George Jagodzinski (00:00):

Today we learned what companies can harness from an alien creature that's been out innovating predators for 300 million years, from three operating modes, or hearts, to nine semi-autonomous brains that somehow avoid total chaos. We talked about designing smarter on ramps to change, avoiding rogue tentacles inside your organization, and why most transformation efforts would go better if organizations acted more like an octopus.

(00:23):

My guest today is Stephen Wunker, founder and managing director of New Markets Advisors, and co-author of AI and the Octopus Organization. It's a fantastic book, I highly recommend it. Stephen's experience building one of the first ever smartphones taught him early on that innovation fails, when you think way too big and way too small at the same time, you have to find that Goldilocks zone. I certainly learned a bunch on this one, please welcome Stephen.

(00:45):

Welcome to Evolving Industry, a no BS podcast about business leaders who are successfully weaving technology into their company's DNA to forge a better path forward. If you're looking to actually move the ball forward rather than spinning around in a tornado of buzzwords, you're in the right place. I'm your host, George Jagodzinski. Steve, thanks so much for being here.

Stephen Wunker (01:25):

Oh, my pleasure, George.

George Jagodzinski (01:27):

I wanted to start with one of your older stories, which is you were part of the team that was part of the very first, or one of the very first smartphones. I'm always curious what lessons can we learn from, and patterns can we learn from there? So, tell me a little bit more about that time.

Stephen Wunker (01:40):

I was really fortunate to be in this position, and I was utterly unqualified to do it, but I guess somebody has to do it. This was back in 2000, and yeah, I worked for Psion PLC, British consumer electronics company that had invented the PDA back in the 80s, and we were creating smartphones for Ericsson and Motorola, on a white label basis. What I took away from that was that you can both think too small and too big at the same time. Look, there's a lot of things we did right, and I'm proud of that, but you learn from your mistakes. We thought too small in that we thought about a smartphone being basically a personal organizer merged with a phone. And maybe you could put a few other things on there, like maps. What a camera could unleash even a low quality camera, because that would've been the only option, we didn't really consider, and so, we didn't have a camera in that device.

(02:39):

Yet, we also were thinking very big, we were thinking about all that it could be. There was one lonely industrial designer on the team who said, "How about we create just a device that's great for messaging? It has a keyboard and that's about it, otherwise, it's a phone." And we thought, "Nah, no, we're going to be creating the future." And so, we thought really big and we created a very expensive, complicated device, and around the same time the first Blackberry came out. And the first Blackberry wasn't even a phone, it was a two-way pager. It was great at sending messages. It did basically one thing, it sent messages, and it cleaned up. We should have thought about what's the on ramp to changing behavior and not been so captured by our grand visions.

George Jagodzinski (03:31):

Interesting. So, finding the on ramp versus jumping ahead to the solution and getting too focused?

Stephen Wunker (03:38):

That's right. So, we could have had a second gen device that did more of what we envisioned. It was a tap interface, it was not a keyboard interface that we had, that could have come second gen. First gen, the guy was right, we should have had just a simple two-way pager, didn't need a lot of bandwidth, didn't need a lot of battery processing, didn't need a huge keyboard that was very fragile, we could have avoided a lot of the issues that we ran into.

George Jagodzinski (04:03):

So, easy to see it looking backwards, right? But when you're in the midst of it and you're working hard, it's so difficult to find that Goldilocks zone.

Stephen Wunker (04:11):

That's right. And it's also easy to get swayed by the market. So, the market in 2000, they wanted the big sexy device. And gee, when I announced the project, and stock spiked like 40%. I understand from that perspective why we did it, but by the time the thing comes out, market sentiment can shift, as it had. So, you really need to be designing for the business and not just what the very temporary pressures are calling for.

George Jagodzinski (04:38):

Yeah, makes a lot of sense. Being in consulting, we're always getting pulled in to solve problems, and we're the first one to raise our hands and say, guys, this is the wrong problem to solve. We're happy to take your money, but not by solving the wrong problem. And which brings us to your book, I've been reading it, I've been loving it. And one of the entry points for me was when you were talking about how people frame problems wrong, and I'd love you to expand on that a little bit.

Stephen Wunker (05:01):

It's very easy to focus on the problems that we have today and look for tools to solve them, and that's what a lot of folks are doing with AI. They're trying to say, how can I make my current job 5% more efficient? Or current process 5% more efficient? That's fine, and I'm not saying that it's a bad thing to do, but you also have to be focused on creating the future. If you're not rethinking the process, rethinking the job, then you're going to get caught flatfooted by people who are. So, unfortunately, we see a lot of process optimization today with AI, we don't see a lot of process rethink, or should we have a process at all? Sooner or later, people are going to have to get there.

George Jagodzinski (05:48):

Yeah, I see that all the time, I was recently working with someone and they were spending months just documenting their old processes as part of the move to reinvent their processes. And I was thinking like, what the heck guys? Why don't you just reinvent them without going through all the old stuff? And I'm curious, what frameworks have you seen that work and how you cast away all that legacy process and move into the future?

Stephen Wunker (06:12):

First, you have to decide which processes to look at because you can't reinvent the whole organization at once. So we-

George Jagodzinski (06:18):

But I'm impatient, Steve, I want to do the whole thing all at once.

Stephen Wunker (06:22):

You will kill the organization if you transform everything all at once. But we do start out, and we try and understand what's the objective function here. Are we solving for cost? Or is it speed? Is it quality? Is it risk? There's a lot of things that AI can do. So, let's frame this as a business issue rather than where can we use AI issue. And once we can frame the key business issues to solve for, then we can go through and work with execs to find what are a handful of processes, maybe a half dozen, that we can look about. And I don't really like to frame it even as a process, but as a set of tasks that need to get accomplished. And then we'll get pretty granular, and we will look at the current process, not because we want to optimize what's there, but we do want to understand what are the issues.

(07:14):

So, what specifically are we solving for? And equally what works well? What don't we want to lose? And we also need to understand what's that transition, right? If we don't understand what people are going from, we won't have a very clear view about what they're going to and how to help the individuals along the way. And then, let's really rethink it, and do it in a collaborative fashion with a client, but figure out what are different ways of doing this, not just one way, let's have two or three options at least.

(07:44):

And usually there are pros and cons with each. Let's map all that out, and then we can choose where to deploy AI and other technologies. And maybe it's not just a tech solve, by the way, George, maybe people's responsibilities haven't been clear from the get go, or there are folks meddling in that shouldn't be meddling in. So, there can be a managerial solve as well as a tech component to what we're doing.

George Jagodzinski (08:08):

And there's real human bias towards change, right? And it's hard to change, and you've got people that are siloed within an organization, they're used to doing it their way, and what I find makes that even more difficult is if they've been successful at doing it that old way. And so, not only do they not want to change, but they are doubling down that they've been successful doing it the way that they've been doing it. And I'm curious how you fight those battles.

Stephen Wunker (08:32):

We need to understand what success is, and it does differ in people's minds. I recall talking with one consumer products exec who was boasting that about 98% of the projects they initiate result in products that launch.

George Jagodzinski (08:51):

Wow.

Stephen Wunker (08:52):

To me, that was a terrible statistic, that meant they weren't really trying risky stuff. They were doing new lemon flavor or whatever it might be, but not the really bold things that would be pushing the envelope in terms of technology, or consumer acceptance, whatever it might be. But if we don't baseline on what we're aiming for, we're not going to get to the right destination. And then, okay, if people are going to want to be super risk averse, okay, I might disagree with that from a business perspective, but let's go with where the client wants to go and then let's design processes that don't necessarily take more risk, but do the sorts of things that they want to accomplish. AI can be perfectly good for that, again, it's not always just a technology solve though, it's often the human component that has to be changed too.

George Jagodzinski (09:41):

Yeah. And getting people comfortable with, hey, we're going to take some big swings and it's okay to have some big misses, right?

Stephen Wunker (09:47):

Yes, I would prefer to come in and solve a business issue, like we aren't innovating big and bold enough. Now, AI can have a role in that, but there are many other components to the answer too.

George Jagodzinski (10:00):

Yeah. Yeah. One thing that I really liked in your book was the three hearts framework, I was hoping you could explain a little bit about that.

Stephen Wunker (10:08):

We chose an octopus for several reasons as a framing analogy, but one overwhelming reason to do it was that it has this alien seeming biology. And octopus has three hearts for different purposes, it can actually give itself a heart attack to direct energy temporarily to the other hearts that need to carry on for certain purposes. We liken that to a company having different operating modes that it needs to be able to switch among. So, it needs to be analytic. Usually companies are pretty good at doing that, but you do need that analytic heart still. You need the agile heart, because there's so much that we are getting into... Look, if we think we're in an era of rapid change now, in five years, this is going to feel like the 1950s. So, we are going to need that agility and adaptation capability. And then, finally, there's the aligned heart.

(11:09):

So, talking to the emotional, the human side of change. Because we're also going to be asking our staff to undertake a huge amount of change in their work and probably elsewhere in their lives, and it's going to be happening all around us all at once. And so, we need to be conscious of the human toll that's going to take, and how we bring people emotionally along that journey.

George Jagodzinski (11:33):

I would imagine it helps for people to know maybe they only align with one of those hearts in their own way of being and operating within the world, and maybe that's okay, right? Because you need each one of those, you don't need to be all three, all at once, all the time, right?

Stephen Wunker (11:47):

That's right. The organization needs to be able to shift, but individuals are probably better at one thing or the other. That's right.

George Jagodzinski (11:54):

Yeah. The other thing that struck out to me, and I'm curious if there's any concrete examples you could share, but rapid rewrite squads. My bias towards action, that got me really excited, so I wanted to hear what that's all about.

Stephen Wunker (12:06):

So, the octopus is a 300 million year old creature, it's 70 million years older than the dinosaurs.

George Jagodzinski (12:14):

That's crazy.

Stephen Wunker (12:14):

It has survived when way over 99% of the species that were existing at the time of its inception have perished. It does that not because of any real natural defenses, it does it because it's incredibly adaptable. It can edit its own RNA in a matter of hours, very few other species can do that. So, organizations need to survive in a similar way, not because of some thick armor against known predators, because an era of rapid change that's not going to do you much good, rather, they need to have that rapid adaptability. And so, it can't always go to the big boss to decide on whether they're going to edit some way of working, there needs to be a group, usually a cross-functional group, that has the ability to say, at least on trial basis, let's do something different. If every experimental change in a process or workflow has to go up to some very senior level committee, you're not going to get much done.

George Jagodzinski (13:17):

I'm curious, before you wrote this book, where was your octopus knowledge at?

Stephen Wunker (13:22):

Oh, I thought they were cute and cuddly, but that was about it. My co-author, Jonathan, who's the futurist at Amazon, had been talking about octopus thinking since about 2022, when he premiered it at South X Southwest. And he'd been talking about this around Amazon, but he mentioned it to me in actually that context of the RNA edit. This is why you need to have some free flowing conversations once in a while, it can't all be structured. So, I just said, "That is really weird, how about we just look up what else is weird about an octopus?" And we discovered things like the three hearts. We discovered that it has nine brains, which is a phenomenal analogy for distributed intelligence in an AI infused organization. So, it was there that we had the framing model of the book.

George Jagodzinski (14:09):

That's great. And now your knowledge of octopus goes so deep, I love it. And you also, I think you justified that it's a-okay to have conversations over beer, to come up with ideas for work.

Stephen Wunker (14:21):

That is absolutely right. The pandemic brought us conveniences and doing a lot more virtual, but you lose that face-to-face interaction that leads to these discursive conversations where innovation often really happens. So, Jonathan and I, he lives in the San Francisco, I live in Boston, we don't get together very much, but we were together for three days talking about the book, and that's how we got the model.

George Jagodzinski (14:46):

I recently just did my first podcast where we recorded at a bar, and there were some technical hurdles, but the conversation before and after was just fantastic. And shame on me, maybe we should try to do something like that since we're both in the Boston area, but maybe next time.

Stephen Wunker (15:00):

I'll buy.

George Jagodzinski (15:01):

The other thing that I'm interested in is as organizations are trying to adapt and move into this direction, the age-old saying is that if you don't measure it, it doesn't happen. And I'm curious what types of measurements or KPIs or things that you've seen out there in the wild that, hey, once you start measuring this, you're going to be more successful?

Stephen Wunker (15:21):

So, you need to start with measurement, understanding what's important to the organization. Is it cost? Is it cycle time, job satisfaction, customer satisfaction, quality? All sorts of different metrics can steer you in different directions. So, understanding, again, the objective function that you're solving for is where it starts. And then, if you don't have very clear measurements that you apply to your AI changes, change isn't going to stick. So, you need to have measurements and time bound goals for achieving them. You are probably going to be off on some sorts of directions, that is natural given that we're all learning all at once, but if you don't have some accountability, it's going to be really hard to get this multidimensional change to happen.

George Jagodzinski (16:13):

It's interesting. I was just recently, there's a little bit of a tangent, but I was just recently at an event with 200, 250 CEOs, and one of the topics was that a lot of folks have gotten away from setting quarterly goals and quarterly measurements, they're just saying, hey, things are just moving too fast now, we need more real time KPIs, we can't do that. And I'm still struggling with that topic to figure out what the right thing is because my initial reaction is, yeah, but are you maybe just choosing the wrong goals and the wrong measurements, or is it truly that we need to move away from that quarterly cadence and into more of a real time? I don't know if you have any thoughts on that because I'm trying to figure it out.

Stephen Wunker (16:51):

So, look, I think you need measures at a variety of timescales. I was with product VP at Adobe yesterday, he was talking about how the moat, the competitive moat, they used to think in terms of six months to a year, and now they think about it in terms of one or two months. So, they have a super quick cadence to how they're doing things. Great, you need to have that very fast sort of cycle time, but then there also need to be things that are on a longer timeframe. People are not going to adjust the way they've worked or the interaction between sales and marketing in two weeks, that's going to take a matter of months. They're not going to attain new capability in a matter of a week or two either. So, we need to have that longer cycle timeframe for the deeper rooted change as well as the quick stuff.

(17:42):

My worry about the quick stuff is that that bias us towards quick solutions, and that, as we talked about, that's hazardous with AI. Great, let's do some quick stuff, but let's also do the really hard things that are going to take a while.

George Jagodzinski (17:57):

Yeah. Yeah, that makes a lot of sense. And then, to move things forward in the organization, regardless of how you're measuring them, you talk a lot about the need to create seamless horizontal communication across the silos, and that to me, yes, 100%, but also I know from experience, man is that hard. It's one of those picture perfect examples of sounds a heck of a lot easier than it is. And I'm curious if you have any tricks up your sleeve where that's been successful.

Stephen Wunker (18:26):

Right. So, this is another aspect of an octopus that's amazing. The arms can all be doing independent things, because they all have their own mini brains, and they can coordinate arm to arm without even involving the central brain. So, effective organizations need to look like that, that makes them super adaptable, rapid, and able to juggle in a multidimensional way, because that's what the future just requires. So, AI creates transparency of data, it structures unstructured data. So, marketing and sales and operations and risk can all have a clear view about what the other functions are doing, and that does enable more de-siloed communication. Now, that has to be accompanied by changes in how the humans interact too.

(19:20):

So, if you have more of a cross-functional organization that also mirrors the cross-functional setup of the data, then they can actually take action, but if everybody stays in their organizational silos and can just peer into the other silo, that invites a degree of chaos with people perhaps taking more initiative than they should, sales goes and creates its own marketing materials, for instance, or people meddling and vetoing what other folks are doing without having clear lines of authority about who's responsible for what.

George Jagodzinski (19:54):

That one example that you just threw out there is an interesting one, that's a little bit of a grenade, with the sales creating their own marketing material. Something I'll even be guilty of as a CEO, if I'm up early and I need something, I'm just going to my various AI tools and I'm like, oh, I'll have this stuff all ready before the day even gets going. And that is really dangerous when it's in the wrong hands.

Stephen Wunker (20:17):

Oh, as the designer of super ugly slides myself, I can relate there, George. But yes, it's tempting when you just need to move, to not have that coordination. So, there just needs to be guardrails against how much autonomy should be taken. And then, also encourage people to take a degree of autonomy. If they're not asked to take any risk and people can peer over their shoulder all the time, then the danger is that you actually get a more conservative, a more slow moving organization, even though everybody has these AI tools, that's definitely not the outcome that you want. So, yes, there does need to be this organizational consideration about how people are going to interact, how much autonomy they should take, water of the guardrails, how should they collaborate... All that needs to go with the AI tool implementation, otherwise, you're not going to get very far down the road you want to travel.

George Jagodzinski (21:14):

Yeah, because kind of going back to the beginning of our conversation, there's a Goldilocks zone to be found there. You're telling your team, I need you to move faster and I need you to leverage the right tools to rethink the way we're doing business, but at the same time, you don't want chaos and you don't want people just completely doing the wrong thing. I'm curious though, in reality, how do you strike that right balance? Does it come back to the measurement? If you're getting people aligned to the right measurement, then it can work itself out, or is there another way to keep them aligned?

Stephen Wunker (21:43):

So, partly I think the answer lies in who is responsible for the change. If it's only IT, you may struggle, because IT isn't used to resetting the relations between people, or functions within an organization. There needs to be some general manager accountability for what's going on, or maybe within a function, like a CMO responsible for the different aspects of marketing and how they're going to be using AI. IT has a role, really, you need that general manager perspective of how this change is going to occur.

(22:19):

Again, if the general manager is clear about what they want to achieve, and we can lay out some options with pros and cons, and the change required in not just IT, but in the human aspects of management, then we can make some intelligent choices around what has to occur, get people on the same page, have some measurements, and then by all means, really assess as you go down that road, what are you learning? You'll learn some stuff, there will be some surprises, guaranteed, there'll be some bumps on the road, but if you have a process for bringing people together and looking at things in a dispassionate way about what's going on, then you can solve for them in a rapid cycle manner.

George Jagodzinski (23:01):

The process for bringing people together, that sounds very important. And it's a tale as old as time, right? This isn't AI specific, a lot of these things that you're talking about, you just think about IT used to have SAP, and the CRM was in there, and the sales team's just really pissed because it's frustrating to work with. And so, someone on the sales team says, ah, screw it, I'm going to throw it on my credit card, I'm going to buy Salesforce. And then they get Salesforce, and they're a new up and comer, and then next thing you know, Salesforce is essentially the size of SAP within the organization and now it's back down to the IT organization. If at the beginning there was more conscious effort put on, hey, how do we solve the sales team's problems and bring people together and align them more, then maybe you get less of those rogue systems or now rogue AI, which would be even more dangerous within an organization.

Stephen Wunker (23:53):

Yeah. Rogue AI happens a lot, and that is really not a good idea. So, definitely IT shouldn't be the enemy of innovation, but this should be the enemy of rogue AI. There's a lot of very bad things that could happen that way. And it's happening because people get frustrated, and they see so many use cases for AI, but if you bottleneck everything up in this process that has to go through central IT, you might wait a really long time. So, again, setting the guardrails, having veto rights perhaps on what goes on through IT, but enabling people to just get on with stuff, great. I know somebody who leads a market research function at a pretty big institution, and in two days, with Microsoft Copilot, she put all of their focus groups and in depth interviews and surveys into a system that is searchable with natural language, and now allows these insights to be accessed through people throughout the organization. Fantastic.

George Jagodzinski (24:57):

That's huge.

Stephen Wunker (24:58):

She didn't want to have to wait for IT to do that. Now, IT can say, no, you did something wrong, they didn't, but I guess they could have, but you do want people to have the initiative to do those sorts of things.

George Jagodzinski (25:09):

Yeah, 100%. Steve, I could talk about this forever with you, I know we're coming up on time. Is there something I should have asked you that I haven't?

Stephen Wunker (25:17):

You didn't ask me the hardest thing about the AI transition.

George Jagodzinski (25:22):

What is it?

Stephen Wunker (25:23):

So, I do sometimes get that question. I think it's actually what I talked about with the smartphone. We need to think big about not just the 5% change, but the 25% or 35% change, what's really going to happen? And then there needs to be the phased on ramp to that change. So, we're not just fixated on some Jetson-style future, but what are going to be the three, five, seven steps, whatever it might be to get to that destination? Folks aren't doing one side of that or the other, and they need to. They really need to if they're going to achieve the sort of results that they're aspiring to.

George Jagodzinski (26:03):

I love that. Yeah, I completely agree. That just made me think of another question for you, which is, I'm curious what you've done in your daily life to figure out how you're re-imagining your business and your life with AI. I'll start, which is, and I'm asking everyone this now, is I'm going to the gym every morning because I'm trying to get back in shape, but I take that hour and a half in the morning and I'm just playing with as many tools as I can. Just so that I know where everything's at, and I think about how can I make my week better, how can I make my month better, my quarter, my year with those tools? And that's what I'm dedicating like an hour and a half every morning too. I'm curious what you're up to.

Stephen Wunker (26:39):

Our firm's pretty simple, it's 15 people, so it's not some humongous enterprise, but there are certainly important use cases for AI, particularly in synthesizing primary research and in conducting secondary research. So, I'm pushing all of our teams... We typically have about five projects at once. So, all of our teams to use this and document what's changed, what went right, what went wrong, and I better hear about what went wrong because I'm certain something did, and what can we take away from this? What would you do differently next time? If we do that in project after project, a typical project is maybe three months, so that creates a pretty rapid cycle way of learning. And already within three years of really adopting these tools in an aggressive manner, we have certainly changed our operations. I'm quite certain that's going to happen at an even faster pace than in the coming couple of years.

George Jagodzinski (27:41):

That's great. I know, I always ask my team, what did we learn? But now I'm going to start adding what went wrong? I love that question. I need to ask that question more. And if there's no answer, then that deserves inspection, right?

Stephen Wunker (27:54):

Right. Well, then that avoids people being afraid to share something bad. They have to share something bad, so let's make it something that we can learn from.

George Jagodzinski (28:04):

That's fantastic. Steve, I always like to end on one fun question, so in your life, in your profession, what's the best advice you've ever received?

Stephen Wunker (28:12):

So, I worked for many years with Clayton Christensen, who's the person who created the Theory of Disruptive Innovation, jobs to be done, a bunch of other things. He was really famous for his business theories, but he had a sign in his office at Harvard Business School that said, "Anomalies wanted." He was always looking for things that didn't fit his theories, because that is how he learned, that is how he improved things. He was lauded, he was called, four years running, he was ranked number one business thinker in the world, but he was always looking to be proven wrong. And that's why he was the number one business thinker in the world, because he never settled for good enough, that was some great advice.

George Jagodzinski (28:54):

That is fantastic advice, I'm going to take that into my life as well. Steve, this was tremendous, I really enjoyed it. Thank you so much.

Stephen Wunker (29:02):

Thank you.

George Jagodzinski (29:04):

Thanks for listening to Evolving Industry, for more, subscribe and follow us on your favorite podcast platform, and pretty please drop us a review, we'd really appreciate it. If you're watching or listening on YouTube, hit that subscribe button and smash the bell button for notifications. If you know someone who's pushing the limits to evolve their business, reach out to the show at evolvingindustry@intevity.com, and reach out to me, George Jagodzinski on LinkedIn, I love speaking with people getting the hard work done. The business environment's always changing, and you're either keeping up or going extinct. We'll catch you next time, and until then, keep evolving.