Ep. 309: Human as Your Competitive Advantage in the Age of AI

Host: It's happening again. Welcome to Work Cookie, a, uh, CBOT podcast as we broadcast around the world. Get bite sized morsels and tidbits from our industrial organizational psychologists, other experts, and the latest research on the workplace to boost your organization's effectiveness. Sign up now@cbok.com that's s e b o c.com to engage with our community, gain a sense of belonging, access our other media and get rapid advice. Our experts@cbok.com welcome.

Dr. Jeremy Lookabal: I'm Dr. Jeremy Lookabal, Industrial organizational psychology

Linda Ann: consultant and workplace communication and negotiation coach. Also, we have Tom Bradshaw with us,

Dr. Jeremy Lookabal: a voice and speech coach and a damn good actor too.

Linda Ann: He is the official voice and speech

Dr. Jeremy Lookabal: coach for the industrial organizational psychology community.

Host: Well, hello and welcome to Work Cookie, our weekly gathering of iOS, HRs recruiters and an actor as we try to make the world of work just a little bit better. And, and to kick off June. Ah, Heather, we're going to have you here for one Thursday, but then you'll be back in July. Um, so today we're going to look at humans, uh, as your competitive advantage in the age of AI. Um, which is kind of interesting to me because I lived through like the last six months where it was like, AI is going to take over the world. If you have a desk job, you're done. And now just in the last week I've been hearing this. No, it's not really going to be that. And we're back to like being hopeful about AI. It's going to make my job easier. But I'm old enough to remember when they put a computer on my desk and they said it was going to make my life easier. No, I was still as busy as ever just doing different stuff. So please explain to me where we are.

Heather: Absolutely. So, uh, yes, we have seen such an advancement with artificial intelligence and it's not new. I mean it's been around since the 19 what, late 1950s, early 1960s. But the generative AI is newer to us, especially in the more recent years. And then the adoption of IT in the workplace is even more newer. And, and now we're seeing where unfortunately people are kind of, I, I don't know, people are losing their jobs. We, we're seeing these articles of like several thousand people are being laid off. And, and is it because AI is replacing them? And I know you can't see people who are listening to this later event can't see me doing quotation marks, but are they being replaced or is it because these jobs are being phased out and new Jobs will be coming in. But are these initiatives, like, are these AI initiatives actually working? And I'm going to send in to the comments real quick, and this will be in the show notes as well. Several articles and scholarly research on AI adoption in the workplace and about how businesses have brought in AI and how this has impacted the workplace. There's some really, really great articles out there telling people like, hey, we have done this, like, qualitative study to get people's understanding on how has AI impacted your job? Has it actually made your job better or has it just caused more work for you? Has it automated the things that you've actually needed automated, or has it just taken away your creativity and then you're still doing the things that you don't want to do? So, and there's a lot of really great stuff in here, but the questions that I want to propose are why are organizations spending billions, if not trillions on AI right now, if we know that upwards of 70% of those change initiatives fail to scale? Because if we think about change management and we're not using effective change management strategies and we're just quickly adopting it and expecting people to take it on without having a good strategy, is it effective?

Host: Well, it really does feel like we're in a world where there is no strategy. It's. Someone told me I needed to implement this. At the same time I'm noticing, you know, I talked to an organization about a year ago and I said, when are you guys going to be incorporating AI? And the response was, oh, we're not going down that path. It's too dangerous. But then within a few weeks they were using like Copilot. So, yeah, it somehow snuck into the organization and then, uh, everybody was using it. So I think you're onto something with like, great tool. We don't know what we're going to do with it and who actually has a strategy to implement it. So I see some hands up. So, Dr. Amy, let's start with you.

Dr. Jeremy Lookabal: Thanks. And great question to start off there in terms of the change process there, um, the reason that organizations are spending a lot of this is not necessarily just though for generative AI, um, there it's AI plus all the things that will come. So AI agents. Right. Actually performing an entire job autonomously. There are other technologies that are being built on it. So robotics with AI embedded. Right. Um, implantable technologies with AI as the background there. So when we think about the investment in the technology, it's not necessarily just so that people can use copilot or chat GPT or Gemini or you know, pick your, your uh, poison, right? In, in that sense, um, there are other things that are happening now. I think the, the most strategic way to look at this for organizations is going to be a what is your market? I don't have the full citation, but in something in the chat, um, that really talks about production, right? So this concept of Industry 4.0 to Industry 5.0, um, what that really says is leveraging the human machine collaboration for production. But that might not be relevant for an organization that's doing software development. It may not be relevant for a medical organization. So you really have to narrow when it looks in terms of strategy, what is the market, what's the purpose, the product? And I think you were alluding to that, uh, Tom as well. What are we even doing with this? Right. Um, one of the clients that I actually just got out of a presentation for have been very, very excited to be human centric and to have strategies. So there are organizations thinking about the how and the why and the impact to humans, which is very, very exciting. And from a strategic perspective, there are really things that you can do. Specifically, uh, one of the areas in terms of change is removing ourselves from this change as a project mentality and thinking about change as a capability within the, and training people to change and adapt. Um, because these things are evolving, right? The latest technologies, the newest gen, all of that, everybody gets excited about it, but without that focus on the what in the heck are we doing with it or what's the purpose, then we really lose the opportunity that evolves there. And even more importantly, we're not necessarily then looking at the risk that's potential and that could be cost risk that can be. I invested $200 billion, um, you know, in this thing and we just wanted to implement something new and shiny. Um, one of the concepts that uh, we just discussed in that presentation I was in was this concept of gap analysis and creating a brand new role or a brand new team construction that might have an agent as a part of it and what are the gaps that my people have and how can I fill in these gaps to help them move to this next generation of what we want to do? But that starts with the what is it we want to do? Conversation. Right? And how do we want to do it? Um, I've implemented an agent for myself recently. I'm very excited about it. He is reading my emails and I promise there is not one human on the planet that wants to read my gajillion emails that are coming through he sends me an email at 9am in the morning and he says, emmy, this is what you missed yesterday. And I've been missing a lot. I've been pretty busy. And so I promise there's not one human that's working in partnership with me that wants to do that. So there are things that are automatable. I knew I needed better calorie calendar management. I knew that I needed help with my emails. I. One time, one. And a contest for the most unread emails. I'm not even joking. I got a lunchbox from it. And so, you know, there's. These are things I identified that I needed to do better.

Speaker E: Right.

Dr. Jeremy Lookabal: And so it started with the problem and then the tech was part of the solution. And the strategy is, like I said, based on the market, the job, the role of company, the purpose. Right. Um, but that's gotta be that starting place. Where are we at today? Where do we want to go and what do we need to do to fill in this gap? Otherwise you're just grasping at straws.

Host: Well, you know, you give me confidence because you know what you're doing. Um, but. And you're like you're using this tool effectively, but sometimes I feel like, you know what, AI is not ready and we're just the world's biggest laboratory and everything goes fine and then something happens. Um, is a, should we have AI this explosive right now or should we be giving it to people like you who know what you're doing? Should we put some, some guide rails on how we're using AI currently?

Dr. Jeremy Lookabal: Um, so the answer is both yes and no, because you asked a couple of questions and I'll qualify that super quickly here. Um, so we can get to Linda Ann. Um, but the acceleration of technology is out of the box. It's the genie's out of the bottle, like that's not going away. And in fact there are billions of dollars invested, maybe even trillions, depending on how you're measuring this invested in the future that has this as a part of just like computers, just like cell phones. The distinguishing factor here though is that these tools are becoming very human, like in a way that your laptop sitting on there were not right. And so, yes, it's out of the bottle, it's already happening. But that is the reason why we need guardrails. There's a lot of research out there showing that the acceleration of technology development is not being matched with the acceleration of ethical frameworks of things that are governing. These things are of, um, frameworks that organizations are implementing even Internally, at the same time as they're rolling out the tech. And in that middle gap, there is a trust, you know, trust chaos happening between that. Right. There are job losses happening without that intentionality, and there's a lot of risk associated. So this gap between our ethics and the gap between our technology development is growing, and that's the reason we have to start putting some things in the middle to bridge it.

Host: Yeah, it feels a little bit like welcome to Pandora's box. Uh, thank you very much for that, Emmy. Linda Ann, over to you.

Linda Ann: One of the things to look at, and Emmy touched on it a bit, is really, is the implementation of AI into an organization truly a strategy, or is it just an operational process? And does it help them win in any way? Have they figured out that? Because what you have is, you know, like Emmy's using it for tasks, right? She's got tasks that it's helping her process well, and if you've got everybody doing that, that's one thing. But to put that framework in place is to make sure that there is a strategy, a purpose in this process. And at what level is that being implemented and how does it help you really win in the marketplace? How does it really contribute to a strategy that's going move your business forward? Because in some cases what you have is all of these employees implementing it, however, because copilot pops up every time you open a word doc, right? So how can you not use it on some level in those. As in that instance, but now, and you've got all this information that it's providing you or documents or process, whatever that is, however you're using it. But we still. But that, uh, um, challenges our quality control, our judgments. And moving forward, looking at what systems are you putting in place, um, to operationalize any of that, to give it some level of control, because, say, for example, you're using it in some ways the way Emmy is, and she's processing and she's generating these frameworks and she's. Or outlines and talk who's. What person can process the. That fast to make sure that the quality level is there, that it's really accomplishing the strategy. You know, if we're just going to turn it over to, um, every level, having it be processed by AI, I think that's. We're not ready for that yet. Um, so what systems are in place that can give you some kind of structure so that people don't just become overwhelmed, like we have become overwhelmed by our email.

Heather: Right.

Linda Ann: So what are we doing to prevent that process Again, where all we're doing is, um, um, responding or evaluating versus actually doing work and having it be a supportive tool. And then is what you're doing with AI consistent and working within the organizational value system?

Speaker E: Right.

Linda Ann: Is it really contributing to doing business in a certain way? And does it support that company's purpose as well? Because it's so easy to get sidetracked by all of this and do more, better, faster. But is it really, is it just the shiny object or is it really contributing to us moving forward as an organization in a solid way that's consistent with the values? And I think that's where the systems and things are not in place yet. To help us process this in a manner that makes people feel good about it and that makes sense for moving the organization forward.

Host: Uh, let me ask you this because I think about HR professionals, those working in organizations, I kind of sit here. You have no idea what might be coming down the pipe. So do you have any advice for HR professionals in organizations who may be about to implement AI? Are, uh, there some key things that they've got to observe and keep in mind and look out for?

Linda Ann: Well, I think that actually human resources is a good example of how some of these processes can go wrong. And if you look at how recruiting and the job market is right, and we've used the ATS systems and the scanning and all those kinds of things and processing for keywords, that has gone horribly wrong, in my opinion. Yeah, um, you know that. And so that's the kind of thing we actually have to be very careful about. I think that that's a good example. Um, while AI is a little bit different than that, that's a, um, that's kind of a macro kind of thing. But, um, it's a little different than that. I think that's a good example of how it can be implemented very, very quickly and everybody jump on the bandwagon and it not serve you the way you wanted it to.

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Speaker F: Yeah I think the general consensus is it's here whether we want it to be or not. Um it feels very much ready, fire, aim. Um I think we're starting to see the first wave of unintended consequences. Uh with AI it's promised to be the end all be all this, that and the other. Uh and it hasn't been. It's one of those where you apply it as important bands and job seeking. I can give you firsthand examples that it is brutal. Ah I had a role. It was an AI company and they thought they were clever by saying go out to our GPT and interact with this GPT in lieu of a cover letter. Um and it became very clear to me this was not a culture I wanted to be in because one that's a very demonizing. You're not worth talking to a real person. Go talk to this thing that we've created. And it was aggressive. It's clear to me whoever crafted or shaped or programmed this had a particular agenda. That's what we forget is this is a tool. We put all sorts of emotion and humanize these things all the time. And they're not. They're built on. It's garbage in garbage out is the old programming. So whatever is going in going to be what's coming out and that will give you as to how people are trying to steer what way they want to use them. We forget that what works isn't always what's best. Kind of this evolutionary sort of thing. Whatever gets the job quickest and all that. Nobody necessarily stops to think of what are we going to do with this extra inbox is clear. What does that free me up to do? Are we going to a four day workweek? No more better. Faster. Do more. It's just this productivity mindset. Uh and it's not trying to. That's bad in and of itself. But where, why are you saving? I struggle. Sometimes it's okay with relationships, with learning. Sometimes you've got to go through the harder, slow process to know what time you are saving. Why did we have to show our work in math? The calculator was saving us all this time. Um, so it's just, it's interesting and I read something recently, uh, and the idea that, you know, we're still using these brand new tools to solve old problems and that might not be what they need to do. They're going to change the way we work fundamentally and we're not good at change because it's big, bad and scary and we don't know what's on the other side. So I think there is that change management. Just almost societally, uh, we have to deal with how are we going to use these tools, what are we going to use these tools for? Um, and also, you know, what, what is the benefit and how do we get the benefit? You know, is it for the few people who own the data centers or is it for everybody who has to work less because we have a wealth of knowledge?

Host: There you go. Thank you very much for that, Nick. And yeah, there's, you know, when it comes to regulations, there are some, some really interesting things happen, uh, geopolitically around the world and how different countries and organizations are going to deal with AI. Um, so we're in the hinterland wilderness right now. Uh, Dr. Emmy, back over to you.

Dr. Jeremy Lookabal: Thank you. What you made me think of there, Nick, when you said that we're solving the same problems is this phrase, there's nothing new under the sun. I just, that's what it made me think of. So our problems will always be the same. Um, I'm going to put a link in the chat here and it is the Deloitte Human Capital Trends report from 2026. So for anyone listening in the future, you can just Google those words and you'll find it there. And in the report from Deloitte, what they have highlighted there is this differentiation between human plus machine and Human times machine. And that really being the collaboration that is providing a strategic advantage. Now they have some graphics in there that talk about two other key areas. Your machine tech focus only, which is where you'll see organizations saying, we're laying off X number to implement Y. Right. But the collaboration that they've called Human Centric, which was very exciting when I read it, um, is what they're saying is actually the strategic advantage. And then really where you're behind the curve is an entire lack of intentionality. And so I want to focus really briefly on this concept of being intentional. And I think it really speaks to what Linda Ann was saying there in terms of understanding the purpose and the outcome before you really ever implement. Um, so the agents that I've implemented in the organization, you know, were derived from analyzing the limited resources that I really have right now at the stage that I'm at. And where do we have gaps and where can the people that are working and partnering with me on my team be able to be supported by agentic operations? And so it isn't necessarily generative AI in that sense. It is that more expansive agentic use. Um, but to be able to email our agents and say, do this, do that, provide me a spreadsheet that does that. Um, it is in our purpose and what we're intentionally designing is to create some margin. That's something that I have not had a lot of over the last year. And I've been feeling the burnout and I've been feeling the exhaustion, and sometimes I totally disappeared to a hole. Um, but that's really part of the purpose. I want to create margin and I want to create space where I can have human to human interaction, where I can have relational impact as well, and where the people that are on my team are really supported by these operations. So it was implemented with a lot of intention, identifying the gaps that we didn't have and where we needed to go based on the proposed future state we're trying to get to, or the vision we're trying to get to. Right. And without that intention, without that direct line, this does this for this reason, then it really would have been spinning wheels. And I've spun my wheels plenty of times, times in my life, don't get me wrong. There have been lots of wheels spinning in my tech career. Um, but that's really been. I think the game changer is that intentionality. And I love this report because it really highlights the how and how organizations are actually moving ahead of the curve and what they're calling human times, machine versus human plus machine and some differentiators that, that really, um, can make a big impact for organizations. And I think, I think that's where iOS have got a really solid space to be moving towards. These are completely new team constructions where you've got three humans and two agents, or 10 humans and 10 agents. Right. Um, it's a game changer and it can be an amplifier. If you are using your humans and multiplying it with the operations of the tech. But that's gotta be designed. And I think that's where iOS have got a super unique understanding. They know the people side. And if you're going to be human centric and have, have what Deloitte is calling the strategic advantage for your organization, partner with one of these awesome iOS because, um, they know the people and they know the impact to the people and the engagement and how they're going to actually be able to amplify and multiply this impact out in the world.

Host: Thank you very much for that, Dr. Jaggedesh. I'm just going to put you on hold for a second because, Heather, I want to come back to you, uh, because I think Emmy, sort of like as I'm sitting here thinking if AI is an ocean, I think I need a navigator and it might be an IO psychologist. So can you talk a little bit more about that role that IO psychologists can fill by coming in and navigating this for us? What do we need? I mean, first of all, just doing an analysis to see how each organization could implement this successfully.

Heather: Oh, absolutely. Um, so I was reading here and I, and I shared it in the comments initially and it'll be in the show notes, but there was an article by McKinsey Co. That says right here that within the next three years 92% of companies will plan to invest more in generative AI and initiatives within the next three years. So as you know, many companies majority will be implementing AI. So iOS can be a huge leverage to use helping navigate this because we are well versed in change management and organizational development and just helping navigate these very, um, turbulent times, especially when change is happening. And we know with humans we don't always like change. So in that same vein, um, there's another statistic here that three times more employees are using generative AI for a third or more of their work than their leaders imagine. And when thinking of. So this is just fresh on my mind because I just got finished doing a contract for a curriculum on change management and one of the lessons was on people who are like innovators versus early adopters versus late majority and then the laggard. So those that are really not like, they're very averse to change. And so we have to consider everyone who is within the workplace because not everyone's going to want to adopt AI. And I was reading earlier in a different article that I shared that there are people who have positive attitudes toward AI versus negative versus neutral and kind of piggybacking off of nick comments from the Last time he spoke, when we have AI doing work for us, are we freeing up a lot of time, or are we just creating more work for us to do? We have to think about that. And as iOS, if a, uh, workplace brings an IO in, then we can kind of assess. Okay, yes, Emmy, we have to choose. Are we going to make more work for us, or are we going to automate certain things and then allow people to do what? Like, can they go home? Can they. Like, what is it? We have to choose something because it can't be. We're going to make all this extra time or create, like, more product, like, more productivity on one front. Like the machines doing all this stuff. And now the employees have certain things that they're not doing. But then expect the employees to start doing so many more new things, um, or make a whole lot of new work. Um, the more optimistic statement Emmy says, is we get to choose. Um, but in this other article that I shared with the people who had positive attitudes toward AIs, one thing that they would neglect to share with their employees or employers. They would not share how much AI they were using because they were afraid that their employers would see how fast they were working, and then it would, like, put their contract payments at risk, which is fair if you think about it, like, if they're working more efficiently, could they be at risk of losing how much they were making? Or maybe it would be renegotiated because now they're working too fast. It is something to think about.

Host: Oh, my mind's rolling right now. Thank you very much for that. How can I get away with doing this work? Um, Dr. Jaggedesh, let's go back to you.

Speaker G: Yeah, I'll actually start with what Peter has stated. It's like a thousand years back, we had no watch, but we had enough time to spend. But now, whereas we have, uh, all of us have watches, but we don't have time to spend together or whatever. See, that's how the evaluation is happening. And, uh, you know, any change that comes with a sense of, uh, say, an anxiety among people or it's a sense of discomfort, now the change is happening. And almost now people are, uh, started to understand or agree to it. But how they are navigating the change is actually making a difference. And right now, at every part of the world, the organizations are adapting AI. So they have maybe, uh, one year to one and a half years earlier that was not the case. But now organization have chosen to adapt AI. But the primary lag, which we are maybe I'm observing Is that uh, they are not focusing on the people who are adapting. Most of the organizations are inculcating that. Okay, I need. I can come up with subscription. You can use a. But say when the. They're not focusing on the user, uh, the employee who is adapting that A. Say what is that? He is perceiving. He is he or she perceiving that A as a threat to their own performance. Like they will be replaced. Uh, like will my experience be valued? Because now uh, I can equally provide a competent output as that of an experienced professional. So is that getting affected? So this is where the human uh, anxiety is coming out. And I think even Heatha was saying that they don't want to reveal that how much of a, ah, they were using in that particular space. So like uh, that's where the like our components of psychological safety. To what extent are they feeling comfortable to state out loud? Like, uh, like maybe very simply. Like I used to tell my students, like do not try. Like I never try to compete with like say a calculator. I know, like I can calculate a calculator can do the calculation, but I never have competed because I know I have much more things to do than the calculator. So I would never say. You don't uh, say compare or get any form of answer because of a. Whether it will replace you. Whatever. Let's focus on the competent. On like, like how what is that we can do else? Uh, certain things which you can. Which I can leave it to a. To help me out to sort things or to do some basic uh, works so that I can focus or direct my energy on a better work. So that is where lagging and that is where like I would say the human, Human uh, aspect is much more important. Say I do 100 percentage agree to Dr. Amy and uh, Peter that say uh, now it's a very huge time for us to focus on that aspect because very less organizations are focusing on the human side of it. And now when we pitch in, uh, that becomes the much more important aspect. As we see now, many are running towards a. But they are figuring they're forgetting the human component. If we are meant to be there for it and we will make it. Yeah.

Host: Thank you very much. Nick, let's go over to you.

Speaker F: Yeah, I think there's you know, a certain level of practicalities that are. That are coming in and kind of a correction so to speak. Uh, you know, these are not proprietary tools. You are running potentially delicate information through a large language model. Now they have that information and work with an insurance company couldn't use Even things like OneNote because the data security issue. Um, I think that there is a certain, you know, corollary where, you know, if a person was doing these same things, making assertions or writing or anything, you have to put your BS filter on a person. AI just does it so much faster. Um, and they get to a point where, you know, we talked about at one point in this podcast, you know, resume writing, kind of this lowest common denominator. Or is it, you know, the minimal acceptable standard where you can go to all the websites and look at all the advice and say, okay, I have crafted my resume to look like this. Well, that could have taken you six hours, whereas the AI is going to be about six seconds. So the result is the same, the time is different. For me right now, where AI is with models, it's very much looking rear view how things have been. You're looking for what is the advice out there, what's going on. But it doesn't necessarily create innovation. It's going to give you what's been shown to work. Um, it is, it is the infinite monkey theorem. They're going to come up with Shakespeare at some point, um, just because they have the time, the resources and all that to just keep throwing words up, uh, against each other, um, to some degree. So I think that, you know, to Emmy's point, having it sit through your email to figure out what you need to pay attention to, that's great because email's been weaponized because, you know, one marketer had an email campaign that worked and they got so many subscribers and now we have a whole spam filter because everybody's doing it. It worked once. And so everybody's got to try it. It's minimal investment for, you know, some return. You get one, you know, realers, they get one sale. It makes, you know, some of the spend on the front end worth it. So I think there's a lot of, a lot of copycat. Um, and I think there's also get into it. You know, people like to break things. We'll see a program or a video game comes out. People are looking at glitches and trying to get it, get it to break. I think people have shown that they're trying to do that with some of these chatbots as well. Um, McDonald's customer service to write Pizzavia. Um, and so there's, there's these abuses. And so what the tool was intended to do and what people are doing with it may not be the same thing. And I think we'll see that because society is an adaptive system, uh, and people throw things against the wall and see what sticks, um, and get people who are going to try and, you know, blaze their own trail. Okay, I have all this for time because I'm not looking at email. Can I really sit and think? Can I finally get to that reading list? Interactive, you're engaging, you're coming up with new ideas, or you're treading something. AI is really good at synthesizing and mimicking, but it's not necessarily so good at creating yet. I still have alarm bells that go off when I see AI generated art or videos or even hear it in the audio. But I think about my kids, they don't necessarily hear that be part of the conversation forward. So, um, data literacy, AI literacy is important so that, you know, when somebody's trying to sell you, it's the same critical thinking that we need. Now when people do something that is either good or bad for you, I think we all go to the mustache twisted, uh, evil villain. But there are good applications of what these things do. Um, who's. Who's at the control board? I don't know. Everybody and nobody all the time. Some regulation, hopefully is coming, um, on, uh, what is permissible, what is not. Um, even if you put a law or guideline out there, not everybody follow it. But some understanding, this is where we're at. This is potentially a public good and not a private thing. Those are larger debates. Uh, Linda's usually smarter than I am, so I will let her, uh, take the floor.

Host: Anytime you want to start a revolution. Nick, I'm right behind you. Uh, Linda, let's go to you.

Linda Ann: I think that one point to understand is, you know, that there's a huge impact on this in that I was listening to something recently, and it really brought back the fact that our survival as humans is based on our ability to be tribal and collaborate and, and work in groups and so forth. So the more we're isolated, the more we're at risk. And so really looking at, as we do this and have, if we move to the point where we are having less human interaction as a result, what is the cost in mental health? Because we're already seeing some of that with screen times and all those kinds of things. And there's a huge, huge shift. So we have to really be good stewards of the technology and protect the basic human processes that keep us healthy as humans. And I think the other thing, like when I talk to some people who are, you know, like, you would think that people in their 30s, 20s or late 20s, early 30s would be all over this. And not all of them are because of the ethical implications, right. With the data centers being built and the fact that now some people, as a result people's water is contaminated. And you look at the, you know, the, the, the huge environmental impact on that that we have to be. Again, consider the big picture. It's not just about getting through your email, right. It's about what is, what's the cost ultimately. And if you think about this, you know, comes to me, if you've ever watched, um, the movie It's a Wonderful Life and um, and Jimmy Stewart's brother who went away to college when he couldn't, comes back and he's wealthy and because he's investing in plastics. This is in the 40s, right. Plastics were going to be that were the next big thing. Right. And what are we doing now? We're trying to get plastics out of our life. So we need to, you know, we don't need to, to maybe, um, learn every lesson in the same way. Right. So I think it's important for us to think responsibly about that. And then as we go forward in the workplace, it's important for you to have AI policies as to how people are allowed to use this in the work environment. You know, I finished recently with someone and one of the things is work product. You are not allowed to, you use AI to create work product and submit it to a client. You can do the research in AI and make sure you have the citations and the validation, all those kinds of things. But you are not allowed to submit an AI generated work product to a client. And if you're using it for certain things, they can monitor or audit your work to see where it's coming from. And so there's, there's important guidelines and, and benchmarks that, that need to be in place so that, that organizations can say, yes, we understand where the service or content or whatever it is that we're providing you is coming from.

Speaker F: Right.

Host: Thank you very much. Heather, let's actually go back to you. I saw your hand come up.

Heather: Yeah, no, I was um, thinking back to what Linda Ann was saying about the plastics being the big thing back in like the 1940s and now we're trying to get rid of it. And then I don't know how chronically online all of you are, but, um, I am one who enjoys reading the comments of everything that I see on social media. Like I don't have a Facebook that I actually use. But I do have Instagram and I used to have TikTok. And I like to see what the comments are when I'm looking at videos or different things, just to see what people are saying. And it's interesting because there's such a night and day difference between thoughts about AI generated content on Instagram versus YouTube versus TikTok. And so when I see companies who post AI generated content, like there, for example, there was a real estate company who had posted and it was kind of cool. It was an advertisement for their real estate company and it was them showing this house and they had like some really cool graphics, but you could tell some of it was AI generated rated. Now the house wasn't, but there were some graphics and I just thought it was kind of interesting. But they got eaten alive in the comments about how you must hate the environment. I hope your company fails. This is not okay. You might like. And they were like getting eaten alive about how their values must be horrible. I mean, it was just. But then the same video. So I, So I went to TikTok to see how it did on TikTok. And TikTok was like, those comments were more positive and it was like, this is such a cool video. Um, they didn't get any of those same types of comments, but it was like Instagram had this whole different feel for it. And I find that a lot like on TikTok. And maybe it's how my algorithm is set up, who knows? But. But it's still me watching like similar videos on both platforms. So I don't know if it's something that I've done to set it up, but it seems like both platforms have wildly different types of individuals who have different feels for how AI is. But there are people on there saying like, if you support AI, you support individuals not getting clean drinking water. Your company must not support like, like, um, I don't even know how they word it, but like, um, small communities, all this kind of stuff, like making these massive generalizations. It's interesting.

Host: So it could. There's a research paper in there somewhere. Thank you very much. Um, Dr. Emmy, let's go to you then. Cynthia, I'm going to come over to you then to you, Nick.

Dr. Jeremy Lookabal: All right. So yes, I think the same can be said also for our shopping patterns via Amazon. People have said the same thing about Amazon. And this, uh, way that people shop there or companies like Shein, they make these, like massive amounts of clothing and the environmental impact there. So I mean, we couldn't really slice and dice that any other way. But just briefly, Heather, I think it's the way they curate the algorithm on how they want people to engage with you. So it's very, very interesting. Um, just generally speaking. So as I was thinking here, one of the concepts that came to mind that I'd heard a long time ago in a meeting that I was in was your business always be hungry. And what that really means is that there's always something to do. There's always something to create or generate or whatever. So we could work ad nauseam, uh, 24 7, and there would still be something that we need to do to feed our business, to get to whatever that next layer or next step is. And even though I made that comment cheekily earlier in the cat in the chat, I really do mean this. We get to choose, right? We get to choose what that intention is. And I think that's where leaders of orchestra organizations are specifically going to make the most impact, is making intentional choices about what it is and how it is they're creating new change in their organizations, how they're reconstructing and transforming their teams. I'm going to pop in the chat a part of the Deloitte Report that I put the link in for earlier. And it was noting in there that many organizations don't change effectively as it is, and even fewer actually implement continuous learning within their organizations. And so the response of how to partner and put the technology in to help with these operations and the intentionality that must be done in the transformation process for their talent to avoid disengaged talent, to really foster workforce growth is what they're calling that competitive advantage. Um, looking at the systemic impact is not just looking at the employees alone inside. That is looking at, at, uh, yes, the employees, the leaders, the stakeholders, the customers and society at large. So that is something that many organizations are not necessarily doing. Some of the names that you've seen in the news probably again recently. YouTube was in the news again recently. Meta, for like the thousandth time, was in the news recently. And so when organizations are not choosing to look at the, uh, ethical factors of what they're doing, that's where you have these great impacts, both financially, you know, via fines or lawsuits, like these organizations I just mentioned, but also reputational impact and lots of other kinds of impact to the people that are associated with the system. Um, and so the intentionality, I mean, I mentioned the email from you, but that was intentional. For a greater purpose, I am freed up a little bit more now to have relational impact for the Purpose of bringing ethics conversations directly face to face, face with people. So the choices that I make, albeit they may be smaller or they may seem like small things, they're always really constructed for a purpose. And I think that's where leaders have to really move in terms of how they're going to integrate tech. It can't be for no purpose. Right. But in our case, it's really to bring that ethical conversation and the human centric development to the conversation of organizations. And, um, some of these things that are happening are going to be transformational and we really can't hide from that. We can't kind of close our eyes and say we're not going to transform because organizations are already doing this. I was at Human Tech Week in San Francisco recently and there were these massive buses going through the city and they were purple and they had the advertisement you might have seen somewhere else that said, stop hiring humans. I mean, it is plastered, it is loud, it is, is out there. It is not something that's going away. And so being intentional is the countermeasure to that and deciding in other ways to construct organizational teaming. And lots of organizations like Deloitte and like McKinsey are putting out these reports that are giving some practical strategies there, but also the why, the why behind how we need to do that. Because coming out of Silicon Valley are each and every tool that is sending a different message that humans don't matter, that you can replace them. Even at Psyop last year they had an advertisement in the ad that said, hire, uh, our AI coach because the human coach, uh, has bias or something along those lines. The messaging is out there and it's even wild in some places. Uh, an ad in London Heathrow Airport that said, um, she'll never ask for a raise is the ad that's on this wall. It made me really, really upset to read it. Um, these are the tools that are being constructed. This is what's being deployed out there. And many people within your organizations are already using these tools for their personal purposes. And so that is why leaders have to get a rein and a wrangle on what they want to do, how they want to do it, and be transparent and elicit feedback from their employees so that they can construct it with intention. It's happening, it's out there, you know, and this is the messaging that's out there. And you know, there need to be some countermeasures. And yes, Lynn, and you know, every AI does have that, and they're not being marketed that way. Right. They're being marketed with this lie built on top of it that like, oh, we have no bias and we're awesome and all of these things, they're not being transparent as they're deploying these tools. And that's why leaders are so critical.

Host: Thank you very much for that, Cynthia. Welcome to work. Cookie, let's go to you.

Speaker E: I've heard so much, so much about this. And you're right, Emmy. I agree with you that there's a lack of communication from the CEOs or whoever that's introducing this, um, AI into the, um, workplace environment. And it's a failure to say, hey, um, maybe we should take a census on our employees, see how they feel about it. And they're skipping that and they're just putting it into the workplace environment. So then it impacts employees, um, also, because there's no purpose or roles, there's no announcement to say we're going to do AI. It's just, to me, it looks like it's just being introduced without even talking to the employees about it. And so then if it's for your emails, that would be great. Like, um, was it Emmy was saying that she uses for emails? That'd be great. But for a person that's actually using it for other strategic purposes, it's taken away the human quality of it. I mean, I've called these places that have the AI and they, they just, they're programmed by what you say. And if you're, what your question is about it, you don't. They, they keep saying, oh, well, you can't talk to anybody. Let me handle your problem. And they can't handle the problem because they lack that human quality. So it kind of impacts the whole organization. They end up losing, uh, employees or throwing them out thinking that this is actually working. There's no, uh, like they said, somebody was saying, looking at the research, the taking qualitative and quantitative research to see iOS should be coming in and say, hey, is this really working for you? You know, finding the data, doing the research and then figuring this out. Like they do trial things. There's none of that happening. I haven't seen any research on the Internet about they're actually doing this. You see trials about other things, but never about introduction of AIs. They're just put into the workplace, oh, this is going to make us better. But what it's doing is, uh, it's killing the human quality of the organizations. They lose their competitive, uh, edge. And eventually that's why you see so many of these organizations closing down. Because they don't consider the ethical part of saying, hey, is this going to be working for my organization? And I think that's where the commitment, the effectiveness is out, actually failing. And then people are getting fired and they think it's working, but when it turns to full circle, they're going to find out that these AIs are actually not effective.

Host: Yeah, I recently heard, I think it was one of the pizza chains in the United States, uh, employed AI and oops didn't work out.

Speaker E: Well, uh, like, they got a machine that ordered to put your order in and all that kind of stuff. I mean, and they're not asking employees anything. They're just putting in there, hey, I'm

Host: upset when I, you know, call customer service and it's not a real person. Um, but that's, you know, that's me, and I want a real person. Thank you very much for that. Nick, let's pop over to you.

Speaker E: Yeah, and sometimes they hang up on you.

Host: Yeah, Nick.

Speaker F: Yeah, I think there's, uh, you know, the customer service is really where everybody wants to because, you know, answer to all those questions takes time, energy, and people on the phone. Um, I was trying to make a change for a rental car reservation called the line of. It's like, go to our website. Go to our website. And now it's farther with interact with this chatbot. Anybody who has a vision in their head about what the answer could or should be, hey, Gemini, give me a layout of this, that or the other. How you ask the question is very much how you're going to get a response. And so fluency in that is not easy to get. It's a lot of trial and error, um, and a lot of frustration and going, well, I could have just done this faster on pen and paper to some degree as well. Um, I think the organizations that adapt to more of a. Alongside, you know, give the people what they want. If somebody wants an automated option, because quicker and they don't want to talk to somebody, that's fine. But also, let's not, you know, throw the baby out with Bathwater and all customer service agents. Somebody needs to be able to react to that one in a million situation. That's not in the data. It doesn't have an expat so far. So I think it covers probably 90%, 10% do want to talk to somebody. Um, and all of that. And I think that there's, you know, everybody's talked about intentionality. What are you doing? What are you doing? Um, that's, you know, I think you'll see people being intentional about going the other way. We are making it not to you, we respect. You know, it almost feels like, you know, made by a human is going to be, let's say, sort of stamp on products, you know, care how things are. Sort of using another, you know, another thought in my head that may have run out on me that did lose it. You know, we're talking about trying to keep things, um, you know, human centric and keep people in the middle of it. Um, I think that that intentionality of how you craft your own interaction and it can be hard to go away with. With national trends and we've seen these technology shifts, obviously history. You have the printing press as the first major disruptor. But even things that I've lived through, like cell phone, I don't remember there being this panic and excitement to change things. It just gradually got smarter and became more ubiquitous and things like that. Uh, but I will put news out there. We were able to get along without these tools before. Can still solve these problems. Sometimes doing it yourself is better than, uh, outsourcing it to something. So it'll be interesting to see how those reactions go. It's all too easy to get colored by sci fi, where you've got the big technological boogeyman that's going to take over and get all the horrible things that we see. But, uh, there's also again, net positives and personify some of these, put human qualities on them. The more we'll be able to kind of see the forest for the trees, uh, and see what it is and not necessarily react to the marketing. And you know, you got to fit in, you got to adopt. You got to be like all these other cool kids, the AI because, you know, keep up with the Joneses.

Host: Thank you very much for that. Um, Heather, we're almost out of time, but let me come back to you for this because one thing I have noticed is that we all globally have a tendency to look to the United States to see, you know, the latest trends. Um, as a Canadian, yeah, I'm always tuning in to see what the Americans are doing. Um, but I've noticed especially now, especially with AI and all of these developments in technology, there seems to be a shift happening. Europe, um, right now is looking at sort of disconnecting itself from American services and using European services. And part of the issue I'm hearing is this ethical issue that, you know, the companies in the United States are just going to go as fast as they can. But there's other places in the world where they're going to point. Can we just put the pause on for a second and start to ask some of the questions I've heard today? So should we as North Americans be looking at other jurisdictions around the world because maybe they've figured something out that we haven't yet?

Heather: Well, I will say there are other countries that have, even just in terms of data centers, have figured out how to make more sustainable data centers and have more sustainable initiatives. Like, for example, China is creating more sustainable initiatives to, like, develop. I know China, like, has a lot of data centers, just like we have a lot of data centers and they are also a competitor in this AI. Um, and it uses a lot of water. We know that. Hello. It's one of our topics. Um, and we use like OpenAI and they, if I'm not mistaken, they use deep sea, the whale one. Um, I kind of am ignorant on like their big one. But, um, there are other countries that use different ones and. Yes. So I think it's good to keep an open mind and stay relevant, not just where we're at, but with where other countries are as well and not just stay in our little bubble.

Host: I, I kind of, I kind of wonder why AI centers, data centers aren't being built. Built in Canada. We're cold and we have lots of water, so I'm sure they're coming probably. Well, thank you very much for this. This has been a fabulous conversation. Once again, it seems like five minutes has gone by and we're already past an hour. So, uh, thank you very much, Heather. Can't wait for you to come back in July.

Speaker G: Next.

Host: Uh, m. Uh, next week we're, uh, going to have a different topic. We're talking about the corporate hamster wheel. Why organizations keep, keep running and going nowhere. Kind of sounds like my life. Uh, thank you very much once again, Heather. Uh, thank you everyone for your contribution today. And hopefully we'll see you in one week's time. So, Heather, if you'd like to count us out, we'll say bye.

Heather: Absolutely. Bye. Four, three, two and one.

Host: Thanks for listening to this episode of work cookie, a CBOK podcast. Don't forget to sign up@cboc.com that's s e b o c.com to engage with our community, gain a sense of belonging, access our other media, and get rapid advice from experts. Would it be a bad idea to make your most challenging workplace problems go away at cbok dot. Com? Sam m.