Best of: What leaders get wrong about AI rollouts and employee adoption

Transcript

Speaker: A lot of folks are still figuring out what AI adoption really means and what is effective use of AI actually look like. And so they focused on leadership alignment. They focused on the change management piece, setting up communications, right, ensuring that people had clarity about what was going on. the one thing that they didn't do was they didn't assess employees, but I think they started off really well. And we know from like studies, I know Mackenzie mentioned that only about 25% of organizations and actually do the things that lead to successful adoption. And so the people side is something that was admitted in a sense of we didn't really get a sense of employee sentiment and we didn't really create a forum to hear from employees about what they felt like they needed, what their fears and concerns were, and what support really looked like.

Speaker: Yo, this is Bare Knuckles and Brass Tacks, the tech podcast about humans. I'm George K. I'm George a And today our guest is Dr. Marissa Alert. She is a psychologist who has carved out a unique niche in helping companies accelerate and adapt their AI adoption plans by addressing the psychological underpinnings in organizations, which...

Speaker: is just something we haven't really explored. We've talked about, I think, tangentially, but this was a really great conversation to get into the weeds. Yeah, i really appreciate it. I mean, as as an executive, um it was very interesting to hear that kind of perspective.

Speaker: I almost looked at this, if you're in leadership and you're listening to this episode, this is free consulting for you, this entire episode, because yes she really um kind of helps you wrap your mind around what it is you're asking your staff to do, um particularly when it comes to adopting all these tools and these pilots and these programs. And there is a human layer impact to these changes and forcing this much change so quickly on people who have to be the ones that have to do the implementation and manage it, um you know, without understanding the impacts on them, it really could cause a lot of damage to your business and to your organization if you don't do so considering human beings first within that implementation process.

Speaker: And I think this episode really does a good job of calling out, you know, where and how that failure occurs when that consideration isn't made. And I'm really thankful that we have Marissa on.

Speaker: Yes. And also to those leaders, the conversation is very much centered not just on feelings, but also on performance and impact to your business. So let's turn it over to Dr. Marissa Alert.

Speaker: Dr. Marissa Alert, welcome to the show. Hey, so happy to be here. Excited for this conversation. Yes, very excited for this conversation. A different take on the human technology interface that we're all contending with these days. So we'll start in the obvious place with your background, right? You're a clinical psychologist. You're not an ah HR consultant, not a change management firm, not at a McKinsey, but you've carved out this very specific lane around the human side of AI adoption.

Speaker: So let's start with how did you end up there and what were you seeing in your other work that told you that this was like a distinct problem? Yeah. um And so one of my, I should say one of the threads that we've all the work I've done over the past that we've used together is identifying barriers and coming up with solutions on how to address them, especially when it comes to people and the work they do.

Speaker: And I was really excited about AI, not just in terms of its capabilities, but just really fascinated by how we as humans would interact with AI. And the first place it started showing up was in the therapy room.

Speaker: where I had clients expressing concern about how quickly things were moving and they weren't sure that they can keep up. And that was also being echoed. we hear from reports, right, in the news, articles coming out. um And as I took a closer look at the readiness assessments and metrics that started to shape how organizations should be adopting AI, I saw that a critical piece was missing. There was very little about the people readiness.

Speaker: are people ready not just to use AI, but to integrate it into their work? How do they feel about it, right? And the reason that stood out for me is that as a psychologist, one of the core things that I do is focus on behavior change.

Speaker: How do I help people, one, not just recognize what needs to change, but to support long-term change? And when we talk about change, there's a whole lot of resistance that comes up for a number of reasons.

Speaker: oh yeah And so if AI is coming into the workplace, that resistance is often predictable, right? And yes, there are a few people who are excited, right? They're enthusiasts, but they're also the folks who might fall in the middle or might be scared to death.

Speaker: And what do you think happens when we're scared, right? We might fight, we might flee, we might freeze. And those things still show up when people are at work.

Speaker: And that freezing might look like, well, I'm not gonna engage. I'm not gonna use this tool that my employer spent who knows how much money on. And so as I noted that, it was like, all right, like, let's let's see what people are doing about this.

Speaker: um I did some work about May last year, trying to have more conversations around this. And i don't think people were as ready to talk about it.

Speaker: And I think as more studies are coming out, I know there was an article in Harvard at Business Review that talked about why adoption fails. And we started seeing more about, okay, this anxiety, this resistance piece, this fear piece really needs to be examined a lot more closely. Nice.

Speaker: Yeah, i'm I'm seeing like a lot of issues with it and, you know, ah from a tech technology standpoint and you're seeing forced um adoption from a process standpoint where, you know, and this is like free consulting everyone, but if you've not figured out your manual process yet and you try to automate it, it's going to fail and you're going to waste a lot of money.

Speaker: Um, I was actually particularly upset yesterday because I read the headline that, uh, Jack Dorsey had, uh, cut about um half his workforce and block but citing purely, um AI, AI efficiency being the cause. And, um, You know, i had ah I had to fight every urge my body not to just scream from the mount to ops on LinkedIn and be like, no, you piece of shit. It's because you didn't properly plan your organization and you're just profit driven because that's all these shareholders are doing is just trying to drive obscene profit and growth based on ruining people's lives because people's lives don't matter. So that's that's one point I find interesting and what you were saying there that, you know, people being fearful of adoption, but organizations, I think,

Speaker: I think organizations intentionally never considered people to begin with, and maybe that's a thread we can of pick off a bit later, but um I kind of want to stick with kind of what what we were where we were talking about, use the word diagnosing rather than surveying or polling.

Speaker: And that's a very deliberate clinical distinction. I came from that, my undergrad base is psych as well, so I remember those days and They're very, very particular about their use of terms. I think we're on DSM-5 now.

Speaker: ah What are organizations actually missing when they treat this as a culture problem instead of something that warrants a real diagnosis or potentially disordered behavior?

Speaker: Yeah. I think one of the things that's missing is that when you look at it from culture and not just honing in on an individual basis is that you miss variability. Yeah.

Speaker: Right. And one of the interesting things that I see is people could feign interest. Right. They can feign adoption in the sense that, yeah, I'm on board. Right. Unless there's super surveillance happening in the organization that can track every single move. Right.

Speaker: A lot of people can just get away with it. But the thing about diagnosing is that it allows for specificity in terms of the interventions that can be used in order to move people forward.

Speaker: So one way I like to think about it is if you walk into your doctor's office and your doctor is like, oh, you look good. You're fine. i think this is what you need to do. There's no assessment.

Speaker: There's no questioning. You're standing up. You're fine. You're conscious. high Great. You're good. OK, ready to run a marathon. A-okay. But the reality is, unless you dig deeper, right, there are things that don't present on the surface, right? Someone might present physically okay, but internally, there could be a lot going on that could be really disruptive to their health and their functioning.

Speaker: Similarly, when we think about organizations making assumptions or just focusing solely on change management or communications or training and not really taking a step back to reflect on, okay, what's really happening with our people?

Speaker: Right? Do we have an accurate diagnosis? Is everyone on board? Are people concerned about AI, not just in terms of its capability, you know, a threat to their job, or even some of the ethical and um environmental concerns around AI?

Speaker: they may not know, right? And so let's say that you're rolling out training, you've invested hundreds of thousands of dollars, which is um actually just started work with an organization that they spent money for six months, they tracked adoption rates and use, and it's still at 20%. And so the work we're doing right now is that diagnosis piece.

Speaker: How do we understand how people really feel, where they are, the ai their AI sentiment, right? Their experience. to figure out, do we actually need more training? Do people feel safe about raising concerns?

Speaker: And based on what we find, how can we then target the solution to where people are? Similarly, you want your doctor, like if you are showing signs of you know heart disease, maybe you don't need to go to a rheumatologist. but right You'd want the right diagnosis.

Speaker: I appreciate that also because I think when you bring this up in the abstract, it feels like something a lot of companies abdicate responsibility on, right? Well, it's not really like we're just in the business of selling widgets. for It feels woo-woo. It's very easy to be like, I don't really care about people's feelings. This is a business operations problem. Yeah.

Speaker: but But I also, but I think there is a reframing. So like in cybersecurity where George and I have worked, mean, that's what, 25 years of tackling stigma of mental health, but- I think we have turned a corner because what we've been able to say is when you invest in these resources, you get more performance out of the team, right? So ultimately, ai adoption, if you're doing it as a business, you're trying to get more out of the business, right? And so I think if you treat it as an operational performance problem, like helping your people perform rather than like, I just don't really get why they won't use the thing that I paid for, right?

Speaker: i think I think that's a helpful reframing. But um the industry narrative, the hype cycle, the hot noise is that speed is the need, right? Like move fast, get it in there. If you don't, your competitors will. They'll outcompete you. You're dead tomorrow.

Speaker: And you are in some ways making the opposite case. So have you encountered cases where speed works or that it needs to be deliberate or is that a false dichotomy?

Speaker: that's an interesting question. um and One that I'm still spending a lot of time reflecting on because I think it really depends on the organization, right? Like I know there's smaller businesses where the sole purpose is to use AI, right? To bring products to market. And so, yeah, there's a lot of enthusiasm there. And so, yeah, we're all on board. There's alignment there.

Speaker: And so, yes, let's proceed. But there are also instances where if you are spending a lot of money up front. And your strategy isn't very sound in terms of, all right, what happens if people don't actually use this, if we can't see the increase in efficiency and performance that we're hoping for and we're shelling out hundreds of thousands, even millions of dollars.

Speaker: And we're not seeing those outcomes. then Or they don't even know how to measure the outcomes. They're like, we're going to be more productive. And you're like, do you have current productivity metrics? it's it's It's funny that she's calling this out because when I brought up Dorsey earlier, this is like relevant to this.

Speaker: um They actually, so analysts cited that this move was made not because of known results, but because of predictive results. But they think that they're going to save money and they think they're going to um exponentially elevate their output.

Speaker: by making this move. And so I'm like, that's a speculative bet on human life. yeah It's wild. I'm like, ah based on what are you guys like making this massive change?

Speaker: And when you see it, not to digress a little bit, but it's like, we talk about a cloud code and like cloud code is going to replace CrowdStrike and all this. And I'm like, do you not understand what enterprise software development is? Like you can't just have some like child vibe code a solution and suddenly like, it's,

Speaker: This is mad. You must look at this, Marissa, and think it's madness. I do. I really do. I really do. And I think you raise a critical point. There's a lot of guessing going on, right? A lot of guessing. And it's one thing if it's based on data, right? Because there's predictive analyses and indices that people can use. But if people are guessing,

Speaker: about outcomes and efficiency and what productivity is going to look like based on not really looking at the data, especially the people data from the people who are going to be responsible for implementation, right? Whether that means actually using the software. Yeah, I sometimes think we're just living with yeah i sometimes think we're just living through the executives fear Right. Like the decision is being made out of panic.

Speaker: Get it in. Right. So like we we talked earlier about the psychology of the people at the bottom up, like trying to metabolize the new technology. But I also think there's the psychological component of the people just like panic ordering it, like just get this thing and do it.

Speaker: Yeah. Let's build a plane while we're flying it. We'll figure it out. but that's That's a regular conversation though because when you deal with a lot of, in my experience, a lot of CEOs and boards, they see the headlines and they see the the stock market value and they're like, why aren't we doing this? And I'm like, you're not a technologist. Please just leave me to do my job and you do your job.

Speaker: but But literally it's like, why haven't we got this tool that suddenly has a massive stock elevation? Like there's no, like we can't force this down our throat. I mean, And it kind of brings us to the next question. Let's say, you know, inshallah, you meet a CEO or or a chief HR officer who actually thinks about the people in their company first. yeah They do exist, I hope.

Speaker: Yeah, they do. They do. When they bring you in, what's the conversation that they think they're going to have with you versus the one that actually ended up happening with you when you're talking about the human impact of massive AI implantations?

Speaker: Yeah, I think the conversation usually centers around like how can we support people? because there is this genuine care about the employees that they're working with and serving. But the conversation often ships to, well, how do you know how your people are doing in order to better support them?

Speaker: And so we spend a lot of time assessing, okay, what's being done? Yes, communications have been sent out. We've added training not training, in terms of AI literally literacy skills alone, but thinking about, okay, how does this relate to the job that people are doing? I'm seeing folks really start to focus more on how is workflow gonna shift?

Speaker: And how can we use this tool to support you in getting your work done and done more efficiently? But the conversation always comes back to, okay, but how are people feeling? And how have you measured that?

Speaker: And there may be some engagement surveys where they ask about, you know, are people, You know, do they have like high confidence in their AI skills? What's their level of experience? Have they been experimenting on their own? But there isn't really this questioning around, well, are you afraid? Do you feel safe raising concerns?

Speaker: Right. Do you feel as if AI is going to come for your job? So are they assessing identity threat? Are they assessing psychological safety? Which simply means I can say what I want in terms of the concerns I have, the fears I have, and I don't have to fear a retaliation.

Speaker: i don't have to fear that I'm going to be ostracized. And so it always comes down to we look deep enough Yeah, it sounds like it's going to lay bare a lot of cultural work that may or may not have already been done.

Speaker: You know, as you said, in those smaller companies where there's alignment, generally the size is like not always an indicator, but it's much easier to maintain kind of a cultural coherence.

Speaker: and You start getting bigger and whole teams start occupying a different floor. You get sort of micro cultures within a larger culture. um i think some of the best implementations I have seen is there is a top down directive to the lieutenants this is the direction we're going to go in next year. We would like to adopt more of this technology.

Speaker: Please meet with your teams and come back with your plan rather than like everyone use this tool, right? Because, Each team will have a better insight into like their actual workflows. Like it's not like the CEO understands like how the marketing team works with the sales engineers all the time if it's a big organization for example. But they understand and if they can like build from the ground up maybe that's like a way to feel safer because they have more ownership rather than just being directed to do something.

Speaker: Yeah, and I think what you're getting at there is alignment, right? Alignment in the sense of not just what each organis organization, not what each organization, but each team or each group is working on.

Speaker: And it's siloed. It's about, okay, folks at the top understand what the folks at the bottom are doing. And the folks at the bottom understand that they're working towards a singular goal, right? What they're working on is going to contribute to a shared outcome, a shared vision.

Speaker: And some of the conversations that I have with leaders is that if you were to ask them about what's AI's role in our strategy, what does success look like in 12 months if you were to integrate this tool or this software, right? What concerns are we most, you know, what what's what are we most concerned about or what outcomes are we really targeting? And I get very different answers.

Speaker: And so the conversations aren't really had around what does alignment look like from us so that when we share information, you know, what folks should be working on or how they should be using the tools, a coherent message is being delivered.

Speaker: And there's a lot of confusion because they don't really understand, like you said, what one team might be doing because they're so far removed from it. And so people are operating out of assumptions, not just at the senior level, but that certainly trickles down throughout the rest of the organization when there is a lack of cohesion and alignment.

Speaker: All right. We'll take a short break and we will be back.

Speaker: Fear of job loss is real, right? It's across a lot of survey data, even though the economic data is like much harder to discern and um has a lot of lag in it.

Speaker: Fear is fear, right? I just had a friend text me this morning. He's university of professor, undergrad business school. And he's like, I can't get my students to care.

Speaker: Like they're super unmotivated. I think they just think they're going into this non-existent job market. So psychologically speaking, fear is a very broad category.

Speaker: um i guess wanted to get your take Is this a rational assessment of fear of job loss or is there something deeper? You had mentioned things like identity, you know, possible loss of meaning.

Speaker: I feel like, quote unquote, job loss is like very broad and I'm interested to get a more nuanced take given the work you're doing with your clients. Yeah. Yeah.

Speaker: And just to start out there are folks who are genuinely concerned about job loss because they've been affected by layoffs. They know family members who lost their jobs. It wasn't said that it was because of ai but...

Speaker: If they see that there are mass layoffs, there's a concern. They often might put together puzzle, OK, maybe this is because of AI. I think the other thing, too, is that people attach a lot of meaning to their job and the roles that they have within organizations. And so it's often a central part of their identity.

Speaker: And if AI is coming into the picture and it can do some of the things that they can do, it begs the question, okay, am I still valuable to this organization? And what would it mean if I can't do this work anymore?

Speaker: And so that's the potential identity threat. Who am i if I'm not able to do this work? If a machine or an AI tool can replace me? I think another aspect of that, and there was actually a study done by writer last year that found about, i think, don't quote me on these numbers, but I think it was about 31% of employees actively sabotage their organization's AI strategy. And that number was 41% for millennials.

Speaker: And identity threat was one of them. There's also a fear of not just fear, but lack of trust, I should say, in AI tools, not trusting the output and also lack of trust in terms of leadership.

Speaker: Right. Do I trust that leaders actually want to keep employees at this organization? Do I trust that they find the work that I do valuable and irreplaceable?

Speaker: There is a human component to the work that I do that at this stage right now, and perhaps, who knows, i may never be able to replace that. And so that's another concern that folks have as well. I think what you're pointing to is also, you know, technology adoption has always been very uneven.

Speaker: And I think this one in particular, probably exacerbated by algorithmic media and like a faster news cycle, is pointing out some other cultural trends, right? I think as a society, at least in the U S I think it has always been a problem that people identify their profession kind of like with their identity, right? Like if I am not this, then what worth do I have in the economy? Which is a sort of neoliberal fever dream, but it's a real issue. Um, and also as you pointed out, lack of trust is kind of pervasive, uh, political, social, and then like even interpersonal, like,

Speaker: Yeah. Anyway, I think it's it's like ah it's like the psychology is laying bare some of the cultural fissures that maybe we've never really dealt with. And and then plus knowledge work was considered...

Speaker: like the, oh, we can automate factories and it's sort of like the quote unquote lower rung, the manual labor. And it's kind of a psychological shock that like paralegals might be replaced or financial analysts, right? These were supposed to be the quote unquote safe jobs. Well, the reason you went to college, the reason you yeah did all this other prep. Anyway, that's my my rant over to you, George.

Speaker: Yeah, I think you're bang on too, though, because I think it speaks to first of all the the one, Interestingly enough, the one job that they've constantly found AI can't replace are nurses, which are of the most.

Speaker: yeah But the nurses and are are in public health care, at least in Canada, it's a massive debate. It's being constantly defunded and then criticized. and And the stress on nurses is absolutely unbelievable. Like I've known a lot in my life and they are constantly...

Speaker: um beaten down by their employer and by the ministry and by funding. And I think in in America, it's probably the same thing if you're not working in a privatized system. You also speak on a really good point, George. I think Marissa, you'd probably agree with this. the The hyper growth that represents the economic bet on AI is has created this massive widening of the of the wealth gap, right, between the working class and the ownership class that I think a lot of people are now um seeing their identities that often got tied to their roles broken.

Speaker: And now they're, they're, they're, The foundation of the economic system in which most of us in the West grew up on no longer applies. And I don't think people have psychologically found a way to exist in the paradigm of today's post-AI revolutionary economy, which is the same thing as like in the 1800s post-industrial revolution, factories started happening, people were moving out of the farms into the cities, and a lot of confusion was occurring and a lot of abuse occurred.

Speaker: which led to a lot of labor ah conflicts and that kind of thing. i think I think we're going through that same sort of cycle. um But that paradigm creates a ah real difficulty, right?

Speaker: And I think that kind of leads into, you know, how much of what solves AI rollout is actually more of a burnout problem in disguise, where people are already running on empty, being handed one more transformation to observe,

Speaker: And they're seeing this transformation as a thing that's going to push them out of a job. So now their security, their economic security, their food security, their ability to provide their family because of this AI investment that their bosses are so excited about that they have to now use, it's going to take away their quality of life.

Speaker: And I think that's what's causing a lot of the burnout, you know, in the last like three, four years. What do you see, Marissa? Yeah, you know, clinically in my practice, I work with folks who are dealing with burnout. And when we think about the state someone's in, when they are burned out, like they're exhausted, not just mentally, but physically and emotionally.

Speaker: And so when the brain and the body is in that mode, even something that's very simple can seem really exhausting. and really ah a way for them to feel as if they don't have adequate resources to cope. And so when we think about that happening in the workplace, like you said, if people are already burdened, they're already burned out and they have to learn something new, that's really taxing for them.

Speaker: And if it's taxing and leaders, execs don't recognize this, Again, it's all right, we aren't really taking care of our people. And we know that when employees well being as prioritized, not just they perform better, but the organization does better as well.

Speaker: And so if you layer the burnout on top of identity threat, it's just multiple stressors hitting folks at the same time. But I do want to acknowledge, though, that there is variability in AI adoption across the organizations, right? Like it's not 100 percent. There's still some industries and sectors where talks of AI isn't as prevalent, right? People know about it. They might use it on their own. But in terms of their organization integrating it and them sensing that fear might be very different from someone who's working in a space where it's not just they're talking about AI. There have been pilots, they're discussing ways to roll it out across the enterprise. i mean, for the vast majority of people in some of the entire sectors of the economy, like retail and food service and stuff, you know, this is like a lot of noise. It's not really changing their day-to-day life. Yeah, yeah.

Speaker: Yeah. And so the not to say like they don't have challenges, but I think when we consider the role technology plays in how they're feeling, right, in the burnout they might be experiencing, I think it's a very different picture.

Speaker: damn um So I'm going to ask this one question, then we're going to start in the negative, but I promise we're going to try to end towards the positive. But ah what do you see organizations get consistently wrong about this?

Speaker: their AI rollouts, whether that's, I think you mentioned clarity before, you know, communication, stuff like that. So what does the bad version look like in your experience?

Speaker: Yeah, the bad version looks like a lot of assumptions being made. Assumptions about not just how people will respond, but the benefits of AI. And an example of this was that I was brought in to help um a newly formed cross-functional AI team that was gonna lead the AI rollout for this 200 person organization.

Speaker: And the executive I was working with, you know, when I started asking about, okay,

Speaker: how are your people feeling? And he's like, what do you mean? i was like, how do they feel about AI? And the interesting thing was a lot of the employees had a tech background. They're either software engineers or developers, um front and back end engineers. And the assumption he made was like, yeah, this is really an exciting moment, right? Like these folks often tend to be early adopters, they're excited.

Speaker: and the other question that I asked was what's the role AI is going to play in the work that they're going to do and what outcomes do you expect? And he was like, well, they're going to use the tool and it's going to, you know, touting all the things that were claimed AI would be able to do.

Speaker: Another organization I'm working with, what the good looks like is... They took the slow and steady approach because they recognize that one, if we're spending all this money, we need to understand why we're spending it.

Speaker: Not just on vibes? but yeah No, they saw beyond the noise and recognize that there's a bias there, right? Everyone's thinking that everyone's ahead of them. But in reality,

Speaker: A lot of folks are still figuring out what AI adoption really means and what is effective use of AI actually look like. And so they focused on leadership alignment.

Speaker: and They focused on the change management piece, setting up communications, right, ensuring that people had clarity about what was going on. The one thing that they didn't do was they didn't assess employees, but I think they started off really well. And we know from like studies, I know Mackenzie mentioned that only about 25% of organizations actually do the things that lead to successful adoption.

Speaker: And so the people side is something that was omitted in a sense of we didn't really get a sense of employee sentiment and we didn't really create a forum to hear from employees about what they felt like they needed, what their fears and concerns were, and what support really looked like.

Speaker: And so I think the bad is making a lot of assumptions, not really basing your decisions on data. And as a psychologist in research, a lot of what I do is data-driven. You know, folks who work with me know that I said, what does the science say? What does the data say?

Speaker: Because we could think and feel and hypothesize and that could so far from the reality. And so taking a data-based approach, taking the time to understand what your goals are,

Speaker: What does a positive outcome look like? And what do we need in order to get our people on board? I think is the best way to approach this so that you are not spending thousands, hundreds of thousands, millions of dollars course correcting after you've already made a huge investment.

Speaker: Nice. I think there's an incredible irony there. At the beginning, we talked about how some people would think like when you ask how are people feeling that they're like, I'm not into feelings because it feels unmeasurable.

Speaker: So then they make business decisions based on vibes. And what you're saying is like, no, asking people how they feel in a systematic way will get you to data to make a better decision.

Speaker: Anyway, over to you, George, to close. Yeah, so I i really um kind of like where you're going with this because it's, you're calling on a lot of things, Marissa, that we have felt that we know to be true, but um I think we have failed to articulate so cohesively as you.

Speaker: um So first of off, like I really want to thank you for your contribution um this episode because i like ah it's I'm trying to be very calm and happy about it, but I'm so filled with rage because as an executive, like I see all this, I experienced it firsthand and like um I still hope I have my sanity because I still feel human empathy for decisions that get made.

Speaker: Um, so I, I kind of to tie it back to how we can help leadership a little bit. So let's say a leader is six months into a rollout that's quietly failing. Adoption is low.

Speaker: People are going through the motions. No one's really actually enthused about it. No one's actually trying to quote unquote, find value out of the new, uh, the new implementation. What's the honest conversation they need to have with themselves, like as a leader in an organization before they can fix it?

Speaker: Yeah, I love that question. I think what you're signaling is that there needs to be a pause before things progress. And that pause needs to include reflection, not just with that leader, but amongst whoever's making the decision, right? um To really test for alignment.

Speaker: Are we all moving in the same direction? And I like to get this visual of tug of war, right? Everyone's in the same organization, And the goal should be the same, right, to win.

Speaker: But then you have people pulling in the other direction. And I think it's important to assess at a leadership level too, are we truly aligned? And what does alignment really mean? i think another thing um that involves that reflection is really thinking about, okay, what role do i need to continue playing in order to ensure that we're not just getting alignment at the top, but that alignment continues?

Speaker: and getting really concrete. Do we need to have meetings where we reevaluate and reassess X, Y, z And is that realistic? What does that really look like? And being honest about, okay, we're failing, things aren't working out the way we anticipated.

Speaker: We have a choice here. We can keep throwing money at the problem. we can keep guessing. But what is it really going to take to, again, one, diagnose and treat this problem and to consider what treatment looks like?

Speaker: Am I going to keep doing the same things that I've been doing before, but just disguising it or dressing it up? Or am I really going to consider, is there an alternative solution that we haven't examined that can really move the needle on this problem?

Speaker: Yeah, I think the most frustrating part about leadership failures is just the sunk cost fallacy. Like, well, we're already in it. So we're just going to keep doing this thing until it works rather than like it's OK to fail. ah You know, the the MIT report that had come out last year that was something like.

Speaker: You know, 90% of generative AI implementations have not yielded any results. Had a lot of headlines. But if you dug in to the report, there was a quote in there about, I'm trying to find it for the show notes, but somebody was very open about like, yeah, we've tried a lot of things and we will continue to wind down pilots when we do don't see results. I was like, that's okay. Like to say like, we're in an experimentation phase. Yes. And like not say, I have all the answers. Yes. You're dealing with executives making high value gambles.

Speaker: And there's an old, i used to play cards quite a bit when I was younger. And there's a term called chasing the dragon. And when you get in the hole, right, just need to win a couple more hands. just need to win a couple more hands. I feel it's the exact same thing. Yes. we love that Yeah. yes In psychology. Dr. Marissa Alert, thank you so much for the time. This was an incredible discussion. And again, as George had said, i think you put two words, a lot of the things that we're feeling and thinking about, but maybe we didn't quite have the vocabulary for it.

Speaker: and Yeah, and I really appreciate the opportunity to have the space to, you know, discuss all of these topics that I think have really been missing from the narrative. And i just want to leave people with the the thought that high usage doesn't mean successful adoption.

Speaker: And it can easily mean that there could be fear-driven compliance. It could be a number of things. And so really looking under the hood. It's going to be one step in the right direction, not just in getting the outcomes people are looking for, but in truly supporting their employees along the way. Awesome. Well, thank you very much. And we hope to talk to you soon.

Speaker: All right. Thank you.

Speaker: all right. Let's leave you with some questions. So top of mind for me after this discussion

Speaker: how are you implementing ai is it top down are you empowering the workforce that you want to use it to come to the table because they better understand their processes and build it up from the ground up that's my question to you listener and my question to you um this is more towards folks who are in leadership roles or on boards or maybe even pd folks who are listening What is the overall purpose of the implementation?

Speaker: Are you trying to provide greater enablement to your stakeholders and to your personnel? or are you just trying to satisfy shareholders and profitability? Because I think the results that you receive from the implementation are going to be directly correlated to what the actual strategic purpose of it was to begin with.

Speaker: Bingo. Take that forward. We will see you next week.

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