HR We Have A Problem: Throwback Thursday: Teri Zipper & Melissa Reeve on Becoming Hyper-Adaptive

[00:00:00] [SPEAKER_04] I've prompted a couple of times, but I'm treating it like a search engine. What do I do? And my guidance there is to follow your curiosity. Pick one thing that you're really passionate about and challenge yourself to think about maybe AI could help me do X. And then go down that path. See if you can find some YouTube videos around it. See if you can find other people in your organization who've maybe dinked around with it.

[00:00:29] [SPEAKER_04] And let me share the corollary to that because I just wrote an article about this, which is if you want people to do that in a serious way, please, please, please free up some capacity because we're all underwater in terms of the amount of stuff we're expected to be done. And so maybe that's an AI learning power hour where again, it's the prompting party thing, right?

[00:00:52] [SPEAKER_04] We need to create safe spaces for people to activate that curiosity and not only a safe space, but capacity like real reserve time for them to do it because otherwise we're just grasping at the lowest hanging fruit, which is probably your email.

[00:01:08] [SPEAKER_00] Welcome to the The HR Huddle presented by Sapient Insights Group, the ultimate resource for all things HR. It's time to get in the huddle.

[00:01:28] [SPEAKER_05] Hello, everyone. Welcome back to the HR huddle. I'm your host, Terri Zipper. I'm the CEO and managing partner at Sapient Insights Group, and I'm back for another exciting episode of HR. We have a problem. This is the show where we like to break down the big and most relevant HR issues of the day. We help you make sense of what they mean for you. And we talk about the things you might do about them.

[00:01:53] [SPEAKER_05] Joining me today is Melissa Reeve. Melissa is the creator of the hyper adaptive model, which I think sounds really cool, and I can't wait to talk about it. And the author of hyper adaptive rewiring the enterprise to become AI native, which I think is coming soon. It's not out yet, right?

[00:02:10] [SPEAKER_04] Available for preorder.

[00:02:11] [SPEAKER_05] Okay. All right. So welcome, Melissa. It's great to have you. I'm excited about this conversation.

[00:02:16] [SPEAKER_04] Me too. It's such a pleasure to be here. Thanks for having me on the show.

[00:02:20] [SPEAKER_05] Yeah. I, you know, our first conversation we talked about like hyper adaptive, which I just think is a really cool word. Hopefully we can explain what it means and talk about how people might engage with it. But, you know, we've been talking about AI everywhere. And personally, I've been talking about it on every show. It's like we can't get away from it. Right. But, you know, I want to be real about it.

[00:02:42] [SPEAKER_05] And, you know, we've been learning about what we can do individually. But what I thought was unique about our conversation previously is, you know, you've been talking a lot about what the organization needs to do. And sometimes, you know, we take a lot of this on ourselves. Right. There are organizational changes that need to occur. And it's a pretty big ask. And so I'd like to talk about that. Does that sound like a good plan to you?

[00:03:06] [SPEAKER_05] Let's do it. Yeah, there's so much ground to cover. Let's get into the huddle then. So before we jump in too deep, Melissa, can you just give a little more insight on your background for the audience? You've got a pretty interesting background and I'm excited about the book, too.

[00:03:21] [SPEAKER_04] Thanks so much. Yeah, I'd like to say that it started on the factory floor in Tokyo, where I was studying Japanese. I was studying industrial engineering and the Toyota production system. And that was really my introduction to how small changes built up over time can impact the entire organization.

[00:03:42] [SPEAKER_04] And thinking about the organization as a system can really help you to solve problems and keep things from being siloed. And from there, I launched a career in marketing and as an executive. The other relevant part of my career is around agile and digital transformation.

[00:04:02] [SPEAKER_04] So I worked for a company called Scaled Agile, and that was where we took lean and agile practices and we scaled them up into the world's largest enterprises.

[00:04:12] [SPEAKER_04] When I started to really dive into AI, I quickly realized the parallels between the organizational change that needed to happen in these lean and agile implementations and the organizational changes that we would need to support as AI started to rewire the roles and jobs.

[00:04:32] [SPEAKER_04] And so then that led me into diving back into something called DevOps, which was the automation of the software delivery pipeline and really researching what the most successful organizations were doing to become more AI native.

[00:04:48] [SPEAKER_05] Yeah. And in all of that, I really didn't hear anywhere where you worked in HR.

[00:04:53] [SPEAKER_04] It's true. I know.

[00:04:54] [SPEAKER_05] Which I love because, you know, sometimes we get really siloed in our in our world of HR. And, you know, I just think it's so critical, the relationships that we have within the business. And also, anytime you have an opportunity to work in in the production side of the business and the people who actually deliver things to to the to the client, right, the customer,

[00:05:20] [SPEAKER_05] it just changes your perspective pretty significantly on how the work gets done and where the focus needs to be.

[00:05:28] [SPEAKER_04] Yeah, there's a there's a saying out there that talks about AI is going to change who can do what in the world. And I feel like what what I heard you describe is what we term adjacent competencies. And so even though I was rooted in marketing and being an executive, I had these adjacent competencies based on the people who surrounded me. And I do feel like AI is going to unlock some of these adjacent competencies.

[00:05:57] [SPEAKER_04] And we're going to find more and more people who are doing really interesting things because they now can with this new capability.

[00:06:05] [SPEAKER_05] Yeah. Yeah. What do you see HR's role in all of this? Or do you have a perspective on that? I mean, there's a lot of dynamics happening there of late, especially with agentic AI. And there's conversations about, you know, the the C-suite is asking HR to deal with this because it's resources. But I don't know how much of that is reality and how much of it is just coming along for the ride.

[00:06:26] [SPEAKER_04] Yeah, there's a great slide that's circulating and it's from Bain & Company. And it talks about what successful companies are doing with regards to AI integration. And the slide, you think about a horizontal bar. And they say successful companies are spending 10% of their time on the algorithms, like making sure that the algorithms are tuned. They're spending about 20% of their time on the technology. What is that? What are the AI technologies we should be using?

[00:06:55] [SPEAKER_04] And then they're spending 70% of their efforts on the people. And so we know that over the next X years, jobs are going to be rewired. And I like to describe jobs as collections of tasks, processes, decisions, and human interactions. And when we deconstruct jobs in that way, they become puzzle pieces. And I believe that AI is going to throw those puzzle pieces up in the air and they're going to land in new ways.

[00:07:25] [SPEAKER_04] And I really feel strongly that it's the role of the people watching the people, HR, to really direct that activity. And so I know that there's this myth out there, oh, we get to lay off all these people. I really don't buy into it. And I think that the role of HR is going to become even more critical in the years to come.

[00:07:47] [SPEAKER_05] Yeah. I was going to ask you where you stood on that whole replace versus enhance conversation. I think everybody's in a little different place. I do think there's replacement coming. But I think to your point about the adjacent skills and capabilities and competencies, those things are going to power people to move into different roles. But those roles may change altogether. I mean, you mentioned value streams and sort of rewiring jobs.

[00:08:16] [SPEAKER_05] And I'm sure HR is like, you know, turning sideways, like, oh, my gosh, does that mean like we're going to, you know, do new job architecture and rewrite job descriptions yet again? But I don't think that's what we're talking about, right? Like these are real skills and capabilities that I think are going to create whole new jobs. This is what happens when we have, you know, upheaval, right? Whole new jobs are created and old ones kind of go away.

[00:08:42] [SPEAKER_04] It's so true. And this is where that DevOps analogy really sings, because what we saw there is we saw people moving from doing the task to building, monitoring and maintaining the automations that were doing the task. And so when you think about the rewiring of jobs, that's some of the rewiring that we're talking about. And those are new jobs. We didn't have a DevOps engineer in the year 2000, nor did we have a social media manager in the year 2000.

[00:09:11] [SPEAKER_04] So I think why people are so anxious is because this isn't isolated to software development. It's not isolated to marketing. It's a massive rewiring on almost all fronts. What I believe to be true is that the people who've spent 20 years processing invoices, yeah, you might not be processing the invoice anymore, right? And automation can do that.

[00:09:38] [SPEAKER_04] But we do need somebody to babysit the AI that is processing the invoices. And who better to do that than somebody with 20 years experience of and has all these vendors in their heads and has all the processes in their heads. And so I do feel like that's the opportunity.

[00:09:55] [SPEAKER_05] Yeah. I think back, and this is going to date me for sure, but when I first got involved in technology, because nobody else wanted to do it, and I kind of raised my hand not knowing what I was raising it for. I was like running, having to do manual backups of data, change out tapes, like do all of these things that nobody does anymore, right? I mean, it's just now it's on a very different scale because it's not just about tech. It's really about everything that we do.

[00:10:24] [SPEAKER_05] And of course, you know, there are certain parts of the organization that are going to be much more impacted by this change than others, right? And have much greater outcomes for the business.

[00:10:37] [SPEAKER_04] Yeah, I think that's yet to be determined. I see a lot of opportunity in many parts of the business. So we'll see who ends up getting impacted the most.

[00:10:46] [SPEAKER_05] Yeah, yeah. I think it's going to be interesting. So tell us about this hyper-adaptive model and what it means and how we, you know, as an organizational leader, regardless of whether I'm in HR or I'm sitting in another part of the business that's going to have an impact on this. What is it? How do I start to think about what these changes mean for my organization and how I can impact them, not just myself, right?

[00:11:13] [SPEAKER_05] Not just my personal skills, which a lot of people are already working on, but how do I upskill my business?

[00:11:18] [SPEAKER_04] Yeah, that's right. So it's a way to think about this transition from what I call a linear organization, which is most of the organizations out there. I like to think of it, I've got this visual in my head and it's strategy to execution on one side of the diagram and it's concept to delivery along the bottom.

[00:11:39] [SPEAKER_04] And when you think about all the handoffs that happen as you're going from strategy to execution and then all the handoffs that happen when you go from concept to delivery of the product, what we know is that AI is going to compress both dimensions. And we know this because we're already seeing these AI native organizations pop up and they are compressed, which is why they get these amazing multiples and amazing efficiencies.

[00:12:05] [SPEAKER_04] And so the question becomes that if you're going from a linear organization, which is what I just described, into an AI native organization, which is what I call a hyper-adaptive organization. And that's an organization that can sense and respond and react in real time. How do you get from here to there?

[00:12:25] [SPEAKER_04] And so the hyper-adaptive model puts forth an incremental and iterative roadmap for you to start gradually rewiring your processes and gradually start rewiring the organization, the entire model, so that you go from this linear organization into what you started to describe, which is orchestrated value streams. And so it's just like, what you're going from here to do is that you go from this environment. And when you think about that transition, that's not something that you can do overnight.

[00:12:53] [SPEAKER_04] And it's not something that you can do without spinning up additional support systems. So I'd like to say that in the 90s, when we put a PC on everybody's desk, we didn't do that in isolation, right? We spun up IT help desks and we spun up entire IT departments. And yet somehow with AI, we're embedding it everywhere and just telling people to go play.

[00:13:17] [SPEAKER_04] And so what the hyper-adaptive model does is it starts to outline these support structures that we need in order to help the organizations make this transition.

[00:13:27] [SPEAKER_05] Yeah. I mean, it's interesting because I think I was trying to think this morning about one of my early jobs. I was like, in my mind, I had a computer on my desk, but I don't think I did. Everything was on paper at the time. And, you know, so it is, I think that's one of the challenges with getting there and thinking through, you know, these kinds of models is thinking beyond what you know.

[00:13:53] [SPEAKER_05] And, you know, if you can't think beyond what you have right now, it's hard to imagine. And that's where I think all the the, you know, the concern comes in. Right. And people start to have fear for their jobs or fear for how this is going to impact their job is they just have trouble envisioning what this is going to mean specifically for them.

[00:14:15] [SPEAKER_05] So I think, you know, clear goals for the organization, communication, those kinds of things are really going to be important to keep that level of anxiety down.

[00:14:27] [SPEAKER_04] Yeah. And I think I think we talked about this on our on our pre-show call where the best organizations out there, the most successful ones are establishing what I call an AI North Star. And the most toxic ones out there are just filling the headlines with we're not going to hire until we figure this AI thing out or even more toxic. We're going to cut all these jobs because of AI. And I do it causes me to think, like, have you really thought that through?

[00:14:54] [SPEAKER_04] Because the most successful organizations are the ones like Moderna and Moderna said we're going to push 15 drugs to the market in five years with the help of AI. And that's such a rallying cry. And it's what I call an AI North Star, something that everybody can get behind. They know why they're implementing AI. They can align their role to that to that AI North Star. It's it's motivating.

[00:15:24] [SPEAKER_04] That's the word I'm looking for, you know, versus this uncertainty or why are we using AI? You know, it just leaves everybody in this this kind of worried and frozen state.

[00:15:34] [SPEAKER_05] Yeah. Do you think a lot of organizations aren't haven't done that? So, you know, if you're sitting in the C-suite and you haven't told people why you want them to make this change, you know, hey, we need to implement AI. That's not enough. Right. That's not going to that's going to cause to me more anxiety than anything, because I'm trying to figure out, well, what am I going to do and where can I implement it?

[00:16:00] [SPEAKER_05] And what would make the most sense instead of thinking about where I need to go, what that North Star is, those five drugs that I'm going to get out by in five years or whatever the timeline was. What do I need to do to make that happen? And I think that, you know, I feel like that's a place where you even have people in the C-suite who aren't quite sure how this is going to pan out.

[00:16:24] [SPEAKER_05] Right. And so if they're not saying the right things and driving the right messages and goals, I think you're going to have a hard time making that adjustment.

[00:16:33] [SPEAKER_04] Yeah. And there's there's a stage that honestly, it just surfaced for me in a in a workshop two two weeks ago. And it's I've labeled this stage AI sensemaking. And I think people need to give themselves permission to go through that stage.

[00:16:49] [SPEAKER_04] And when we talk about the C-suite and this this kind of inability to articulate a solid direction with AI, I think it's partially because they're in the process of this AI sensemaking. And I'd love to invite any listeners on this this podcast to just acknowledge that and take a breath and say, you know what, I do need to pause. And I can let my board know that we're in this phase called AI sensemaking.

[00:17:19] [SPEAKER_04] And we're going to slow down to speed up. We're going to take a minute to identify why we're using AI, how it's going to help move the business forward before we we spend another million, hundred million, whatever amount you're spending out there to implement AI.

[00:17:36] [SPEAKER_04] Because what I've noticed is that without that AI sensemaking, because AI is so big, because it's so amorphous, it's really hard to contextualize and contain it to an area where you can really move the needle. And so I just want to call that out as a step.

[00:17:55] [SPEAKER_05] Yeah, well, and I think the other thing you're seeing and we're seeing, of course, is organizations are putting structures in place, putting groups in place in the organization to help facilitate that we are not in this alone. Right. What kind of structures are you seeing, you know, organizations form? And, you know, what have you seen be successful at this stage?

[00:18:18] [SPEAKER_05] I know we're, you know, we're still early, but there are a lot of people that have been way ahead of this, this curve, doing some of the right things. And what can people learn from that?

[00:18:27] [SPEAKER_04] Absolutely. Yeah. And I've outlined some of these in the hyper adaptive model. And it's interesting to see the market pick these, you know, identify these structures. So, for example, in the foundation stage of the hyper adaptive model, I talk about your AI councils and getting your AI leads in. And I've seen a lot of organizations pick up like, oh, we need AI leads. We need our champions. What I'm what I'm seeing most organizations miss is the programmatic support for those AI leads.

[00:18:57] [SPEAKER_04] Like you don't become an AI lead on your own. How do you nurture an AI lead? How do you make sure that they're they're sharing their knowledge with other people? How do you make sure that they're causing the change that you're intending? They're also your frontline guardians. And so how do you equip them to be your frontline guardians? And then as far as the AI councils go, is this a static council? Is it cross-functional? Is it dynamic?

[00:19:24] [SPEAKER_04] Do we have dynamic governance because of the speed of AI? How are you equipping those AI? I advocate for a network of AI councils because you have the centralized guardrails and then you might have functional AI councils. So those are just your foundational ones. And then I start to advocate for what I call AI activation hubs. And so, again, this is a network of hubs within the organization that is taking care of your AI leads, which are then taking care of your AI practitioners.

[00:19:54] [SPEAKER_04] And the role of these activation hubs is to do what I call atomize the learning. They have a few roles, but one of them is to atomize the learning. So when you think about that AI sensemaking that we just talked about and you think about all the information that's coming at us in terms of AI development, somebody in the organization has to be tasked with making sense of it. Let's just say that Sora 2 launches, which is the video generation platform for open AI.

[00:20:22] [SPEAKER_04] They know who that impacts in the organization and how to get the newest information to the front lines as quickly as possible. And when I think about L&D professionals, I think of them moving from this centralized organization that does learning at a point in time to becoming a disseminated learning empowerment organization that atomizes this learning and gets it to the front lines as quickly as possible.

[00:20:47] [SPEAKER_04] So this just gives you a sense of some of the structures that we need to spin up in order to support the organization.

[00:20:55] [SPEAKER_05] Yeah. And are you also seeing people up and down the organization being part of these groups? Because I feel like there's people, you know, you don't have to be the leader. You don't have to be the manager to know what's going on or to maybe have ideas, right? A lot of these ideas are coming from the people on the ground who are touching this work every day. That's right. Yeah. Better ideas than any of us could imagine because they know what they go through every day.

[00:21:21] [SPEAKER_04] That's right. And so I'd like to say that AI learning is social learning. And because there are so many use cases, they can't really be contained in a curriculum. You can't just teach somebody, here's your use case. Because it's like telling people to draw a cat. Everybody draws it a little bit differently. So what's better is to have everybody say, draw their cat and then compare notes. Oh, that's interesting. You put stripes on that. I didn't know that was possible. And so again, the job of the organization is to create these learning arenas.

[00:21:50] [SPEAKER_04] And I like to cite PricewaterhouseCoopers as a great example of this, where they created what they called prompting parties. And it just, it sounds very attractive when I hear it. Like, oh, let's get some pizza and let's have a prompting party. And then when you do that with your peers, all of a sudden you get this AI sparking happening where people see what's possible. And then the next step is to catch those sparks and start to spread them and sustain them.

[00:22:19] [SPEAKER_04] And that's what I call the AI learning flywheel.

[00:22:21] [SPEAKER_05] Yeah. Yeah. I love the idea of the prompting parties. Anything that's got party in it, I think people are like, yeah, let's try that. Let's at least give that a whirl and see what happens. But I do, it's a good example of something that, you know, when we first introduced the World Wide Web, just how good some people were at finding the things they needed to find. Because they would, you know, their questions or their prompts or the things that they would develop to sort of figure out the answer

[00:22:50] [SPEAKER_05] were vastly different than other people who kind of were just sort of straight line. A cat doesn't have, you know, stripes or whatever, you know, whatever it is. So they wouldn't think to ask that. I think AI is very similar to that where I see, you know, people writing like practically, you know, a five page memorandum telling it what to do, right? With all these sort of interesting and strange ideas that I wouldn't have even thought of. So true. And getting back some really cool stuff, right?

[00:23:19] [SPEAKER_05] Like it really thinks outside the box. Although I have to say, like, I don't know what your reaction was to the Maltbot conversation over the weekend. But, you know, that's the kind of thing that just sort of blows everything out of the water right now. Everybody's like, oh my God, you know.

[00:23:37] [SPEAKER_04] Yeah, listeners who may not have been tuned into that channel. Maltbot was basically like you're submitting your AI agents and all the AI agents are talking to each other as if they're on social media. And they created their own religion. They created their own prophets. Somebody decided they wanted to be a king. And it's so fascinating to think about AI on its own. What does it do, right? Yeah.

[00:24:00] [SPEAKER_05] Yeah. It's really interesting. But it's also the kind of thing that then sort of strikes fear in a portion of the community. And people start to think, oh, do we really want to do this? Like, is this really the right thing to do? And there's just so much interesting and so much misinformation out there. But I do think people, you know, we need to be thinking about how we're using technology to do things better, faster, smarter.

[00:24:30] [SPEAKER_04] And that's what I actually love about the Maltbot experiment. And in one way, it's relatively harmless. You know, they can shut it down at any point. And it's a great learning experience. What will AI do when it's on? Like, what kind of governance do we need to prevent that or encourage this? And it is a whole new world. As you were talking about the Internet and the advent of the Internet, my mind kept going back to the role of the webmaster.

[00:24:56] [SPEAKER_04] And at one time, we had one person, the webmaster, who did everything. And you think about how that role split apart into a front-end engineer and a back-end developer and a UI UX professional. What we're facing now is unprecedented in terms of its scale. And we do have enough signals from the past to give us hints of where this might land.

[00:25:21] [SPEAKER_05] Yeah, yeah. I think, I feel like we're moving in the right direction. But there's so much more to go. You know, what are you seeing in terms of leaders in the organization? Because the other thing I saw, Gallup poll, you know, some research they did recently where, you know, leaders are still the most likely segment to be using AI.

[00:25:48] [SPEAKER_05] I'm not sure how they ask the question because I think a lot of people are using it and maybe don't even know it. But in terms of those people who are actively using it or believe that they're doing something to use it, tends to be much more significantly higher numbers of leaders than, say, you know, managers and employees. Like the numbers go down from there pretty substantially. With leaders using it, you know, multiple times a week. Employees, you know, not so much. What do you see as the difference there?

[00:26:17] [SPEAKER_05] I mean, is it just the role that the person has? I mean, obviously, if I'm a leader, I'm doing a lot more writing. So I'm, you know, maybe getting help with how I'm going to convey this message versus, you know, somebody on the front line who, you know, unless they've got new technology that they're implementing, they're not necessarily using AI during the day.

[00:26:36] [SPEAKER_04] Yeah, I would love to see that study. The people that I talk to, you know, on a daily, weekly basis, they really struggle to measure usage. And I have seen everything from the number of prompts that people are using to, but I feel like, is that really a good measurement? Are you looking for recipes? Are you, to me, that's not a good measurement.

[00:27:01] [SPEAKER_04] And so really, honestly, without looking at the study, I would have a hard time commenting on it. And it actually is counterintuitive to what I'm seeing because what I'm seeing is more pockets. I'm seeing one, a bifurcation. We have the power users, you know, 10 or 20% of the organization. We have kind of a mucky middle that's using it for email. And then we have a group who's like, I don't want to touch that.

[00:27:26] [SPEAKER_04] So that, I feel like that's more indicative of the split that I'm seeing rather than levels. And even there, it's hard to say like usage. Okay, if you're using it for email, is that really achieving your business outcomes?

[00:27:41] [SPEAKER_05] Yeah, yeah. That was kind of my reaction. It was sort of what, you know, why would levels dictate that per se, right? I mean, unless you're, I don't know, I could think of a few reasons, but it was just sort of, you know, I thought I'd love to see the questions. Like I didn't take the survey. So I, you know, don't know the detail. But yeah, I thought it was, I thought it was interesting that they were trying to break it down that way. I do think there's an opportunity for us as individuals. And I don't know if you have some advice for people specifically to, you know,

[00:28:11] [SPEAKER_05] if they're not being currently engaged on this topic, you know, how do they get engaged? And, you know, what could they do to, you know, to start some engagement?

[00:28:21] [SPEAKER_04] Yeah. So I think there's, there's two sides to that coin. One is as an individual, I've, I've chatted with people who are struggling to find their starting point. Like I've prompted a couple of times, but I'm treating it like a search engine. What do I do? And my guidance there is to follow your curiosity. Pick one thing that you're really passionate about and challenge yourself to think about maybe AI could help me do X.

[00:28:49] [SPEAKER_04] And then, and then go down that path. See if you can find some YouTube videos around it. See if you can find other people in your organization who've maybe dinked around with it. And let me share the corollary to that because I just wrote an article about this, which is if you want people to do that in a serious way, please, please, please free up some capacity because we're all underwater in terms of the amount of stuff we're expected to be done.

[00:29:17] [SPEAKER_04] And so maybe that's an AI learning power hour where again, it's the prompting party thing, right? We need to create safe spaces for people to activate that curiosity and not only a safe space, but capacity, like real reserve time for them to do it. Because otherwise we're just grasping at the lowest hanging fruit, which is probably your email.

[00:29:39] [SPEAKER_05] Yeah. Well, and also how about opening up opportunities for them to talk about it or make suggestions? Because I mean, there are tons of people that would never come forward and, and make a suggestion unless somebody asked or sort of pulled them out of them. And they may have some fabulous ideas, right? But they're just not willing to, to say them, right? They don't want to be countered or they don't want to be wrong.

[00:30:04] [SPEAKER_05] Or there's not a, as you said, there's not a safe way for them to give their ideas without ridicule, you know, or just being brushed off. So I think as organizations, we need to give people that opportunity because there's probably some crazy ideas out there that are going to turn out to be really good ideas.

[00:30:22] [SPEAKER_04] Well, absolutely. And this is a chance for all organizations to reinvent their processes at every level. And we're going to have to reinvent our processes over and over and over for the foreseeable future. One of the things I advocate for in the book is the integration of feedback loops into the process reinvention. And it's for exactly what you said, which is we've got great ideas everywhere and we need to be surfacing those.

[00:30:50] [SPEAKER_04] And so if we're deliberately reinventing our processes and we're deliberately injecting learning and feedback into those processes, then we're going to surface the great ideas. And then we need to, alongside that way, create these, what I like to call idea funnels at every level of the organization. My individual feedback, my team feedback, my department feedback, the organization feedback.

[00:31:15] [SPEAKER_04] And in that way, just to put a full circle on our conversation, that is exactly what happens in that Toyota production system. Is they have these interconnected feedback loops that then result in continuous improvement. And those micro improvements over time created the massive giant of Toyota that we know and love today.

[00:31:39] [SPEAKER_05] Yeah. Yeah. The other thing I was thinking about as you were talking about things people could think about. Everybody's got at least one or two things in their job that they hate doing. Like, start there. Like, there's a lot of passion around the things that you hate, right? How do you fix those things? Or how do you stop doing those things, right? The ideas that could flow from that would be massive, right?

[00:32:05] [SPEAKER_04] For sure. And one of the case studies I cite in the book is a company called Biziant. And their developers were not eager to lean into AI and coding. And what they did is they took somebody who was well-versed in empathy mapping. And they conducted empathy interviews with the development team. And it surfaced exactly what you're saying, is they were like, we really don't love chasing down bugs. And we really don't love testing. And they started there.

[00:32:32] [SPEAKER_04] And from there, they were able to get the mindset shift from AI is going to take our jobs. Or it's going to take the more enjoyable parts of the job. To, wow, AI can really support me in doing more of what I love. Yeah. Yeah.

[00:32:45] [SPEAKER_05] That's the key. Do more of what you love. Less of what you hate. And use that as your guidepost, I guess. That's awesome. Well, I'm very excited about the book. Can't wait to check it out and read it. I hope other people will check it out because it sounds like something that's really going to help organizations start to deploy this capability. Right? It's not just so much about AI.

[00:33:07] [SPEAKER_05] It's more about the capability and the people within your organization and giving them the ability to do what needs to get done to drive these organizations forward. So I'm super excited about it.

[00:33:20] [SPEAKER_04] Absolutely. Well, thank you so much for giving me some airtime to share with your audience. And it's such a pleasure to be here.

[00:33:26] [SPEAKER_05] Yeah. Thank you so much, Melissa. I also want to remind people the HR Systems Survey is out there and talks about all of the HR tech that's available. And actually, there's a whole new section this year about AI specifically and what HR vendors are doing in that space. So go check that out. Also, be sure to listen to Stacey and Cliff next week while they spill the tea on HR tech. Melissa, where can people find you online and on social?

[00:33:53] [SPEAKER_04] Feel free to connect with me on LinkedIn just under my name, Melissa M. Reeve. Hyperadaptive.solutions is my website and looking forward to connecting. Awesome.

[00:34:01] [SPEAKER_05] I also want to thank our producers, Brand Method Media Group. I want to thank our marketing team who get all this stuff out there to the audience. And I want to thank you for tuning in. That's all the time we have for this week's episode of HR We Have a Problem. If you enjoyed this episode, you can subscribe to it on your favorite podcast app. Leave us a review or let us know what other topics you want us to talk about. We'd love to make sure we're talking about the things that you want to hear about.

[00:34:28] [SPEAKER_05] We will be back in two weeks with another episode of HR We Have a Problem. Thanks, everybody.

[00:34:35] Thanks. Thank you. Thank you.