Why 44% of Employees Are Actively Sabotaging AI at Work

A 2026 report reveals that 44% of Gen Z employees admit to actively sabotaging their company's AI initiatives. This behavior is part of a broader, more prevalent resistance to workplace AI, often driven by fear of job displacement and FOBO, fear of becoming obsolete. And today I have Dr. Serena Huang, one of my friends, uh, who's a premier AI keynote speaker and former Fortune 500 tech executive, chief data officer. All the cool things, uh, here to talk about this. And one of the things that's been on her mind lately is exactly this. And a quote, uh, she shared with me is, AI will not fail because the technology isn't good enough. It will fail because leaders are not changing fast enough. She works with big organizations doing talks, driving change, and I brought her here today to talk about this. AI sabotage, FOMO, uh, change management. What's going on in this world and what are the pain points that executives are going through and organizations are going through. Uh, and you've mentioned to me, we really got to focus on humans at the center. So do you want to say more on just that topic of AI sabotage? You were the one that shared with me that stat, 44%, like almost half of Gen Z's sabotaging AI initiatives at work. That's wild.

Got, uh, so much to say. First of all, I think that's so expensive. And anyone who's in leadership role can just think about all the wasted training, the enablement programs that you put together, and including the very expensive bill of AI and all of it, half of it for your next generation is going to waste. And even for general population, it's like, not just Gen Z, it's about 30% are actively sabotaging. And what do we mean by sabotaging? It's like including not going to the training that you've paid for and brought in either external partners or even have internal teams if you have a good, you know, L&D function that's well-funded. And then even going as far as inputting terrible prompts. So that the output will be unusable. And then when you send out those surveys, like, how is AI going? What can we do better to improve the tools? Then your employees are telling you, well, it's totally unusable, and it's partially because I actively made it so. And I think that is almost unthinkable, probably, to some people who are not in the day-to-day of AI rollouts. I spend a lot of time purposely talking to people who refuse to use AI no matter what. Because I work with the change leadership on AI, and I made it a mission to better understand why people won't use it and what's getting in the way. And I think if you just spend a couple minutes talking to someone who said, nope, I'm not touching AI, that will open your mind to different things. So yes, the reasons vary from one organization to another, from one person to another. But it's definitely happening. And I wish more organizations are spending more time with those people, the resistors, loud or quiet ones, rather than just focusing on your champions and your fast, you know, the first adopters and getting them to the next best day. Because you can't move a ship without moving everyone. And if you have people who are really just not willing to get on your boat even, then, then, um, you, yeah, you can only go so far.

Yeah. AI adoption is such an interesting thing and you're a data person and change is something that I think about, uh, as a very difficult thing to measure around AI adoption. So when you're working in these companies, like what does a measure of success look like for quote unquote AI adoption? And could you speak more about that?

Yeah. So one of the activities I'm starting to see a lot at the senior leadership level is how do we measure the true ROI of AI investment? And how do we then incentivize people to behave differently so that we can adapt faster and get to the outcomes that we desire? And unfortunately, a lot of organizations are stuck in the middle of measuring what is easy, which is, you know, the number of prompts, maybe time spent on the platform, maybe number of logins, how many days someone is on a particular platform. And that's great because, well, it's easy and it is a signal because let's say that— let's be honest, if you don't have any usage, you are not adopting AI and there will be no change. So that's minimum. But I'm really asking leaders to think beyond that. Just because you have lots of prompts doesn't mean your work output has changed, right? So sometimes it could even be worse. This famous word from last year, work AI work slop, right? Where you actually spend more time fixing AI output from either your colleagues or yourself than getting what you need out of AI. That's counterproductive. But where does that show up? Well, it's actually the output. If you just measure the usage, it could look like everyone is using AI or a lot of people are using AI or they're prompting more, perhaps because they're terrible at prompting. And like, who knows? There's a lot of things that underneath that we need to start measuring. So I also advocate for things like a combination of both quantitative and qualitative approach to measuring and actually asking people, what are you using with AI? What are you doing with AI? What do you get out of it? What has changed because of AI? What could you not have done without AI? And once you ask those questions, right, people start to really think, oh, you know, maybe it's, I'm able to close deals faster because AI is taking notes on my calls for a sales team. Maybe it's engineering team that's focusing more on, you know, debugging. They're able to debug faster, for example, or they're able to translate an old language to Python overnight and instead of paying another company $1 million to translate all the code. So these are some real use cases that I have seen and heard recently. And I think every organization has different use cases and it's not just about usage and number of prompts and time spent on platform. Important, but what are you using it for? And does it get you to, you know, the outcome you need faster? And ultimately, the question is, what KPI for the business are you moving because of AI? Is it more revenue? So, you know, the other thing I say is money always talks, and it's not just because my degree is in economics, but really, is it top line or bottom line impact that you have? And that's it. Those are the two things that matter. Is it more revenue? Is it lower cost? And when you are able to speak those things, the CFO is listening to you. The CTO who needs to invest further in AI is listening to you. And I think that's where a lot of organizations are not pushing more beyond the usage simple metrics. Yeah. Got it.

So scaling.

Yeah.

I, I love that. I, I love that. And just to reflect what I'm hearing in your work with executives at big organizations trying to drive change, it's really getting to the heart of what is success for you? Yeah. Not for somebody else, not some other, another company. And for you and your business, uh, top line and bottom line. There's a quote that I, uh, am remembering from Jack Dorsey in reflecting on this, which is money is one of the most honest signals in the world. And it totally is because we live in capitalism. So yes, it is. Um, and on, on that, I love your human-centered approach, which is don't just talk to the champions who are gung-ho about it already. And there are 100Xers, talk to the people resisting. So tell me, what are you hearing from when you sit with people that are resisting? What are they saying about AI? What are their fears?

There's, there's so much anxiety. I don't think I've seen this level of anxiety, especially among Gen Z in a really long time. There's so much fear of what is— what does this technology mean for my future and my career? And the truth is no one is having those candid conversations. So, you know, what happens to our brain when we don't know? Well, we're filling the blank, right? And we're filling the blank with sometimes worst-case scenarios because our brain wants protect us and keep us safe. So when I don't know what AI is going to mean for my career, do I get paid more if I learn AI? No one is telling me these things. They're just saying, go do it and use more of it. Does that mean it's no longer valuable to continue as a coder or software developer? What does that mean for those more technical roles? And in the past, it was like a pretty linear path, right? I, you know, it's like you have competency framework laid out and I can, you know, eventually be a staff engineer senior, whatever, principal, and then you just keep progressing and then you get to a CTO. That's like the path. I honestly am not sure what the path to CTO anymore today. And anyone who says they know is probably not being super honest because there's so many unknowns, right? What kind of skills are required? And is it supervising, you know, agentic AI in your workplace? Along with humans? Does leadership skills still matter and which part matters more? Like all of that really is different. Do we, are we going to see different roles getting combined, right? There was a famous pharmaceutical company that combined HR and IT together. It is now under one umbrella, right? Do we know anything from that experiment? How is it going? Does that actually work? 'Cause if we're getting squeezed at the top, if we're seeing even fewer opportunities at the C-level, what does that mean for the leadership pipeline? That, you know, like, so, so yeah, I think there's so many questions. So people are really most worried about what does this mean for my career? And, and so I think that's number one. And basically, you know, if I'm not hearing my leader telling me this won't impact my job, and I still have a future here, then I'm going to do things to protect myself. And that could include not using the tool you are forcing me to use. So that's one. The other is environmental impact. This is huge, and this is philosophical ideology, if you will, but it's also very real, right? And there's so much misinformation out there. So part of my learning recently for myself is upskilling myself on AI infrastructure. So, how do we actually get more out of the same compute? How do we get more out of the same chips? Because we're constrained. And there's a lot that is happening behind the scenes from various AI companies trying to minimize environmental impact. And I think it just takes more curiosity from all of us to better understand before we jump in and say, oh, I'm going to only use the model from this company because they're doing more. Like, it's— we may not know everything. So, so anyways, I think there's a lot going on in sustainability side of AI that I'm paying more attention to, and it's definitely moving really quickly. So that's two. And then I think the third that is a little bit more strange is like related to next generation. Do my kids still have a future? I've heard parents say, I don't want to use AI because I don't want to advocate for a future where my kids don't have jobs or they don't have good jobs. And again, like these are soundbites that I have gathered. And my encouragement to all of you is to gather your own and actually talk to people. Because there's a lot of stats out there, like women don't use AI as much as men. And then like even Reese Witherspoon is advocating for a woman to use AI to not get left behind. And there's all kind of backlash on her. And like, again, gather your own data because in the organizations I work with, when I looked at the disparity by gender, I actually don't see one. And perhaps, right, there's some bias in this data sample, right? The organizations that hire me obviously are very heavily invested in AI, and I also speak very loudly on, you know, well-being, inclusion. So they're not going to pick me if they don't care about those things. So perhaps, right, there's a relationship there. But nonetheless, you have to know, are the female employees in your organization actually using AI less? Are they pushing back more? Or is there something that is out there? And maybe it's not just gender, maybe something else, maybe it's generation. But you have to understand your own data so that you can create a strategy that targets the groups that maybe need different help than the general population. Yeah.

Interesting. Yeah. And it all goes back to just centering on the humans, right? And focusing on them and talking to them. And so that sounds like a very actionable takeaway if you want to drive AI adoption. As a leader at your company, go talk to your people, not just the people that are gung-ho about it, the champions or innovators, but talk to the resistors and talk to people of different, uh, diverse backgrounds, gender, race, age groups, right? That's so interesting to hear about that people are wondering about, um, their future and that leadership pipeline. Like, what is leadership pipeline if there's less seats at the top? Like, then what's going to happen. Uh, and it's also interesting to hear about the moral and ethical philosophical dilemmas, uh, around sustainability. Um, hmm. Okay. Another question is now, so you've worked with a bunch of organizations on this, so you've seen probably more mature, let's say, and then less mature in terms of AI adoption. What are the more mature orgs doing right that the less mature orgs could learn from? In terms of AI adoption?

First of all, everyone feels behind, like everybody, right? Everybody, it doesn't matter.

So maturity is not the right word.

Yes, like I think there's so much, oh gosh, yeah, there's so much FOBO and FOMO right now. My initial conversation with anyone, any company, any level is like, we are so behind, we just want to catch up. And then they went on to say, you know, all the amazing things like already having AI governance in place, the whole COE set up, and the latest tech. They're not tied down to one platform. They're actually experimenting and gathering data on multiple. I'm like, wait a minute, you're actually very advanced. So just chill. And yeah, and you know, like sometimes they're skeptical. Oh, of course you will say that, Serena. Like, no, no, no. Really, there are people who are very behind and they're very risk-averse. And then others who are experimenting. I do think there's a wide spectrum right now that's happening. And we're starting to see this almost like, I worry about the haves and have-nots in AI that we're going to see because some organizations were so conservative. Remember, like 2 years ago, they locked down any AI platforms. right? And especially in like financial services, there were some big banks, I think, that came out and said, we just won't allow anyone to use AI. And I'm sure they've changed their mind since then, but that still means there's a lot of variation in what is allowed inside the AI ecosystem within the company. So I'll give a really simple example. So you have all seen ChatGPT at this point, likely. And some companies actually lock down the ability to upload anything because of security risk. So imagine that. So imagine if you have a ChatGPT that doesn't allow any uploads. So you just, you know, or maybe it doesn't have the web search functionality. So you're constrained by all the information, or you're constrained by what you can copy and paste into the window. And suddenly, like all the context window and whatnot, like those issues become really real. And I think that then creates different risk, right? Like if my own AI on my phone is actually better than the work AI, then guess what? I'm going to use my own device probably, right? Because I want to get things done and want to be productive. And that might suddenly create overnight really high risk for your data and your work IP. And I think people don't think about that too much, but like AI that is implemented inside the company, is actually vastly different from one organization to another, even if it's the same platform, because of different philosophies around risk management and around what data is acceptable and the governance maturity. So, yeah, you look like you were going to say something.

No, I want to hear more about that. That whole risk, I think I've heard that too with my clients and prospects. That, um, just cutting the chase, I think IT teams gotta level up is what I find. Like, if you don't have the right people running your IT in this age of AI, if they're not using AI on the weekends nerding out about the latest OpenCL or Hermes, then they don't know what the surface area of risk is, and they don't know how to right-size the recommendation to you as the C-suite on how do we roll this out in enterprise. That's my take. Might be a hot take, but I think all the Fortune 500, Fortune 1000s out there with a legacy CIO that doesn't actually play with the tools, they're just, just the blind leading the blind. That's what I think. And then you're going to cap your people and then your people are going to go backdoor your data and IP and proprietary data out to ChatGPT on their phone. And then God knows what happens after that.

Right?

So that, that's my take. And I want to hear more about that from you, like, cause you've had your own data points. So I'd love to just share that.

I agree. And I think back to your earlier question about what are leading organizations doing differently, it's the leaders that are doing it differently, really at the very top. And I see it from, you know, both the tech and human side of things where I see a different level of investment in their own C-suite. So, yeah, so yes, you can't just tell everyone else to use AI and then you yourself as a leader, this is no different than, gosh, when computers first came out, right? Or the internet first came out. You know, we would hear sometimes the C-level leaders saying, well, I don't need to use AI because I have people for that, right? I have an EA who schedules my meetings. I don't need to figure out how to do that scheduling tool that you are using AI for. I have a group of people who take notes for me on calls. So I don't need to use the AI note-taking thing. Plus, my stuff is all confidential. So, you know, you hear all these things, very logical, but at the same time, like, you could quickly get left behind and come across as a leader who are, you know, not like, do as I say, but not as I do. And that doesn't work, especially for a technology that is moving so quickly. So, so what, what does work for the C-level, right? I think companies that are leading the space are thinking about talent differently at the top too, whether that is hiring someone who like, okay, so side note, I don't think I see a lot of job postings are asking for not just unicorn, like purple unicorn, right? Like people who, I mean, like, I don't know, it's like people who can, what is it? Employee or coach, CDAO or CCAIO, and they know how to set up governance. They also have run LLM ops for years. I mean, like, LLM ops doesn't even exist in most companies. So, I mean, MLOps doesn't even exist in every company yet. So, like, that's asking a lot, but then also you need someone who, you know, is like visionary, can do all these things. Can speak to the business and at the same time can monitor what AI is doing. Like that doesn't really exist. So the leading companies I see are actually thinking about talent in a more realistic way. Do we have someone who can do the governance side and then we have someone who can lead the strategy and transformation and vet AI vendors, whatnot? Like, are those skills going to exist in the same person? And if not, let's be realistic and build a team that can work together and really focus on the human skills as opposed to the resume and the experience that likely doesn't exist in the general population. And then have the team create an environment where the team actually can work together. So, I think that's one. And the other is I've been brought in to do these, I'll call them white glove sessions, with C-suite leaders where no questions are too simple and there's actually hands-on time to play around with either building an agent or just prompting better or solving a real problem inside the organization using their own AI. And it's about the safe space. It's just creating the space and time and psychological safety for your most senior leaders to actually get hands-on and be willing to use AI. I think that that just, that moves the needle the fastest when they then come, coming out of the session, say, wow, I learned so much in collaborating with my peers, and now I'm going to use X, Y, and Z for this particular problem I have. And I encourage all of you to do the same. Like that carries a very different weight than some, you know, comms team or PR firm drafting a beautiful message around AI. And then, yeah. And then in the elevator, when someone asks you, hey, CEO Robert, how have you used AI? And you can't come up with a single thing. Like people can see when you are not honest and candid these days.

So, yeah. I, I find that in working, um, with different companies, I find that it really is down to the leaders. And it's what you said, it's people will see what you do, they'll feel that energy of whether you know your shit or not. Yeah. And whether this is just a PR statement wrapped up in a PR statement, right? Um, and I love that, that you're actually on the front lines with those, uh, executives helping them understand what's the art of the possible so that they bring that back into the science of their business. And I think that's such a key thing. And I find that change is just hard for everybody. Like change is hard for me, it's hard for everybody. And especially folks, uh, that have been entrenched in these leadership positions for decades. Now here's a new technology that just like changes the entire landscape of business, right? Uh, and that will dictate what kind of talent pipeline you're driving, which drives the bottom line ultimately that you want, and whether you're still relevant in a few years. That's a big thing. So change is just hard. And I think, um, I, I, I really think that if people just embrace that, hey, I should just try this for a little bit every week, just try new stuff, then they start seeing the art of the possible and they start bringing that back into the science of the business. And, um, that's really interesting though about how people are trying to hire a purple unicorn. I, I was just thinking how I'm sure there's gotta be job descriptions out there with like 20 years ChatGPT experience. And it's like, bro, it's been out for, it's been out for how many, 5 years? It's like there's, when I graduated college and there was all these entry-level positions that required like 8 years of experience, or it's the same crap. But, but it's like, who's writing this? Who does, who is writing OM Ops? Like, who's doing that? And you have to have this many years. The thing hasn't existed this long. Like, what are we talking about? There is something I want to get your thoughts on. Agent operators. That is a new term that's coming out. I've heard of this and there's more jobs. This, like, you can Google it. There's like a lot of new job descriptions where it's essentially I think of it as like a plumber, like a mechanic that like goes in and they like know how to look at a workflow and then go install the thing, go run the thing, go teach a team how to operate the thing, and then they bounce to the next thing. That, that's high level. There's more to it in the job descriptions you'll find out there and different companies I think are tailoring it different ways. But that person that understands the economically valuable problems to solve, and could actually go implement with the team, teach them how to do it. I think that's becoming such an important role that bridges that gap that I see between aspirational, like, leaders being like, go AI everybody. And then like people on the bottom, they're like, what is this? Okay, doo doo doo sabotage, build crappy prompt. Like, cool. I AI'd my, I'll show up on your dashboard now that I used it. Right. Like, but that agent operator where they could see the business and tie that back to the boots on the ground of what do we actually do here? Um, I think that's becoming a very important role and I bet that'll be an important role for a long time. Uh, towards that, what, um, so we spoke about maturity, we spoke about, um, the IT function and you even mentioned like IT and HR collapsing in certain orgs. We spoke about, uh, leaders needing to lead from the front with know-how and expertise and not being afraid to roll up their sleeve and just figure it out, and that will help them evaluate better what is good and what is bad to then figure out the talent pipeline. On that note, uh, what are like the top 3 sort of change management tactics you would give to leaders at a big org where they're struggling with AI adoption? Um, it sounds like maybe the first step is just baseline and figure out your particular measures of success. Uh, it sounds like another one might be like you know, leaders go, go like figure stuff out, play with the tools. What other things would you recommend to leaders struggling with this?

Yeah, I think definitely getting an understanding of why is really important and be transparent about what you don't know, especially as a leader. Like nobody knows what tomorrow will look like with AI because there's so many unknowns. And being candid about, I don't know everything, and I can't promise that you will all have a job 2 years from now, but what I can promise you is X, Y, and Z. And maybe that is getting you a new path to a new job. Maybe that is training opportunities, what have you. And I think we are not having those conversations. And the thing that is I would say the concept that's underneath all of this is trust. Is do people trust you with their future, right? Do people trust the AI to produce the right output that is usable? And I think sometimes we don't and we're not talking about it, but in various surveys or focus groups and conversation, you can quickly get a sense of is there trust trust in this equation. And maybe the lack of trust is holding us back from getting more out of AI. And therefore, because I think the sabotage is just a symptom and the cause is actually lack of trust. So, yeah, so I think that the trust part is, it's like, it seems like it's pie in the sky, but it really isn't. It can be as simple as, do I trust that you will take care of my career and not just have me train the AI and then you're going to lay me off? Do I have that basic level of trust? And then even for managers, do they trust the AI output, right? Do they trust it enough to use it? And then incentives. We talked about incentives a little bit and nothing works. What is it? Nothing changes when the incentives are not changed, right? So, right. What is the KPI we are driving for? What is the thing that I'm supposed to be chasing? Is it just usage or is it actually concrete things like we are going to save X billions of dollars because of AI? We're going to have, you know, X number more customers because of AI and be very clear about that. So, I think there's so much that any organization can do by getting started with something different, doing something that we haven't done before. Whether it's measuring things differently, whether it's actually talking to the resistors, whether it's, hey, pulling up the usage stats for your C-level, you might be shocked. What if your CIO hasn't logged in to your AI platform ever, right? What if your CTO hasn't touched it, right? You might be surprised, like, how can that person then be the champion? And then you might be pleasantly surprised, like, oh my God, the chief legal officer is the one who's loving AI. Who knew? And you can have new champions and new resistors because you now know the data behind your own AI platforms. So I think that is huge. And if we just get started somewhere and try something that we haven't done before, it will go.

Yeah, I love what you said there, which is start with trust. And that comes to psychological safety, not just at the C-suite level, because change is hard. Like I said, like change, nobody likes it. Um, but creating a culture and an environment where people feel psychologically safe to say, I don't know, and empowering people to say, I don't know, and then building that trust in collaboration over time. And not only that, but also looking at the data to see, well, who actually are my champions? Like, are the people saying go use AI, are they like actually using it themselves? Like, that's, that, that, that's such a basic thing, but I'm now going to start recommending that too, which is Like, yeah, go do that. See, like, like that's, that's only a great place to start. Are there, are the people that should be using it even using it? Like, right. That's valid on your dashboard. No, depends on who's running the dashboards as well. Um, and okay, one last thing, which is, um, I guess I'll share one of my experiences, uh, to, to wrap up, uh, when I was leading product architecture at Workday. I was responsible for change to inculcate data-driven product management and data-driven decision-making. And, and basically in enterprise software companies, B2B enterprise software companies, they're not incentivized to deliver on adoption of product. They're incentivized to deliver on features and then they go work on the next feature. You probably know about this. And in business to consumer, you can't do that. Simply because the economic transactional value is so close to the features being shipped. Like you have to be measuring and the switching costs are much less. B2B used to be switching costs are big enterprise contracts, integrations, data conversions. Nobody wants to deal with that change, but now AI is making it so it's way easier to deal with that change. So I'm starting to see in my work, big organizations like enterprise software companies are being like, oh crap, like we need to be more data-driven. To this nth degree. Otherwise we're going to become dinosaurs or what I call like the walking dead. They're just like, they're just, they have cash in the bank and so they'll walk for a while, but they're just dead people walking is how I see it. Backing up a little bit. Um, one of the things that I did there and one of the things I did at Dayforce to drive like 2x adoption of AI in like a couple months was simply, um, 2 things, like, to, to give evidence to what you say. And, uh, as a recommendation, I just became sort of the thought leader around this space, and I was like, hey, I don't know everything, but I do know measuring what we do is a lot better than measuring nothing. And, and, and then people were like, oh crap, Robert, who is, you know, our lead or whatever, he's saying he doesn't know, but he says in this direction it's less bad. I'm going to trust that because they could feel that, right? It's not just like fancy vision stuff. It's like, yeah, I think this way is a little bit better. We should go there. And in particular, I did this where I was chief product architect at Salesforce. So I was kind of expected to know my stuff and around AI. So I just, you know what I did to drive like 2x adoption of AI tools in 2 months? Like it went from like, actually, I think it was 3.5x. It was like 15% to 50% in a couple of months. All I did was I invited the whole D-list of product and engineering and I was like, hey guys, we'll meet every other Friday. I'm going to show you how to use AI and that's it. And anybody else that wants to show the other people how to use AI, it's going to be raw, messy demos. That's all. And, and the, when I kicked it off, I literally said, hey, you know how I'm out, you know, I'm like in this position. I, I nerd out about this stuff, but I don't think I know everything. and I think I feel kind of dumb about this. I literally said that in front of like 1,000 people or so. I was like, yeah, I don't think I know everything. And I want to be a student because the tools are changing every day. You have a new tweet or a new GitHub going viral and it's like suddenly the world's changed, right? So there is no way that one person will know everything. And so we all need to just put on our student hats and guess what? Like 2 months later, everybody just started using it and that then Over 50% of the org just started using it simply because one of the most influential leaders at the company who's expected to know just admitted they don't know everything, but here is what I do know and here's how I'm using it for me. And that invited other people to have a safe space to be like, oh, he doesn't know shit. I don't know either, but also I'm gonna show you what I do know. And then, and then everybody started coming outta the woodwork to be like, oh, I don't know either, but like, here's what I'm doing. Here's my workflow. And what that did was create More sparks of the imagination be like, oh, they're using MCP with Jira. I don't ever have to go write Jiras again. Heck yeah. I'm going to move out from my IDE or like, hey, I could have AI code review at scale with my taste. Holy crap. I should do that. And, and then it just created way more innovation. And so back to your point of, uh, people just creating the space to say, uh, especially influential leaders at companies, I would highly recommend. Just get one of your influential leaders that do know some stuff about AI, but doesn't, obviously doesn't, nobody knows anything. Um, and just host raw, unfiltered sessions where people play around with the tools and you will see adoption go up literally. And, uh, yeah, that was like a big experience where it really opened my eyes to just how fearful people are of change. But when you have somebody that kind of has social and political capital in an organization, that says in front of a lot of people, I don't know everything. Here's what I do know. It will drive your adoption. Like I found 3x in a couple of months. So yeah, I know you're totally spot on about that.

Yeah, no, I can, I can definitely relate. And I've seen it play out as well in different organizations where the leader says go and it makes such a big difference when they make the time and when they show up with the vulnerability that you did of I don't know everything, but I'm curious. This, and I invite all of you to learn together. That's huge. I've seen the same tactic work in different organizations as well, like the Friday. It could be a, you know, like we can call it lunch and learn, and maybe someone just take turn sharing what they had done with AI in the past 7 days. And it's like, you know, a little bit of peer pressure, but also a lot of, you know, setting examples that goes a long way. The other thing I've seen, and I was able to 10x the usage in 6 months, so was building a champions network of sorts. Like, it's like an organic community. We actually debated, how do we get people to come? Is it nomination? Is it self-nomination, volunteer? Is it assignment? Do we set X percentage by organization that you have to have these AI champions? And a lot of companies now have these networks and they are like a community of practice, if you will. So if you think about the big AI tools or big data tools out there that have a good community where people do actively show up, support each other and share, that's what I wanted to build. But inside the organization, you have so many things at play. It's actually really hard, right? It's not just like, let's get on WhatsApp group or let's get on Slack and No, right? It's just like, wait, hey, you know, whose promotion is at risk if something happens? Like, there's a lot of politics inside organization. You have to make it work. So I've found that helpful to kind of go both directions, to both have leaders suggest, like, whether it's, hey, I know you're really interested in our career conversations. I think you should consider being a champion, or I can nominate you. And then also have people just like nominate their peers. Who is someone in your network that you've talked to who's got this figured out, who's experimenting, who can help drive change? Because once you have those real use cases of like, you know, having simple things like I asked Copilot to analyze my calendar and it's telling me amazing things that I haven't thought of, or like the mind map that I created. Right, as we're preparing for this call, those simple things, and it's the sharing that really makes the change last. It's not more preaching. And like, I want to speak a little bit about training too, because so many companies are spending gazillion dollars on training, as it's not effective.

AI training?

AI training, yes.

So the orange are spending like millions of dollars wasting money on AI training, but it's not effective. Okay. Tell us how it'll be, how the mistakes that people are going through and the pitfalls and how do they, how they solve this.

And the mistake I see is a lot of the training look more like features demo. It's like you can do X, Y, and Z. Here's a new feature that just came out. You know, you can create an image or you can create an image faster, what have you. Okay. But who cares? Like the what's in it for me is not clear. When the what's in it for me is not clear in your training, you are going to have people show up for the free food, for, you know, whatever, and they're not going to actually change the behavior, or they come hang out with their friends, whatnot. And I think the connecting AI usage to your context inside the organization is super important. And that means probably more tailored sessions for a particular function, for a particular job. You don't have to boil the ocean. You don't have to have one session for each function. Like, who has money for that? But focus on the key ones, right? Like, if you're expecting HR professionals to use AI in the same way as finance, you are not going to see adoption. Like, there is very, very small overlap of AI use cases that will appeal to to both groups. So think about the audience, and yeah, and then like, again, get feedback, get data. What was helpful? What was not helpful? Actually ask them. And here's a really simple trick for anyone who has run training or tried to gather data and survey on, you know, on feedback before, is do it within the session. Don't do an email follow-up. Like, so I would I will pause my audience actually throughout. I'll be like, hey, how is this use case, what we have covered so far, and then ask them candid questions like, I can apply this to my day-to-day tomorrow, and just a simple, you know, scale that you can pause and then be ready to take that feedback and actually pivot. So, it's great to know what has worked well, but if you are not willing to change, whether it's the vendor, whether it's the training content, you're going to continue to get very little adoption or less than ideal adoption.

I love that pro tip for if you're facilitating something, definitely survey within the thing because you have their attention right there. Get the data right there. Don't get the data low fidelity later when nobody gives a shit. Get it right there. Like, I agree. Yeah, yeah, yeah, totally. Because, because there's this, um, so I love everything you said and, and it made me think of this thing, which is, uh, it stuck in my head from, from trying to do change at scale, which is when you have these big town halls and events and stuff like that, what you have to remember is it is 1 hour out of the 40 hours a week that they have and they're going to go to their Scrum or the next fire and it's going to go out one ear and in, in the, uh, in one ear out the other. So one thing I think about is no matter how good of a presenter you are, the whole goal is like getting them to change habit there and carry it with them to change, to like inculcate that change. And I always say like when people, 'cause people get, there'll be energy in like, it'll be like, oh my gosh, like I can do this. Like, and you wanna capitalize on the energy. And I always say this, which is the half-life of that energy is like 2 meetings later. So, right? Like you need to, you need to capitalize on the energy. Now. So, uh, is that, is that relatable on your end?

Like, absolutely. Yeah. And, and I try to, if I have time, I ask people at the very end, share something that they're going to do differently starting right now. Yeah. Right. And I have them, like, you know, sometimes in a smaller group that is leadership focused, I'll have them make the commitment, right? And have accountability partner of, okay, I'm going to use AI for X, Y, and Z going forward. Who's your accountability partner? Who's gonna check in with you a week later or on a monthly? I'll take this one, right? So make your—

that's a good one.

Yeah. But it even here, like sometimes I teach thousands of people at once. You can't really do that, but I might have a couple of people share and say, hey, what's something that surprised you today? What's something new you learned today? What's something that you will do differently starting right now? And then we get ideas from our peers all the time. It's like, oh, Robert said he's gonna use it for meal planning purposes, I might look into that, right? Or something that is, you know, I'm stuck in and now I suddenly have a solution because you shared. So that gave people to share. So it's a two-way dialogue in any training session as opposed to a one-way webinar. It's so important.

Yeah, it's so important. Okay, I know we're almost out of time, but one thing I wanted to go over is carrots and sticks. In management, it's always about carrots and sticks. We sort of spoke to that. Uh, you know, uh, carrot is I see person in role that I want doing thing that I don't know how to do. I should go do thing, right? Like that's like a carrot is I see a CTO, he's doing AI, he's setting up an Open Cloud or Hermes agent. Like he's nerding out about this stuff. If I wanna be a CTO someday, maybe not thinking this, maybe it's subconscious, maybe it is conscious. That's a carrot. A stick is like, go AI everybody, otherwise I'm gonna fire you. You know, like that's a stick. Um, there's variations to carrots and sticks. It's on, it's on the spectrum. But, uh, one thing that I have always recommended for people trying to create change in behaviors at scale is look at your career framework. It is the most underrated tool I find for the right incentives for different functions. To go change behavior. If your, uh, job description in your career framework to get promoted from like associate product manager to senior has nothing to do with AI competency, well, guess what? They're not gonna do it. They're probably gonna sabotage your rollout cuz it has nothing to do with it.

Right.

And time and again, I always hear from leaders I work with, hey, my people aren't using AI, Robert, how do I? And then I ask, well, what's in your career framework? I look at it, they're, Ctrl+F, no AI. It's like, well, what the hell are we doing here? Like, what are we, what are we even like, what are you like basic stuff? That's like the most basic thing to me. And I think a lot of people are sleeping on it, honestly. So any executive out there, if your people aren't using it, literally go to your career framework for your function, Ctrl+F, AI, artificial intelligence. See if you have a Beechee kind of scale up of this parallels your career track. You will get more competent at a higher breadth. And depth of AI tools going forward to get more money, you know, get more RSEs, that kind of thing. So tell me about that. That's been my experience. And I want to hear from you because I think you've had a more diverse range of experiences consulting on these things.

Yes. Yeah, no, that's spot on. I think the, the what's in it for me has to be clear for everyone. Speaking of carrots and carrots or sticks, right? So if there's nothing for me, and there's nothing in it for me, then I'm not going to change, right? Why would I? It doesn't make sense. So I'm actually seeing lots of organizations set KPIs now at the top, even ones that tie to, and I don't know if it's going to be most effective because we're early in the day, but I bet it's pretty effective, is tying it to executive compensation, meaning everyone in the C-level, including the CEO, has skin in the game, right? Skin in the game of we need to get something out of this AI investment. It's not just a cost, right? We are not just signing contracts and donating money to AI companies. We're actually getting money out of it, right? So, like, we are not charities here. So what is the thing that we're getting out of AI and are we on track? So anything from, you know, maybe it's for, for example, in R&D or companies that really focus on R&D, it could be faster discovery time. That could be a very concrete KPI, you know, cutting it by X%. Maybe it's more patents because of AI usage, right? So in pharma, we see this a lot, like we're discovering cancer treatments much faster because AI because it takes that kind of compute power to look at images to see whether or not there is cancer in the scan versus not. Right. So, so whatever your business is like, make sure it is at the top. I've seen that to be very effective because guess what? At those levels, that is, you know, the bonus at 6 to 7 figures that makes a difference. That's going to change incentives at the very top. And then also when you cascade those KPIs downward, does everyone that you think will, should be using AI actually have a KPI that matters to them that's touching it from one month to another? And not just like when I get promoted, because guess what? People don't get promoted every year. We would like to think so, but the reality is not everyone will get promoted every single year. So if that's an event that only happens 2 to 3 years or 5 to 7 years at senior level, then what is it, what is it in it for me on a daily, weekly, monthly basis to keep using this thing, especially if I don't love it, especially if I have personal, you know, um, uh, personal, personally I'm against it because environmental issue, whatever the why is, um, are you changing the incentives enough for people to actually use it? And yeah, and then be candid about it, have those tough conversations. And even the leaders who are, you know, some organizations now have AI usage dashboards. So as a leader, you know who is using AI, who is not using AI on your team. Are you having those conversations with the people who are not using it? And do you know why? And maybe you need to do something with the incentives, whether it's carrot or And just, yeah, just my little soapbox. I really don't think we should use it for punishment. I don't think we should use AI usage to eliminate people from either having a job completely or promotions. Like if you don't use it, then you're out. I just, I think there's so, so much we don't understand. And it's like, it's making a very naive assumption that AI is the right tool for everything. And it certainly is not. We know there are other tools out there. And also it shifts the mindset from, hey, I thought only the output and, or the outcomes matter. Now you're saying how I get there actually matters too. So what does that mean on the psychological level? When people have lost their autonomy to achieve the outcomes that they need, people are going to behave differently and not in a good way. So yeah, autonomy is really important. So that's something that I don't think people talk enough about either. Like when we're pushing people to use AI and you're taking away people's autonomy, and I'm not talking about like losing jobs, but simply I I used to, you know, use my eyes to look at data anomalies. I used to, you know, code something by my bare hands, and now I'm using AI for it. And let's say I'm really good at that. Maybe I'm really fast, but now you're forcing me to use another tool. I've lost my autonomy, and I may not feel as productive. Um, I was talking to someone about this earlier today about the IKEA effect. I don't know if you've heard this before, but yes, tell me. Like, there's IKEA furniture behind me there I feel very proud of. Um, if I put it together, then I, I have more ownership, right? So even if it doesn't look as good as like, you know, I don't know, Pottery Barn furniture, but I am proud because I made And the same thing with AI, where our output is now through the roof, or it can be if we use AI. But when I talk to real people, sometimes people say, you know what, my output is up, but I don't feel as good. I actually feel less productive. How could that be? Well, it's because you didn't actually do the thing. So, um, so I think sometimes that it's very complicated. Human minds are very complicated. And, uh, how do you design incentives then? To make sure people still feel good about their skills, to still have a purpose, even if they're using AI to do what they used to do, even if they're using AI to do what they spent 10+ years getting expertise in. What does that mean for their identity? Like, um, and, and these are very real problems that we face now, and it's not philosophical, even though it sounds like it is, um, because a lot of us, um, get purpose out of work. And when that purpose is gone, when we don't feel like we are as capable because we're using another tool to do it, that's a different conversation to have. And we need to think about the system and the incentives to design to actually change behaviors. So, all right. Off my soapbox now.

No, no, you can stay there. Stay there. You're great. I love it. No, I, I, I really enjoyed hearing what you said about focus on, um, the autonomy piece. And it goes back to how we started, which is why are Gen Zs, 44% of Gen Zs actively sabotaging their company's AI initiatives, right? And when you spoke to some, you realize that they're worried about what is my future here then? Am I being, am I automating myself out of a job? What does this mean for the future of the family that I want? Kids that I want in the future, like, am I contributing to a dystopian reality, like, where they won't have a future? And under that is a fear of agency, lacking agency and autonomy, right? At its core, it's like, do I get to make the right choices to be happy and healthy for me and my family, right? Um, and yeah, I think that's a great way to wrap this, which is, you know, we went through tactics, we went through change management tactics, we went through both of our experiences. I'll give a quick shout out, Dr. Serena Huang. She's available for consulting around these things. She's great and has made economically valuable, I should say, she's made economic valuable change at companies that are dealing with this problem. So shout out to any of you who are watching. If you need her services, I'll throw that in the show notes. You can take a look, hit her up, and she's just friendly and really great to work with. That's been my experience. Always happy to have her on, uh, my channel and the pod. Um, so yeah. And oh, and I guess one last shout out, she has her book in the background there, The Inclusion Equation. I'll also drop, I'll drop a link to this book. It was a good book. Uh, we should have actually another pod where we just talk about that particular thing, because that's a whole deep topic in itself. Uh, which is how do we ensure we're designing systems that work for us and not against benefits us, right, across diverse people. So, Serena, thank you, my friend. I really appreciate you going through all of the strata of maturity of AI and organizations, AI adoption, leaders got to roll up their sleeves, all those different change management tactics you shared. I'm sure the viewers will get a lot of value out of this. Any last things you want to say before we end?

Yeah, thank you so much for having me. I feel really good covering so many different topics in a short amount of time. My last message is let's stay human in the age of AI. Let's keep the human in the center. And because, yeah, because when AI can do a lot of things, there are unique human skills that we have that cannot be replaced. And I cherish those. And I, you know, things like connecting with you on a human level, that cannot be replaced by AI. And I I really encourage everyone to, you know, if you have the power to influence, keep the human at the center. And if you feel like you are powerless, think about what you uniquely contribute, what you uniquely do that cannot be replaced by AI and focus on that.

Absolutely. Love that. Keep the human at the center. All right. Thanks, folks. Catch you next time.