Why 70% of Your AI Budget Is Being Wasted on the Wrong Thing ft. Ex-Google (Fitbit) Executive

Before, if I was implementing SAP as an example, I could wait to see how that worked with my competitors, or I could watch and monitor an organization in a different market to see how it goes. Organizations don't have that time right now. That luxury is going. There's this model called 102070. It's actually flipping the 702010, learning and development model on its head, but the premise of that model is the 10 and 20 percent. You add that together. That's 30 percent of investment towards making AI work is the technology itself. The 70 percent is still on people and practices and culture. Good day everyone. I'm back here at 10X AI with GLSdale. I am here with a special guest who has a years of experience in technology and consulting. He used to work in Accenture. His name is Jason Williams. Jason Williams can you introduce yourself and give us the background from four hour users and listeners right now? Yes. Hey everybody. Thanks for listening. I am Jason Williams. I am a leader in talent, learning, organizational development, culture and engagement. I have built a career working at a number of well-known brands doing this work. As Jules mentioned, I started my career at Accenture, but I've also worked at Pfizer, BlackSouth Smith Klein, American Express, Fitbit, which is now acquired by Google. I was also a head of talent for Expedia, Warner Brothers Discovery, as well as a light solution. So I've been in a number of industries doing this work, which is my passion. Yeah. So you have a very diverse, you have pharmaceutical, you have entertainment, you also have consulting, so it's a very good background of your soul. Currently, so you have led talent strategy through multiple tech waves. So you have cloud, mobile digital transformation. What fundamentally feels different about AI right now compared to those shifts? Yes. The fundamental difference, I think, between those technologies and AI was those technologies, I will say you had to use them because they became the workflow. And so in order to follow the workflow, you had to learn to use them. There was no if-answer buts. The difference with AI is a couple of things. One is there's a level of individual competence that everyone needs, and that varies. And so if I was using an ERP, there's a certain amount of competence. I'm not a super user, but we had a super user group that you could ask questions to. Imagine now being in a role where you have to kind of learn it on your own. There's not a super user group that you can ask how to do it. And someone else doing your job could be very well skilled at writing prompts and doing things better than you. And there's an individual aptitude that drives the impact and effectiveness of how people use AI in the workplace. That is not something that really impacted people in the past in the same way. Do you think this is hype or through inflection point? I believe it's an inflection point for a couple of reasons. One is there are a number of AI providers coming from all angles. Not just the names you know, that's one. Second is every platform that you have in an organization is introducing AI into that platform, which means there's no standardization. And so those LOMs don't all think or act the same. Yes, there's some commonality, but if I'm using one application and then moving to another to do work, I've got to change some elements of how I'm interacting with them because it's not standard across. And I think that's introduced the level of complexity. I would also say the pace in which organizations have gone out and bought implemented AI systems is unprecedented. We didn't see organizations I think as fast lean into the internet or internet of things. And I don't think we saw even the adoption of ERP systems or you name it go as fast as it is now before it was who had the money and who was willing to be an early adopter. Right now people are racing to be organizations are racing to be early adopters. Why do you think that a lot of organizations now are going into the adoption, adoption phase? What is the real reason behind it? Is it financial? Is it organization or structural? What do you think is the reason why? Honestly, my opinion is they don't want to be left behind because it is moving so fast before if I was implementing SAP as an example, I could wait to see how that worked with my competitors or I could watch and monitor an organization in a different market to see how it goes. Organizations don't have that time right now that that luxury is going. And so they need to get it in as quickly as possible, figure out how to use it. And this year, which I've also written about is trying to now turn that investment back into some type of tangible ROI, whether it's revenue, return on invested capital, greater customer retention, something that shows that we haven't just implemented AI, it's actually giving us an organizational benefit that we can quantitatively value. So you already mentioned about the return on investments. If an organization right now is implementing AI, it's very in terms of financial is very financially straining, it's very expensive. What are the other things that organizations should look at when those organizations who are not yet implementing that because of financial, you know, constraints? What are the things that they need to look at besides financial? Yeah, there's this model called 102070. It's actually flipping the 70, 2010 learning and development model on its head. But the premise of that model is the 10 and 20 percent, you add that together, that's 30 percent of investment towards making AI work is the technology itself. In my opinion, all companies are not approaching that 70 percent with the fervor that is needed. An example would be we've made AI training available for individuals in our company. That's great, but that has not gotten to the level of how does my job change? What workflows now should be different? Have we been able to now quantify what work should be automated, what work requires me a human to do it with an agent and what work is solely in the purview of just human creativity? I think that 70 percent is really where the challenge is in the last part I'll mention. It's just kind of the leadership alignment in terms of the vision that they're casting and then the operational execution. Many times senior leadership, they've got a vision that's out and it's far ahead of what the operational reality is in the company. That takes tensionality. Again, it's part of that 70 percent, but we also need organizations to start really being focused on what are the levers that are going to bridge the gap between that executive vision and then that on the ground operational execution and the day-to-day lives of their employees. So you mentioned about leadership that is a very even my previous podcast. It's already initial about leadership. So they stay C.A.I. as a technology driven project or you know, transformation, but in reality it's like what you said, it's 70 percent people. But the only thing is the people who are going to be trained for this, they're also fearful or scared about implementing A.I. in their work or task. So because they're thinking maybe they're going to get laid off in the future. If A.I. is going to automate task rather than jobs, what do you think is the should be the talent strategy in a world where roles are constantly changing? Yes, this is I think the million dollar question. I would start by saying don't be scared of A.I. And the reason I'm going to say don't be scared of A.I. because it's everywhere, it's not just in work. It's on your phone. It's in the apps in your phones. When you call your bank or utility company, A.I. is being used to screen and route your phone call. So it's actually pervasive in life. So you actually can't hide from it. I would suggest leaning into it and doing a couple of things. One is figuring out how are you good at using it because you've got to build some skill and also some self-confident. So that is number one. Two, when you look at the work that you do, everybody has elements of work that they don't like doing or they wish could get done faster. How can you or your team or you know whoever is really good at writing prompts or creating an agent help to figure out let's automate this? These are areas where you need to figure out how to work with A.I. to get to work done. But how can you carve out the most valuable work for where you can be creative or you can use experience where you can use collaborative partnerships with other team members from across the company to get it done? I don't think you'll ever be able to do enough to not allow yourself to potentially be laid off. It's an organization has made that decision, right? Because there are different factors that go into that decision. But I do believe that you can make yourself the most impactful, the most efficient and one that is demonstrating value. you, if you take the time to learn it and figure out what are the right applications for you and your team. And actually, believe it's probably better to do it at a team level than just at an individual level because at an individual level, you're alone at a team level. You have community, you have camaraderie, and collectively, most teams are gold against kind of the same objectives or outcomes that they need to deliver. So if you work it as a team, I think that makes you a bit stronger. And lastly, it is very possible to identify a new skill or capability that you have that could take you in a different direction in your career. And so I don't think we've got enough examples of that happening because right now everybody's just trying to learn how to use it in their current work. But I do believe that we'll start to unlock other opportunities and you'll just have to figure out where to those lead you in terms of a career. Do you think the companies are underestimating how much AI adoption is a culture problem rather than technology problem? Absolutely. I was talking to an HR leader the other day and she was telling me Jason, it's still the old adage of, you know, what is it? What's the numbers? It's 70% of change efforts fail, right? Because there's not enough emphasis placed on moving people through the different stages of change and upskilling and teaching leadership new behaviors. I mean, all the elements of culture, I think, are things that need to either be tweaked or redefined in this age, especially since we don't know what the next five, you know, we're going to be in the next five years, but we are anticipating that AI is going to continue to grow and change things in an unprecedented way. And we have to start having more of a forward looking element to our culture, to leadership, to how we think about work in our organizations to begin to shift the culture now so that it's at a place of sustainability in the next three to five years. So there are also, I asked a lot of people about their organizations and there are also organizations like what they are talking about. They are already, you know, early adopters, they wanted to implement AI, but they're also, you know, organizations who are not yet adopting or they're still waiting to see. So what can you say about those organizations who are just not even started or not even in their plan, adopting or implementing AI transformation? So I think there's actually the element of an advantage here, depending how you view it, because let's take a midsize company, let's just say there's a company of 10,000 people and they're spread across 13 countries, driving enterprise change at that scale around AI, just like any other change is a massive undertaking in that organization. They've invested in AI technology and their current vendor partners have added AI features to the platforms they already have. So you've already got a complexity in terms of making that transition. However, a company that's slow to adopt could just say, you know what, we've got three core platforms, you know, I'll make these up, work day service now, Slack may not be a good example, but I'll throw it in there. If all of those providers have added AI elements, let's just focus on getting good at what's in the tools we're already using. Let's not think about this at an enterprise wide level. Let's start working on it at a job and team level. I think they will have a little bit of an easier path because they are approaching it in us slower, but contextual driven change approach, right, which is how does this change the work that I do, how does this change what we're trying to achieve on our team as opposed to what's the top down imperative that everybody in the organization is trying to go after. So yeah, I agree with that. But currently, for example, in an organization, there are two things that having, you know, they are implementing, like for example, they wanted to have a high performance team. And then at the same time, because AI, there's also future ready workforce. How do you, how do you, how would you identify both? Is it, is it intersection of intersection of the future workforce or is it only high performance or is it future ready? Because when you, when you implement a high performance projects, it's a little different from AI, right? So what do you think about it? I think it's two different, maybe three different things, right? So the, I'll start with the third, the overall goal is to get to a high performance culture, right? So that's the high level goal. Part of that is driven by we have high performing leadership teams. Do those teams exhibit high performing behaviors that can be replicated by the teams and individuals that report to them? That is a practice that I believe needs to be facilitated by a human, right? One who can observe, coach, provide feedback. Yes, you may use some frameworks. Yes, you may use 360 feedback assessments as part of that process. But in order to become high performing, it's something that I believe you need to be counseled through. And as you start to make improvements, you should start to see other examples of that show up in the parts of the organization that report up into that team. So that's one, and that's a project that can take 12 months, 18 months. It depends on where the team is starting. And that was one of the things that me and my team did at the center, that's our center, sorry, at Expedia, is that we were working with the C suite teams to help those leaders become high performing so then there could become a cascading effect. Because most organizations are look up and look up. I see what the leaders are doing. They're doing good or they do in poor behaviors, right? We want the good behaviors replicated. On the other side is future of work. That is more of a process that I think can be AI enabled, but there are definitely steps that need to happen in your talent and workforce strategy to get there. One is some level of workforce planning. You know, is that a skills based approach to workforce planning as opposed to doing headcount. The approach that I would suggest being able to align where the business is trying to go with the skills that are needed, the roles that are most critical for those skills and then working backwards through to how many actual people do we need. How do you segment that work? What can be automated? What can be augmented or what purely needs to stay human? And then what's the development or upskilling that occurs and also to ensure that there's a future ready workforce and also how does that further up line impact the recruitment of external talent. You know, if you don't have the ability to get that those talent pulls from within your organization. So I think it's two paths. One is you have to keep working on the high performance and effectiveness of the leadership teams. The other is for the future ready workforce. I say, you know, kind of human and AI workforce planning that's based on a skills architecture and the combination of those two things will start to move the needle towards a high performance culture overall. How do you see a future ready workforce in maybe five to ten years or even two to three years because of this fast face of advancement? I had a premise a number of years ago that has not yet come true. It's come true in the fact that we have a gig economy, but that gig economy is largely through services. I would reframe that idea, which is in the age of AI, we have a greater ability to get to a point where people become gig workers, particularly for work that is seasonal is the only work that comes to mind, but let's just think about work and finance. We know we have to do quarterly results because we are a public company and close to books. Maybe my gig is I just rotate from company to company and help them close the books either at the end of the quarter or in the year. I think gigs within an organization are going to significantly rise similar to how I start at my career in consulting. What happens to you moved around an organization based on your skill set and what projects needed you? That is a way I feel that we will start to see a shift. Secondly, the organizational structure is likely to change over time from a pyramid shape to something maybe that's more akin to a diamond as there may be fewer entry-level roles in the middle of the organization may swell a bit, but I think that's where we will see a lot of internal mobility and people working on multiple projects and maybe staying less departmentally siloed and it's their technical skill and capability that allows them to move across the organization. There's one guest that I had four weeks ago and he said that it was a different take about the middle management. He said that the middle management because of AI, AI can analyze a lot of things for the middle managers that the middle management will disappear. [BLANK_AUDIO] on your analysis, which is a diamond structure, the mirror will be a little bit bloated. So what can you say about the take from your side? Yeah, well, he talked about the role as a manager. I didn't explicitly call out the role of the manager because there's going to be a couple of different approaches at stake. So if I'm just working on a project, maybe I have a project manager. That project manager could be a human, that project manager could be a gentick AI, right? So that's going to be a big shift in of itself. How does a human now become the direct employee or team member of an automated agent as opposed to a human? I think we have to, we have to, even as we look over the next couple of years, we have to be mindful that there are some psychological changes that are going to have to happen as part of that. And so, maybe we'll see more pods emerge and the role of a manager is more of a community manager. Think about companies who have community managers that organize their clients and groups, what do they call them? Like a success manager. We may see managers move into more advisory and supportive and community engagement type roles as opposed to I just lead a team of X amount of people. And again, that's because there will be some that will be choir human managers and there'll be some that will be managed at to say it, but by by an agent. Okay. Yeah. That's a good take. Yeah. It can be. It can be. It can be now again, because some people are already like, for example, the solo printers were just one one one one man or one woman CEO, they have these agents are already looking that they have this in the C suite level. They have these agents that helping them with the decision-making and day to day and day to day they work. So as in a corporate setting, so we have already talked about the analysis of be having an agent. So how should leaders sitting performance management when AI tools are quietly doing 30 to 50% of the knowledge work? You know what I mean, right? Sorry. No. If AI can analyze things for you, what how are we going to how are we going to have this performance? How are we going to align with a KPIs based on his or her work? When in just few seconds, he or she have already did this data. This is one of the questions I wrestle with and I've wanted to write an article and I've been struggling sometimes to land on a core thesis to fight the article on. So a couple of things. One is I've talked to former colleagues recently through this past performance cycle and they said, AI wrote my performance reviews. It was great. It saved me so much time. I mean, I'm like, that's fine. I mean, depending on the manager, they can spend one minute on a performance review. They can spend an hour on a performance review, right? It just depends on the individual, but that's actually not been the problem, right? The problem has been actually then having the conversation with the employee, planning development, keeping them engaged, having better alignment between how they were assessed and how they are incentivized in terms of you know, reward and recognition. So those things still have not been fully solved for, but I think the monkey wrench for me that makes performance management going forward so challenging is the fact that if I have a team of five people, I may have two that are highly proficient at using AI and so they are getting, let's say, more work done. They are demonstrating more efficiency than the other three people, but it's because of their individual intelligence and capability, not because they're not trying hard. And so how do you then view performance through a lens of they got more work done because they're better at using AI, but the other people were trying hard, but they're just not as good at using AI. I think that's going to be, you know, one of the key challenges that leaders are going, managers are going to have to wrestle with. And then what does incentive look like when now part of the work is done by an agent and part of the work is done by a human or I'm a manager and I'm managing a team of agents, not humans, right? So there are there, there are going to be a few, you know, maybe we can boil it down to, you know, three to five kind of core scenarios that will need to start planning for and then be able to take a step back and maybe just blow up the design of performance management and say going forward, how do we think we need to assess the value of someone's contribution to the organization? Yeah, it's a hard question. It's also connected with, for example, your example, two people who are more AI fluent, right? They have this good and from engineering and then the other guys are doing the other works. And how are you going to structure their salary, for example, because, you know, in HR, we have salary greatly. They're all engineer one, for example, and this guys are not very good at from engineering and the from engineering guys are very good at what they're doing. And then this guy's where saying, okay, we're very good at trying to technically, but how come our salary is the same? Do you think there will be a driving, right? You can go, you can pass the test to get a driver's license, you can drive on the road, but everyone who drives, there are some who are really good and maybe they become formula one drivers, right? But the average person, they can be good enough. Maybe they don't get in any accidents, they don't get in any speeding tickets, but, you know, how to insurance companies, then compare and contrast the goodness of drivers. And so they are actually trying to use, you know, more data by giving you plugs into your car so they can see, well, are you speeding more or are you coming to hard stops? And so maybe there will be some new approach to identifying and tracking performance value that will help us. And maybe also I think the name, I think we were probably to shift away from performance management. Is it performance, activation, you know, performance, engagement, enablement, but I think some other terminology needs to also lead the way with this shift. Yeah. So if, for example, if AI becomes a co-worker, like what we are talking about, but we are discussing right now, so not just as a tool. So how does the change accountability and ownership, who gets credit and who gets the blame? Like this, well, that's who gets the credit and who gets the blame. I think there's opportunity. One is, you know, many organizations are training people to get to some level of AI fluency. I think there will need to be an organizational baseline that companies have. And so this is the standard. If you can't pass that standard, maybe you can't work here because we need everyone to be able to reach that standard in terms of AI fluency. I think that's number one. I think two, depending on the work that you are doing or your team is doing in an organization, we may need to think about is work value differently so that you can better align reward to the value that work is delivering. So for example, maybe if you are, you know, helping to create and launch products that your customers use based on the amount of use, maybe that drives a higher type of reward structure versus, and I'm going to get in trouble for probably is saying some example, but you know, a wellness program. Right? There's different levels of organizational value on those two types of work. And so maybe we start to think about how do we bucket and group work? We still have salary bands, but maybe the variable becomes bonus level RSUs if you're giving RSUs. And what we would also then have to do from a learning standpoint is offer people the opportunity to evaluate themselves on other skills. So if they want it to move to a higher value ad area, there's the ability to understand where am I starting from? What's my gap and what are some personalized recommendations in terms of both formal learning and hands-on experience that can help me start to shift there? But that's a hypothesis, but I mean, it seems plausible because also some of that work will go away. At some point, in the same way that the agents have, you may have recently heard this, created their own social media platform. Right. So you can imagine that maybe some agents will get together and go, we're going to create a wellness marketplace. And so organizations no longer have to dedicate a human towards finding wellness or medical plans for an organization. So then it's how do we turn those individuals based on their skill and capability to higher value work. That may also free up some capital in the company as well to allow to sustain a reward and salaries in an organization because the lower valued work has become automated. So the cost is fractional. Have you in your experience ever seen an AI adoption damage trust internally? Wow. I don't think I've got an example of it damaging trust. I think the damage that I see right now is not specifically AI. it is the impact of AI. So let me explain what I mean. A lot of well-known organizations have spent a lot on AI and have started laying off people and have started eliminating roles. And I think we're going through this process and it's still happening right now is where organizations are making tech first decisions. They are not making decisions that are anchored in humanity or human centricity. And not that it has to be 50/50, but it's very hard right now to see where the human element is. We're all humanity and now you're saying, we told you we need computer scientists. You go get a degree in computer science and now you can't get a job. Right? Where's the love for humanity? And I think that's the disruption and trust that we're seeing. Not the technology itself. It's been the reaction of leaders. And so I want to be involved in how do we start to help leaders and organizations start to move more back towards maybe it's a blend. Human plus tech. Maybe we can get to a 60/40 or 55/45 split where it's a little bit more on the humane side and not completely being driven by tech for decisions that impact people. Yeah, so moving on to, you know, because we're talking about the tech first because this is a leadership decision that they see AI as a tech first. So what new leadership capabilities become non-negotiable in an AI first organization? And an AI first organization, I think empathy, need to move higher on the list because humans are made created to do some type of labor. Even recently there was the video of the tribe down in the Amazon, I believe, that was taken. They don't have technology, they don't have pay, but they are laboring in order to live and survive. I think that's a core element that needs to be moved up higher on the leadership wrong in the age of AI. I would also say that adaptability or agility needs to be moved up. We're asking a lot of society right now to learn and adopt a lot of new things right now specifically in regards to AI. But again, I mentioned it's ubiquitous through life. I mean, they're talking about you won't have to drive anymore. There'll be autonomous cars. So you can't drive, you know, your food gets delivered by a drone. All these things, your children are being taught by an agent, you know, all of these things are changing. So it's not just the work change, it's a fundamental life change. And so I think people who are thinking about others, agility and ability to manage through all of this complexity, I think that needs to be higher on the list as well. And if I could pick one more, I would just say, you know, either values or community. I don't, we all have to coexist together whether it's at work or outside of work. And I think leaders need to also think about what type of community or village and my forming, leading, taking care of. And so the empathy and agility fall into that. Yeah, so you're already mentioned about, you know, community and then, you know, diversity and then. So currently, right now, a lot of organizations, even though we're always talking about inclusion, we've always talking about diversity and ethics. A lot of organizations are still struggling to do that to have this equality and inclusion. So as AI promises objectivity, for example, in hiring, but we know algorithms have inherent bias in it. So in your view, is it more likely to reduce or amplify inequality? I think it's going to amplify inequality. Let's go back to the example of everyone has differences in how a new ability to use AI. So if I'm applying for a job, right, if I'm really good at using AI, I can figure out what are the triggers for an ATS to read my resume or my CV moves higher in the list of talent being considered, right? That's a, that doesn't help in terms of, you know, a diverse and inclusiveness because it boils down to capability. There are still going to be biases based on relationship, right? I know someone in the organization, I know someone in leadership and that connection bumps you up and I don't think that's something that AI can override, right? Because a human is going to advocate for a human no matter what the technology platform says. And I'm going to go on a limb here, but I think, and actually, I see this right now. Technology plays on our senses. Let me give you an example. When I turn on Netflix on my television and it says, here, you're suggested shows. I'm a, I'm a black African-American. I see a lot of snippets from shows that show me the black person in the show and say, maybe I watched the show and that it's an hour long show, but that person's only in the show for three minutes. What the AI and machine learning did was it figured out somehow my racial ethnic identity and it played on that to get me to watch a show. So that goes to your point of there's bias built into the system. It's going to take human decisions and human creativity to be able to unravel those things in order for inclusive and diverse organizations to be able to flourish. People are going to have to be intentional. And I don't think that's something that we can just hand over to AI to do. I think we're still going to need targeted outreach, targeted development and different types of efforts to ensure that we've got good representation from females, that we've got good representation across racial, religious, socioeconomic areas. And also, you know, we live in a global environment, right? We live in a global world. How many organizations can be successful if they don't have perspectives at minimum from, you know, the major continent to populations around the world? Yeah. Yeah. So I agree. I asked this because I was when I was in Sweden, I have this, it's a startup, a big startup, European, European supported startup in a battery, battery industry. And we have around 110, 170 nationalities in that company. And we are very, very diverse. And I was so surprised that, you know, all of us, whatever your Asian, American, British, or from Iceland or whatever, we think the same, you know, the division, we all believe in the vision. And we are, you know, leaving the values. So that's why it's the first time in my whole career that I have seen that 80 to 90% of the people are so homogenous that they think the same. You know, the values are the same, you know, the, the personalities almost the same. So even if you're talking with a Polish or some, you know, English, it's the same. You know, every, so I thought that, okay, their talent acquisition team is very, very good. Because like for some Asian, we have some Malaysians too. We have some Indonesian and different religions. And we have, you know, it's like a culture melt out, a melting pot of culture in that organization. That's why I was so surprised about it. And then now I was thinking if it fits AI is going to filter out these people, because I believe that the reason why they were able to filter out these people, because they were able to talk to them, you know, like this, we thought you feel, okay, this guy is a cool, you know, he's a, right? So, what do you think? How do we, and how do we ensure AI doesn't quietly encode cultural feedback at scale? Yeah. So one of the things I learned, maybe when I was leading diversity, it actually, I started working with a design organization, it's figuring out how to design for inclusion and diversity from the beginning, as opposed to making it an add on. There's likely someone who's working on an LLM that is targeted towards designing for inclusion from the beginning. Maybe it's designing a workforce for inclusion from the beginning. Maybe it's designing a marketing strategy for inclusion from the beginning. I think it's probably going to take first a customized LLM that's focused on, you know, diversity, equity, inclusion, belonging, kind of all of those things. And hopefully that will start to gain some traction and become integrated as maybe the wrong word, but the only word I can think of, but it becomes part of major, large language models. So then it doesn't just exist as a separate platform, but starts to permeate the AI ecosystems of organizations. So it shows up in all the functional areas and is an influencer on the work. So you work also in Warner Brothers Discovery, so PG and the entertainment. So in entertainment, where you know, this right where creative videos are is everything. So now you do see this AI now is doing a lot of a lot of good videos or creative creative designs and all this even cloning the Hollywood stars. So do you see AI's creative amplifier I'll try to be human. and the originality. - It's a threat. The reason I tell you this is, we have a friend who writes movies, love-ish-and-chose, and what she has been saying to us for the past, at least two years and not three years, that there's a formula now. And the company's look for, does the story fit into the formula? And it's because they have data. So they're less interested in ideas that break from the formula because the formula has a track record. But what that does is it does stymie creativity and what it forces. And fortunately, we've got some examples of where it's led to success, but I think it's now getting harder is that forces those creative people to YouTube. It forces them to TikTok, to those Instagram, but those are also very saturated social spaces. So they really have to get a mass following to shine. So I do think in the near term, it is going to aggregate some elements of creativity, at least in what we consume in terms of music, television shows, maybe even news writing. But my hope is that those individuals won't take no form answer and they'll use one of these other platforms and they'll cultivate an audience until it gets to a point where, the mainstream companies cannot ignore it and they have to embrace it and adopt it. - You nearly tried that, it's already there. They cannot avoid it anymore. So how should companies protect creative talent while still leveraging generative AI for efficiency in the entertainment industry? I mean, in the entertainment industry. - Yeah, yeah. Well, I mean, I think creative talent, let me back up. Humans understand humans the best. I don't think that while technology can give you data, it doesn't create anything that is completely new because it works on a factual set of data. Whereas humans, you and I can talk and we can come up with an idea that neither you nor I thought of, right? And then we can whiteboard that out and figure out what to do with it. I don't think anything replaces that. And so maybe what we are doing more of is helping creatives to nurture their creativity together kind of technology free and then bringing in technology when it's time to further that idea, you know, turn it into a script to write a pitch document, you know, those things and then it can help you button it up. But I don't think that technology understands humans better than humans do. And so I want to just continue to nurture creatives. And also I'm gonna go back because you asked me question about leadership, the creativity and the value of creativity is something that leaders really need to better, more strongly focus on because it's not just what do we think is creative with technology. Humans have continued to show that we create and invent new ways, new ideas all the time that have revolutionized the workplace that have also revolutionized our lives. And so that's where it starts. - In the near future, do you think there is, because right now we have this generative AI and there are AI avatars that if you have a business you don't wanna talk to your customers or you want to have create a video, you can just select, you know, the faces of your company. So do you think there will be a new industry like an AI Hollywood where the movie stars, you know, the movie stars are created? They don't even exist, but it was created by AI. They are beautiful, perfect, you know, handsome. - I do. - I do. While I don't feel I'm qualified to answer this question, I will say that there might have been one, maybe there were more, but last year there wasn't a musical artist that was completely AI, that was generated. You can Google this, the person had songs on the radio, they had a name, they had a social media following, and people found out it was completely AI generated. There's two camps here. I think there's gonna be one camp that rejects it and says, no, I want humans. Why do I wanna look at a screen and see something that is completely fake? And there will be others who will be open to it. And I think there, I don't know that we'll get past those two things, but I think that's where we will land. And I think what you said also has huge implications for, you know, your next manager is going to be an agent. If your next manager is going to be an agent, do you just wanna interface with that manager in a text-based interface? Or would you prefer an avatar to give you some semblance of I'm talking to a person even though you're not? I think some of those human senses are going to demand that we adapt some of those techniques within organizations as we become more reliant on AI tools to enable us to work and perform successfully. - So if you are, for example, if you are going to advice, CEO or C-H-R-O, so one piece of advice that about leading through AI era, what would it be? - Right now. - My first piece of advice would be the manager, so not leadership, talking about the average manager who has five people on his team. That is where you need to spend time to drive true adoption and operationalization of AI, because that's where the rubber meets the road between what we need to deliver and how we get it done. And unless we are helping managers cope with that complex change and how they need to do their work, re-evaluating, rewriting their roles, taking some things off their plate 'cause we've put a lot of things on their plate and helping ensure they have the right both capability with AI that they can coach and if they can't, where can they go? I think that's going to be the biggest, 'cause that's also where community and the feeling of belonging starts within companies is how do I feel with my manager? - Correct. - Yeah. So thank you for that. So we are now moving on to the last round, which is the Rapping 10X. So I'm going to ask you, how you're going to thank you very much for all the insightful. It's very, I knew Tic, I learned a lot from you. I like our conversation because we dealt into different parts of the conversation, which I haven't had the conversation before. So I very, very insightful and I'm glad that you were here, but we are moving on to the last part. It's almost an accord now, because our discussion is too exciting and too interesting in discussion. So I'm going to ask you 10 questions that about your personal, you know, personal view about AI. So what's one AI tool you actually use every day? - I use Gemini every day. I've created gems and two gems are kind of agents but I've actually created two apps, although I'm struggling to actually get them to work. They seem to be created correctly, but I use Gemini every single day. - Okay, so what's one task AI has already 10xed in your life? - My ability to, I've been writing a series on AI and talent strategy since last year. It has really helped me synthesize a lot of articles, research, so I don't have to spend as much time doing it. I can drive the core idea. I can find a couple of articles in the research, so it helps me with that. And it has helped me with formulating, I'd say a cohesive story around my main thesis that still includes me, 'cause I try to write mainly from, this is my experience with AI as opposed to, this is what I think about AI, so that has helped me tremendously. - Okay, what's one human skill AI can ever replace? - One skill. Oh gosh, I don't even know how to quantify the skill. Love is not a skill. (laughing) - Yeah, yeah, yeah. - One skill that AI cannot replace. I'm gonna say, you know, effective communication. - So if you could automate one annoying thing forever, what would it be? - Me, I know this is going to not sit well with a lot of people. We have a dog and I don't like walking the dog. If there was a way to automate walking the dog, that would be fantastic. (laughing) - And picking up the poop. - What's the biggest misconception about AI right now? - I'm gonna go back to what I said before. There's an assumption that everybody can use AI well or equally well, and I think that is a big misconception and we're gonna continue to see, you're gonna be broad differences in people's capability with it. - Okay. What advice would you give someone who wants to be 10x starting today? - In order to be, starting today, identify the two to three things, 'cause it's not gonna be more than that. Two to three things that you are so both passionate and skilled at that to not do them would make you sad and figure out how AI can help you in each of those areas. - What would you tell your younger self about the future? - It is not like you thought it would be watching book Rogers. So growing up, we thought by this time, we would have flying cars and, you know, super walkie talkies that it's not going to be. be that way. But what I, what I would have said is I took a class in C plus plus in graduate school. I think I got to see, I probably would have told myself, keep trying. Keep trying and understanding how to write some code because, although now we can talk to AI conversationally, I do think there's some deeper level understanding. Think up like an if-then loop or something that can help accelerate your capability. And so if I told my other self, I probably say, you didn't do as great in that C plus plus class with maybe taking another class in a programming language and just try again. So what's one book, podcast or resource that's shape you're thinking about AI or innovation? I would just say I'm a person with a lot of high goals. And so when things change, I try to adapt quickly and set high expectations for how I'll do the thing that change at a higher level. So maybe not a book, but personal motivation or your personal motive. Yeah. If you had to bet on one AI trend that would be massive in three years, what is it? I think self-driving cars. Yeah. I think it's going to happen. Yeah. It's going to happen. I think the rub is that people, like I like driving my car. So I think we'll just have to figure it out. You know, I don't know that it takes over, but I think we're going to be very, very close. And I like the idea of it, especially every time you hear about it and accident. I think everyone, especially one that is fatal, everyone's heart churns. Imagine something that happens all over the world having a higher likelihood of decreasing the probability of accidents while driving. So autonomous driving. Okay. And in one sentence, what does 10x mean to you personally? Next means to me, it's growth mindset at the most exponential level. So there's probably only one thing in life that are maybe two things in life that I'll be able to 10x. But that is because there are areas where my growth mindset is just I'll call it exponential or extreme, meaning I'm willing to keep going the extra distance because I'm so, you know, connected to it passionate about it, love what it is. And I think that the combination of those two, the growth mindset and just kind of that exponential effort is the 10x unlock. Thank you very much for that insightful rapid 10x fire. And if our viewers or our listeners wanted to talk to you about everything about AI or other consulting, consulting, things that they wanted to, you know, to ask you. So how do they connect to you? Wow. So on x I am it's at the talent chief on x ch i e f all one word. And then on LinkedIn, you can find me Jason Williams, Jason to spell J a I s o n. And if you probably just type in Jason Williams Fitbit, you will get me. All right. Thank you very much for that insightful is over one hour now. So I don't usually go over one hour, but I have I wanted to ask a lot of questions for you. But it's already one hour. So I wanted to capture the one hour. So it's very interesting. I'm very happy that you grace this event now and this activity that we have this conversation. I learned a lot from you. And I hope our viewers also are our listeners will also learn a lot from you. Awesome.