speaker-0: We're gonna be AI first and we're gonna be AI forward and that's how we're going to accomplish more. The company strategy should have been set, the North Star should be existing prior to any tools that you implement.
speaker-1: Everyone wants to get better, everyone wants to feel motivated to embrace AI, but we need to have in place the right space where we celebrate people coming into not knowing anything and learning, right? And it's a phase approach. We cannot expect anyone from getting one day and become an expert. If you try
speaker-0: Truly have a labor cost issue. You have a labor cost issue with or without AI. So just address that head on, right? If you anticipate that AI is going to come and defray all of these human capital, then you have to be really honest with yourself and say, like, well, my goal truly is to cost cut my way into competitive advantage and differentiation, which is very hard to do because, like I said, that can be copied by your competitors.
speaker-1: Companies write algorithms, governments write laws. Someone has to write the human story. People don't resist AI because they are irrational. They resist when the story is unclear, when the purpose feels threatening, and leaders demand adoption without building trust first. This episode is about the leadership work that comes before anything else. Welcome to the Human Protocol, the podcast about staying human. While building the official, I am your host, Michael Solomon. Most leaders think their AI problem is a technology problem. My guest today is gonna tell you it is a change management problem. And that the way that you frame AI decides everything that happens next. Call it cost cutting and your people hear layoffs. And quietly stop trusting you. Call it growth and they start building with you. MJ Mingyan Yan is the chief strategy officer at the ThinkTech Leader Credibly, where she leads Company's Gen AI transformation. She's a Ford veteran who built a startup, spun it out, and watched it fail, then rebuilt. She's a Yale Digital Ethics Fellow and my favorite detail, a literature major from Harvard, now leading an AI overhaul. MJ, welcome to the Human Protocol.
speaker-0: Michael, I'm so glad to be here. Thank you so much for having me.
speaker-1: It is an absolute pleasure. I'm very excited about this conversation because I know this is a topic that you are passionate about as well. And when we had our intro call, you mentioned something that really stuck with me since then, which is this concept of lazy leadership, right? And when we're talking about lazy leadership for our audience, what do you have in mind?
speaker-0: Well, I think ⁓ that's a great question. There's a a few things I want to touch on, right? One is I often see change management being delegated or outsourced. So a lot of top level leaders will say, ⁓ we have something really difficult we need to accomplish, such as the adoption of a new technology. Hello, VP of whatever, you go execute and implement that. I've heard a lot of leaders talk about how. They're middle management, especially receiving direction to say, you're gonna, you're gonna be responsible for making sure that your team implements and adopts AI, but also you're responsible for delivering everything else that you're delivering for the company at the same time. So to me, that is lazy because you're taking the hardest part about leadership and you are delegating it to someone who doesn't have necessarily the resources or the bandwidth to do that. The second part of being lazy in my mind is when you take strategy and you outsource it to AI. When people ask, well, what is this transformation that you're leading? And the answer is, well, we're going to be AI first and we're going to be AI forward. And that's how we're going to accomplish more. Well, the company strategy should have been set, the North Star should be existing prior to any tools that you implement. So this tells me that there was a gap in thinking that is now being Proposed with AI, but it's actually covering up the fact that maybe the company didn't have a North Star or a believable strategy to begin with. But because AI is such a hot buzzword, it has taken the place of actual deep thinking. So those are kind of the two examples. And the last one I would say is ⁓ I will also often see leaders who will mandate AI usage without actually doing it themselves. They come to the table with very Ambitious proposals of what it can do. And hey, why can't we cut head count here? ⁓ we should be able to cut costs here. But they've never actually really played with and understood the constraints of AI in a real way. They've only heard about what it can be done. So not understanding the leadership failures and then reflecting that failing with AI, for example, I think is great. I think leaders should share stories of when they experimented. and failed with AI. And that I think will build more credibility than the three things that I mentioned earlier.
speaker-1: Yeah, and this last part that you touched on is extremely important, right? Because how are you gonna make people to follow your message and follow your mission and feel empowered about adopting AI if you haven't had the time to adopt it first, right? And it's not that we're gonna get into this topic anyways or any shape, but it's it makes me think about how do we think our government or governments are gonna make laws, right? To make sure that ⁓ AI is there for us if they don't even understand the technology behind it. So everything should start at the top. You understanding what the technology is and the strategy should follow after. But it's not that we as leaders can remove ourselves from this equation when we need our people to follow our direction. That's our
speaker-0: Yeah. And I think, you know, there's a difference between the word empower and mandate, right? So AI empowerment is a very different tone than AI mandate, because mandate seems to me like you're not actually helping people use this technology in a way that makes them feel capable. You're just saying they need to use it or else, which I'm not saying doesn't work, but I think that there are risks and longer term. Percussions and the way you would execute that strategy would be very different than if your mindset was, no, I actually want to empower my organization with AI, not I want to mandate that people in our organization use AI because I'm frustrated by the lack of adoption or progress. Then I would say, you as a leader, that is your job to solve for that frustration and lack of adoption, right? And there's a better way to do that than just say, well, everyone has to do it or else.
speaker-1: Absolutely. All this conversation, it comes around a topic that I also know that you're passionate about, which is when we talk about AI just from the lenses that we're gonna implement it or we're gonna adopt it just to cut cost, right? and you mentioned something to me that it makes a lot of sense. It's it's all we do, it's implementing AI to cause cost. That's what competitors can do. They can copy that. That's not on a strategy in a way, right?
speaker-0: Right. Yeah. And I think, you know, there's been a lot of literature lately about how maybe it's not cutting costs super effectively. And I'll give you very concrete specific examples, right? So when we first started implementing AI adoption ⁓ credibly, that was what the executives were excited about. ⁓ we can increase employee productivity, we can cut down time, maybe we can eventually think about, you know, shrinking these teams. These are all fun and interesting things to entertain. But if that is your only benchmark, a few things very quickly start to happen. One is I didn't see the initial wave of employee excitement around AI. We know everybody was using it, right? They were using it to make their own work efficient, but they didn't feel sanctioned to say they were doing that as part of a company permissions. So, you know, you had shadow sort of IT situations pop up. You had the fact that people felt very hesitant and also ⁓ distrusted. The company message because the first thing they're going to think about is like, what about my job? Is it coming after my job? And no one's going to say it, but it's you can clearly hear, you can hear it in the silence, right? And then I think the other thing that happens is you start to remove really good ideas off the table because the first set of ideas are so what everybody wants to chase because they are low-hanging fruit. And that distracts you from maybe doing the more value-added thing. ⁓ and then the last thing is like you really have no. Better ROI because everything is going to be down to, well, how much does this cost me? And have I implemented the right cost measures? I will tell you that our API costs have gone up. I'm on the billing. So I've seen these costs, these costs go up. Right. And now we're being questioned like, hey, are you guys spending like too much with open AI? And yes, there are some things we have to do there, but these costs don't necessarily disappear. They just get shifted in different ways. And so If your only benchmark for AI is cost and how much you're not taking out, then you're not looking at other benchmarks like is your organization more nimble? Are your people more keep work working with AI? Are you changing and adapting faster? Are you creating and doing projects that are worthy for the company that have a longer term return on investment that you are not seeing right now because they're using AI to do it? There has to be at least five metrics that companies look at when they're thinking about AI adoption. And so much only just look at have I saved human capital without looking at the other factors, some of which are qualitative. So even in my own personal experience, it's only when we got into the second wave where we had concrete use cases, where training was implemented, where we specifically told the employees, no, this is about growth and new ideas and not about cost cutting and efficiency, that I really see another generation of adopters begin and be excited and confident enough to work with this technology.
speaker-1: And I'm sure that was in part, MJ, because of the strategy that you put together was for people to feel like AI is here to augment what we do, not to really erase their jobs or anyway, right? Because really we are human beings. If we think something is coming for our jobs, we're not gonna adopt something that is gonna hurt us, right? It's just a natural reaction to it. But how we frame it, how we implement it, and the strategy and the communication that we put around that, it makes total difference in in these projects because at the end of the day these are still human beings implementing this.
speaker-0: Yes. And I think leaders have to make tough decisions. And I've had to make tough decisions. But making tough decisions to use AI as a scapegoat for prior bad decisions or for suboptimal decisions is not necessary. Right. So you if you're if you truly have a labor cost issue, you have a labor cost issue with or without AI. So just address that head on, right? If you anticipate that AI is going to come and defray all of these human capital. And then you have to be really honest with yourself and say, like, well, my goal truly is to cost cut my way into competitive advantage and differentiation, which is very hard to do because like I said, that can be copied by your competitors. So I think that when people are talking about AI strategy, and I've spent quite a bit of time on this, I will say I did not start with a strategy. We started with building culture and excitement and training. Then we built a ⁓ in-house Skunk Works Lab that works with AI agents and then we rotated people through it. I've done nine plus hours of training with the entire company. I've gone to classes myself and brought that back. Then we said maybe we should have a strategy, right? Because now you've learned enough, you've seen how people work, where you've seen some of the gaps. And now we said, what does this future of work look like? I developed an internal framework. And one of the things that I think is also really important, and leaders often eclipse this when they're thinking about AI adoption. Is that AI is really good at introducing complexity. And so just as with change management, which by the way, everyone is terrible. I don't want to hear anyone say they're good at change management. They're not. Just because you've done it many times, every time it looks different. The people who are best at change management are really humble about the fact that it's it it's it's hard every time because it's unpredictable every time you're dealing with people. So first, if you think you're good at it, you're already off to a bad start. ⁓ With AI adoption, when a AI is really good at introducing complexity. And what that means is like I'll give you an example in underwriting, right? When we do underwriting and risk assessment, you can have a human decide the definition of is a business seasonal? Okay, well, what does that mean? That means that for some businesses, their their high months are the summer season because they're in a tourist place like Virginia Beach or whatever, and their low months are in the winter. Okay. for college towns. Maybe it's the opposite, right? They have students back in the school, but then the summer they're out. Okay, so humans can understand some variation of this. And they have big pictures in their head, but an AI can understand down to the individual business in that exact location. And we've tested this, right? And something that takes someone a lot of time to train to develop rules that they can then execute by. And AI can actually come up with a very dynamic model that is very specific to that business, which is great because it actually delivers more customer value because there's more knowledge and precision around the health of that business in the business of lending. However, it's complex because now you don't have rules you can just apply. You don't have things you can just automate. Now you have to really think about, well, what is the prompt that I'm gonna really work with AI to generate this. And by the way, like does do I agree with the output? ⁓ it looks normal for this business in this area, but just a few zip codes over, it's going to look very different. So when you take that example and you sort of compound it over business cases, it's very important for leaders to understand when AI starts to change work, if you're using the power of the technology, you're likely making a lot of work streams much more granular and complex, which means people end up managing more work streams, even if they're agents. Right. Versus true automation, which you can just process the same thing over and over.
speaker-1: And now one out to expand.
speaker-0: And it's one process. And a lot and and I've seen a lot of companies use Gen AI to do what is actually just automation, which also means you're not taking advantage of the full power of these models. So there are a lot of like if you're a leader trying to implement AI and you're not thinking about how it's going to increase complexity, increase decision ⁓ decision flow, increase the smaller granular branches of work stream and taking one big work stream and starting to create. These tiny branches, which are then more complicated and varied to manage, I don't think you're using the power of Gen AI in the right way.
speaker-1: So many layers. So many layered, right? And and I love the first one which I think a lot of people still make that mistake. Gen AI is not an automation bundle up with a chatbot or with an open AI API in the back end. Right? There's so many layers to it. And that representation of what leaders need to think about when the whole picture is in place is really extremely important because it's ⁓ There's so many layers. The more you work on it, the more your people will have to analyze different outputs and different ways. And you know, human beings, our brain is limited in a way when we are analyzing, we usually focus on one thing at a time. With the help of AI, we're probably getting multiple things to take a look at. So that whole idea that the work is getting synchronic, that AI is really getting a lot from us. I see human beings still doing more with the help of AI.
speaker-0: Yeah. And I I think, you know, I see a lot of LinkedIn posts where people are saying, like, ⁓ you know, now my teams are managing, you know, dozens or hundreds of agents. And I have to really ask, is that realistic? Right. Is that is that should your team be managing hundreds of agents or are you truly talking about monitoring erroneous output or something where that you're seeing you're seeing a model and you're seeing ⁓ anomalies? So you're doing anomal anomaly monitoring and detection. Which is not the same thing as managing thousands of agents. That's not that's not that's not ⁓ sincere. That's not an accurate description of what's going on. Cause I I can't I can't realistically say like that's a good use of people's time and nor do I believe any person can actually
speaker-1: Monitor all that that's great.
speaker-0: It's not possible. But if you're talking about anomaly detection where things are running autonomously, but you have a way to look at something is getting too long. We need to investigate that's great. And I think that's that's we should be upskilling people to do that. That to me is a system AI is orchestration deployed the right way. But if you just have a bunch of workflows and humans are just actively looking at all of them, I think we need to rethink the sort of the design of the overall system.
speaker-1: Things go wrong. Yeah, exactly. One thing I've learned through going through all this, and I'm sure you had as well, is there is no one size fits all for this conversation. It's not because people are saying on LinkedIn or whatever, this is what's happening, that this is really what's happening. I really loved the approach that you follow ⁓ at your organization, which was let us try, let us learn from our organization what works, what doesn't, and then we can continue. you know, to refine the message and see where we want to go. And I think that's the absolute best way of handling this, because we don't know everything and we are all learning in a way. And when you have the whole organization that is composed of people with multiple roles and and different cultures trying to embrace this, of course you need to, you need the organization need to have the time to really understand how our people are responding to it. And then you keep going and you keep going. I think that's the absolute best way to really understand how we can best implement AI in any organization, because all organizations are different from one each other.
speaker-0: Yeah. And I think, you know, there's not enough credit or conversation being really given to the actual experience of learning with AI, right? Because so much of the authority and ex ⁓ subject matter experts is on like, I've implemented this great saying, look at the capabilities of AI. It's like we I I get it. I understand these models are very capable. I think everybody understands that. How many of us are PhDs in Sanskrit? One point two percent of the population. And this thing can do, you know, better than most humans on really hard mass math exams. And I I get it. I get it. Okay. Ninety-five percent of that capability is irrelevant for the work that you're doing on a day-to-day basis because you're not using those capabilities. So because AI operates on an abundance model, because it is so intelligent, it's actually harder for humans to harness that intelligence and figure out how it works for them. And so Learning is such a crucial part. And that's why I think there's so much conversation with AI around education, right? Like people just don't give themselves enough credit and organizations have to l often lack patience to really learn. I when I was in a class at Wharton, one of the ⁓ people in my class talked about he came from a very large company and he was just you can just see he was just sitting there like, I'm hearing you guys say all these amazing things about AI. All of my databases. Are separate. The same piece of data that lives in these different databases have a different label and mean different things to different teams. So the first thing I have to do is to invest millions of dollars in data engineering so that I can connect these databases and at least apply some semblance of a common label so I can run an MCP server so that I can actually give data to AI to analyze. And by the way, We have to then figure out because I can't do it all at once. What is the most valuable piece of data or or clusters or groups or parts of that database that I can do that to begin with? Right. And you hear these problems and they're very real. And that's one company. And so the fact that companies are not acting or are later in adoption is not because people are just waiting around being like, we don't believe in the capability of these models, right? I don't think People, I think people are generally at this point understand these models are very capable, but you have to give the organization time and process and investment to actually make change happen. This is not unlike when you invite, like, like for example, if you have a brilliant PhD who comes into your company and gives a and delivers to your manager just like an amazing lecture on how you should run a company. And optimize for its profits and how you can turn this into a billion dollar venture. And that person is a hundred percent right and spot on. How long would it take you to actually get to that result? It's not tomorrow. You have to actually do all those things that the person is recommending. It doesn't make the person less intelligent. It doesn't make your staff, you know, not recognize that this person is ex brilliant and saying all the right things, but the execution still takes time and there's still change management and learning and failing and restarting in all of that, right? It's not one happy path. It's very messy. And that is how learning actually happens. It doesn't happen in a contained, predictable way. It happens with detours. It happens through failure. It happens through variability. That's actually what learning is. And I think people don't recognize that in adopting new technology.
speaker-1: Yeah, I'm wondering if part of it is people, you know, being worried of trying and failing. We don't give enough credit for those that try and fail. We are a society that we are usually just, you know, acknowledging the good things when something when people get something right. But behind the scene, as you were saying, the feel and the journey, it's a lot messier than just the destination when you reach up there. So this is what I also believe We need to empower our people to be okay with trying and failing and recognizing when something didn't go the way we expected, because that's how we learn what works for us. What happens in organization is when you amplify that to the whole organization that can be hundreds, thousands of people, of course, you need to take all that into consideration. And that's why it this thing takes time. If you really want to implement it the right way. It's about empowering your people to give it a try, to use it and make sure that you get data from them, what is working, what's not working, and be open to feedback at the end of the day.
speaker-0: Yeah, I think so. There's some practical things we can do here, right? So I think first of all, I was telling someone else the other day, I think especially like America has this very interesting, I I I almost think like just in relationship with the word failure, right? It's like we are always told you learn through failure and failure happens in everyone's lives and it's really important and of overcoming adversity and all that. And then we celebrate. the people who are really, really successful. And often in organizations, the language around cellure and the artifact around failure are different, which means, which means like people say they celebrate cellure, but they don't actually celebrate failure. So there's a usually there's a gap because now it's like, ⁓ you cost this money and blah, blah, blah. Right. Like, I I'm lucky enough to work for an organization where this is not the case. I cannot tell you how many projects I have failed at and spent hundreds of thousands of dollars of the company's money and I got no backlash for it because I learned. So with AI, there has to be different metrics, right? So first of all, I would expect 90% of your tiny project to fail. That should be a very flat expectation. I can name off the top of my head multiple agents I've had sales reps and other people deploy that didn't work and it was fine. Because they learn how to use AI. The second has to be there has to be a metric around AI proficiency for the company, which means you do this thing, you launch this little agent, you build this gem, you wrote this piece of code, you built a UI dashboard. It didn't go anywhere. No one adopted it. You're not even using it for yourself. Okay. But did you get better at working with AI? And if the answer is yes, then we should celebrate that, right? So much of our Metrics are outcome based because we've been taught to think about that in a business context. But in learning and adopting new technology, there has to be metrics that are learning based. And learning is is qualitative and subjective and difficult to measure, but it doesn't mean it's impossible to measure. I've done surveys in our company around employees' expectations and satisfaction and usage of AI, saying nothing about did you produce something that the company is actually using? Which by the way, we give people prizes if they do, but just seeing that percent of people who are whose comfort and ability to work with AI has gone up, that's an overall corporate success metric. Right. So I would suggest that if we shifted or at least added one metric of success onto learning, are your people learning? And are they because if they're learning, they're also more adaptable to change, right? Which is what you need that. You need that in a resilient organization. Then keep going. Maybe tweak how much you're spending, how you're incentivizing people. We had a very recent debate where we were talking about opening up Claude to anybody in the company who was interested. And our CFO and our executive team, part of them were like, Well, that's a that's gonna be quite a bit of money. So we need to get people to write a business case and suggest why they want to use it and what problem they're trying to solve. And immediately, Myself and the VP of product was like, nope, we're not gonna do that because you're setting up a barrier and we wanna be AI forward. So what we need to do is actually give people free access, let them play with it for 30 days, and then have them tell us what they tried to do with it and was it interesting or not. And guess what? People got access, they played with it. A lot of them ended up not retaining the access, but we learned very one very important thing, which is what I just told you. My my own team played with Claude, right? And we had a meeting. They had access to cloud. And they said, and I said, what kind of problems are you trying to solve with Gen AI? And they gave me, well, I want to run this report at this time of the day. And I want to send it to like this merchant and blah, blah, blah. And then I said, that's not a Gen AI problem. That's an automation problem. So you're trying to solve for a different problem, not using this tool. Okay, so now we have our data analytics person who was learning to build automation. And now we're creating these tools that allow these people to be better. They learned. What is Gen AI use case? What is not Gen AI use case? And they didn't end up using the more sophisticated models after 30 days. It actually got shut off for them. That is all success because look how much you learned in the process, right? Without creating that barrier to entry. And you learn things you never thought you would learn. That's that's the power of opening it up and encouraging people and not putting a limit on it, but also letting them know like they're all off ramps. You can still solve your problems and do better, but you don't want to. prevent people from trying new things.
speaker-1: I I love that angle of, you know, turning people from passive recipients into really builders. 'Cause that's what we really need. We need to give people the tools and you know, the education and the ability to go there and try, see how it can help you and how you can learn. And we are human beings at the end of the day. We know human beings. We love when we learn and when we feel ourselves useful, right? That's how we should be looking for. That's why our people, everyone wants to get better, everyone wants to feel motivated to embrace AI, but we need to have in place the right space where we celebrate people coming into not knowing anything and learning, right? And it's a phase approach. It cannot be we cannot expect anyone from getting one day and become an expert. ⁓ we can embrace it. but one thing I love ⁓ about the things that you're doing, ⁓ MJ, is ⁓ I think that you mentioned to me that you have stood up an internal academy, right? And you're running pods of people building real agents. And I'm wondering if you can share with us how this activated people and how it's motivated them. So people can, you know, they came in and they embraced this instead of feeling that this was a mandate for them to do it.
speaker-0: Yeah, so ⁓ this is now I would say round probably three. So I would say ⁓ what I'm about to describe to you is probably four years in the making now, right? So it's not something you achieve on day one. So I run the internal innovation incubation team called Skunk Works, which is modeled after Lockheed Martin's ⁓ very famous Skunk Works project, where they took a bunch of really talented, cross functional people, gave them a goal, gave them very little resources and said, Go solve this problem and because we need you to solve this problem. And so I often joke ⁓ when I talk about the Skunkworks team because I said, you know, hey, I'm not buying anyone lunch. There's no t-shirts. The work and the companion, the the work and the company you keep, which is the people who you work with, those are your rewards. Okay. So the first iteration of that team, we were always giving them, this is pre AI, pre-gen AI, ⁓ unsolvable problems. Right. So the question, so immediately you set the culture of, These problems, no one has easy answers to. In fact, no one has answers to. So you don't come in with your expert hat on, thinking you know everything. You come in ready to challenge, you come in ready to voice your opinions, you come in ready to fight for your beliefs, and it gets better. What's interesting is part of the automation problem in the beginning, I knew from the very beginning it was going to be a failure. Because people, like I said, ⁓ when we did end up introducing ChatGPT and Gen of AI, it was a a very human construct to look at the best underwriter, the best processes we have in place and say, how do we take this general intelligence tool? And then how do we mimic all of that? How do we make it do exactly what this is doing? And by the way, we're going to compare the answers against our best underwriters answers. ⁓ and it's not working. It's not even coming close to humans. Okay. Great. Is that how you automate? Because that's general intelligence for very specific tasks. And these tasks are optimized for humans. Who can only remember 17 things versus a really intelligent model? So the first wave of that, which by the way was a year, okay, because this is how long it takes people to change how they think and how they actually work, failed. Failed. All right. And so second round, same people, same question, right? Now the people were like, Great, we just failed. Okay. Now they started asking questions differently. Okay, why are we doing it this way? What's the risk if we didn't do it this way? Okay, now we have some real answers. Now we have two patents from that part time of how we worked. And you can see this is for year one, we produce nothing. Nothing. All right. Year two, two things. Year three, six things. Year four, which is where right now, multiple teams working at the same time. I've cycled people through. I've done multiple trainings. Now the people who are building are levels like junior level people in the company. Who have never even before touched AI. But because their managers had been on the team and now empowers them and feel confident, that confidence and inspiration, and yes, you can do this. And by the way, I know how to do this and I'm supportive and I've done this before, trickles down to them and they feel like they can. So now I have literally two agents that are built by junior level people in the company who are not technical, who have never been on Skunkworks. They were pulled in and I asked their manager and I said, How do they feel? ⁓ working with AI. And he's like, they love it. They're having a great experience. Right. So building the right culture, actually being okay with failing, and then knowing that this cycle is going to come, but it takes a little bit of investment in time up front. So if anyone is going to build this incubator, I would suggest that you start the same way we did small elite team, give it unsolvable problems. Make sure though that the first round is like usually the company goes in with like, we need to have some quick wins. If we can just show that we got some quick wins and then we can show that this team is worth its value. It's like, no, your your output is not did you deliver the quick win? Your output is did you change how people think about how they're supposed to work? And that then meant that this becomes a self-sustaining cycle to the extent that I don't really run that team anymore because I just sit in the meetings, but now people are just running these things by themselves. Right. So now it's a truly a self-sustaining body. That can take new technology, bring other team members in as needed, train them, push them out. And then the next version of that is one of the agents that we are building. It's not an agent, is a skills repository. And what he's actually doing is taking all the different pieces of knowledge around the company and putting them in one central area, but in a way that anyone can go in, access what they need to access, and then build agents on their own. So that is now what we're trying to do. And that's only because people have gotten to a point where like, People are comfortable, first of all, even giving you the information for you to work with, right? Because if you think AI is going to take your job, why would I give up my secret sauce of like manuals or stuff that I'm doing? So now everything's in one place, right? I'm seeing the QAs and I'm seeing the feedback loops and I'm seeing like MCT servers being built. So those are the kind of things that will come and they will happen faster and faster. But that initial phase, the learning phase, is going to take a little bit of time. It probably took me. three years to find a version of AI human hybrid models that work. Yeah. It took me three years to do it. And and if you take less than three years, like congratulations. But if you told me you found it on year one, I will have a hard time believing you.
speaker-1: That works for you guys. Would too. It doesn't happen from night to day. It it really doesn't. And and and you know, it's ⁓ that's what I want people to really understand, especially on organizations implementing this. The way we do things matters and it takes time, right? The quick ROI that people are looking for, mm, that's not gonna materialize tomorrow. Right? You really need to give time and you need re it really needs to be part of the ⁓ the overall strategy. When I listen to what you were saying, a few things come to mind. One, it takes time, right? It takes effort. Second, the training creates awareness. But really, the building with it, it's what creates the belief that people have on working with this. And that's where the magic happens. Because once you get people to feel part of this mission and they like what they're doing, they like what they're building, they will continue to embrace it, right? And the more they work, with it probably they will continue to find more use cases where things can get better with the help of AI. And to me, that's really kind of what we all need to think around. How we can get people to jump on it with us so they feel like they're part of the mission and they believe on it. Cause once people believe on it, we know what happens. They keep going and they follow they follow the direction. Yeah.
speaker-0: I think working with AI is very much like a lot of the intangible things that we often talk about in businesses, but we give we understand those things take time. So for example, a business building trust that takes time. A business building customer loyalty, that takes time, right? You may want to have the trust and the loyalty. And you may think you earn it and you deserve it. And if people can just do this faster, but I think Leaders understand that building the trust in a brand and building the trust and the loyalty of customers, their time has to be a component because you need to be stress tested, right? Those things are the product of having been tested. And so AI adoption is the same thing. You want your people to trust using AI. You want AI to become truly embedded in the work of your company so that it's not just. functioning on a mandate, which then kind of goes away, or even worse, people are actively sabotaging you. You want it to be truly adopted as a tool of excitement and people are working with it and want to work with it and are advocating for it and being evangelists for it. Then that is like building trust and loyalty. It's going to take some time and that's okay because when you get there, then it's self sustaining.
speaker-1: ⁓ it is fascinating to hear from, you know, real builders like yourself how you know how things started working for you, the time that it took. It's always, you know, it's always to me fascinating to hear really the honest take on what is happening with AI out there. Right. Because the more we hear people in the news, some people may think because they, you know, everybody just has a recipe for implementing AI in three days. and the ROI in a week, which this is not real life. Yeah. And we need people to really understand and w what is real in all this conversation, which it takes me to to my next to our next segment, ⁓ MJ. If I recall from our previous call, I think you mentioned to me that you you stopped hiring junior engineers, right? And now you have a need for more senior engineers. that are stuck with reviewing complex AI written code that demands debugging. Things have changed, right? The work has become more complex. And I wonder if you can work me through on when did you see that shift happening?
speaker-0: Yeah, well, we didn't stop hiring junior engineers. It's more that I saw the the we have junior engineers and senior engineers. And the way that our tech department started with working with AI and how they asked people to work with AI was which is very common, right? In the beginning, the junior engineers are like, Great, I'm gonna use AI to help me write my code. And so they would deliver and try to and code for code review for senior engineers who then spend a lot of time looking at this junior engineer's work, A, because it's complex, and B, they don't even know how to debug it because they can't even find where the error is. So then they're using AI to try to it becomes a maddening because they feel like they're not really ⁓ exercising their value as engineers. And also the code that they're given doesn't allow them to coach and develop people because it's all pre-packaged and written by Claude Cook. Right. So eventually we kind of had to really think about How are we asking people to actually work in a way where the senior engineers can still mentor the junior engineers? The junior engineers have to be able to explain why the code is written in a certain way. How do they think about it? Right. Understanding and explaining how they're thinking about it is very, very important. And so it's not that the team stopped working with AI. It's how you work with AI, as we all know, has different consequences and repercussions. And if you really want to preserve the value. Of how someone with more technical expertise wants to present themselves and their work, then you need to have them work with AI and their junior developers in a way that sustains that. It is not to cut AI out. It is to use it in a way that develops and continue to develop that human expertise while increasing productivity. But it is not okay to say, now that you have these tools, I'm gonna expect you're gonna code three times as more because we're gonna get. Is three times the amount of garbage that then somebody else has to review and make right. Because at the end of the day, they're still on the hook for code that makes it to production. So this is where I think like setting arbitrary standards without really understanding how humans are motivated along those work streams, you're going to shift value to different parts that you didn't anticipate. And you may have to learn from a couple of iterations, but I think that's what I see. And I've heard people talk about mandating. more output for the junior developers. And then the developers would do more outwork, ⁓ output that is not of the right quality, which then requires more heavy ⁓ monitoring and code reviews and in-depth code reviews, which then take up people's time who are actually technical experts who have scarcity, right? Because they're fixing other problems in the company. So you're creating more problems in a way, ⁓ depending on how you choose to work with AI.
speaker-1: Yeah, that that makes total sense. And and that's what the shift we are seeing in not only with developers and engineers, with in any other of of the roles that that our teams have. Things are changing and them depending on how you work with AI, these are different problems and different kinds of, you know, ⁓ tasks that you are probably gonna be responsible for. Because now it's only it's not only human beings in charge of the whole process. Now we have AI in the middle. And that itself creates a different set of challenges and potential ⁓ processes that we need to follow to to kind of manage the whole process. Managing processes now is not only humans, it's humans plus AI and automations, right? And that's the shift.
speaker-0: Yeah, I I mean I think that's also why like I'm not as concerned as a lot of people about AI taking over creative content or writing, because now we are all so used to reading AI generated things and there's such a now there's even more demand for ⁓ original writing and voice and art, right? For c for things that are different. There's even more demand for that. Humans want to go after the thing that is not easily produced. That's human nature. So for artists and musicians and others to say like, well, now they can just take over. There's a piece of it that's true, but real higher quality original, originality and creativity is not going to be easily commoditized because humans will want that authenticity. And so I think there's a market for that. ⁓ and because I've I've definitely read enough AI generated stuff that And people, I'm starting to hear people complain about it, which means that when you read really good writing, it feels even better, it feels good because you're like, man, this is really a genuine voice. Like it's human, they're talking, it's raw, it's authentic, it's vulnerable. Like those are all good things, right? And so I think there's actually ⁓ space for creators ⁓ going forward as well.
speaker-1: Feel like us. Yeah, I I believe that too, really do. And and I want to ask you something, 'cause I know that you are a leader ⁓ implementing this, ⁓ but not every leader or not all leaders may be in the same type of organization that really embrace ⁓ innovation, ⁓ risk takers in a way. And some organizations, MJ, they are just lesser focused on cutting costs. Right and creating efficiencies, right? Where is from your point of view, where is that thin line or that line between efficiency and try and take the time that you need to really understand how we can implement AI in our organization?
speaker-0: Yeah, I mean, I I actually don't think that efficiency is a wrong thing to pursue. Right. My argument is never you should not pursue being more efficient. I just don't think it's the only thing you should pursue. And so I've ⁓ written and I'm I I've always thought about if I were to advise those leaders, I would say, okay, write down all the ideas you have that around AI and how you can use it to cut costs and increase efficiency. Okay. Then put those away. And then I want you to write down ideas that it's going to not do that. It's going to take you longer to write those ideas down because they're not going to be obvious. But I would challenge them to say, at the end of the second round, look at those ideas. Are they more different? Are they more differentiated? Are they more original? Do they grow revenue? Are they things your competitors can't copy? Okay, maybe still do your efficiency and cost cutting measures. Maybe pick one idea from your second list and also execute on that. That's the only thing, right? It's not to replace one side of the equation with the other, but it is to acknowledge that just because you came up with all these ideas that are low-hanging fruit that you think is really great, don't let that detract you from at least trying one idea from the side where AI is empowered to do something that has nothing to do with efficiency and cost cutting because you might be surprised with what you find there. And you might really like. what you're writing down there, because those probably are things that's not easily duplicable by somebody else in your category or in your competitors set.
speaker-1: Yeah, I I I agree with that as well. The whole conversation, it's a layer conversation. It's not we can just focus on that. The pursuit of efficiency, it's one that we all should always be looking for one way or the other, right? How we can make our processes and our people more efficient. But there are so many other layers that we should embrace that it's not only having our, you know, looking at efficiency at the end of the line. when it's actually how you can bring people together that they really can embrace AI and and then continue that conversation. And and I'm curious, ⁓ MJ, because now we get to a point that we are implementing AI in different parts of the organizations. You know, the goal is probably that every department ⁓ one way or the other start implementing AI or have AI implemented. What does trust will look like operationally speaking, right? Once AI start touching customers, communication, and things related to IP, right? Reputation of the company. Curious to see to hear from you, you see this going next.
speaker-0: Yeah, I mean, that is a, I think, very important question. And I would say I don't have the answer for that. I have some thoughts around it and I can maybe help people understand. So, first of all, ⁓ in terms of AI being implemented in different parts of the company, I think that is absolutely the future. It will not get all implemented at the same time, nor in the same industry at the same time. Industries with a lot of compliance and complexity is going to take longer. Industries that are very small but don't have any resources are going to be outsourcing more. And then those in the middle are probably doing a combination where they're also ⁓ natively building, right? So ⁓ so first of all is to recognize that these things are gonna happen with different clock speeds for every company that's and that it's gonna be unique to the company's culture, their appetite for risk in the industry that they're in, their size, their compliance requirements. etc. A Walmart that's operating globally is going to be very different than a mom and pop shop that's based out of one zip code in in ⁓ in one state. Okay. So having said that, a couple of things that I've seen is like often companies that are ⁓ you know, sometimes they'll take the most, the strictest requirements and they will use that as a way to set up trust, right? To say like, hey, we're gonna take our compliance requirements. This is really strict and we're just gonna apply it across the board because above that we're fine. And we're going to work within those constraints. ⁓ compliance and trust are different things, but they kind of build off of each other. The second one is like people try to centralize it, right? So there is a ⁓ you know, you can imagine there is today it's usually rolled up under tech or legal, where ⁓ employees are given permissions on how to access certain models, the kinds of models they can access, the kind of information they can pass into models. ⁓ the good companies will have very clearly written employee handbooks on what's permissible. Or not. But the idea is that like in the future, I example see, I expect to see organizational models where there's like an AI ethics and safety committee, right? That like looks at the content, looks at the output. So there's a way to do that there. I think the last thing which I would encourage that people should do, regardless of what model you adopt, is you have to make sure that every person in the company understands and is trained on the risk associated with AI so that they can use their best judgment. We have a lot of trainings, right, around things like ⁓ you know, ⁓ how do you deal with workplace harassment, whatever. So there should be trainings like that in the future where you teach employees what does it mean for confidential data, what that's the impact that you're gonna have on customers, et cetera, et cetera. Right. So there's going to be ongoing training around how AI is being used, but ultimately it's gonna be up to each employee. To understand the impact of using AI the wrong way or using AI to generate trust. For a lot of companies, having that initial definition of what does it mean for our customer and stakeholders to trust in our use of AI is already a tough question. What does that mean? What does your content look like? What does your brand look like? Do you disclose you're using AI, right? What are you using internally? Is it cut touching customer data? So having even a discussion around that topic alone, what does trust in my AI production looks like, written down and having discussed at the executive level is would be a first step. And the second step is having an employee handbook that would that documents fair use and making sure that it's updated in a dynamic way. I would I mean like that question I'm not even sure I've taken to our team. I gotta really think about this after this podcast.
speaker-1: I keep thinking about it. That's why I it's it's it it's something that it makes me, you know, I keep thinking about all this stuff because it's still, you know, when we look at this and and when we look AI everywhere, how we can keep up with security, right? And the way I see this going forward, it's still the human layer. It's kind of the security layer, right? That will continue to be as we are part of this conversation. And and I think we in a way we keep underestimating or or or maybe not having this conversation enough because the more we implement it, the more we need to make sure that we keep, like you say, employee handbook and our internal processes up to date and updated for this new era. That there are so many layers in between. More to come on that, I guess, right? As we continue to learn on it. Absolutely. Yeah. And MJ, we are approaching the last segment of our conversation. Which is really one general question intended to be asked to multiple groups. So I'm gonna start with the first one, which is what would be your piece of advice for those individuals that are trying just to figure it out how to use.
speaker-0: My piece of advice is if you're trying as an individual, think about it as something that you're playing with and not something that you're trying to control or something that you're trying to understand. Because the more you can take an interactive and play mentality to working with it, the more fun you're gonna have and the more you're gonna wanna do it. If you're trying to implement it in the ⁓ like an inner organization, then those three components that I talked about earlier, you need to have the crowd, which is ⁓ you know, you need to have like people, individuals building that culture who are excited, you need to have an innovation lab, the crowd, the lab, and then the strategy which is coming from the leadership. And that comes from Ethan Mollock's co intelligence, that framework. So every organization trying to implement it should at least think about having those three pieces and how they could work together.
speaker-1: Right. For a CEO thinking on implementing AI in his organization, what he should stop doing immediately.
speaker-0: ⁓ stop delegating that to someone else. Right. Like this is this this is gonna if you truly think AI is the most important strategic work your company is gonna do, you should do it yourself. Or at least get some people to work with you, but don't just ask your competent people to execute. They already have a lot on their plate and they're probably doing all kinds of other things. So the first thing I would encourage people to do is to play with it themselves and get a really good understanding of what it can and cannot do. And then work with the smart people in your organization. But if you don't interact with it yourself, then we go it goes back to the lazy leadership idea.
speaker-1: Okay, love it. To policy makers, what safeguards matter as AI reshap reshapes works and trusts?
speaker-0: Wow, policymakers. I would say it would be great if all of you could get together and establish one standard for everyone, which I know is really difficult. ⁓ but that is what most companies would like, right? We have global standards around. But I mean, like, come on, like that would be nirvana if people can actually do it. ⁓ but I I find that to be very difficult. I would say like having some sort of ⁓ I Europe has the AI Act, right? I think having some sort of federal level view on what this means that is meant to spur innovation, but also takes into account real issues with data privacy, trust, things like that. If if there's something, an umbrella direction of what things mean, and that you can evolve over time so that there is a bit of guidance ⁓ for companies, right? I mean, we know cybersecurity is huge. That's already being discussed in the news, right? But the idea of fair use, the idea of using AI to create deep fakes and things like that. There there's going to be some societal backlash from that. So the sooner that policymakers can at least start to develop a point of view on it before, you know, I would say the ⁓ the court of public opinion decides for themselves.
speaker-1: Love it. One last more, I promise. What would be your piece of advice for a younger version of MJ watching her today, getting ready to start transitioning to this future with AI?
speaker-0: Huh, I would just say maybe to other younger people, how about that? ⁓ I would say I mean this is very basic advice, but I would say like one, read. Okay? Don't give up reading because I've heard a lot about how people are trying to take shortcuts with just, you know, taking the easy knowledge head first. And two, remember that learning comes with friction and it's important that you have that. And if you are trying to learn without friction, then it is not learning. And learning and learning how to learn is one of the most important things in life and AI cannot teach you how to
speaker-1: My friend, what a beautiful advice. Thank you very much for dedicating part of your day to be with us today. ⁓ I have really enjoyed this conversation, so I cannot appreciate you more.
speaker-0: Thank you so much. I'm really happy to be here. Thanks for spending an afternoon with me.
speaker-1: Absolutely. And for everyone watching out from home, ⁓ there's gonna be another opportunity next week. Share this with your friends, with your colleagues and talk soon. Take care everyone.
speaker-1: Everyone wants to get better, everyone wants to feel motivated to embrace AI, but we need to have in place the right space where we celebrate people coming into not knowing anything and learning, right? And it's a phase approach. We cannot expect anyone from getting one day and become an expert. If you try
speaker-0: Truly have a labor cost issue. You have a labor cost issue with or without AI. So just address that head on, right? If you anticipate that AI is going to come and defray all of these human capital, then you have to be really honest with yourself and say, like, well, my goal truly is to cost cut my way into competitive advantage and differentiation, which is very hard to do because, like I said, that can be copied by your competitors.
speaker-1: Companies write algorithms, governments write laws. Someone has to write the human story. People don't resist AI because they are irrational. They resist when the story is unclear, when the purpose feels threatening, and leaders demand adoption without building trust first. This episode is about the leadership work that comes before anything else. Welcome to the Human Protocol, the podcast about staying human. While building the official, I am your host, Michael Solomon. Most leaders think their AI problem is a technology problem. My guest today is gonna tell you it is a change management problem. And that the way that you frame AI decides everything that happens next. Call it cost cutting and your people hear layoffs. And quietly stop trusting you. Call it growth and they start building with you. MJ Mingyan Yan is the chief strategy officer at the ThinkTech Leader Credibly, where she leads Company's Gen AI transformation. She's a Ford veteran who built a startup, spun it out, and watched it fail, then rebuilt. She's a Yale Digital Ethics Fellow and my favorite detail, a literature major from Harvard, now leading an AI overhaul. MJ, welcome to the Human Protocol.
speaker-0: Michael, I'm so glad to be here. Thank you so much for having me.
speaker-1: It is an absolute pleasure. I'm very excited about this conversation because I know this is a topic that you are passionate about as well. And when we had our intro call, you mentioned something that really stuck with me since then, which is this concept of lazy leadership, right? And when we're talking about lazy leadership for our audience, what do you have in mind?
speaker-0: Well, I think ⁓ that's a great question. There's a a few things I want to touch on, right? One is I often see change management being delegated or outsourced. So a lot of top level leaders will say, ⁓ we have something really difficult we need to accomplish, such as the adoption of a new technology. Hello, VP of whatever, you go execute and implement that. I've heard a lot of leaders talk about how. They're middle management, especially receiving direction to say, you're gonna, you're gonna be responsible for making sure that your team implements and adopts AI, but also you're responsible for delivering everything else that you're delivering for the company at the same time. So to me, that is lazy because you're taking the hardest part about leadership and you are delegating it to someone who doesn't have necessarily the resources or the bandwidth to do that. The second part of being lazy in my mind is when you take strategy and you outsource it to AI. When people ask, well, what is this transformation that you're leading? And the answer is, well, we're going to be AI first and we're going to be AI forward. And that's how we're going to accomplish more. Well, the company strategy should have been set, the North Star should be existing prior to any tools that you implement. So this tells me that there was a gap in thinking that is now being Proposed with AI, but it's actually covering up the fact that maybe the company didn't have a North Star or a believable strategy to begin with. But because AI is such a hot buzzword, it has taken the place of actual deep thinking. So those are kind of the two examples. And the last one I would say is ⁓ I will also often see leaders who will mandate AI usage without actually doing it themselves. They come to the table with very Ambitious proposals of what it can do. And hey, why can't we cut head count here? ⁓ we should be able to cut costs here. But they've never actually really played with and understood the constraints of AI in a real way. They've only heard about what it can be done. So not understanding the leadership failures and then reflecting that failing with AI, for example, I think is great. I think leaders should share stories of when they experimented. and failed with AI. And that I think will build more credibility than the three things that I mentioned earlier.
speaker-1: Yeah, and this last part that you touched on is extremely important, right? Because how are you gonna make people to follow your message and follow your mission and feel empowered about adopting AI if you haven't had the time to adopt it first, right? And it's not that we're gonna get into this topic anyways or any shape, but it's it makes me think about how do we think our government or governments are gonna make laws, right? To make sure that ⁓ AI is there for us if they don't even understand the technology behind it. So everything should start at the top. You understanding what the technology is and the strategy should follow after. But it's not that we as leaders can remove ourselves from this equation when we need our people to follow our direction. That's our
speaker-0: Yeah. And I think, you know, there's a difference between the word empower and mandate, right? So AI empowerment is a very different tone than AI mandate, because mandate seems to me like you're not actually helping people use this technology in a way that makes them feel capable. You're just saying they need to use it or else, which I'm not saying doesn't work, but I think that there are risks and longer term. Percussions and the way you would execute that strategy would be very different than if your mindset was, no, I actually want to empower my organization with AI, not I want to mandate that people in our organization use AI because I'm frustrated by the lack of adoption or progress. Then I would say, you as a leader, that is your job to solve for that frustration and lack of adoption, right? And there's a better way to do that than just say, well, everyone has to do it or else.
speaker-1: Absolutely. All this conversation, it comes around a topic that I also know that you're passionate about, which is when we talk about AI just from the lenses that we're gonna implement it or we're gonna adopt it just to cut cost, right? and you mentioned something to me that it makes a lot of sense. It's it's all we do, it's implementing AI to cause cost. That's what competitors can do. They can copy that. That's not on a strategy in a way, right?
speaker-0: Right. Yeah. And I think, you know, there's been a lot of literature lately about how maybe it's not cutting costs super effectively. And I'll give you very concrete specific examples, right? So when we first started implementing AI adoption ⁓ credibly, that was what the executives were excited about. ⁓ we can increase employee productivity, we can cut down time, maybe we can eventually think about, you know, shrinking these teams. These are all fun and interesting things to entertain. But if that is your only benchmark, a few things very quickly start to happen. One is I didn't see the initial wave of employee excitement around AI. We know everybody was using it, right? They were using it to make their own work efficient, but they didn't feel sanctioned to say they were doing that as part of a company permissions. So, you know, you had shadow sort of IT situations pop up. You had the fact that people felt very hesitant and also ⁓ distrusted. The company message because the first thing they're going to think about is like, what about my job? Is it coming after my job? And no one's going to say it, but it's you can clearly hear, you can hear it in the silence, right? And then I think the other thing that happens is you start to remove really good ideas off the table because the first set of ideas are so what everybody wants to chase because they are low-hanging fruit. And that distracts you from maybe doing the more value-added thing. ⁓ and then the last thing is like you really have no. Better ROI because everything is going to be down to, well, how much does this cost me? And have I implemented the right cost measures? I will tell you that our API costs have gone up. I'm on the billing. So I've seen these costs, these costs go up. Right. And now we're being questioned like, hey, are you guys spending like too much with open AI? And yes, there are some things we have to do there, but these costs don't necessarily disappear. They just get shifted in different ways. And so If your only benchmark for AI is cost and how much you're not taking out, then you're not looking at other benchmarks like is your organization more nimble? Are your people more keep work working with AI? Are you changing and adapting faster? Are you creating and doing projects that are worthy for the company that have a longer term return on investment that you are not seeing right now because they're using AI to do it? There has to be at least five metrics that companies look at when they're thinking about AI adoption. And so much only just look at have I saved human capital without looking at the other factors, some of which are qualitative. So even in my own personal experience, it's only when we got into the second wave where we had concrete use cases, where training was implemented, where we specifically told the employees, no, this is about growth and new ideas and not about cost cutting and efficiency, that I really see another generation of adopters begin and be excited and confident enough to work with this technology.
speaker-1: And I'm sure that was in part, MJ, because of the strategy that you put together was for people to feel like AI is here to augment what we do, not to really erase their jobs or anyway, right? Because really we are human beings. If we think something is coming for our jobs, we're not gonna adopt something that is gonna hurt us, right? It's just a natural reaction to it. But how we frame it, how we implement it, and the strategy and the communication that we put around that, it makes total difference in in these projects because at the end of the day these are still human beings implementing this.
speaker-0: Yes. And I think leaders have to make tough decisions. And I've had to make tough decisions. But making tough decisions to use AI as a scapegoat for prior bad decisions or for suboptimal decisions is not necessary. Right. So you if you're if you truly have a labor cost issue, you have a labor cost issue with or without AI. So just address that head on, right? If you anticipate that AI is going to come and defray all of these human capital. And then you have to be really honest with yourself and say, like, well, my goal truly is to cost cut my way into competitive advantage and differentiation, which is very hard to do because like I said, that can be copied by your competitors. So I think that when people are talking about AI strategy, and I've spent quite a bit of time on this, I will say I did not start with a strategy. We started with building culture and excitement and training. Then we built a ⁓ in-house Skunk Works Lab that works with AI agents and then we rotated people through it. I've done nine plus hours of training with the entire company. I've gone to classes myself and brought that back. Then we said maybe we should have a strategy, right? Because now you've learned enough, you've seen how people work, where you've seen some of the gaps. And now we said, what does this future of work look like? I developed an internal framework. And one of the things that I think is also really important, and leaders often eclipse this when they're thinking about AI adoption. Is that AI is really good at introducing complexity. And so just as with change management, which by the way, everyone is terrible. I don't want to hear anyone say they're good at change management. They're not. Just because you've done it many times, every time it looks different. The people who are best at change management are really humble about the fact that it's it it's it's hard every time because it's unpredictable every time you're dealing with people. So first, if you think you're good at it, you're already off to a bad start. ⁓ With AI adoption, when a AI is really good at introducing complexity. And what that means is like I'll give you an example in underwriting, right? When we do underwriting and risk assessment, you can have a human decide the definition of is a business seasonal? Okay, well, what does that mean? That means that for some businesses, their their high months are the summer season because they're in a tourist place like Virginia Beach or whatever, and their low months are in the winter. Okay. for college towns. Maybe it's the opposite, right? They have students back in the school, but then the summer they're out. Okay, so humans can understand some variation of this. And they have big pictures in their head, but an AI can understand down to the individual business in that exact location. And we've tested this, right? And something that takes someone a lot of time to train to develop rules that they can then execute by. And AI can actually come up with a very dynamic model that is very specific to that business, which is great because it actually delivers more customer value because there's more knowledge and precision around the health of that business in the business of lending. However, it's complex because now you don't have rules you can just apply. You don't have things you can just automate. Now you have to really think about, well, what is the prompt that I'm gonna really work with AI to generate this. And by the way, like does do I agree with the output? ⁓ it looks normal for this business in this area, but just a few zip codes over, it's going to look very different. So when you take that example and you sort of compound it over business cases, it's very important for leaders to understand when AI starts to change work, if you're using the power of the technology, you're likely making a lot of work streams much more granular and complex, which means people end up managing more work streams, even if they're agents. Right. Versus true automation, which you can just process the same thing over and over.
speaker-1: And now one out to expand.
speaker-0: And it's one process. And a lot and and I've seen a lot of companies use Gen AI to do what is actually just automation, which also means you're not taking advantage of the full power of these models. So there are a lot of like if you're a leader trying to implement AI and you're not thinking about how it's going to increase complexity, increase decision ⁓ decision flow, increase the smaller granular branches of work stream and taking one big work stream and starting to create. These tiny branches, which are then more complicated and varied to manage, I don't think you're using the power of Gen AI in the right way.
speaker-1: So many layers. So many layered, right? And and I love the first one which I think a lot of people still make that mistake. Gen AI is not an automation bundle up with a chatbot or with an open AI API in the back end. Right? There's so many layers to it. And that representation of what leaders need to think about when the whole picture is in place is really extremely important because it's ⁓ There's so many layers. The more you work on it, the more your people will have to analyze different outputs and different ways. And you know, human beings, our brain is limited in a way when we are analyzing, we usually focus on one thing at a time. With the help of AI, we're probably getting multiple things to take a look at. So that whole idea that the work is getting synchronic, that AI is really getting a lot from us. I see human beings still doing more with the help of AI.
speaker-0: Yeah. And I I think, you know, I see a lot of LinkedIn posts where people are saying, like, ⁓ you know, now my teams are managing, you know, dozens or hundreds of agents. And I have to really ask, is that realistic? Right. Is that is that should your team be managing hundreds of agents or are you truly talking about monitoring erroneous output or something where that you're seeing you're seeing a model and you're seeing ⁓ anomalies? So you're doing anomal anomaly monitoring and detection. Which is not the same thing as managing thousands of agents. That's not that's not that's not ⁓ sincere. That's not an accurate description of what's going on. Cause I I can't I can't realistically say like that's a good use of people's time and nor do I believe any person can actually
speaker-1: Monitor all that that's great.
speaker-0: It's not possible. But if you're talking about anomaly detection where things are running autonomously, but you have a way to look at something is getting too long. We need to investigate that's great. And I think that's that's we should be upskilling people to do that. That to me is a system AI is orchestration deployed the right way. But if you just have a bunch of workflows and humans are just actively looking at all of them, I think we need to rethink the sort of the design of the overall system.
speaker-1: Things go wrong. Yeah, exactly. One thing I've learned through going through all this, and I'm sure you had as well, is there is no one size fits all for this conversation. It's not because people are saying on LinkedIn or whatever, this is what's happening, that this is really what's happening. I really loved the approach that you follow ⁓ at your organization, which was let us try, let us learn from our organization what works, what doesn't, and then we can continue. you know, to refine the message and see where we want to go. And I think that's the absolute best way of handling this, because we don't know everything and we are all learning in a way. And when you have the whole organization that is composed of people with multiple roles and and different cultures trying to embrace this, of course you need to, you need the organization need to have the time to really understand how our people are responding to it. And then you keep going and you keep going. I think that's the absolute best way to really understand how we can best implement AI in any organization, because all organizations are different from one each other.
speaker-0: Yeah. And I think, you know, there's not enough credit or conversation being really given to the actual experience of learning with AI, right? Because so much of the authority and ex ⁓ subject matter experts is on like, I've implemented this great saying, look at the capabilities of AI. It's like we I I get it. I understand these models are very capable. I think everybody understands that. How many of us are PhDs in Sanskrit? One point two percent of the population. And this thing can do, you know, better than most humans on really hard mass math exams. And I I get it. I get it. Okay. Ninety-five percent of that capability is irrelevant for the work that you're doing on a day-to-day basis because you're not using those capabilities. So because AI operates on an abundance model, because it is so intelligent, it's actually harder for humans to harness that intelligence and figure out how it works for them. And so Learning is such a crucial part. And that's why I think there's so much conversation with AI around education, right? Like people just don't give themselves enough credit and organizations have to l often lack patience to really learn. I when I was in a class at Wharton, one of the ⁓ people in my class talked about he came from a very large company and he was just you can just see he was just sitting there like, I'm hearing you guys say all these amazing things about AI. All of my databases. Are separate. The same piece of data that lives in these different databases have a different label and mean different things to different teams. So the first thing I have to do is to invest millions of dollars in data engineering so that I can connect these databases and at least apply some semblance of a common label so I can run an MCP server so that I can actually give data to AI to analyze. And by the way, We have to then figure out because I can't do it all at once. What is the most valuable piece of data or or clusters or groups or parts of that database that I can do that to begin with? Right. And you hear these problems and they're very real. And that's one company. And so the fact that companies are not acting or are later in adoption is not because people are just waiting around being like, we don't believe in the capability of these models, right? I don't think People, I think people are generally at this point understand these models are very capable, but you have to give the organization time and process and investment to actually make change happen. This is not unlike when you invite, like, like for example, if you have a brilliant PhD who comes into your company and gives a and delivers to your manager just like an amazing lecture on how you should run a company. And optimize for its profits and how you can turn this into a billion dollar venture. And that person is a hundred percent right and spot on. How long would it take you to actually get to that result? It's not tomorrow. You have to actually do all those things that the person is recommending. It doesn't make the person less intelligent. It doesn't make your staff, you know, not recognize that this person is ex brilliant and saying all the right things, but the execution still takes time and there's still change management and learning and failing and restarting in all of that, right? It's not one happy path. It's very messy. And that is how learning actually happens. It doesn't happen in a contained, predictable way. It happens with detours. It happens through failure. It happens through variability. That's actually what learning is. And I think people don't recognize that in adopting new technology.
speaker-1: Yeah, I'm wondering if part of it is people, you know, being worried of trying and failing. We don't give enough credit for those that try and fail. We are a society that we are usually just, you know, acknowledging the good things when something when people get something right. But behind the scene, as you were saying, the feel and the journey, it's a lot messier than just the destination when you reach up there. So this is what I also believe We need to empower our people to be okay with trying and failing and recognizing when something didn't go the way we expected, because that's how we learn what works for us. What happens in organization is when you amplify that to the whole organization that can be hundreds, thousands of people, of course, you need to take all that into consideration. And that's why it this thing takes time. If you really want to implement it the right way. It's about empowering your people to give it a try, to use it and make sure that you get data from them, what is working, what's not working, and be open to feedback at the end of the day.
speaker-0: Yeah, I think so. There's some practical things we can do here, right? So I think first of all, I was telling someone else the other day, I think especially like America has this very interesting, I I I almost think like just in relationship with the word failure, right? It's like we are always told you learn through failure and failure happens in everyone's lives and it's really important and of overcoming adversity and all that. And then we celebrate. the people who are really, really successful. And often in organizations, the language around cellure and the artifact around failure are different, which means, which means like people say they celebrate cellure, but they don't actually celebrate failure. So there's a usually there's a gap because now it's like, ⁓ you cost this money and blah, blah, blah. Right. Like, I I'm lucky enough to work for an organization where this is not the case. I cannot tell you how many projects I have failed at and spent hundreds of thousands of dollars of the company's money and I got no backlash for it because I learned. So with AI, there has to be different metrics, right? So first of all, I would expect 90% of your tiny project to fail. That should be a very flat expectation. I can name off the top of my head multiple agents I've had sales reps and other people deploy that didn't work and it was fine. Because they learn how to use AI. The second has to be there has to be a metric around AI proficiency for the company, which means you do this thing, you launch this little agent, you build this gem, you wrote this piece of code, you built a UI dashboard. It didn't go anywhere. No one adopted it. You're not even using it for yourself. Okay. But did you get better at working with AI? And if the answer is yes, then we should celebrate that, right? So much of our Metrics are outcome based because we've been taught to think about that in a business context. But in learning and adopting new technology, there has to be metrics that are learning based. And learning is is qualitative and subjective and difficult to measure, but it doesn't mean it's impossible to measure. I've done surveys in our company around employees' expectations and satisfaction and usage of AI, saying nothing about did you produce something that the company is actually using? Which by the way, we give people prizes if they do, but just seeing that percent of people who are whose comfort and ability to work with AI has gone up, that's an overall corporate success metric. Right. So I would suggest that if we shifted or at least added one metric of success onto learning, are your people learning? And are they because if they're learning, they're also more adaptable to change, right? Which is what you need that. You need that in a resilient organization. Then keep going. Maybe tweak how much you're spending, how you're incentivizing people. We had a very recent debate where we were talking about opening up Claude to anybody in the company who was interested. And our CFO and our executive team, part of them were like, Well, that's a that's gonna be quite a bit of money. So we need to get people to write a business case and suggest why they want to use it and what problem they're trying to solve. And immediately, Myself and the VP of product was like, nope, we're not gonna do that because you're setting up a barrier and we wanna be AI forward. So what we need to do is actually give people free access, let them play with it for 30 days, and then have them tell us what they tried to do with it and was it interesting or not. And guess what? People got access, they played with it. A lot of them ended up not retaining the access, but we learned very one very important thing, which is what I just told you. My my own team played with Claude, right? And we had a meeting. They had access to cloud. And they said, and I said, what kind of problems are you trying to solve with Gen AI? And they gave me, well, I want to run this report at this time of the day. And I want to send it to like this merchant and blah, blah, blah. And then I said, that's not a Gen AI problem. That's an automation problem. So you're trying to solve for a different problem, not using this tool. Okay, so now we have our data analytics person who was learning to build automation. And now we're creating these tools that allow these people to be better. They learned. What is Gen AI use case? What is not Gen AI use case? And they didn't end up using the more sophisticated models after 30 days. It actually got shut off for them. That is all success because look how much you learned in the process, right? Without creating that barrier to entry. And you learn things you never thought you would learn. That's that's the power of opening it up and encouraging people and not putting a limit on it, but also letting them know like they're all off ramps. You can still solve your problems and do better, but you don't want to. prevent people from trying new things.
speaker-1: I I love that angle of, you know, turning people from passive recipients into really builders. 'Cause that's what we really need. We need to give people the tools and you know, the education and the ability to go there and try, see how it can help you and how you can learn. And we are human beings at the end of the day. We know human beings. We love when we learn and when we feel ourselves useful, right? That's how we should be looking for. That's why our people, everyone wants to get better, everyone wants to feel motivated to embrace AI, but we need to have in place the right space where we celebrate people coming into not knowing anything and learning, right? And it's a phase approach. It cannot be we cannot expect anyone from getting one day and become an expert. ⁓ we can embrace it. but one thing I love ⁓ about the things that you're doing, ⁓ MJ, is ⁓ I think that you mentioned to me that you have stood up an internal academy, right? And you're running pods of people building real agents. And I'm wondering if you can share with us how this activated people and how it's motivated them. So people can, you know, they came in and they embraced this instead of feeling that this was a mandate for them to do it.
speaker-0: Yeah, so ⁓ this is now I would say round probably three. So I would say ⁓ what I'm about to describe to you is probably four years in the making now, right? So it's not something you achieve on day one. So I run the internal innovation incubation team called Skunk Works, which is modeled after Lockheed Martin's ⁓ very famous Skunk Works project, where they took a bunch of really talented, cross functional people, gave them a goal, gave them very little resources and said, Go solve this problem and because we need you to solve this problem. And so I often joke ⁓ when I talk about the Skunkworks team because I said, you know, hey, I'm not buying anyone lunch. There's no t-shirts. The work and the companion, the the work and the company you keep, which is the people who you work with, those are your rewards. Okay. So the first iteration of that team, we were always giving them, this is pre AI, pre-gen AI, ⁓ unsolvable problems. Right. So the question, so immediately you set the culture of, These problems, no one has easy answers to. In fact, no one has answers to. So you don't come in with your expert hat on, thinking you know everything. You come in ready to challenge, you come in ready to voice your opinions, you come in ready to fight for your beliefs, and it gets better. What's interesting is part of the automation problem in the beginning, I knew from the very beginning it was going to be a failure. Because people, like I said, ⁓ when we did end up introducing ChatGPT and Gen of AI, it was a a very human construct to look at the best underwriter, the best processes we have in place and say, how do we take this general intelligence tool? And then how do we mimic all of that? How do we make it do exactly what this is doing? And by the way, we're going to compare the answers against our best underwriters answers. ⁓ and it's not working. It's not even coming close to humans. Okay. Great. Is that how you automate? Because that's general intelligence for very specific tasks. And these tasks are optimized for humans. Who can only remember 17 things versus a really intelligent model? So the first wave of that, which by the way was a year, okay, because this is how long it takes people to change how they think and how they actually work, failed. Failed. All right. And so second round, same people, same question, right? Now the people were like, Great, we just failed. Okay. Now they started asking questions differently. Okay, why are we doing it this way? What's the risk if we didn't do it this way? Okay, now we have some real answers. Now we have two patents from that part time of how we worked. And you can see this is for year one, we produce nothing. Nothing. All right. Year two, two things. Year three, six things. Year four, which is where right now, multiple teams working at the same time. I've cycled people through. I've done multiple trainings. Now the people who are building are levels like junior level people in the company. Who have never even before touched AI. But because their managers had been on the team and now empowers them and feel confident, that confidence and inspiration, and yes, you can do this. And by the way, I know how to do this and I'm supportive and I've done this before, trickles down to them and they feel like they can. So now I have literally two agents that are built by junior level people in the company who are not technical, who have never been on Skunkworks. They were pulled in and I asked their manager and I said, How do they feel? ⁓ working with AI. And he's like, they love it. They're having a great experience. Right. So building the right culture, actually being okay with failing, and then knowing that this cycle is going to come, but it takes a little bit of investment in time up front. So if anyone is going to build this incubator, I would suggest that you start the same way we did small elite team, give it unsolvable problems. Make sure though that the first round is like usually the company goes in with like, we need to have some quick wins. If we can just show that we got some quick wins and then we can show that this team is worth its value. It's like, no, your your output is not did you deliver the quick win? Your output is did you change how people think about how they're supposed to work? And that then meant that this becomes a self-sustaining cycle to the extent that I don't really run that team anymore because I just sit in the meetings, but now people are just running these things by themselves. Right. So now it's a truly a self-sustaining body. That can take new technology, bring other team members in as needed, train them, push them out. And then the next version of that is one of the agents that we are building. It's not an agent, is a skills repository. And what he's actually doing is taking all the different pieces of knowledge around the company and putting them in one central area, but in a way that anyone can go in, access what they need to access, and then build agents on their own. So that is now what we're trying to do. And that's only because people have gotten to a point where like, People are comfortable, first of all, even giving you the information for you to work with, right? Because if you think AI is going to take your job, why would I give up my secret sauce of like manuals or stuff that I'm doing? So now everything's in one place, right? I'm seeing the QAs and I'm seeing the feedback loops and I'm seeing like MCT servers being built. So those are the kind of things that will come and they will happen faster and faster. But that initial phase, the learning phase, is going to take a little bit of time. It probably took me. three years to find a version of AI human hybrid models that work. Yeah. It took me three years to do it. And and if you take less than three years, like congratulations. But if you told me you found it on year one, I will have a hard time believing you.
speaker-1: That works for you guys. Would too. It doesn't happen from night to day. It it really doesn't. And and and you know, it's ⁓ that's what I want people to really understand, especially on organizations implementing this. The way we do things matters and it takes time, right? The quick ROI that people are looking for, mm, that's not gonna materialize tomorrow. Right? You really need to give time and you need re it really needs to be part of the ⁓ the overall strategy. When I listen to what you were saying, a few things come to mind. One, it takes time, right? It takes effort. Second, the training creates awareness. But really, the building with it, it's what creates the belief that people have on working with this. And that's where the magic happens. Because once you get people to feel part of this mission and they like what they're doing, they like what they're building, they will continue to embrace it, right? And the more they work, with it probably they will continue to find more use cases where things can get better with the help of AI. And to me, that's really kind of what we all need to think around. How we can get people to jump on it with us so they feel like they're part of the mission and they believe on it. Cause once people believe on it, we know what happens. They keep going and they follow they follow the direction. Yeah.
speaker-0: I think working with AI is very much like a lot of the intangible things that we often talk about in businesses, but we give we understand those things take time. So for example, a business building trust that takes time. A business building customer loyalty, that takes time, right? You may want to have the trust and the loyalty. And you may think you earn it and you deserve it. And if people can just do this faster, but I think Leaders understand that building the trust in a brand and building the trust and the loyalty of customers, their time has to be a component because you need to be stress tested, right? Those things are the product of having been tested. And so AI adoption is the same thing. You want your people to trust using AI. You want AI to become truly embedded in the work of your company so that it's not just. functioning on a mandate, which then kind of goes away, or even worse, people are actively sabotaging you. You want it to be truly adopted as a tool of excitement and people are working with it and want to work with it and are advocating for it and being evangelists for it. Then that is like building trust and loyalty. It's going to take some time and that's okay because when you get there, then it's self sustaining.
speaker-1: ⁓ it is fascinating to hear from, you know, real builders like yourself how you know how things started working for you, the time that it took. It's always, you know, it's always to me fascinating to hear really the honest take on what is happening with AI out there. Right. Because the more we hear people in the news, some people may think because they, you know, everybody just has a recipe for implementing AI in three days. and the ROI in a week, which this is not real life. Yeah. And we need people to really understand and w what is real in all this conversation, which it takes me to to my next to our next segment, ⁓ MJ. If I recall from our previous call, I think you mentioned to me that you you stopped hiring junior engineers, right? And now you have a need for more senior engineers. that are stuck with reviewing complex AI written code that demands debugging. Things have changed, right? The work has become more complex. And I wonder if you can work me through on when did you see that shift happening?
speaker-0: Yeah, well, we didn't stop hiring junior engineers. It's more that I saw the the we have junior engineers and senior engineers. And the way that our tech department started with working with AI and how they asked people to work with AI was which is very common, right? In the beginning, the junior engineers are like, Great, I'm gonna use AI to help me write my code. And so they would deliver and try to and code for code review for senior engineers who then spend a lot of time looking at this junior engineer's work, A, because it's complex, and B, they don't even know how to debug it because they can't even find where the error is. So then they're using AI to try to it becomes a maddening because they feel like they're not really ⁓ exercising their value as engineers. And also the code that they're given doesn't allow them to coach and develop people because it's all pre-packaged and written by Claude Cook. Right. So eventually we kind of had to really think about How are we asking people to actually work in a way where the senior engineers can still mentor the junior engineers? The junior engineers have to be able to explain why the code is written in a certain way. How do they think about it? Right. Understanding and explaining how they're thinking about it is very, very important. And so it's not that the team stopped working with AI. It's how you work with AI, as we all know, has different consequences and repercussions. And if you really want to preserve the value. Of how someone with more technical expertise wants to present themselves and their work, then you need to have them work with AI and their junior developers in a way that sustains that. It is not to cut AI out. It is to use it in a way that develops and continue to develop that human expertise while increasing productivity. But it is not okay to say, now that you have these tools, I'm gonna expect you're gonna code three times as more because we're gonna get. Is three times the amount of garbage that then somebody else has to review and make right. Because at the end of the day, they're still on the hook for code that makes it to production. So this is where I think like setting arbitrary standards without really understanding how humans are motivated along those work streams, you're going to shift value to different parts that you didn't anticipate. And you may have to learn from a couple of iterations, but I think that's what I see. And I've heard people talk about mandating. more output for the junior developers. And then the developers would do more outwork, ⁓ output that is not of the right quality, which then requires more heavy ⁓ monitoring and code reviews and in-depth code reviews, which then take up people's time who are actually technical experts who have scarcity, right? Because they're fixing other problems in the company. So you're creating more problems in a way, ⁓ depending on how you choose to work with AI.
speaker-1: Yeah, that that makes total sense. And and that's what the shift we are seeing in not only with developers and engineers, with in any other of of the roles that that our teams have. Things are changing and them depending on how you work with AI, these are different problems and different kinds of, you know, ⁓ tasks that you are probably gonna be responsible for. Because now it's only it's not only human beings in charge of the whole process. Now we have AI in the middle. And that itself creates a different set of challenges and potential ⁓ processes that we need to follow to to kind of manage the whole process. Managing processes now is not only humans, it's humans plus AI and automations, right? And that's the shift.
speaker-0: Yeah, I I mean I think that's also why like I'm not as concerned as a lot of people about AI taking over creative content or writing, because now we are all so used to reading AI generated things and there's such a now there's even more demand for ⁓ original writing and voice and art, right? For c for things that are different. There's even more demand for that. Humans want to go after the thing that is not easily produced. That's human nature. So for artists and musicians and others to say like, well, now they can just take over. There's a piece of it that's true, but real higher quality original, originality and creativity is not going to be easily commoditized because humans will want that authenticity. And so I think there's a market for that. ⁓ and because I've I've definitely read enough AI generated stuff that And people, I'm starting to hear people complain about it, which means that when you read really good writing, it feels even better, it feels good because you're like, man, this is really a genuine voice. Like it's human, they're talking, it's raw, it's authentic, it's vulnerable. Like those are all good things, right? And so I think there's actually ⁓ space for creators ⁓ going forward as well.
speaker-1: Feel like us. Yeah, I I believe that too, really do. And and I want to ask you something, 'cause I know that you are a leader ⁓ implementing this, ⁓ but not every leader or not all leaders may be in the same type of organization that really embrace ⁓ innovation, ⁓ risk takers in a way. And some organizations, MJ, they are just lesser focused on cutting costs. Right and creating efficiencies, right? Where is from your point of view, where is that thin line or that line between efficiency and try and take the time that you need to really understand how we can implement AI in our organization?
speaker-0: Yeah, I mean, I I actually don't think that efficiency is a wrong thing to pursue. Right. My argument is never you should not pursue being more efficient. I just don't think it's the only thing you should pursue. And so I've ⁓ written and I'm I I've always thought about if I were to advise those leaders, I would say, okay, write down all the ideas you have that around AI and how you can use it to cut costs and increase efficiency. Okay. Then put those away. And then I want you to write down ideas that it's going to not do that. It's going to take you longer to write those ideas down because they're not going to be obvious. But I would challenge them to say, at the end of the second round, look at those ideas. Are they more different? Are they more differentiated? Are they more original? Do they grow revenue? Are they things your competitors can't copy? Okay, maybe still do your efficiency and cost cutting measures. Maybe pick one idea from your second list and also execute on that. That's the only thing, right? It's not to replace one side of the equation with the other, but it is to acknowledge that just because you came up with all these ideas that are low-hanging fruit that you think is really great, don't let that detract you from at least trying one idea from the side where AI is empowered to do something that has nothing to do with efficiency and cost cutting because you might be surprised with what you find there. And you might really like. what you're writing down there, because those probably are things that's not easily duplicable by somebody else in your category or in your competitors set.
speaker-1: Yeah, I I I agree with that as well. The whole conversation, it's a layer conversation. It's not we can just focus on that. The pursuit of efficiency, it's one that we all should always be looking for one way or the other, right? How we can make our processes and our people more efficient. But there are so many other layers that we should embrace that it's not only having our, you know, looking at efficiency at the end of the line. when it's actually how you can bring people together that they really can embrace AI and and then continue that conversation. And and I'm curious, ⁓ MJ, because now we get to a point that we are implementing AI in different parts of the organizations. You know, the goal is probably that every department ⁓ one way or the other start implementing AI or have AI implemented. What does trust will look like operationally speaking, right? Once AI start touching customers, communication, and things related to IP, right? Reputation of the company. Curious to see to hear from you, you see this going next.
speaker-0: Yeah, I mean, that is a, I think, very important question. And I would say I don't have the answer for that. I have some thoughts around it and I can maybe help people understand. So, first of all, ⁓ in terms of AI being implemented in different parts of the company, I think that is absolutely the future. It will not get all implemented at the same time, nor in the same industry at the same time. Industries with a lot of compliance and complexity is going to take longer. Industries that are very small but don't have any resources are going to be outsourcing more. And then those in the middle are probably doing a combination where they're also ⁓ natively building, right? So ⁓ so first of all is to recognize that these things are gonna happen with different clock speeds for every company that's and that it's gonna be unique to the company's culture, their appetite for risk in the industry that they're in, their size, their compliance requirements. etc. A Walmart that's operating globally is going to be very different than a mom and pop shop that's based out of one zip code in in ⁓ in one state. Okay. So having said that, a couple of things that I've seen is like often companies that are ⁓ you know, sometimes they'll take the most, the strictest requirements and they will use that as a way to set up trust, right? To say like, hey, we're gonna take our compliance requirements. This is really strict and we're just gonna apply it across the board because above that we're fine. And we're going to work within those constraints. ⁓ compliance and trust are different things, but they kind of build off of each other. The second one is like people try to centralize it, right? So there is a ⁓ you know, you can imagine there is today it's usually rolled up under tech or legal, where ⁓ employees are given permissions on how to access certain models, the kinds of models they can access, the kind of information they can pass into models. ⁓ the good companies will have very clearly written employee handbooks on what's permissible. Or not. But the idea is that like in the future, I example see, I expect to see organizational models where there's like an AI ethics and safety committee, right? That like looks at the content, looks at the output. So there's a way to do that there. I think the last thing which I would encourage that people should do, regardless of what model you adopt, is you have to make sure that every person in the company understands and is trained on the risk associated with AI so that they can use their best judgment. We have a lot of trainings, right, around things like ⁓ you know, ⁓ how do you deal with workplace harassment, whatever. So there should be trainings like that in the future where you teach employees what does it mean for confidential data, what that's the impact that you're gonna have on customers, et cetera, et cetera. Right. So there's going to be ongoing training around how AI is being used, but ultimately it's gonna be up to each employee. To understand the impact of using AI the wrong way or using AI to generate trust. For a lot of companies, having that initial definition of what does it mean for our customer and stakeholders to trust in our use of AI is already a tough question. What does that mean? What does your content look like? What does your brand look like? Do you disclose you're using AI, right? What are you using internally? Is it cut touching customer data? So having even a discussion around that topic alone, what does trust in my AI production looks like, written down and having discussed at the executive level is would be a first step. And the second step is having an employee handbook that would that documents fair use and making sure that it's updated in a dynamic way. I would I mean like that question I'm not even sure I've taken to our team. I gotta really think about this after this podcast.
speaker-1: I keep thinking about it. That's why I it's it's it it's something that it makes me, you know, I keep thinking about all this stuff because it's still, you know, when we look at this and and when we look AI everywhere, how we can keep up with security, right? And the way I see this going forward, it's still the human layer. It's kind of the security layer, right? That will continue to be as we are part of this conversation. And and I think we in a way we keep underestimating or or or maybe not having this conversation enough because the more we implement it, the more we need to make sure that we keep, like you say, employee handbook and our internal processes up to date and updated for this new era. That there are so many layers in between. More to come on that, I guess, right? As we continue to learn on it. Absolutely. Yeah. And MJ, we are approaching the last segment of our conversation. Which is really one general question intended to be asked to multiple groups. So I'm gonna start with the first one, which is what would be your piece of advice for those individuals that are trying just to figure it out how to use.
speaker-0: My piece of advice is if you're trying as an individual, think about it as something that you're playing with and not something that you're trying to control or something that you're trying to understand. Because the more you can take an interactive and play mentality to working with it, the more fun you're gonna have and the more you're gonna wanna do it. If you're trying to implement it in the ⁓ like an inner organization, then those three components that I talked about earlier, you need to have the crowd, which is ⁓ you know, you need to have like people, individuals building that culture who are excited, you need to have an innovation lab, the crowd, the lab, and then the strategy which is coming from the leadership. And that comes from Ethan Mollock's co intelligence, that framework. So every organization trying to implement it should at least think about having those three pieces and how they could work together.
speaker-1: Right. For a CEO thinking on implementing AI in his organization, what he should stop doing immediately.
speaker-0: ⁓ stop delegating that to someone else. Right. Like this is this this is gonna if you truly think AI is the most important strategic work your company is gonna do, you should do it yourself. Or at least get some people to work with you, but don't just ask your competent people to execute. They already have a lot on their plate and they're probably doing all kinds of other things. So the first thing I would encourage people to do is to play with it themselves and get a really good understanding of what it can and cannot do. And then work with the smart people in your organization. But if you don't interact with it yourself, then we go it goes back to the lazy leadership idea.
speaker-1: Okay, love it. To policy makers, what safeguards matter as AI reshap reshapes works and trusts?
speaker-0: Wow, policymakers. I would say it would be great if all of you could get together and establish one standard for everyone, which I know is really difficult. ⁓ but that is what most companies would like, right? We have global standards around. But I mean, like, come on, like that would be nirvana if people can actually do it. ⁓ but I I find that to be very difficult. I would say like having some sort of ⁓ I Europe has the AI Act, right? I think having some sort of federal level view on what this means that is meant to spur innovation, but also takes into account real issues with data privacy, trust, things like that. If if there's something, an umbrella direction of what things mean, and that you can evolve over time so that there is a bit of guidance ⁓ for companies, right? I mean, we know cybersecurity is huge. That's already being discussed in the news, right? But the idea of fair use, the idea of using AI to create deep fakes and things like that. There there's going to be some societal backlash from that. So the sooner that policymakers can at least start to develop a point of view on it before, you know, I would say the ⁓ the court of public opinion decides for themselves.
speaker-1: Love it. One last more, I promise. What would be your piece of advice for a younger version of MJ watching her today, getting ready to start transitioning to this future with AI?
speaker-0: Huh, I would just say maybe to other younger people, how about that? ⁓ I would say I mean this is very basic advice, but I would say like one, read. Okay? Don't give up reading because I've heard a lot about how people are trying to take shortcuts with just, you know, taking the easy knowledge head first. And two, remember that learning comes with friction and it's important that you have that. And if you are trying to learn without friction, then it is not learning. And learning and learning how to learn is one of the most important things in life and AI cannot teach you how to
speaker-1: My friend, what a beautiful advice. Thank you very much for dedicating part of your day to be with us today. ⁓ I have really enjoyed this conversation, so I cannot appreciate you more.
speaker-0: Thank you so much. I'm really happy to be here. Thanks for spending an afternoon with me.
speaker-1: Absolutely. And for everyone watching out from home, ⁓ there's gonna be another opportunity next week. Share this with your friends, with your colleagues and talk soon. Take care everyone.
