Ep 142: Disrupting Ourselves for the Evolution of Work with Jess Von Bank

Speaker A: Foreign. It's Bob. Welcome back to Elevate your aiq your go to source for insightful conversations on human centric AI readiness, talent transformation, responsible innovation and the future of work. Today I am very excited to share my conversation with my friend Jess Von Bank. She's the co founder of the new AI studio Now to Next. She is the co founder of the now of Work community. She's a keynote speaker. She is the co host of the Edge of Now podcast and the author of the brand new book Mother of Disruption, Reshaping the Human Contract in the age of AI. I've known Jess since 2021 when she was at LeapJN, later acquired by Mercer. And whether she's on a global stage writing on her substack, leading the non profit Diverse Daisies or crossing the finish line of a tough mudder race, she is a gifted storyteller and a role model to so many people, including young girls, starting with her own three teenage daughters. Her book brings all of that together. Part memoir, art challenge to the systems we work in and asking what people should demand and deserve as AI uh reshapes work. And in our conversation we explore why people aren't in the way of transformation, but are the transformation, why organizations should invest as much in their workforce as in AI tools, and why it's time to rebuild how work gets done rather than just speed it up. I was really looking forward to this one. So stay tuned for an inspiring and hopeful conversation. And thank you as always for listening to the show. Let's go talk to Jess. Hey everyone. Welcome back to Elevate youe aiq. I am your host, Bob Pulver. And today I am very excited for my. My conversation with Jess Von Bank. How are you today, Jess?

Speaker B: I'm so good for a Monday.

Speaker A: You're green, right? Green.

Speaker B: I am green. Yeah.

Speaker A: Excellent.

Speaker B: Green.

Speaker A: I am green as well. Unleash, I'm sure was a whirlwind. We missed you at HR Tech, but sounds like things went well. Unleashed. Have you

Speaker B: ever fallen asleep while walking, do you think? Like,

Speaker A: I don't think I have.

Speaker B: Like, if it's, if it's even possible. I think it was a big week. It was a long week. It felt like an endurance race. And so if there was a finish line, I think I've crossed it. But it was a good week.

Speaker A: Yeah. Well, I know racing is one of, is just one of your many talents, being the renaissance woman that you are. So we'll definitely get into that and, uh,

Speaker B: we'll get to that.

Speaker A: Yeah. So, Jess, uh, for Those who happen to live under a rock and don't know who you are. Why don't you just give us the abridged version of your trajectory from North Dakota now to Next?

Speaker B: We want to start there. I love it.

Speaker A: Yes.

Speaker B: Well, first of all, congratulations on knowing that it's North Dakota, where I was born. My partner in crime, Jason Averbook, likes to say I'm from the Dakota territory because he can't remember which one I'm actually from. But yes, Nice to meet your audience. I'm so happy to be in your guest seat today, by the way. Nice to meet everybody who's listening. I'm Jess Von bank and I'm co founder of a firm called now to Next. But as Bob alluded to, I. I have kind of grown up in this industry. I've been on the talent side as a practitioner as well as a vendor, as well as consultant, advisor, industry analyst. I started a community called the now of Work at the onset of COVID I, uh, do all the things, as we all do, trying to keep up with the future of work conversation. I was born in. Well, actually I was born in Milwaukee. I'm going to claim Milwaukee right now because the brewers are looking really, really good. Born in Milwaukee, but raised in small town North Dakota, which always raises an eyebrow or two. Like, how did you, like, go from that to like, running around the world on stages talking about AI and the future of work? I also don't know. Some things just happen. I will say growing up like I did, and I wouldn't trade a single second of it. Growing up like I did made me very curious about the world. I was a bookworm from the time I was a toddler. I couldn't read enough, I couldn't know enough, I couldn't understand enough about the world. Probably because it was just outside my view, maybe in a strange way. So as soon as I hit college, I started studying abroad, traveling the world. I think I've always just been a really hungry, curious knowledge seeker, which is a great thing to be these days.

Speaker A: And somehow in whatever spare time you have left, you've got a three daughters. Is that right? Three daughters and a community around, like, empowering young, young ladies. Right. Diverse, diverse Daisies. And. And then you're a tough mudder, you know, competitor. I, uh, just.

Speaker B: People like, read my bio back to me. I'm like, who is doing all of this? Yeah, it's been an incredible year.

Speaker A: It seems like you have more than 24 hours in your days to do all of this. I, uh, don't get it.

Speaker B: I think it's the old expression, however cliche it is when you're passionate about what you do, it doesn't feel like work. I don't know. I think we just find time for the stuff that's really, really important. And raising three teenage girls. Holy cow. They're all three in high school at the same time. This year, only year, all three are in high school. And they remind me of this daily. They're. They're, like, competing for attention and social calendar time and, like, there's a lot going on in this house, but it's also a good. I still say. I literally say almost every single day, I learn more about the world by observing it through their eyes than I do through my own direct participation. I think we, especially in our HR transformation, AI future of work conversations, there's so much like gazing into crystal balls and, like, making predictions and trend spotting and sort of analyzing this and predicting that. Honestly, the. The most honest reflection, the most transparent reflection I get of the world and what's happening and what we make of all of it is through the eyes of young people, especially teenagers. I mean, there's no BSing with teenagers. They call it like it is that, you know, everything is just very. It's, uh, actually kind of refreshing because it gets a little exhausting, I think, for all of us. There's always something to be paying attention to and sorting out and storytelling around and positioning. It's quite exhausting, I will say, from someone who's also in it and someone who's trying to stay a step ahead of it. It's pretty refreshing to sort of go to my kids and see what's. What's real, you know, put our feet in the grass for a second.

Speaker A: And, uh, I think it's also an important perspective as you and I both think about, you know, the future of work and how we get there in a constructive and responsible way to understand what the future workforce is experiencing and how they bring all of those life experiences through Covid and, uh, all kinds of other typical teenage challenges as they think about what path do they want to take, and is that a linear path? Is it a nonlinear path like I've had in my career? But how do you bring each of those experiences to bear, and how does it help with things that I know you and I take very seriously, are, uh, part of our sort of Persona, which is systems thinking? Like, how do you think about how these different parts of our personal and professional lives, you know, interconnect? And how do they affect each other. And I think that's probably some, some things you talk about in your brand new book, which I want to talk about.

Speaker B: You know, I, I, I love giving proper attribution when I can and, and I will not remember who came up with this. But you've probably heard the quote about marketing too. Marketing is 50% right. At 50% wrong, you just never know which 50% is right. That's how I feel right now, honestly. I think all of our, even like our hopeful predictions, there's hopeful and then there's sort of like the doomsday side of things. Like what could happen? What could happen if everything goes right? What could happen if things go wrong? Whatever we think and whatever we predict, like rewind 20, 30, 50 years ago. Half, maybe like May. And that's even generous. Like half of this stuff is going to be anywhere close to happening or to happening when we expect it to. And we just don't really know what of that, what we can possibly guess will actually come to fruition in good ways in ways that maybe we would have done differently had, huh. You know, if we had the chance. And I think that's the tricky part here. But it's up to us. And you talk about responsible AI, we talk about ethical guardrails for organizations deploying technology and trying to innovate and you know, sort of press the gas as hard as they can. There is a pace of things to this conversation that makes it a little bit daunting. But I also think we have to remember not everything is going to happen. Not everything is going to play out as we predict and we sort of have to be prepared for, for all of those possible realities. And the only thing, the only way we can prepare is to keep our heads on straight, to be responsible and um, to hold ourselves accountable and to remember that we are the ones like this is our future. To your point, raising kids this like it's a reminder every day. Whatever we're designing is the workforce our kids are going to walk into in five, 10 years. So it's kind of up to us to, to as much as we can, like hold the steering wheel of a speedboat that feels like it's going really fast. Keep holding the steering wheel.

Speaker A: No, uh, I think that's really important. And so I mean you talk about, I definitely want to talk about some of the themes of the book because I think it's so relevant and so important to everything that we're talking about in our professional lives and with our kids, just how we need to Put things in perspective. How we need to understand why we do what we do, how we get, you know, meaning and satisfaction from the work that, that we do. And, and because of that, you know, the, the humanity of it all, the human centricity of it all is, is why we do all these things and why when we try to redesign work, it is certainly challenging and there's plenty of friction, but we have to have some level of comfort with the discomfort. And paraphrasing another old quote, right.

Speaker B: You know, it's really interesting. I have been, I've been writing a lot of. You could say I've been a thought leader, evangelist, storyteller. I've, I've been, I've generated a lot of storytelling, a lot of content around this topic for the last decade or so. So I had uh, have a lot of like searchable snippets and content in the archives. And I did something, every so often I do this actually. I search for terms that we're using now to see if I've used them before and in what context. And I literally just searched for humanization of work. I just searched for this in my Notes app. Like you wouldn't believe how much I have in my Notes app. My next book is probably already in my Notes app. It just needs to be organized. I searched for the humanization of work terminology and there it was multiple times. Around digital transformation, around digital workforce experience design, around Persona development and journey mapping, around whole person approach. We've had this conver, we have been having this conversation for a really long time. Personalization of experience, like uh, everything. Like these are not new concepts. And so I'm a little bit concerned that this is not, these aren't new concepts, they're just more relevant and they're more urgent right now. They are more urgent because AI is the fastest tech we've ever seen in terms of how it's being built and shipped, built and shipped, built and shipped. But these aren't new concepts. So I'm a little concerned that we've already been having this conversation. We already know what we need to do. So the same excuses are going to be present this time that we already had. And so now we need to, we, we honestly need to do away with our excuses to transform our organizations and our workforces the way we know we must. I, I started saying two years ago and I probably wasn't the only one, but I felt a little solo at the time. You should, A couple of years ago I started saying you should be investing more in workforce transformation. Than you are in AI transformation, meaning the tools and the technology. Whatever your budget is for tools and tech and rolling out licenses across your organization and standing up pilots and testing proofs of concept and all these AI initiatives that started running rampant two and three years ago, you should be investing as much or more in workforce transformation. Understand who you have, what are their capabilities, how do they work, how do they best produce value? How are you measuring that? What are your KPIs? Your measures of success should have nothing to do with technology deployment or tool adoption. They should be the capacity you're creating in your workforce, how you're redeploying people against the work they should be doing. Highest value, high performing teams, that kind of thing. And lo and behold, two years later, we're having this massive aha moment. Like ah, turns out we shouldn't be measuring adoption of tools, we should be measuring people. Yes, yes, that's always been the answer. Since the dawn of time. That has always been it's people, people, people. But in somehow we struggle to have these like full circle moments of realization Every time a new wave of innovation comes along. We get caught up in the shock and awe, uh, of the new technology, figuring out what it is and it's like a, you know, it's like a new species. What is this and what can it do? And let's play with it for a little while, which is fine. I think that's the messy middle that we all that we also talk about where you kind of have to have the clumsy toddler phase before you step back and go, okay, well now that we understand, what do we do with it to deploy this and sustain it at scale in a way that brings the organization forward and doesn't attempt to skip or bulldoze the people in the way. People are not in the way of transformation, they are the transformation. And I feel like what are we at, uh, like the four year anniversary of ChatGPT at the end of like right, right about now?

Speaker A: Yeah,

Speaker B: like it's taken us this long to get back to this realization.

Speaker A: There's all the, as I look back at my career, there's all these like puzzle pieces that maybe didn't. You didn't have the, the full picture so you didn't know like where they might fit. And then even more recently to your point around, you know, focusing on the people, everyone sort of shifted to this, you know, skills first mindset. Oh, I've got to build this skills thing or whatever. Well, skills aren't. Skills are tied to, tied to the people so you're, you are actually focused on people. You just didn't realize it and you didn't see how that fit with the larger sort of transformation, you know, redesign kind of picture. Because just to sort of point to, you know, the theme of, at least the title of, of the book, disruption requires, you know, going even beyond discomfort to say, how am I going to sort of reposition myself, my team, how am I going to redesign the work? What are my people capable of? So you've got to look at all of these things in concert to see how those pieces, you know, fit together. I don't know if Rubik's Cube is a good sort of example, but you've got to sort of think a couple moves ahead. And how do these things need to align across perhaps people, processes and technology so that everybody's sort of moving in the same direction and aligned to that, the business strategy. And I think to your point, Jess, the. These AI investments that everyone is so concerned about, like, you realize that it's people using, you know, augmenting themselves with this technology that is going to make the difference. And so your AI investments have to also have a pretty significant portion be a, uh, people investment.

Speaker B: And that's honestly why when I started writing this book, I realized I had to sort of like, I felt like it was important to zoom out a little bit. We've reached a pivotal, pivotal enough point in the evolution of work that we do actually have to ask more foundational questions. If this was just another, like, you know, iterative leap forward, but it's basically business as usual. This is just, you know, progress year on year, but this isn't that. This is a pretty pivotal moment. And so if we're really going to capture what's possible now, I think we have to go back a little bit further than we think we do. When you think about the purpose of a business, like I'm going to go back that far. Purpose of a business or an organization is to create something somebody else will buy. Goods, services, product, whatever. So there's R and D, there's product development, there's producing the actual thing, there's taking it to market, finding your customers and getting it in their hands. I mean, this is. Most businesses understand this and they rent the time and talent of people to help them do that. That's, that's business as we understand it. I think the AI conversation has, has reimagined all of the ways that can, that work can happen from down to core business processes, how R and D, how customer discovery, how Market discovery, where does this belong? How can it get made? How can it get procured? Uh, through what channels? Like, enough of those questions beg re asking, what are all of the ways work can happen? And the answers have, have changed. So the conversation gets to change, which also changes the social, the social contract of work itself. You're not renting the time and talent of people alone. You're creating digital environments. You're creating the actual ecosystem that allows that, that product, that production and the output to happen. So you have to redesign the ecosystem. The, the, the online world in which we work, the tools and systems, the technology that we touch, even how we consider measuring the value of people. Is my, is an hour of my time worth an hour and how do you quantify that? Or are you now measuring my performance and my outcome achievement, my, my value by what I can actually produce, regardless of the time it takes? What an interesting question. But these are the questions that we have to go back to the drawing board on. And I am a big fan like you, I, uh, think of everything in terms of system design. So to have these conversations, we need components of design. And the only thing we know, it's very hard. There's a lot of muscle memory in organizations. So the only thing we know to think about when we think about components of design is, are people and the jobs they perform and the jobs themselves and the requirements of those jobs, the tasks, the workflows, the systems. Like those are components that we know how to think about. And we put them in architecture that we feel like we can optimize and move around. I think that's where we're getting a little wrapped around the axle. You mentioned skills, for example. I'm still, uh, hearing. So I. The stage that I was responsible for emceeing last week in Paris was the skills stage. So I heard a lot of skills based organizations presenting their strategy and their progress and the, a lot of that and the what for. I didn't hear enough advancement on the use cases. We're still trying to figure out how to get the rubber to meet the road on these conversations. And having a skills based strategy isn't a bad thing necessarily. It might be foundational work that's necessary to get something else to happen. We're just spending so much time pouring the foundation and building the house. Like nobody gets to live in their house yet. We've been, we've been designing these palaces for so long. We need these use cases to start. Are you doing skills for hiring skills for mobility, skills for performance, like skills for What? I still feel like organizations are waiting for their chance to answer that question. This could be the right time to answer that question in a meaningful way. If you're doing workforce transformation thanks to AI, from the starting point of AI could be useful fodder. But I beg us not to get too wrapped around the axle on the details and old artifacts and old design components of work. I don't think we're ripping up enough yet. We need to rip up and reimagine and use different, different design components in how we reimagine all of the ways work can happen and the way we deploy, prepare and redeploy people in that ecosystem going forward.

Speaker A: Yeah, no, I think that's really well put. The employment contract itself, I feel, uh, like needs an overhaul and I don't even know how it's going to evolve from here because I've been talking to a lot of folks recently about not just the redesign of like a role or, or a workflow, but the, the teams themselves. Do they need to be so. So fixed and, and rigid with a typical, you know, org. Org structure and a typical, you know, this team has this manager and this leader or whatever. But does that give you the, the agility that you actually need? I think a lot of people have trouble conceptualizing what it means. You know, I'm going to go back to, to the disruption kind of, kind of theme. And I know you've also used the term demolition. Like just thinking about how, how do we just sort of rebuild this as if we were starting from scratch. Like, if you could build an organization today where you didn't have, you know, the baggage and the sort of legacy mindsets of how orgs typically work, how might you redo that and build for agility the same way, you know, consultancies, which, uh, I know you're intimately familiar with, like consultancies, have, you know, sort of teams that move, you know, from client to client or project to project or whatever. How could you have that type of agility and structure inside, you know, perhaps a larger organization? Because you've got to change incentives and metrics and really understand what this contract is. Because at the same time you've got AI capabilities increasing day by day, and you can't necessarily get distracted by every model that comes out, but you still have to recognize that the trajectory of someone's career or their skills growth or what have you, or even the half life of those skills may change. So this is, uh, very much a trying to, you know, fix the, fix the plane while it's flying kind of situation.

Speaker B: Yeah, totally. I love all the analogies. Sometimes I say you eat the elephant one bite at a time. One of the, who was it? One of the chief learning officers, maybe from Docebo last, last week said eat the bear one leg at a time. Regardless of our favorite analogy. How about another one? Boil the ocean. All of these things feel like, where do I start? Where do I start? And what am I actually trying to do here? And then there's sort of maintaining it. And I get that big organizations feel a lot of complexity and pain around this because you have different geos, you have works councils, you have like, there are a lot of things that require compliance and require literal structural organization and maintenance. I totally get that. I spent an afternoon last week at uh, Schneider Electric and they're well into their skills based organization journey. I'm not sharing anything they wouldn't share publicly themselves. They were so happy, like literally so happy to get 300 job families. Not job titles families, big broad families. 300 job families down to 100. That was a reduction of complexity. That was important to them. But they still have thousands of job titles to be maintained. And there's a. So these are, it's like a necessary evil. I get, I get asked this question more often lately than I have before. So, so maybe because I write about things like disruption and demolition. So how would you, like how would you abandon job families and ski and job architecture and skills? Architecture. Like doesn't this serve a purpose? It does if you're a. If you're using it to actually keep accurate inventory of relevant capabilities so that you can support mobility, redeployment of people to where the work needs to happen. So you can literally, I think of it like an investment portfolio. How healthy is your investment portfolio? Can you see what is maturing and performing well, what isn't, what's declining, what needs to be shed or managed differently? And not that people need to be shed, they can be rebuilt and redeployed. And that's what, that's the helpful nature of having accurate inventory of what you have. This is a powerful portfolio, but only if you know how to see it and manage it well. And that's what most organizations are trying to get to M. The only thing that I would poke holes in is that if it doesn't actually serve its purpose. I hear too many cases of like, uh, skills for hiring. But then you don't carry that conversation through. You're not pulling that thread through to say, well then how are you allowing employees to keep up their skills passport, to keep it relevant so that they can continue to grow their career, and so that it's visible from a succession, planning, promotion, redeployment, mobility perspective. It often dies on the vine. Whatever the initial use case is that brought that conversation into the organization, it dies on the vine in too many places to be truly helpful. If that's. If you're, if this is the commitment you're making, you need to figure out how to make it. Oxygen that everybody breathes. You literally the day before I spoke with, with Schneider, I spoke with the chief learning officer from l' Oreal, and I literally, how can we just get everybody to carry a valid skills passport that's current and accurate and ready to travel? That's what organizations are trying to do. And we're looking for all of these vehicles to get us there. So the vehicle is probably the tech, the architecture itself. It's all of your organizational baggage. You need to figure out how to free these things up or get them out of your way and get to the end goal, get to the outcome that you're imagining. So what. I can't even remember your original question. What are we all trying to do here? You need to design against the outcome and not let all of the vehicles you think will get you there be your actual shortcoming.

Speaker A: Yeah, I mean, I, uh, think the skills thing is important so that you've got visibility. So, you know, what your sort of, I guess, more dynamic, you know, path forward is because you've got to account for the changing nature of what skills are in demand and the trajectory of AI capabilities. And then your own AI, what I would call your aiq, your AI readiness to basically roll with those punches, as it were. But wherever your organization is on their own sort of adoption curve when it comes to the technology, because you can't just look at it in isolation. You say, how, how quickly can the organization itself absorb some of this capability and put the right guardrails on it so that everyone's using it, you know, constructively? And people aren't just bringing, you know, their own personal agents to work and exacerbating the shadow AI problem that we're seeing. But I also, not to go too far off on the, on a tangent, but I feel like people should have ownership of that skills passport or whatever you want to call it, because people do move around at least every couple years, I think average 10 years, less than two years now for at least knowledge, work. And so. So how do you give people more control over, you know, who sees,

Speaker B: even allow them to be a free agent in their own organization. Moving people to work is um, like I truly think the most competitive, differentiated organizations of the future are going to be those organizations that are first learning organizations. They're never static, they're literally growing and building and self optimizing. The stronger your organization can be is the mo like you want people getting better individually every single day, adopting new ways of working, like adding value to high value work, you know, to projects that are urgent and important and relevant in the organization. That's an unlock and it's the business getting in its own way that's preventing that from happening. So organizations that are learning organizations and that can mobilize talent on demand are going to be the absolute most competitive organizations of the future. They're sort of self adapting and I don't think those are individual problems to be solved. There is some bottom up, you know, top down stuff. Certainly there's bottom up stuff. We can create permission and agency for people to self actualize and develop and you know, overcome their own fear, uncertainty, doubt, you know, around new ways of working. Sure there's some of that, but it pales in comparison to the organizational barriers that are the actual transformation barriers. And so I think creating that kind of culture of transformation and capacity for learning that, that the workforce needs and wants and then removing the actual organizational barriers that prevent you from putting people where the work needs to happen and new ways of working once they get there. This is a, you know, like uh, we're creating a new ecosystem for people to know how to sit down and produce high value, high, you know, high quality work. That environment for them is changing and so we need to better support and equip them in that environment. And I don't think that's making everybody high powered, super user high adopting uh, sure, some of those people too and make them champions and Sherpas and evangelists and stuff. Mostly people need to know how to use what they already have. Their own expertise, their own sense of discernment, judgment, critical thinking. All of these new buzzwords we're throwing around as like sort of the new power skills of the AI era. Oh my gosh, people already have them. They're native to them. This is domain experience, lived experience, human judgment. We already have. We're not putting them in the types of workflows and system design that allows that to produce something valuable. The uh, system is almost the constraint right now. So it's up to us to sort of reposition people and the systems and environments they work within to get to that higher value work.

Speaker A: Yeah, I absolutely agree. I feel like we're getting bogged down in the uh, workflows and you know, what, what tasks can I automate and things like that and aren't, uh, communicating the importance of, like you said, these innate skills that people already have and leaning into that. And instead we're thinking about, well, what can you, what can we take off your plate? And, and people are now actually going sort of leaning in the wrong direction and saying, you know, look at how easy AI, uh, can make this good enough, you know, can I, can I produce more throughput? Because this is how I'm being, you know, measured, uh, while at the same time they're both, they're sort of sacrificing or deprecating their own, you know, critical thinking in some ways and rubber stamping instead.

Speaker B: I think we have, so far, I think we have used AI to perfect the way we used to work because that's just, uh, easy and natural and we just sprinkled AI glitter on top of everything. And it looks better, it looks a little faster. It looks kind of magical and fancy in the short term. And there are some immediate cost savings. We've seen the productivity metrics. People who use AI feel better, helped they feel more efficient and productive. They feel like they're doing some pretty good work. That's fantastic. But it doesn't actually unlock the organizational productivity and new ways of working. It's sort of captured at the individual level. Unless you make it an organizational effort, an organizational transformation. I feel like it's kind of like going on a fad diet. You can say you made a good decision one day. You know, you ate an apple instead of a candy bar one day, but that didn't give you a healthy lifestyle. That's what organizations we need to get over. Sort of the fad diet phase of AI. Great. You handed out a bunch of licenses, you got a bunch of power users, you watched your adoption dashboards turn green, but then you still looked at the organization itself and said, well, why haven't we as an organization actually transformed? That's because you just looked at fad diet numbers over here. You actually need to change your lifestyle and adopt a healthier lifestyle and commit to it over the long term. That's changing the business, that's reinventing a culture, perhaps that's changing the way people understand their own contribution and value and giving them capacity and permission and agency. This is a leadership mindset. Those are all the foundational things that you need to put underneath Your technology so that it can actually stick and actually change ways of working.

Speaker A: Yeah, I mean I, uh, when I talk about this concept of AIQ or AI, ah, readiness, there's an individual metric of someone, if you were to give someone a self assessment or test a job candidate on their AI readiness. Um, but then there's a team AIQ and then an organizational aiq. And so if you're not tracking all of those things, which may have different sort of attributes of success or sort of tiers of maturity, if you're not looking at all of that to understand where the organization sits, then you know, you can invest all you want in the technology and the latest models, you know, frontier models, and give everybody all these tokens to use, et cetera. But like if your people aren't ready, I'm not sure like if you're accurately, you know, representing your preparedness for, you know, how to execute against, you know, your, your business strategy. The, the other thing I wanted to hit on, Jess, like we were talking about sort of the getting a little bit to the, to the ROI of some of these initiatives, like when you, when you are seeing returns in, in whatever form you're tracking, right? Cost savings, cost avoidance, you know, revenue growth or what have you, you've got to understand and communicate like how you're going to reinvest some of that savings, right? You're not just going to, you know, buy more models and more, invest more in technology. You're going to make sure that your people are taking advantage of the momentum that they're hopefully feeling on the ground and move everyone forward so that they understand how and why everything that you're doing will, will be mutually beneficial to both the organization and to the individual.

Speaker B: I think the biggest miss is around that, you know, we're sort of playing a short game a little bit. And I get it. If you have Wall street and shareholders and you know, quarterly earnings reports to appease, the short game is easy, is easier or maybe more urgent to say, look, we made these investments and we're already seeing some payoff. The, the only payoff you're really going to have to show so far are the individual productivity metrics or sort of the speed, speed to market or speed to product, speed to whatever output. That's fine. Those are a little bit vanity metrics that, and, and that's sort of part of the bigger picture. But I think the bigger picture is the long play and it's a little bit of a harder story to tell of the um. I guess I probably use McKinsey's State of AI report most because it's recent, you know, just out a month or so ago. And it's the stat around, you know, sort of the 37%, 88% of people are using AI and organizations and reporting productivity gains. Great, fantastic. Only 30% of organizations say they're seeing any impact whatsoever to EBIT. And most of those aren't really able to attribute it to AI. They're just sort of guessing maybe it's because of some of their AI initiatives. The 6% number is the one I land on most often. 6% who are reporting any impact to EBIT whatsoever attributable to AI are pursuing growth and innovation, not productivity hacks and not efficiency vanity metrics. In other words, don't make people faster, incrementally faster doing work as they already know how to do it, but actually change the company, pursue growth and innovation. And if you're mapping that over the longer term, you're trying to understand how to get there. Like that's the story you're telling and you're positioning the kind of company and the kind of workforce it would take to achieve that and sustain that. And if that's the way you're thinking, this becomes a much more exciting conversation because you are thinking about the people, the, the, the strategy, the actual strategic workforce planning, you know, that you've tried to do for the last 10 or 15 years. Like now that becomes uh, a really fascinating and possible conversation that uh, most of the problems AI creates, it can also do a great job of solving. So deploy AI against that sort of longer term forecast planning and understanding who's in your workforce and who can do what and, and pivot them toward that longer term strategy. I promise you, you probably would not be thinking cuts, you would be thinking long term people growth and development strategies to support business growth and development strategies.

Speaker A: Yeah, I mean those are the people that I want to have on this show. That was the premise of what you know, I, I wanted to do and the themes of the show when I started it two and a half years ago, I was like, I want to talk to people who are having a positive impact or taking a human centric, you know, lens to this transformation and they're thinking, you know, long term about how to succeed, you know, that value creation and that, huh, competitive uh, advantage, like what is your, what is your moat? And it probably involves, you know, the very people who you're thinking about cutting, which would clearly be a myopic decision. And yet I talk to leaders and board members all the time and say well yeah, well we're a public company and so what'd you expect, right? And it's just like uh, I expect you to invest for long term growth. How about that? Right? And so it's, it's frustrating but you know, efficiency and automation, which is automation is usually tied to, tied to efficiency. It's like there's, there's diminishing returns there and you gotta recognize that. And if you wanna think long term, then you've gotta think about augmentation, not automation. And that means, you know, finding the right sort of balance of human plus AI across how you do your work.

Speaker B: Yeah, totally. That's why one of the most like it's literally the simplest, most fundamental framework we talk about in our business at now to next is the hands, heads, hearts model. Hands work is uh, probably fully automated. It's the stuff that machines are better at. They're more accurate, error free, they're faster. Like give it like it's like using a computer versus doing math by hand. Give it to AI. I like to say if it's a no brainer, give it to AI. If it's a brainer, if it actually requires a little bit of brain, it can be better supported, better augmented by the good and proper use of AI. I still think we're sort of like offshoring a little bit of that work too much. But if you're using AI as a thought partner, as a strategist, as a sounding board, not handing over the question, not even using it for questions and answers, literally using it as a critical thinking partner for research, for discovery, for like, you know, all kinds of things. That's the, that's the head's work, that's the augmented work that you're talking about. So if it, if it's a no brainer, give it to AI Automated. If it's a brainer, please keep it. But figure out how AI can better support and augment you and hold like look at all the things that models are great at. Holding context, applying, reasoning, M M Memory. Far better than my human brain is better at memory. So think about the things that models are truly, truly better at or good at in support of our own human capabilities and judgment. I swear judgment is like our new favorite word lately. That's great, that's fine. Judgment is what you apply when the rules run out. So if, if it's not fully automated and if it's, it's augmented but the rules eventually run out. Judgment is what you apply when the rules run out. That's human work, it should be hardly touched or it should be brought to a certain level, a certain place for the human to handle. That is heart's work. It's empathy, judgment, critical thinking. It's most of the decisions around human outcomes. Yes, that includes hiring, firing, promotion, succession, pay. That needs a heavy, heavy, heavy human hand. But even that can be pretty well supported by better data in real time and closing loops and that kind of thing. So it's just us understanding where AI belongs and where it doesn't. And I sort of hate the humans in the loop, even humans at the helm. Like we keep playing with this terminology. It's humans in the lead always, literally humans in the lead always. And just wrap our heads around where AI. If you, if you reimagine any process from the starting point of AI, you can do it better, differently to get to the same high value outcome you're trying to achieve. That's the fun design thinking part of the process. But if it's a core business process, drag it back to the drawing board and reimagine uh, everything from the starting point of AI with that kind of framework in place. And it will help you imagine what needs to be redesigned or even ripped out altogether. A lot of, a lot of things probably don't need to be carried forward.

Speaker A: I get really frustrated when people who are reasonably well known in our space are just talking about essentially leading with the technology like at your workflow and then just start, start assigning, you know, building and assigning agents to do, you know, all these things or whatever. And then, and then take a step back and say, you know, well, what could, what should actually stay human? It's like, what, Wait a minute, what? That's not a very human centric way to think about it. What should, what should start with what should stay human? What are, Think about human potential. Think about all the things that your people are capable of that you're not even allowing them to do is, you know, not that you're not allowing them to do, but it's not within like the scope of, of what they do. But you know, work is more than just a collection of, of workflows and workflows are more than a collection of tasks. There's tacit knowledge and judgment and, and you know, decision making that goes into that. And it's not necessarily a single person's decision in the moment. It might be a, uh, decision making body that is deliberating over something that a workflow just isn't capturing. So you really need to understand the Connective tissue of all that makes M work work, I guess, is one way to think about it.

Speaker B: I, I do agree with you. I think this is more than a sorting exercise. You can't take a core business process. Like, imagine what every company does. They hire people, they process payroll, they make products. They have a customer acquisition strategy. We often call that marketing. But core business processes, regardless of what type of company you are, there's some things that still need to happen. Uh, even in the future of work, we imagine these, these are core business processes. If all you do is a sorting exercise to say, well, customer acquisition marketing usually looks like this. So let's sort out marketing between the hands, heads, hearts exercise. This stuff can be automated. This stuff should be supported, but still kind of human. This should be that. The sorting exercise, unfortunately, will carry forward too much legacy stuff. It won't truly bring you back to a clean slate. First principles, thinking Art of the possible. We all, we all love an art of the possible workshop. But the whole point of an art of Art of the Possible workshop is to forget what you know, forget what you already know. And that muscle memory is so hard to shake. It really is. There's some situations where it's really helpful, where you can just rely on instinct and lived experience and a little bit of wisdom you've gathered along the way. But there's sometimes you just need to turn that off and say, what are all of the ways to find and acquire a customer? What are all the ways to find and acquire a person to join our workforce? You need to ask bigger. I truly think we need to break more. We need to be willing to break a little bit more. Otherwise you're going to say, well, we need an ats. Do you maybe for what, like, ask all of the questions like that you literally need to earn the right to be carried forward in a, in a design, if you still have a valid purpose, like, but that you need to like, earn the right to be brought forward rather than just inherited and assumed by default, which is most of what's happening right now. When I say we're just sprinkling AI glitter on everything. I haven't seen that many hiring processes truly, truly, truly, truly change because of AI. We're sort of just, there's a lot of, like, mimicry that's just a little bit faster. We're still doing most of the micro processes that make up, uh, a hiring transaction. Not to say there's not innovation out there and there aren't people trying some wildly different things. And that's a Lot of a B testing. We're going to learn a lot about whether or not that worked or not. Was that a good way to assess talent? Was that is lights out hiring? No human touch point ever needed from apply to hire M. Who knows? Like, um, some of this stuff we'll figure out by hopefully responsible testing. But we have to be willing to ask the question, does this still need to happen and for what purpose? Have we asked enough questions around how else might this happen? How do we still remain compliant? How does this get sustained at scale? And how do we measure whether or not it's doing anything good? Those are some good questions to keep at your foundation. Otherwise throw everything like almost everything is subject to scrutiny now.

Speaker A: Yeah, no, I agree. I mean, question everything. Right. And, um, let's really think deeply about this and let's make sure we're gathering the thoughts and opinions of, you know, the extended workforce as well. Like how do you. Because I think that the, you know, the trust. There's a trust gap here. Right. Uh, we're trying to manage. Right. Are people using technology responsibly? Is the organization putting the right guardrails in. In place? And then of course, do we trust the output of technology? But I can't stress enough to, to the audience that, you know, we can't. Human in the loop to your point, Jess, is like, you can't. This isn't about rubber stamping something and just being part of the approval process. Oh, that looks good. You know, send it along. It's like, no. What. This required human judgment before, and it was there for a reason. And just because the technology exists doesn't mean we. This is the appropriate place to use it. And it shouldn't take legislation and the threat of a lawsuit for you to do the right thing. So let's be responsible by design. We're all building. I know you and Jason and the team are, you know, you're an AI studio, you're building things. Right? And, and everyone has that capability now within the constraints of, of your, uh, work, you know, relationship. But we've got to lean into people's curiosity and creativity, but they've got to do it in a responsible and ethical way. And so I think those fundamentals are really important when we think about concepts of human centric design which were instilled in me back when I worked at IBM and all the researchers working on those kinds of projects. So those things still hold true.

Speaker B: This is where I, I think governance can be a hero, and we're not letting it be a hero. Most organ, a lot of organizations are still struggling to even like, uh, we're talking very futuristic frontier use cases like what if we did all of these things? And you know as well as I do there are so many organizations that are still at a complete standstill, even banning AI use because they don't have a policy yet or they don't have their plan straight. They're still trying to figure out security and data privacy and data management and all of the what if scenarios. And you can spend forever playing out the what if scenarios and then in the, like, you're never out of the gates. It's uh, it, I, I get how hard this is to figure out. That's why if you can create little innovation councils or little, you know, sort of safer proofs of concept pilots, you can run test things, play with things in a way that you can bring more broadly in your organization. Use, uh, partners. Uh, not just me, there's lots of partners out there who, I mean, we sign almost all of our clients on under a design partner premise, meaning we're going to innovate together, we're going to try this together, and every step of this is together and we'll get through all of your, you know, whatever it takes to bring this into your organization at scale. But there has to be that like tiny little bit of safe trust fall. So you can even. It's hard to understand what's possible if you can't even touch it yet. And so I'm just acknowledging that. I'm well aware that's a very real barrier for some organizations. There are ways to get started while you're getting sort of that safety structure under you that I know your organizations need.

Speaker A: Jess, always awesome to talk to you and it's so cool I get one on one time with you without Jason getting in the way of our, um, conversation.

Speaker B: I know. Look at that. This is what we have to do if we want to have a good conversation. We'll just record a podcast and record it.

Speaker A: Right, Right. Just kidding, Jason.

Speaker B: Totally.

Speaker A: So by the time this airs, your book will be officially available. They don't have to pre order, they can just order.

Speaker B: Isn't that crazy?

Speaker A: Motherofdisruption. AI. Is that right?

Speaker B: Yeah, that's the landing page for the book. So it'll, by the time this episode's out, you can just go to Amazon and find it. But Mother of Disruption is also where you can get on the book list and get any other goodies and stuff like that. Sort of, uh, a behind the scenes. So yeah, I Kind of like, do you know how happy it makes me that I own the domain? Motherofdisruption. AI. I want to form a punk rock band with that name just to claim. Just to like, oh my God, there's so now, like, the domain's mine. I can do whatever I want, right?

Speaker A: Yeah, uh, you can, you can play with that. Just like I have Elevator aiq. I'm gonna play with that and hopefully write a book myself. So.

Speaker B: I love it. You. You said. But yeah, grab the book. And I do mean it. Everybody should read it. I want you to feel a sense of hope and optimism. I want you to feel a little bit disruptive and transformative yourselves. I'm not like a. I'm. I'm pro AI. I'm pro people. I'm pro human. I think we can advance. I think we can fix a lot of old problems. The system is not designed all that well right now to support everybody in it. We're a burned out society. We're like, like we're literally fighting for our own survival, uh, let alone thriving inside of not just work systems, but societal systems. Like, this is such a great moment to tear down what's broken anyway and rebuild something better for generations to come. We have the most powerful, consequential technology of our time to sort it out and maybe make that happen. And I, and I don't. It drives me crazy that everybody doesn't feel like they have a stuff stake in that conversation. We all have a stake in the conversation. And, and it's. It's easier to feel like you are part of it if we address the scary side as well as the hopeful, optimistic side. I think we need more honest conversation around what people should be demanding, what people deserve, what we need to be willing to protect in this moment, and what we get to design for more people to benefit. That's. I mean, honestly, that's the real conversation I wanted to have around the book. I wrote a whole book about AI without ever talking about AI. I talked about what people need to demand and deserve in this time as we're designing the future of work.

Speaker A: Thank you so much. Congrats again on the book.

Speaker B: Thank you.

Speaker A: Thank you for spending some quality time with me. I greatly appreciate it. Thank you, Jess. And, uh, thanks everyone for listening. We'll see you next time.