← Using AI at Work: AI in the Workplace & Generative AI for Business Leaders
Using AI at Work: AI in the Workplace & Generative AI for Business Leaders
102: The AI-Native Company: What Comes After the Org Chart with Melissa Reeve
00:52:12
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- [00:00]
A
It's like a piano. Because AI is easy to use, but it's not necessarily easy to learn.
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B
How should a leader be looking at this when they're really their only reference is oh yeah, we rolled out, you know, Salesforce a couple years ago.
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A
I 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.
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B
I totally get it with the larger organizations. If they pivot too fast, there's going to be a lot of chaos. Does this paradigm give smaller organizations an advantage?
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A
It 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 going to see giants fall. And don't discount the big guys because they have a trillion dollars sitting around that they can throw it at these types of problems.
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B
What does a hyper adaptive organization look like in practice?
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It's one that can sense and respond and learn in near real time.
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B
Melissa Reeve is a leading voice in AI native organizational transformation and author of Hyper Adaptive 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. Welcome to Using AI at Work. I'm your host Chris Stain. 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 and Melissa is the author of a book that's soon to be released by the time this podcast comes out, I guess it might might already be available on AI native organizations and this concept of the hyper adaptive operating model and I will be one of the first ones I think I may have already purchased. My
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A
thanks so much.
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B
The book is called Hyper Adaptive Rewiring to Become an AI Native Organization. As you listen to the interview today, I think you're going to 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 on a Tokyo shop floor studying the Toyota production system. Take a second and just walk me through how that experience became a book about AI transformation.
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A
Yeah, thanks for that. So I do like to say that the book started on a factory floor. I was studying the Hino Motor Company which is 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 could pull what's called an andon 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, and that paved the way for integration into an organization called Scaled Agile. They were part of the whole digital transformation wave. It's about adopting new ways of working and integrated new ways of working. It was DevOps, it was a little bit of John Kotter, it was a little bit of Clayton Christensen, Peter Senge and learning organizations. When AI hit, I could see that this was going to be the next wave of transformation. I thought to myself, wow, 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 Hyper Adaptive was born.
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B
I love it. So you know, one of the things that we talked about pre interview when we were getting to know each other was this recent update from Block and Jack Dorsey about their approach to reorganizing what the hierarchy of a business looks like moving forward. And it just seemed like with the release of your book and the depth of experience you have in exploring optimization of an enterprise that the timing on this was perfect.
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A
Yeah, it's great.
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B
Business leaders 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?
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A
Yeah, so I describe linear organizations as most organizations today. So you have strategy to execution, you have concept to delivery, or 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 making from concept to cache as we move our way through the functions. And 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 going to 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 going to make better decisions. And as we on 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, 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?
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B
So you just used the word gradually. Is gradually an option?
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A
Well, I mean, how fast can you turn an air tank, an aircraft carrier around? And so when I say gradually, it's because we know that these enterprises aren't in speedboats. And so whether they want to or not, it's going to happen as fast as they can, but not as fast as they like. And what we see is, and this is what we know from digital transformation, is that if you try to go too fast too soon, 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 actually move the needle in a meaningful way so that we can move together?
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B
So I totally get it with the larger organizations, if they pivot too fast, there's going to be a lot of chaos. Does this paradigm give smaller organizations an advantage in it sure does.
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A
Okay, yeah. 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 going to 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.
- [09:26]
B
By the time they do get the aircraft carrier turned around, they're making big waves. So you introduced this concept a minute ago and I like it a lot because I see it and experience it and see it in client companies. Once somebody who has a label or a domain expertise in the business, oh, they're the marketing person, but they get fluent in using AI, not just 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 marketer or this payroll analyst or this HR specialist or whatever the role is, they now are able to influence 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 that play into this concept of the hyper adaptive organization? And what does a hyper adaptive organization look like in practice?
- [10:29]
A
Yeah. So let me start with the second part first, which is what does a hyper adaptive organization look like? And it's one that can sense and respond and learn in 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, 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 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 going to be able to unlock that innovation. But in order to do that they have to take people out of their boxes.
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B
Okay, 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, this brain of where all of the information about the business was being collected, said we could do that because we were a remote company, that 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 remote artifact creation opportunity. Do those companies, are they at a disadvantage? Are they left out of this, this particular, you know, model altogether?
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A
No, not at all. So I'll just give you a few examples. 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 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, back to the factory line. In fact, Toyota did something also similar where they empowered their frontline factory users to use their internal LLMs in this way. We're getting this cross pollination just like the cardiologist who's using Claude code, just like the musician. Really forward looking companies are recognizing that we need to unlock, unlock the innovation and power even in these physical environments. My other favorite example is McDonald's and they, you know, they've got AI all over their supply chain, they've got sensors in the McFlurry machines, but they're also, 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 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 organizations or these physical environments, I do think that AI has a place.
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B
Are there any Industries that might be too linear for the AI era. Do you see some models or some industries that may not make it through the end of the decade?
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A
Well, yeah, I mean, I do think that giants will fall, whether it's because they're too linear or quite frankly, it's just that their cultures won't be able to evolve. And 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 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 want to be managing a bunch of people and managing a bunch of budget and you love your quarterly bonuses, I think the, the newcomers are going to blow that model out of the water. And if you can't pivot away from that, then I think, I think you get stuck.
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B
So 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. Which, you know, kind of hits this maxim that AI is not a. It's usually not the issues with the technology, it's the issues with the people. There's AI problems, right? So a lot of leaders that we talk to are still treating AI as this, I mean, I guess because they don't really have anything to compare it against. But like a software rollout, right? Is that the position that you would take if you were in charge of a company 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. 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.
- [16:53]
A
I 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, on Microsoft Windows 2007 and be relatively safe until Microsoft Windows 2010. So that's a nice three year window. We have AI that changes every week in meaningful ways. What that means is that the learning goes from being static to being dynamic. What you need to do is create the Infrastructure through which ongoing learning or always on learning can flow. Because AI has so many use cases, there's no way to build a curriculum around it. What we need to do is we need to create learning arenas where we can share the learning with each other. Then this is part of the structures we spin up in the hyper adaptive mode 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 activation hub can say here's, here's Claude 4.7. Here's what it means to you legal people in this business unit. We're going to atomize that learning. We're going to create a couple of 15 minute videos for you. We're going to 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 bidirectional. 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.
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B
So is this hub. It sounds like it's a group of people, but it's also a platform as well.
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A
It can be. 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 so maybe they have some software that's there too. I talk about an AI knowledge engine to house the best practices, but it's infrastructure that I think most leaders haven't figured out they need to be funding.
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B
So is this hub participation? Is that a part time role for an employee or a staff member? Is it somebody that is their job? They're part of the AI education environment?
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A
I think that's for each organization to figure out, like if they want to beg Borrow and steal or if they can fund it. 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 recognized we needed an IT help desk, we recognized we needed entire IT departments to support this new powerful technology that we're putting on everybody's desk. 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.
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B
I 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 think they're, I think they're using this and oh, she's got an account with someone. 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 guess, what's the disconnect? Yeah, yeah, what's the disconnect?
- [21:13]
A
Yeah, 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.
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B
I like that.
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A
It feels like you should just be able to show a video and it'll happen, but 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 part of the disconnect, you know, so one
- [22:03]
B
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, you know, departments. Ideally it's a decision maker in that department plus 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 Coltrum 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 like to start with those wins and 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 going to be much more open to, hey, when the meeting's over, do you mind showing me what you were doing, what you were talking like? And that organic 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, 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 IT help desk makes a lot of sense. There's, 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 going to give me an answer. So what is the AI equivalent of this help desk? Is it the, is it the hub? Is it somebody? Yeah.
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A
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 space, you might have heard of John Cotter. And so John Kotter 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 AI because leading organizations that I researched for the book are already using them. And I'll give your listeners just a sense like PricewaterhouseCoopers 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 what I call a learning arena for that social learning to happen? That's a prompting Party and we get people with, like, work together. It sounds fun. 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 this momentum that starts to build in the organization as people have their aha moments around AI and then we've got the support structures to spread it and scale it and sustain it. And that's 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.
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B
You know, coming from marketing, you might be familiar with Robert Cialdini and his concepts with influencer. 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. They're caught up maybe in the shiny side of the contagion. What breaks when companies are giving everybody a license to ChatGPT or Gemini or Copilot or whatever. But there isn't one of these support systems or operating structures that you're talking about.
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A
Yeah, I think the shift around governance, in my mind, we talked about static versus dynamic learning. And I think we also need to be thinking about static versus dynamic governance and layered governance. So I feel like the way governance has looked in many organizations is that 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 two things happen. One, it promptly gets forgotten. And two, L&D 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. Is that really what we want for our organizations and so 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.
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B
I like it. Yeah, yeah.
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A
So 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.
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B
That makes a lot of sense. So let's talk about this because we typically will address the governance and the creation of a use policy in the same kind of conversation. Right. Initially because a lot of companies and they're not necessarily the same thing. I like this idea. A lot of, okay, we've got a governance 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 tool? 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.
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A
Sure.
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B
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 your leader. And then red light is like you can't do it. Maybe somebody else in the organization can, but you can't. So how would you recommend that I move forward with the idea of the dynamic governance, which again, I love that. Is there a dynamic capability with a use policy?
- [29:49]
A
Yeah, I think it looks the same, I think it looks similar. And I think every organization should build this for themselves and they'll figure out what and how it looks like. I could almost see it as you're invoking something 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 success patterns. Has anybody Ever done it before? What did. What did they learn?
- [30:20]
B
Okay, so this is a. I'm going to leverage this, actually. We're going to update our procedure here. Big takeaway. Okay, Changing topics just a little bit. What should a company do with these AI gains that they're getting? So 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, 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 Liam Otley call it layers, not leaps. Right? Those layers, they compound. How can a company capture those? Like, beyond just, 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 redeploying that human bandwidth or, you know, leveraging the gains?
- [31:26]
A
Sure. I think there's a few things I'd love to tease apart here. One is that the measuring is part of this AI activation hub. Again, they're the ones who are saying, hey, we just got a huge win over here. Save these guys 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. That's one thing. I think the same second thing is leaders or listeners need to 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 where you're slow down to speed up. And then 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 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 of 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, are we gaining productivity or is really quality improving, our outcomes improving? You know, are we delivering better customer experiences like McDonald's is trying to do? 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.
- [33:22]
B
So you know, from again experience talking with leaders, when you say clarifying what it is that you want out of AI, 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 generative AI. But beyond just the, the productivity, what are some common goals that you think they should have?
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A
Yeah, so I just gave a talk 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 for one. That's going to take a pretty big hit to your public facing audience, right? Do I want to go work with him right now? Maybe not. I think the other thing that organizations need to think about is 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 going to redeploy people into R and D or we're going to reimagine our organization. So in the fifth stage of the book, 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 going to 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.
- [36:01]
B
So how does a company know do we reduce headcount or do we. Because look, the people that I deal with, they're busy. The idea of reimagining our organization is. 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?
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A
Yeah.
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B
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 see some opportunities here as a health, 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?
- [36:51]
A
Yeah, and that's where the hyper adaptive model really shines. Right. Because it says this isn't going to 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 and till 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 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 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 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? That's how we start to dip our toes into this new way of working without blowing up the business.
- [38:29]
B
How 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, X, Y, Z, like that
- [39:05]
A
sort of thing, Right, Yeah.
- [39:06]
B
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.
- [39:16]
A
Yeah. So 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 funnels for the practitioner backup. Simultaneously, you should be implementing and using things like design, design thinking tools. So you're starting from the customer back, which is exactly what Ping on did is that what do our customers need? And then how does that intersect with what we're really good at?
- [40:03]
B
So I want to go to a topic that's been, I guess, top of mind for me, and it's going to require a little bit of a context for the listener. Jack Dorsey, who we mentioned earlier with Block, he at the end of February announced the layoff of 40% of their 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 basically the thesis was they evaluated what the roles were doing and that in an organization their size, about 60% of those roles were, were ingesting, receiving information from different departments. They were analyzing that information. They were figuring out, you know, 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?
- [41:16]
A
Yeah, so a couple of layers here. One is he harvested the gains. So we talked about that's one option. 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 going to pick up that redeployment cost? Is it going to be, I'm just going to lay off 4,000 people, they're going to 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 want you, I don't need you. Or is it a company that looks more like the unilevers of the world and the metlifes 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 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 infrastructure. Knowing that people can probably do more than just shuffle information around and knowing 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.
- [43:02]
B
Now one of the things that came out of this, and I think I might have mentioned it in the pre interview that we did, but this quote from Henry Ford about if I asked my customers what they wanted, they would have said faster horses because they couldn't really conceptualize what this hyper adaptive organization would look like. Right. And in this case it seems like most of what's being taught 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?
- [43:36]
A
Well, I think, 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 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. 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 wielded a hammer is he was like, well, let me just lop off 40% of the organization and then just try and accelerate into stage five. Okay, that's fine. An 8,000 person organization or whatever it was. I don't know if you are one of the giants, if that works that well.
- [44:48]
B
So, okay, so I mentioned this concept of the faster horses. 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 Workspace Studio to build a 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?
- [45:48]
A
Yeah, well, I think you can. 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 2 is kind of our validated near term, next cash cow and our Horizon one is our cash cows. There are frameworks and there are models for us to reinvent ourselves as we're going forward. I actually think the mistake 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, if you want orchestrated agents and you haven't put down the layers, then what I have seen and heard happen is they fail. We've got this 80% failure rate and they burn through political capital, they burn through monetary capital. People start to get cynical about the technology, and it makes it that much harder to move forward. If you haven't gathered already, I'm an advocate for very deliberate approach. The bigger you are, I think the more deliberate you have to be, as painful as that feels to hear, because everybody's got fomo and everybody feels like we have to do this yesterday. But I do think there's something to being very deliberate, and these patterns are tried and true. I'm not just pulling this out of thin air. It's like, this is the proven ways on how organizations change.
- [47:22]
B
As the saying goes, history may not repeat itself, but it sure rhymes.
- [47:26]
A
Yeah, exactly.
- [47:28]
B
So you mentioned quick wins are quick wins. Now, I hope that my listeners 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 my industry who I, you know, 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?
- [47:58]
A
I love them as foundations. You know, when. When they don't have focus, when 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 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 create these structures that help them to do that. And I think all of a sudden, the flow 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.
- [48:51]
B
So 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, 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 the number one takeaway or something that you would want a leader to think about as their anxiously awaiting the arrival of their pre order of your book.
- [49:37]
A
Well, I think start thinking about what your version of the help desk is. I've put forth some structures, we've talked about them today and 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 about building some permanent infrastructure and start planning for how that looks, the people you tap and how you would fund that.
- [50:07]
B
Love it. So everybody again 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 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 as the technology accelerates, as your people are becoming more literate, as the demands for this type of, of contribution to your organization are going to only increase. So a big endorsement for literally pre ordering the book. So Melissa, thank you so much for taking the time and 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.
- [51:02]
A
Thanks so much for having me on the show and really appreciate the time.
- [51:05]
B
Thanks everybody. And for those of you still on, 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 going to say we figured it out, but we're working on it. So 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 especially 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.chiefai officer.com. follow us on Twitter at the handle using AIATWork and visit www.usingaiatwork.com for free resources to help you harness AI in your role.