It's like a piano because AI is easy to use, but it's not necessarily easy to learn.
SPEAKER_02How should a leader be looking at this when they're really their only reference is, oh yeah, we we rolled out, you know, Salesforce a couple years ago?
SPEAKER_00I think the crux of it is around the learning. We have AI that changes every week in meaningful ways. And so what that means is that the learning goes from being static to being dynamic.
SPEAKER_02I totally get it with the larger organizations. If they pivot too fast, there's gonna be a lot of chaos. Does this paradigm give smaller organizations an advantage?
SPEAKER_00It sure does. It's why we saw Uber disrupt taxis, it's why we saw Airbnb disrupt Marriott. They move fast, they can scale quickly, they can disrupt it. We're gonna see giants fall, and don't discount the big guys because they have a trillion dollars sitting around that they can throw at these types of problems.
SPEAKER_02What does a hyperadaptive organization look like in practice?
SPEAKER_00It's one that can sense and respond and learn in near real time.
SPEAKER_01Melissa Reeve is a leading voice in AI native organizational transformation and author of Hyperadaptive. From the Toyota Factory floor to agile marketing, she teaches leaders how to rewire their companies for the age of AI and make change actually stick.
SPEAKER_02Welcome to Using AI at work. I'm your host, Chris Dag. Each week we'll be learning how today's business owners, entrepreneurs, and ambitious professionals are getting more done with smart use of tomorrow's tech. Let's get started. Right now, every business leader is asking the same question. What are we going to do about AI? If this is you, ChiefAIOfficer.com has the answer. We give you a simple path forward where we provide executive and team training so your people know exactly how to safely use generative AI in their day-to-day. We also manage the deployment and implementation to make sure tools actually get adopted and deliver results. And we'll also guide company-wide transformation so AI becomes part of your operating system, not just another shiny object. The companies that act now will increase productivity, cut costs, and grow faster than their competitors. Those that wait will get left behind. So if you want to make AI work in your business, visit ChiefAIOfficer.com and see how we're helping companies of all sizes finally get results from AI. Hey everybody, welcome to another episode of Using AI at Work. My name is Chris Daigle, and I'm the host of the podcast. And today our guest is Melissa Reeve. Melissa is the author of a book that's soon to be released. By the time this podcast comes out, I guess it might uh might already be available on AI native organizations and this concept of the hyperadaptive uh operating model. And I will be one of the first ones. I think I may have already purchased my uh my copy in advance. The book is called Hyper Adaptive: Rewiring to Become an AI Native Organization. And as you uh listen to the interview today, I think you're gonna realize that Melissa is the perfect person to talk to us about this. In that pre-interview, you mentioned that this whole journey for you started from uh on a Tokyo shop floor studying the Toyota production system. Uh take a second and just walk me through how that experience became a book about AI transformation.
SPEAKER_00Yeah, thanks for that. So I do like to say that the book started on a factory floor. I was uh studying the Hino Motor Company, which is uh a subsidiary of Toyota. And the Toyota production system, for your listeners, anybody who doesn't know, is just all about lean manufacturing. And it was there that I really saw how somebody on the front line uh could pull what's called the dandon cord, stop the production line, and make an improvement that rippled throughout the system. And so with that as my foundation, I didn't actually have some of these words in my vocabulary then, but I was a systems thinker. So I always thought holistically about cause and effect and second and third order effects, even through my career as an executive, primarily in the marketing function. But I was always sitting at the intersection of technology and marketing and often embedded in tech forward organizations. So in 2011, I discovered this thing called Agile and started applying it to Agile marketing. And uh that paved the way for uh integration into an organization called Scaled Agile. They were part of the whole digital transformation wave. And it's about adopting new ways of working and integrated new ways of working. It was DevOps, it was a little bit of John Cotter, it was a little bit of Clayton Christensen, Peter Senge and learning organizations. And when AI hit, I um I could see that this was going to be the next wave of transformation. And so I thought to myself, what lessons have we learned from the other transformations from things like DevOps, the automation of the software delivery pipeline, factory automation, that we could integrate and really ground ourselves in as we're looking forward into the future. And that's where hyperadaptive was born.
SPEAKER_02I love it. So, you know, one of the things that we talked about pre-um interview when we were getting to know each other was this uh recent update from Block and Jack Dorsey about their approach to uh reorganizing uh what the hierarchy of an organ of a business looks like moving forward. And it just seemed like that with the release of your book and the depth of experience you have in exploring uh optimization of an enterprise, that the timing on this was perfect. Yeah, it's great. Business leaders uh are asking the wrong question if they're only asking how do we add AI tools. In your perspective, they need to be asking a deeper question, something along the lines of how does this organization itself need to change? And one of the concepts that you talk about is this concept of a linear organization, and that needs to change into this hyper adaptive organization that you talk about in your book. Can you explain to me what is a linear organization in your definition?
SPEAKER_00Yeah, so I describe linear organizations as most organizations today. So you have strategy to execution, you have concept to delivery. When you think about that, there's a lot of handoffs and delays as things are making their way through the hierarchy. There's a lot of handoff and delays as things are baking uh from concept to cash as we move our way through the functions. And that that way of organizing the functional silos really emerged after World War II as we started to globalize our companies. And it made sense when things moved slower and people could really only hold one specialization in their mind. But when you think about AI, AI is gonna compress both of those dimensions. We don't need as many layers of the hierarchy because a lot of that, to be honest, was trying to make better decisions. And if we get more brains on it, we're gonna have make better decisions. And as we on the the other side of the concept to cash, as we spread into adjacent competencies, meaning you're no longer just a marketer, you can do other things as well, enabled by AI, we don't that functional specialization and siloization isn't going to make as much sense. And if you look at AI native organizations today, they're not necessarily organizing around functions, they're organizing around value streams. And so the question is if you're a linear organization, how do you get from here to there? How do you like gradually rewire your people, your processes, your roles, even your operating model to become more AI native?
SPEAKER_02So you just use the word gradually. Is gradually an option?
SPEAKER_00Well, you I mean, how fast can you turn an air tank, you know, an aircraft carrier around? And so when I say say gradually, it's because we know that these enterprises aren't in speedboats. And so whether they want to or not, it's it's going to happen as as fast as they can, but as not as fast as they like. And and what we see is, and this is what we know from digital transformation, is that if you try to go too fast too too soon, the the you have tissue rejection in the organization. They just can't move that fast. And so why not harness that knowledge that we learned and figure out how do we how do we actually move the needle in a meaningful way in so that we can move together?
SPEAKER_02So I I totally get it with the larger organizations. If they you know pivot too fast, there's gonna be a lot of chaos. Does this paradigm give smaller organizations an advantage in the world? It sure does. Okay.
SPEAKER_00Yeah, it's it's why we we saw uh Uber disrupt taxis, it's why we saw Airbnb disrupt Marriott. You know, they they move fast, they can scale quickly, they they can disrupt it. We're gonna see giants fall, and don't discount the big guys because they have a trillion dollars sitting around that they can throw at at these types of problems.
SPEAKER_02And by the time they do get the the aircraft carrier turned around, they're making big waves. So you introduced you introduced this concept a minute ago, and I like it a lot because I I see it and experience it and see it in client companies. Once somebody who has a a label or a domain expertise in the business, oh, they're the marketing person, but they get you know fluent in using AI, not just the the large language models in the chat environments, but they start to do, I don't know, maybe build some custom apps with some vibe coding tools and things like that. This this marketer or this uh payroll analyst or this HR specialist or whatever the role is, they now are able to uh influence uh domains or get access to expertise from domains that were previously unavailable to them unless they scheduled the meeting or they went to the conference and those sorts of things. Does does that play into this concept of the hyperadaptive organization? And and what does a hyperadaptive organization look like in practice?
SPEAKER_00Yeah, so let me let me start with the second part first, which is what is a hyperadaptive organization look like? And it's one that can sense and respond and learn in near near time, near real time. So you think about the AI systems that are in place that are doing the monitoring and figuring out what's going on in the market, what's figuring out going on in the inside the organization, what's going on with individuals in those organizations. And because there's so much information that's being munged on an everyday basis, we now can respond that much more quickly. But when you think about that much information coming in from all of these sources, being interpreted, analyzed, responded to, like that's a totally different way of operating than what we have now. So, what does that start to look like? I think this is the tip of the spear of innovation. So Claude recently held a hackathon, and the the top winners of the hackathon were a cardiologist, a musician, a civil engineer, uh, and I think it was one software developer. And that represents in my mind the untapped innovation that every organization has currently that if they are deliberate about how they play their cards over the next year, two years, five years, they're gonna be able to unlock that innovation. And uh not, you know, but but in order to do that, they have to take people out of their boxes.
SPEAKER_02Okay, so some organizations obviously, again, referencing Block and Jack Dorsey, you know, he said one of the reasons we were able to do exactly what you just said, which is have this brain of where all of the information about the business was being collected. He said we could do that because we were a remote company. Everything that we did was creating an artifact, whether it was the email or the Slack or the call transcript and things like that. Now, you know, we work with a lot of construction companies and manufacturing companies and things like that that are physically present. There's not a whole lot of this r remote artifact creation opportunity. Do those companies are they at a disadvantage? Um, are they left out of this this particular model altogether?
SPEAKER_00No, not at all. Uh so I'll just give you a few examples. Um Mercedes Benz comes to mind, and they have something called the D-Shift program. And it takes frontline factory workers and it says, who on the front line uh has aptitude around data that might be suitable for learning about AI? And they put them through this D Shift program. And the goal here is to bring some of that AI knowledge goodness back to the factory line. In fact, Toyota did something also similar where they empowered their frontline factory users to use their internal LLMs. And in this way, we're getting that this cross-pollination, just like the cardiologist who's using clawed code, just like the musician. And so really forward-looking companies are recognizing that we need to unlock the innovation and power even in these physical environments. Um, my other favorite example is McDonald's. And they um, you know, they've got AI all over their supply chain, they've got uh sensors in the McFlurry machines, but they're also uh they're also using it for things like scheduling. And if you've ever been in a McDonald's, I used to work at a Burger King uh during during the peak hours, it's it's madness, it's crazy. And it's stressful. And so their goal in doing in introducing the AI is to relieve the the stress so that they can deliver better customer experiences. And so whether you are in these knowledge-based uh organizations or these physical environments, I I do think that AI has a place.
SPEAKER_02Aaron Powell Are there any industries that might be too linear for the AI era? Do you do you see some models or some industries that that may not make it through the end of the decade?
SPEAKER_00Well, yeah, I mean I do think that giants will fall, um, whether it's because they're they're too linear or quite frankly, it's just that their their cultures uh won't be able to evolve. And I'll I'll try and paint that picture for you. A lot of this has to do with incentives. And I know that's an odd place to point. But when you think about how organizations have operated for years, it's that you climb the career ladder and when when you get to the top, you have a lot of budget and you have a lot of people. And AI blows that apart. And so now if you have a big ego and you wanna be managing a bunch of people and managing a bunch of budget and you love your quarterly bonuses, I think the the newcomers are gonna blow that model out of the water. And if you can't pivot away from that, then I think I think you get stuck.
SPEAKER_02So it's interesting. So it's not so much the model that that would be the impediment, it would be the culture behind the model that was okay. Interesting. Um which uh you know kind of hits this maxim that AI is not a it's it's usually not the issues with the technology, it's the issues with the people that there's AI problems, right? Um so uh a lot of leaders that we talk to are still treating AI as this I mean, I guess because they they don't really have anything to compare it against, but like a software rollout, right? Is that the the position that you would take if you were in charge of a company or or how would you advise them to look at this? Because it is different. It's it's a we just talked about the culture element of it. Um the the learning curve on the software is pretty quick. Like how should a leader be looking at this when they're really their only reference is, oh yeah, we we rolled out you know Salesforce a couple years ago.
SPEAKER_00I think the crux of it is around the learning. And there's two aspects here I'd like to focus on. One is when you think about the rate of learning for a new piece of software, you could train everybody on Microsoft Windows 2007 and be relatively s safe until Microsoft uh Windows 2010. So that's that's a nice three-year window. We have AI that changes every week in meaningful ways. And so what that means is that the learning goes from being um static to being dynamic. And so what you need to do is create the infrastructure through which ongoing learning or always on learning can flow. And because AI has so many use cases, there's no way to build a curriculum around it. So, what we need to do is we need to create learning arenas where we can share the learning with each other. And so then this is part of the structures we spin up in the hyperadaptive model, where we create these structures through which the training can flow. So, for example, we have AI activation hubs, and this is fractal. So if you're a small organization, have one activation hub. If you are a large enterprise, you might have 50, 100, 150, who knows how many activation hubs for business units, for functions, for areas. You don't want everybody in the organization to have to keep on top of Claude's release of 4.7. But your AI, your AI activation hub can say, here's here's Cloud 4.7. Here's what it means to you, legal people in this business unit. We're gonna atomize that learning. We're gonna create a couple of 15-minute videos for you. We're gonna hand it off to our AI leads. These are your AI power users, who can pair with the individuals on the front line and get that knowledge into the front line. And I like to call that the AI learning flywheel. And what's so magical about the AI learning flywheel is it's bi-directional. So not only do you get that learning flowing through down into the front line, but if somebody has a breakthrough on the front line, that's really cool automation that they built, they send it up to their AI lead, it gets codified in the AI activation hub, it gets shared through the network of activation hubs, and all of a sudden you have an organization that's updating itself.
SPEAKER_02So is this hub, is it it sounds like it's a group of people, but it's also a platform as well?
SPEAKER_00It can be. It's it's it's a group of people who are tasked with things like how is AI moving the needle? How can we atomize the training? What are the best practices that should be shared? And uh so maybe they have some software that's there too. I I talk about an AI knowledge engine to house the best practices. Um, but it's infrastructure that I think most leaders haven't figured out. They need to be funding.
SPEAKER_02So is this hub participation is that a part-time role for uh an employee or a staff member? Is it somebody that that that is their job? They're they're part of the AI education environment.
SPEAKER_00I think that's for each organization to figure out like if they want to to beg, borrow, and steal, or if they can fund it. Here's here's what I say. When we rolled out PCs in the 1990s, we didn't really treat our IT help desks as part-time positions. Like we we recognized we needed an IT help desk. We recognized we need an I entire ID departments to support this new powerful technology that we're putting on everybody's desks. Somehow, with AI, which is even more powerful, we think that it'll just magically happen. And I feel like that's one of the disconnects.
SPEAKER_02I totally agree that we call it, you know, bring your own AI. We talk to companies and they say, Oh, yeah, we're using AI. Well, what are you using? I I think they're I think they're using this. And oh, she's got an account with so and so like they haven't thought past the fact that we're using it. Yeah, but are you using it safely? Are you using it in an orchestrated manner? Are you using it to its full leverage? So, like, I I guess what um What's the disconnect? Yeah. Yeah, what's the disconnect?
SPEAKER_00Yeah, so I like to say it's like a piano because AI is easy to use, but it's not necessarily easy to learn. So anybody can walk up to a piano, start dinking on the keys, but it needs a lot of reps, it needs some practices, it needs lessons to really be able to play a song. And that's the deception of AI. And so it feels like it should just install itself. It feels like you should just be able to show a video and it'll happen. But it's it's more nuanced than that, and it takes time, and so we need to support our humans in this transition. And we're reinventing processes along the way. And so, what happens then? What happens to the communication? What happens to the decisions? And all of this takes some deliberate action, and I feel Like that's that's part of the disconnect.
SPEAKER_02You know, so one of the things that we do when we're working with the company is we our version of the AI hub is we call it an AI council, and it's representatives from the um you know departments. Ideally, it's a decision maker in that department, plus an one or two AI enthusiasts. They come together, and the idea behind the council really is to push pilots through. Yeah, but we always open the meetings with sharing wins. And the reason that I like doing that is because we've mentioned culture. I'm I I've made some mistakes with change management earlier in my career. I know how important this stuff is to not just come in and go, you know, do it, right? And the reason that I I I like to start with those wins in the conversations is because I can look over there and go, oh, they're they're not smarter than me. They're not, you know, higher up the chain than me. They're a peer. I trust, I know, like, and trust them. Therefore, if they're doing something cool with it, I'm not going to be intimidated by it. I'm not I'm gonna be much more open to, hey, when the meeting's over, do you mind showing me what you were doing, what you were talking about? Like in that organic um facilitation that has nothing to do with me as a chief AI officer or the CEO of the company, that is where we've seen, which is following this model, a lot of the enthusiasm behind the adoption, the lack of skepticism, and the lack of like, is this taking my job? Oh no, I just get to do the fun parts of my job now because I learned this trick from you know bill over in marketing. So the the IT help desk makes a lot of sense. There's uh, you know, all these settings and configurations and blah, blah, blah, right? I need some help with that. With AI, I can ask it a dumb question, I can ask it a smart question, it's gonna give me an answer. So, what is the AI equivalent of this help desk? Is it the is it the hub? Is it somebody? Mm-hmm.
SPEAKER_00Yeah. And so these are what we're starting to articulate and what you've naturally gravitated toward are what I call support structures. And if you've spent time in the transformation um space, you might have heard of John Cotter. And so John Cotter and leading change talks about exactly what you said. How do you take the people who are at the front lines of the organization and start to activate those to get your early wins, to build the credibility? And how do you create the peer-to-peer support networks that cause things to spread? So, what I've done is I've codified those and started to articulate those and articulate the relationships between those different support structures so that it can spread. And organizations, and we know that these patterns hold with the AI with AI because leading organizations that I researched for the book are already using them. And I'll give your listeners just a sense. Like Pricewaterhouse Cooper's has something that they call prompting parties. And when you think about everything we've been discussing, that peer-to-peer learning, how do you create a what I call the learning arena for that social learning to happen? That's a prompting party. And we get people with like work together. It sounds fun. It sounds like there'll be pizza, it sounds like it'll happen on a Friday afternoon. And all of a sudden, we start to get what I call social contagion and learning contagion. It's it's this momentum that starts to build in the organization as people have their aha moments around AI. Yes. And then we've got the support structures to spread it and scale it and sustain it. And that's the the equivalent of the help desk is the investment in these support structures. Great, you have AI leads. How are you supporting them programmatically? Do they have a dedicated role? Great. You have your AI Center of Excellence, what I call an AI activation hub. Great. Are those dedicated people, is it being funded? And that's if there's one unlock for your listeners, I think that's it.
SPEAKER_02You know, you coming from marketing, you might be familiar with Robert Cialdini and his concepts with influence. And one of those concepts is social proof. And essentially that's what this social contagion is, is that like, oh, they're doing it and they like it, they're not scared of it, they're using it, oh, they're doing cool stuff with it. I must as well. So I love that a lot. I mentioned it earlier, but you know, we talked to executives, they're all excited about AI, but there's very little oversight, very little governance in a lot of these companies, and very little like, it's not that they're naive, but they're just caught they're caught up maybe in the shiny side of the contagion. What breaks when when companies are uh giving everybody a license to Chat GBT or Jim and I or Copilot or whatever? But there isn't one of these um support systems or operating structures that you're talking about.
SPEAKER_00Yeah, I think the shift around governance in my mind, um, you know, we talked about stay static versus dynamic learning. And I think we also need to be thinking about static versus dynamic governance and layered governance. So I I feel like the way governance has looked in many organizations is that it's it's a committee of people who are deciding the guardrails, they're meeting on a quarterly basis, whatever that looks like, and then they're codifying out on the internet, where uh two things happen. One, it it promptly gets forgotten. And two, LND picks it up and turns it into some training and makes sure that everybody checks the box. And if they're like my husband, he's listening to it on 3x speed and takes the test four times until he gets it right. Like, is that is that really what we want for our organizations? And so what I what I advocate for instead is what I'm calling dynamic governance. So you still need that cross-functional group of people at the top who decide the guardrails for the organization. But they're now meeting probably every two weeks, every month, a much more frequent basis. And they're housing their policies into a custom GPT so that anybody at any time can query it and say, hey, I'm thinking about doing XYZ.
SPEAKER_02I like it. Yeah. Yeah. Yeah.
SPEAKER_00So it's that. And then there's a couple of other layers, right? You might want to interpret that at the functional layer, your AI leads, your AI activation hubs, they're also your frontline guardians. And so, in this way, again, you're creating these systems that can keep up with and monitor the governance as in a much more fluid way.
SPEAKER_02That makes a lot of sense. So let's talk about this because we we typically will address the governance and and the creation of a use policy in the same kind of conversation, right? Initially, because a lot of companies uh and they're not necessarily the same thing. Um, I like this idea a lot of okay, we've got a governance uh element that meets within the organization, and as they make changes, they update a mechanism like a custom GPT that anybody in the organization can do and get really real-time. Hey, is this how do I do this, or is this allowed, or what tools? Love that idea a lot. Can you do the same thing with a use policy, or do you kind of see them like intertwined here as far as and I'll tell you like some examples? Sure. Um, some like red light, yellow light, green light. Hey, you can always use the tools for these activities. You can use the green light, right? Yellow light is you can use the tools, but like clear it with the the hub or or your leader. And then red light is like you can't do it. Maybe somebody else in the organization is can, but you can't. So how how would you recommend that that I move forward with the idea of the dynamic governance, which again, I love that. Is there a dynamic um capability with a use policy?
SPEAKER_00Yeah, I I think it looks the same. I I think it it looks similar. And you know, I I think every organization should build this for themselves and they'll they'll figure out what what and how it looks like. Yeah. I I could almost see it as you're invoking skills, you know, in the language of Claude and Anthropic, where, you know, now it's the governance skill. Run this, run what I'm trying to do by governance, run it by my use cases, and run it by uh success patterns. Has anybody ever done it before? What did what did they learn?
SPEAKER_02Okay, so this is a uh I'm gonna leverage this. Actually, we're gonna update our our procedure here. Big takeaway. Okay, changing topics just a little bit. Um, what should a company do with these AI gains that they're getting? So we we've got the hub, we've got adoption, we've got the social contagion, people are sharing the stuff. We're starting to see, you know, um measurable but anecdotal wins and the different departments and that sort of thing. But all of a sudden, first off, how do we how do you recommend that a company because here's here's the issue. We're working with somebody and they're people, they're getting wins, but it's 15 minutes here, it's it's an hour once a week here, and it seems like small things, but I've heard uh Leah Motley call it layers, not leaps, right? Those layers, they compound. How can a company capture those, like beyond just the oh, I know they're doing it over here, but I don't know how much time they're saving. You have any suggestions on how a company could capture it? And then once they have captured it, how do they plan on uh redeploying that human bandwidth or or you know, leveraging the gains?
SPEAKER_00Sure. So I I think there's a few things I'd love to tease apart here. One is, you know, that the measuring is part of this AI activation hub again. So they're the ones who are saying, hey, we just got a huge win over here, save these guys, you know, 40 hours a week. Where else can we redeploy this throughout the organization? But you need to have somebody whose job it is to monitor that stuff and spread it. So that's one thing. I think the second thing is leaders or listeners need to be really be thinking about the J curve. So we talked about how AI is easy to use, not easy to learn. And so, yeah, you're getting 15 minutes here and there. Where are you on that J curve? And uh, you know, where you're slow down to speed up. And then the the other thing is really thinking about what you're getting out of it beyond productivity. What I mean by that is in my own experience, I have or I've noticed that uh I've been taking shortcuts all over. I don't do the analysis analysis that I really should have been doing. I'm not doing the research I really should have been doing. The quality of my slides isn't that great. And so we as organizations, especially in organizations that have been just whittled down to the bone over years and years and years and layoffs, have really started to cut corners. And so I think if you're a leader, really start looking at your teams and saying, hey, is are we are we gaining productivity or is really quality improving? Are our outcomes improving? Uh, you know, are we delivering better customer experiences like McDonald's is is trying to do? And um and and think about what your AI North Star is. Like, what is it that you're trying to achieve with AI, and then measure against that, not necessarily your 15 minutes gathered here or there.
SPEAKER_02So you know, from again, experience talking with leaders, when you say clarifying what it is that you want out of AI, I I most of them I would say, you know, certainly productivity, maybe increase EBITDA by slashing you know, SGNA costs, because the knowledge work is where there's a lot of, you know, obviously the quickest gains from the uh generative AI. But beyond just the the productivity, um what are some common goals that you think they should have?
SPEAKER_00Yeah, so I just gave a talk um where we talked about what do you do with the the excess. You know, there is excess that's being produced. And do you just harvest the gains? Like we talked about Jack Dorsey, he just harvests the gain. He was like, okay, well, bye-bye, 4,000 people. Yeah. That's a that's gonna take a pretty big hit to your public-facing audience, right? Uh, do I want to go work with with him right now? Maybe not. Um, I think the other thing that organizations need to think about is um job displacement and how we're going to shepherd people from one area to the other. Because you could also be reinvesting in people and saying, we're gonna redeploy people into RD and or we're gonna reimagine our organization. So in the fifth stage of the book, I um I profile an organization called Ping On Insurance. And this is an insurance company out of China who started their AI journey in 2008. And the first thing that they said, that's a while ago, they said they were gonna get their data house in order. And they just started as an insurance company. But as they grew, they realized the connections between insurance and healthcare and finance. And so they were able to start to reimagine a new customer experience where they learn you get laid off from your job and you might need a different financial support. You might need mental health support. Your health might be impacted because of this layoff. And they've created this customer ecosystem where they they leverage the data to create new types of experiences for people. There's an organization that has grounded themselves in their customer and grounded themselves in what they do very, very well. And I feel like those leaders who are at that point need to be reimagining what the purpose of their company is and how they can recreate experiences for customers.
SPEAKER_02So how does a company know do we reduce headcount or do we because look, uh the people that I deal with, they're busy. The idea of you know reimagining our organization is uh I'll think about it when I'm walking the dog, but I may not like like that's a big that's a big effort, or at least it sounds like it's a big effort, right? Yeah. So how do these leaders know what are some signs maybe on look, it's time to harvesting the gains in this case might mean laying off the 4,000 people, versus hey, I I see some opportunities here as a health or as an insurance company to also leverage some of these insights into you know financial intelligence and health intelligence for our customer base. What are some signs I should look for as a business owner?
SPEAKER_00Yeah, and that's where the hyperadaptive model really shines, right? Because it it says this isn't gonna happen overnight. And although we know we need to change our organizational model, we got to keep the plane flying while we're rewiring. And so, you know, we've we've primarily up until this point in our conversation really focused on stages one and two, which is getting your foundation in place, your governance in place, identifying your AI leads, spinning up this AI uh, this AI activation hub. But what starts to change in stage three, which is where you start to do some of that reimagining, is you start to think in in terms of more value streams and the AI impact hub. And your AI impact hub is saying, okay, as more and more automations take hold, and we know that the jobs shift from doing the thing to building, monitoring, and maintaining the automations that do the things, job roles really start to change. And so let's spin up dedicated people to take a look at what's happening. What's happening to the people? What's happening to the jobs? Are we going to redeploy people? How do people have the attitudes and aptitudes to move into this new new space? How do we start to experiment on a small scale, which is what we do in stage three, and run small experiments around this reimagined business before we start to scale it in stages four and five? And that's how we start to dip our toes into this new way of working without blowing up the business.
SPEAKER_02How do I come up with those experiments or those? I mean, I guess I could work with the models and say, you know, give me some ideas on how we could reimagine our business, of course. But are there some more like but I think that way. I think in AI, the models are my default, you know, for any type of question that I have. But if I want to because sure, the executive leadership team, I'm sure they've got some great ideas, but they're not they're not doing the thing that you talked about, right? The people further down the hierarchy are doing the thing, and their insights could be like I always wondered why we didn't do you know XYZ, like that sort of thing, right? Yeah, how do how do we tap people or give them permission or train them to start thinking about what a reimagined version of our company looks like?
SPEAKER_00Yeah, so um there's no one answer, but again, if you've if you've laid down some of these new pipes, right? Your AI leads, your AI activation hubs, your AI impact hubs, what you're also doing is laying down new infrastructure for ideas and learnings to flow, right? So we talked about how you atomize the learning from the AI activation hub to the lead to the practitioner, you're also creating those those funnels for the practitioner backup. Simultaneously, got it. You're you should be it uh implementing and using things like design thinking tools. So you're starting from the customer back, which is exactly what Pingon did, is of what do our customers need? And then how does that intersect with what we're really good at?
SPEAKER_02So um I want to go to a topic that's been uh uh I guess top of mind for me, and it's gonna require a little bit of a context for the listener. Jack Dorsey, who we mentioned earlier with Block, um he uh at the end of February announced the layoff of 40% of their uh entire team of 10,000 people. They fired 4,000 people. And me, I heard it, I was like, oh, you know, AI optimization got them. They just automations or whatever, right? But a few weeks later, Dorsey came out with this paper called From Hierarchy to Intelligence. And it basically the thesis was they evaluated what the roles were doing, and that in an organization their size, about 60% of those roles were ingesting, receiving information from different departments, they were analyzing that information, they were figuring out, you know, uh signal versus noise, and then they were passing that information up, down, or laterally in the chain. And Dorsey realized, hey, wait a minute, AI is pretty good at that. We don't need humans to do that anymore, right? So perfect timing with with you know the stuff that we're talking about here with the release of your book and all those sorts of things. And I know that you're familiar with this. What was your reaction to his hierarchy to intelligence idea?
SPEAKER_00Yeah, so a couple of layers here. One is um he just heart he harvested the gains. So we talked about that's one option. Um the second is the World Economic Forum has stated that I think it's like 72 million jobs are going away and 98 million jobs are being created. And when you think about that level of displacement, that's a lot of redeployment. And so the question becomes who's gonna pick up that redeployment cost? Is it going to be, I'm just gonna lay off 4,000 people? They're gonna have to figure it out how to reskill themselves, how to upskill themselves. And then Jack Dorsey, when he figures out, like, oh, I actually did want to grow my company in a different direction, has to try and hire all those people back. But now he's got a little bit of a ding on his name because he just laid off 40% of his company and said, guess what? I don't, I don't want you, I don't need you. Or is it a company that looks more like the Unilevers of the world and the MetLife's of the world? And both of those organizations are saying, hey, let's do two things. Let's break down our jobs by skill, and let's break down our humans by skill, and let's figure out what our needs are, and let's use AI to match the skills to the needs, regardless of where they sit in the the uh infrastructure, knowing that people can probably do more than just shuffle information around, and knowing that um that things that things are going to change. And one of our biggest expenses is hiring and retraining people. So let's figure this out in a little bit a smarter way, in my opinion.
SPEAKER_02Now, one of the things that that came um out of this, and I think I might have mentioned it in the in the pre-interview that we did, but you know, I this quote from Henry Ford about if I asked my customers what they wanted, they would have said faster horses, because they they couldn't really conceptualize what this this hyper um adaptive organization would look like, right? And and in this case, it seems like most of what's being taught, if if okay, first let me ask you, is block a warning sign, a model for what we should be striving towards, or both, or neither?
SPEAKER_00Well, I think it let me articulate how I think organizations look in stage five. I think they look like they have innovation circles. So this is your internal venture capitalist trying out ideas, experimenting, has its own funding mechanisms. The middle layer starts to look like uh value streams and cross functional groups. Of people being funded long-lived value streams. And then you might have a stable layer of people who keep the lights on. You know, they're funding the infrastructure. That has its own funding stream. So is Jack Dorsey wrong in his vision? No, he's not. Absolutely, the hierarchy compresses. Absolutely, the organizational model starts to evolve. I think where he, you know, he wielded a hammer is he was like, well, let me just lop off 40% of the organization and uh and then just try and accelerate into stage five. And I, okay, that's fine. He's an 8,000-person organization or whatever it was. I don't know if you are one of the giants, if if that works that well.
SPEAKER_02So, okay, so I mentioned this concept of the faster horses. If if this hierarchy to intelligence model is viable, and we will start seeing maybe not every company, and maybe not all in, but that there will be more adoption of agents that are feeding on context that the business is generating and blah, blah, blah. The really like the basics, the entry level of what's possible with these tools. Right now, that again, the hierarchy will compress, as you mentioned, but right now we've got consultants and efforts internally and chief AI officers and all this stuff. And they're not doing that necessarily. They're going into the organization and they're they're doing the things, those layers, not leaps that we talked about. They're teaching them how to use LLMs, they're teaching them how to use, you know, Google uh Workspace Studio to build uh uh light automation or whatever those things are. So it seems like the companies now are we're making the faster horses as compared to preparing for a stage five environment. How do we do both simultaneously? Or can we? Yeah.
SPEAKER_00Well, I think you can. Um, you know, this is also the horizons of investing, right? So horizon three investing is is the future, and let's invest in that. Horizon two is kind of our validated near-term next cash cow, and our our horizon one is our cash cows. So so there are frameworks and there are models for us to reinvent ourselves uh as as we're going forward. Um, I actually think the mistake that that too many organizations are making is they're trying to go too fast too soon. And I know that feels counterintuitive, but the thing is, is if if you want orchestrated agents and you haven't put down the layers, then what I have seen and heard happen is they fail. You know, we've got this 80% failure rate, and they burn through political capital, they burn through monetary monetary capital. People start to get cynical about the technology, and it it makes it that much harder to move forward. Where I, you know, if you have if you haven't gathered already, I'm I'm an advocate for a very deliberate approach. Uh the bigger you are, I think the more deliberate you have to be. As um as painful as that feels to hear, because everybody's got FOMO and everybody feels like we have to do this yesterday. But I I do think there's something to being very deliberate, and these patterns are tried and true. I'm not just like pulling this out of thin air. It's like this is the proven ways on how organizations change.
SPEAKER_02As the saying goes, history may not repeat itself, but it sure rhymes.
SPEAKER_00Yeah, exactly.
SPEAKER_02So you mentioned quick wins. Are are quick wins now I I I I hope that my listeners are are looking at this not just through theoretical or this is interesting, but really like how can I maintain like economic viability with my business? How can I be competitive as those in in my industry who I, you know, uh who have some of the addressable market, they're doing things with AI. These guys are doing things, they're doing things with AI. Are quick wins necessary?
SPEAKER_00I I love them as foundations. You know, when when they don't have focus, when uh everybody's left, doesn't have a North Star and they're just left to their own devices, they can turn into random acts of AI. And that's where I feel like people get a little stuck because there's just like little pockets of quick wins and they're not coordinated. You know, they don't have a an AI North Star like let's do 15 drugs in five years. And so everybody's just kind of doing stuff where they can. And we just need to give people a little bit more focus and and create these structures that help them to do that. And I think I think all of a sudden the flywheel starts to turn, we start to go to momentum, we're measuring it, and and we see our organizations like make meaningful leaps forward.
SPEAKER_02So I'm looking forward to the book because like this is this conversation is where I spend a lot of my time thinking. Using the tools, easy stuff. Like thinking about how to use the tools, I've already kind of like rewired my brain. But what does it mean for the business of tomorrow? Is a subject that um like as a value for my clients, I want to make sure that we're we're bringing them valid perspective about what's around the corner. And I think that the conversation for sure today opened that up, but the book will as well. So I'm looking forward to that coming out. Melissa, what would be the kind of like the number one takeaway or something that you would want a leader to think about as they're uh anxiously awaiting the arrival of their pre-order of your book?
SPEAKER_00Well, I I think start thinking about what your version of the help desk is. You know, I've I've put forth some some structures, we've talked about them today, and that that you can invest in this side of the business, that AI is more than just shopping for licenses. It's more than building a video training library. It's it's about building some permanent infrastructure and start planning for how that looks, the people you tap, and how you would fund that.
SPEAKER_02Love it. Um, so everybody, again, uh, you know, we we get authors on here and that sort of thing, and we're always interested in the topic. But this is one we're paying a lot of attention to what this uh organizational structure of tomorrow looks like here at Chief AI Officer. So this is a a topic of all the topics that are out there, I think as a leader for sure, this isn't necessarily the tactical stuff, but it is something that you need to be thinking about uh as the technology accelerates, as your people are becoming more literate, as demands for this type of contribution to your organization are gonna only increase. So um a big endorsement for literally pre-ordering the book. So Melissa, thank you so much for taking the time and um uh I wish you all the success with the launch. And again, literally, this will be one that we'll be studying internally with all of our chief AI officers.
SPEAKER_00Thanks so much for having me on the show and really appreciate the time.
SPEAKER_02Thanks, everybody. And for those of you still on, uh listen, if you got something out of this episode or any episode, the only thing that I would ask is that you share something with those of your peer group or your friends that are on this AI journey and they haven't quite like figured it out. I'm not gonna say we figured it out, but we're working on it. So if you if you think that this could help somebody that you know, please let them know about using AI at work. We'd really appreciate it. And we'll see you next week with another amazing episode. Thanks everybody. Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer, for empowering businesses with AI education and training. Visit their website for a free AI readiness assessment and AI strategy guide to help you get started using AI at work. That's www.chiefaiofficer.com. Follow us on Twitter at the handle UsingAI at work and visit www.usingai at work.com for free resources to help you harness AI in your role.