're, we're kind of moving from this period of like, I don't know, let's experiment towards like actually use cases with roi. And so like, how does a leader think about that? Is that the ambidextrous organization kind of concept or how do you manage both?

Speaker B: Yeah, it's a little bit of a mix. Right. So I think the ROI stuff, stuff should absolutely start to be put on. Well, I think there's a basic, I'll call it hygiene level of productivity. So like giving somebody copilot is kind of effectively like giving them a PC or uh, a smartphone in the past and say this is just a productivity tool trying to measure the ROI of like, yeah, it looks like they're saving 10 minutes every time they do their time entry or do, you know, kind of do a browse on the web for competitive research where. So there's that productivity layer that I think companies just need to invest in because it's just modernizing the uh, workplace. Right. And what tools employees are using to stay current. But then there's the next layer which are distinct, I'll call them almost AI products or solutions. So that's like, hey, we have a unique solution that can build a proposal faster just based on our historical best proposals as companies that should become like a product or solution that gets better over time, has a product owner gets reused that should have an R, uh, because it's going to require more significant dedicated effort to stand up, probably some technology and business support and then eventually to run it and expand it. And it should have an ROI like any other product would, even though it might only have internal users. So I think you're going to have a layer of those things where we need product owners mostly in the business or function supported by the technology group to go run with those, put the business cases together and drive them like a product and pivot when they need to pivot. Then there's another layer which are those bold AI things like we're going to basically, and we did this at my last company, Everson, we're going to take an entire slice of the company, in this case, you know, the marketing agency, and rethink it with agentic AI clean sheet of paper. What does it look like if we completely redesign this? And that's where you're going to get a massive gain. But also that's where the tension gets higher. Right, because you're challenging your business model the way you deliver things. Maybe some of the core business leaders feel the tension, but at the same time that's where you can stay ahead of disruption by creating a brand new version of something you offer today, a product or service that needs a dedicated team, it needs protection to go run fast. It probably needs some external partners to help you, whether it's a venture or a tech partner. And that's a different model that's almost like kind of a stage, gate, growth options, lean startup kind of model where you're probably not going to get it right out of the gate. You're going to have to build a feature, set an mvp, test it with some of those early adopter clients, pivot, figure out not only what the tech product stack is, but what's the fee model going to be that's going to work. Are we going to charge for outcomes? Are we going to charge for the tech stack? So all those things have to be iterated very different than taking an existing thing like market research or proposal development and building, you know, kind of, I'll call it a fairly near term product where you have a clear ROI usually and you just need to prove it out versus the baseline. So I think you're going to have these three different types of AI initiatives going on. One ROI is going to be a little bit more clear and expected. One almost needs to be like a portfolio, like a venture investments to play out these brand new things that we. But we have to invest in them to stay ahead of disruption.

Speaker C: Yeah, that makes sense. And I had a follow up question there. But I want to make sure that we also talk about your AI life and then we'll come back from there into that kind of personal AI adoption and kind of put it on the, in the mind of the employee or just the citizen, your AI life. I can see why it's a logical next step, but it seems like a nice divergence from what you have been writing about. Why did you decide to take that on?

Speaker B: Yeah, very different than a uh, business or managerial book. I don't know. I always wanted to write a mass market book. And I also, I think it kind of happened organically as I started getting questions from my neighbors, people in my community, concerned moms saying, like, you know, what should my kid be majoring in and how should my high school be thinking about using AI for them? Or, you know, some young professional saying, should I still major in software development or should I change my career? Or, you know, retiree saying, what's this AI thing? And like, how do I use it to make my life better and stay ahead? So that was really. And even within companies, recognizing there's about 50% of the employees, uh, a lot of those in frontline roles that are still struggling with like, where's the value of AI in my life and how's it going to make my work better, not worse, or take my job. So that's really your AI life was really meant to say, listen, anybody can find their AI moment. Anybody can really elevate themselves and supercharge their life with AI. You just have to start from wherever you are and recognize like, where your unique path is going to be and then be willing to put the time in just like any other habit you want to form. How do you build up your AI readiness? And that's really what the book's all about. And I intentionally took life arcs like parenting, education, health, finance, retirement, just because I recognized that, uh, somebody's going to be attached, be able to attach to something in the book, you know, that relates to their yes, for sure.

Speaker C: So, uh, yeah, no, I love that because I think that it goes back to what you said before is if we can't envision what it's going to look like afterwards, what my jog is going to look like. And stories are a great way to transmit that and have you sit there and practice that mental time travel and kind of imagine it in the story. Tell us about what an AI superpower is.

Speaker B: Yeah, I mean an AI superpower can take on lots of forms, but it's, it's anything that would have been incredibly difficult without dedicated education and training for you to have that level expertise available to you. And there are lots of good examples of it. I mean, on the more professional, technical end. My son's a fourth year medical student and he now uses open evidence as an AI tool that as a primary care physician looking at a patient, he can now summon a GI doctor over his shoulder or a respiratory specialist or a cardiologist. Now he's smart enough and has critical thinking to say, I'm m not going to depend on that for my only opinion, but as a second opinion or a starting point for the patient, it's really valuable. Right? So that's once again, to be a specialist in cardiology or GI takes years, right. But now he has that kind of, of world knowledge at his fingertips. One of the more fun examples for me is like, I like wine, but, um, I'm nowhere near a wine expert and being able to take a massive wine list and put it into chatgpt and say, tell me about these wines and which one would be for under a certain price point, which one would be the most unique given here's my palate. And like, that's the kind of stuff like, yeah, if you're a sommelier, it might take to you you2 years to train on that. But now all of a sudden I have that my fingertips. So I think there's so much cool stuff that's both on a personal level, but also on a professional level that we're just figuring out. Yes.

Speaker C: And I think that like, on the professional level, the stakes feel higher than, you know, if I get a bad wine goes. But like your son, what do you say to people who say, well, you know, you know, I live in a, I work in a very high risk and, you know, big, high consequences environment, you know, regulated, we got life at stake, you know, and like, I can't trust that this thing is going to give me the right answer. What do you tell them?

Speaker B: Yeah, I was just talking to a lawyer in a law firm the other day, and they've just introduced Harvey as, you know, a kind of a large language model to do knowledge management and contract creation. And I think the answer is the responsibility doesn't leave. And I tell my students the same thing, like, you can use AI all you want, but you own your ideas, you own the output, good or bad. And if there's errors, you own them too. And you have to be ready to stand up and explain your answers. Sometimes I'll ask students to provide their prompt to understand how they're thinking. So we have to learn to teach differently in that case, and make sure that students are still retaining the fundamental concepts and their unique thinking and not outsourcing that to AI it's meant to augment, not over overtake your thinking in the attorney example, like, listen, if that can help you generate, uh, an estate plan for somebody, or will, you know, 80% faster, but you still have to do that final review. You're just gonna have to learn to work differently. And I Think that's the big difference. And listen, there are certain tasks where AI might not speed us up because the amount of scrutiny we have to put in that human in the loop, where it's super high stakes, super complex and nuanced, but there's other stuff where it's a repeat and step and repeat process, where we're going to find it can save us massive amounts of time. So I think we just have to take a step back and just make sure we're doing the trade off. I think you put it well, like, what's at stake if I get this wrong or if the model gets it wrong? And am I willing to take that risk? And if not, how could I grab some of the efficiency but still put the right oversight and controls in place? And I think we need to think of each use case on that matrix of like, what's at stake and how, you know, how easy or hard it is to implement and then figure out which use cases make sense to go after.

Speaker C: So where my mind is going is at the beginning, the idea of mindset. It's making me think of a metaphor. Maybe there's like an underlying metaphor, how I think of things. And you've seen many technology adoptions. Is there a helpful metaphor that you would suggest that someone hold when they think about how to use and when to use AI?

Speaker B: Uh, yeah, I think, you know, one of the things I talk about with LLMs in general, and then we'll talk about agents is, you know, I, I like to say it's a genius in intern with access to the world's knowledge, but no common sense. So, uh, if you had that kind of person walk in your door and you recognize they have no common sense, they have no street smarts, how would you guide them? How would you give them the context? How would you challenge their answer? Because, you know, it could be completely off base, but the power of what they can access. Like, a doctor could never read all the scientific journals that are published every day in the world in cardiology, you know, just like I could never read everything about AI so what is. How would I use that resource? And how would I also have the critical thinking to never accept the first answer, to look at it as somebody who can work alongside me, get a lot done, but ultimately I still own, um, the output. And I think that's kind of the LLM m. The agents, I think of, uh, as kind of, I'll call them co workers or an extension of you. And so, like, having an agent that can go look up problem, paying Customers and then get me back a list so I could then be ready to email them with a uh, pre crafted message. That's useful. I'm uh, still making the final decision. But that agent needs access to my email system. It needs access to the finance system. Just like using my credentials or if I, in an e commerce consumer world, I want the agent to go on Walmart, look for the best deal and then actually purchase something and have it delivered to my house tomorrow for a party based on, you know, these people attending. That's pretty valuable and pretty exciting and it's already happening. But like giving it uh, that agent access to my payment credentials, that's kind of a big scary step. So those agents, you have to be comfortable that you're willing to let them reflect you because those agents are going to be acting on your behalf and doing things. And I think that level of accountability and control is not there in enterprises yet. And that's why you're seeing agent deployment. You know, it's exciting. It's the thing that could probably move the needle the most. But it's still early because we're figuring out those controls. You probably saw the news that Amazon had a software agent overwrite or you know, push out a bad procedure that caused AWS cloud to crash, you know, uh, a week and a half ago. Not good. Right. But then even Amazon, this super tech savvy company, said we need more human oversight. Right. So they're trying to figure it out like where's that boundary of control and oversight and human agent interaction? So we're early, it's exciting but still lots to figure out.

Speaker C: Okay, great, we're reaching the top of our time with you. But you know I think it, it keeps coming back to mindset and I'm wondering if, how you define mindset and what do leaders need to do to be able to uh, overcome or replace or however you think of it, unhelpful incumbent mindsets.

Speaker B: Yeah. So I think mindset to me for, for something like AI or any new technology that's going to change the way I work. I think it starts at you know, getting employees to the first step on the scale which is openness. Like are uh, they even open minded to experimenting or trying or sitting through a uh, training? Because if they're not and they're closed minded, arms folded, this is a bad thing, you're not going to get anywhere. So I think how do we get them to the open minded piece? A lot of that comes from peers, peers saying hey, it's okay, this is Going to make you better, or seeing examples where this isn't going to replace me. Just like everybody said AI was going to replace radiologists five years ago. And now we have a shortage of radiologists and we have more radiologists and need more because now we can apply radiology as so many more fields because it's more efficient. So like, you know, there's something called Jevons paradox that, ah, the more efficient we make something, the more demand it creates. So, so I think that's the first thing is just getting people to recognize, get over the fear, be open, say this is a good thing. You know, yes, it's got risks we got to manage. The next layer up is kind of getting them satisfied that, uh, they can do this. Right. It's not this, you know, it's like riding a bike. We can get on this thing and once we are able to task AI for one thing, we could start to do it for 20. And you know, once we can build one agent, I can build a bunch of these. But getting them over that anxiety curve that they, this can actually, they start to see the value of actually making their job better. And then the last one, I call it like the excitement or satisfaction layer. That's the ultimate where they're coming into work every week thinking there's other stuff I can now do, there's things I can innovate, there's things I can reimagine. And not only am I excited, but I'm willing to kind of work alongside AI and be accountable for it. Like, that's the ultimate to move people there. We need the safety net of experimentation, the leadership, messaging, the incentives. And I think this notion that I can improve myself, it's not just about the company and making them better and more efficient with less people. It's about I can be a multiplier for myself, my career and for the company. And it's doing more with more. And I think that's the main message.

Speaker C: Yes, yes.

Speaker B: Yeah.

Speaker C: And it's all intertwined. There's something in it for me and I'm not going to be.

Speaker B: Yeah, absolutely. Yeah.

Speaker C: We've reached the top of our time with you. I've got a number of other questions I'd love to ask, but people can certainly find your work and we'll, we'll share with them how to do that. But yeah, it keeps anchoring down to mindset. And we've talked about mindset inside a company, inside a person. But let's look at the whole, like, metaphor, the whole like, question of AI Adoption in society. Is there a mindset that is wrong or that we need to change?

Speaker B: Yeah, there's this knife edge of, you know, some people are calling AI dependency. You know, this notion that it's really important that we bring people along in terms of their critical thinking skills and the ability to work alongside AI is fundamentally based on how well their soft skills come along, not just their hard skills. Judgment and bias detection and EQ and collaboration and creativity and all the things that they need to effectively challenge AI and make sure they're not just accepting the answer. And we're starting to see this in even K through 12 schools. There are certain parts of the brain that gets shut down when you become overly dependent on AI and that's not good. And we actually might be suppressing critical thinking and the very things we need to elevate to make people effective. So that's the big thing is like I think empowering people is one thing getting them excited about AI, but also making they can use it safely, responsibly and in a way that's not going to degrade the things we actually care about that make us uniquely human. So I think that's big. And then the last thing is leaders have to stop talking about cost reduction. They, you know, every leader in the tech companies, you know, you know, block laying off 40% of their employees, which I think was AI Washington. But all this stuff about, you know, this is we can do, you know, the same thing with 80% less people. That message, yeah, cost cutting will come along with this. But the idea that AI is about creating more opportunity, multiplying what we can do, reaching more customers, allowing our employees to do the things they couldn't get to before. Like that's the main message. And yes, will we get efficiencies and productivities out of it? Absolutely. But I think if you lead with cost cutting message, you will destroy your culture. And I think, you know, we're starting to see that play out in the markets.

Speaker C: Yeah, it seems like that is sort of the two visions. Right. I think it was in m mid-1900s people started investing in leisure, in, in, in, in golf because people thought, oh my God, what are we going to do? We're going to be able to do all the work in half the time, but we're as busy as ever.

Speaker B: Yeah, we fill the jar. Yeah, we find new things and, and I think, I think that's the way it's going to play out.

Speaker C: So it's got to be, it's got to, it's got to thank you. I, um, mean, we didn't get to through everything. You've got great. You got three great books, you've got a number of articles and a lot of it on knowledge at Wharton. You've got a book website. What's the best way for people to continue to connect with you and learn from you?

Speaker B: Yeah, I think look for me on Substack. So I'll continue to post articles there for Scott Snyder, not the one who wrote Spider man comics, but I that

Speaker C: I was about to. I was almost here ready to ask, ask about the hundred of Spider man

Speaker B: comics I know there is. That's the more famous Scott Snyder. But then you can also go to yourai Life, which is the book website and then certainly you can find me on LinkedIn or shoot me an email. So.

Speaker C: Great. Well, Scott, thank you for exploring this and bringing it together and packaging it so nicely for us and taking some time to unpack it for us. We really appreciate the work that you do.

Speaker B: Thank you. Thank you for giving me the opportunity.

Speaker A: Thank you again to our sponsor of today's episode, lhh. We encourage you to check out their executive solutions and learn more about their beautiful working world@lhh.com. thank you to our guest, Scott Snyder. Thank you to our executive producer, Zach Ness, our producer Nazanin Humayung Jam and our editor, James Pierce. If you like what you heard, please follow, download and subscribe. I'm your host, Kyan Krippendorf. Thank you for listening. We'll catch you next time with another episode of Outfinkers.