What makes organizational change so hard and why does AI make it even harder? Tim Creasey and Paul Gonzalez from Prosci join Jeremy and Henrik to unpack what 30 years of research reveals about transformation at scale. From bold leadership to cultural resistance, they explain why change always comes down to people, and what it actually takes to make new behaviors stick.
Generative AI is moving fast, but most organizations aren’t. Tim Creasey and Paul Gonzalez have spent their careers studying why. As leaders at Prosci, they’ve worked with thousands of teams navigating complex change, and in this episode they share what their research says about the human side of transformation.
They discuss why traditional tactics like comms and training break down in the face of rapid AI adoption, and how successful organizations create the conditions for people to actually change. From hands-on leadership and peer-driven learning to the power of experimentation and the ADKAR model, this conversation is packed with practical tools and hard-earned insights.
Tim and Paul also explore how AI is reshaping organizational structures, what “exposure hours” reveal about executive readiness, and why culture beats mandates every time. Whether you’re leading change or stuck inside it, this episode offers a grounded look at what actually works when everything is in motion.
Key takeaways:
LinkedIn: Prosci: LinkedIn
Website: Prosci | The Global Leader in Change Management Solutions
00:00 Introduction to Change Management and AI Adoption
00:25 Meet the Experts: Tim Creasey and Paul Gonzalez
01:51 The Challenges of Change Management
04:07 Generative AI Transformation: Unique Challenges
07:44 Key Ingredients for Successful AI Adoption
15:18 Building a Culture of Experimentation
20:43 The Role of Leadership in AI Transformation
25:54 Future Organizational Designs with AI
27:02 Disruptive Organizational Changes
28:00 Examples of Innovative Enterprises
28:15 Military Analogies in Business
29:30 Challenges in Organizational Change
30:36 Timeless Principles of Change Management
31:36 The Role of Leadership in Change
33:13 ADKAR Model for Change
35:51 Addressing Resistance to Change
40:05 Effective Communication Strategies
47:48 Concluding Thoughts and Reflections
📜 Read the transcript for this episode: Transcript of How Science Suggests You Change Your Organization - with Prosci’s Tim Creasey and Paul Gonzalez |
[00:00:00] Jeremy Utley: Change management is something that comes up again and again and again in these conversations. And today we are so excited to have Tim Cree and Paul Gonzalez from Prosci. These folks have been studying change management in organizations for the last 30 years. And today we're gonna talk about how the timeless techniques of change management. Are being applied at the cutting edge of AI adoption today.
[00:00:25] Tim Creasey: Hi, Tim Creasey, chief Innovation Officer of Prosci. Excited to talk about the conditions of successful AI adoption and here with Paul Gonzalez.
[00:00:33] Paul Gonzalez: Thanks, Tim. I'm uh, Paul Gonzalez, VP of product at Prosci. I've been working in technology and product for about 10 years now.
Um, and I'm excited to share what we're seeing with organizations and teams that are using some of the key change management tools and frameworks to drive successful AI transformation.
[00:00:51] Jeremy Utley: So maybe we just start there. So for folks who are normal listeners to our show and who don't know, Prosci, give us a little teaser for why should they turn it up this week.
[00:01:03] Tim Creasey: Very good. Thanks, uh, and excited to be here. Prosci is at its core, an organization focused on understanding why there's a pile of successful change and what pile of unsuccessful change. So founded by an engineer 25 years ago, just to start to explore what are the differentiators when we, uh, overdeliver on change in outcomes and when we end up struggling.
And sure enough, the people side of change was right there at the very top. Uh. So for as long as we've done change, we've designed, developed, and delivered great technical solutions and then often wished and hoped that the other side of that change coin where people engage, adopt, and use the solution actually comes to life.
And uh, so yeah, we put a bunch of science behind. How do you put people in a position to succeed when you ask 'em to make change inside an organization? So
[00:01:51] Henrik Werdelin: maybe we start with why is change so difficult?
[00:01:55] Paul Gonzalez: I mean, at its core, I guess I'll take this one, Tim, but inertia, right? Like, I think at, at fundamentally, when it comes to why change is hard, um, is that people are hard, right?
People have a lot of inertia, organizations have inertia. Um, and at the end of the day there's tends to be large parts of organizations that, I mean, as you think about, you know, 10,000, a hundred thousand person organizations, a lot becomes like calcified and ossified about the way things operate. To make real change happen.
A lot of times you have to sort of thaw that out and move people in a new direction. And that's just a tricky endeavor, right? But from the technical side, there's like requirements. You can sort of set them in, you can build your solution. There's no one sort of like resisting that. The technology you might fight with a little bit, but ultimately you can sort of control the outcome.
When you think about changing behavior, there just tends to be a lot of baked in, in terms of systems, um, incentives, culture, all those things have like a massive amount of inertia. And at the core, that's really why change tends to be hard. And when it's done right, it can feel so magical, right? 'cause this is like a huge mountain to climb.
That's really what Prosci has been working on for the last 30 years, is how do we help organizations have the right tools, frameworks, methods, to make change a little bit easier. It's not gonna be easy, it's still hard. Um, but uh, that's really at its core why it's so, so challenging. I don't know if you have anything else to, to add on that, Tim?
[00:03:19] Tim Creasey: Well, I think this general AI change is, is a fascinating. Even a different type of change for organizations to navigate. And so Ethan Molik talks a lot about the secret cyborgs, right? The folks that are sneaking AI into work in their pockets. While at the same time we're watching enterprise AI deployments really struggle to gain traction.
And so. Not only has change hard, you know, human inertia and all the making sure we answer the questions people have when they have them. This particular change is u really unique in terms of the individual and enterprise size of the coin, um, and how that's going.
[00:03:57] Jeremy Utley: I think a lot of folks are experiencing that for sure.
A lot of the organizational leaders feel like this is a particularly, uh, gnarly change management effort. Why do you think that is? What's, what's unique about the generative AI transformation?
[00:04:11] Tim Creasey: Well, I think, uh, the first thing I had to start with is that nobody ever smuggled an ERP to work in their pocket before.
Like the commercial accessibility or the, the consumer accessibility to the very top models is like nothing we've ever seen in comparison to a lot of the other technology deployments that were rolled out. And so I think that's one of the big, big changes. And then Paul talks a lot about just the pace of this change and.
What it truly means to the human side of, of organizational change.
[00:04:42] Paul Gonzalez: Yeah. I think a lot of leaders that may not be taking change or change management all that seriously, a lot of times approach change as like a comms and training endeavor. Let's tell people what we're doing and then let's give them training.
If you just believe that's true, which by the way, we do not, um, at its very core, from a comms perspective, it is a tough message to deliver, right? If you're, if you're leaning in from a leadership perspective and just saying, this is about cost. That's like not a really inspiring way for people to get on board.
[00:05:10] Jeremy Utley: Let's go.
[00:05:12] Paul Gonzalez: Why am I gonna like work myself out of this? Right? On the training side, I mean, when you look at the, the pace here, you look at things like, you know, performance is doubling every six months. We're just not used to that type of pace of change, of like transformation technology. So what that means is our old playbooks around, Hey, we're gonna like, you know, tell people what they need to do and then we're gonna give them training on how to do the thing.
Within three months that might be obsolete. Right? Everything's changed. Mm-hmm. I would've taught all this stuff about prompt engineering and chain of thought, and then all of a sudden reasoning comes around or like, Hey, you can't do this with image models. And now all of a sudden now bananas around, like there's so much change that's happening that if you just take our old tactics around, you know, comms and training, again, that's always been limiting.
Certainly the pace is different here, so we need a different approach for how we like get people. Do
[00:06:00] Henrik Werdelin: you think there's some reason why some people seem to be excited about this new stuff and kinda like, see this in opportunities, learn, see the opportunity for getting more autonomy and there are some people that seem almost kind of angry at it where it's like, Hey, this thing is coming from my job.
I, I want to resist it. Is there a profile or how do you think about that?
[00:06:21] Tim Creasey: I think not necessarily. I, I don't know. I'm not sure about the profile of person. I think your first exposure matters, like when you watch it truly do something that's value add above and beyond. You know, give you a recipe for tomato soup and Snoop Dogg's voice.
Um, I think when you see people go into that moment of this is a partner for me to. To work with, to achieve whatever it is I've set out to achieve. I think that's the moment when you see people kind of tip over.
[00:06:51] Henrik Werdelin: I love the new goal when they see something of true value. Tomato Is this new power's voice?
Oh,
[00:06:58] Jeremy Utley: I'm with you on the record. For the record. Truly valuable. Yeah. I wonder by the
[00:07:02] Tim Creasey: way, like it's contextual, hyper personal, right? Because the thing about AI is it can do stuff hyperly. It gives us the ability to actually get done what's right in front of us. And so when I ask it to teach me about MCP servers.
I ask it to teach me in the terminology of barbecue grilling, because I grill a lot and, and so like the ability to mash up different bodies of knowledge is, I think one of those things that I don't know. I, I leaned into it. And I think when you watch people mash up it's capabilities and truly something they're trying to unlock that that's, it's that hyper personal experience.
[00:07:37] Jeremy Utley: So you're kind of bringing in, we've heard what the bad is, right? Bad approach to change management is comms and training. If you could wave a magic wand and kind of, uh, prescribe what are the ingredients to bend the odds in the direction of success, what are the kind of, call it top three or four key leverage points.
Assume you have way too much demand, that you've got a thousand less spots than customers are demanding, and you have the ability to sort through a customer list and say, we're gonna take on one client this quarter that is primed for transformation. What are you looking for in terms of being prime for transformation?
[00:08:16] Tim Creasey: So rather than wave a magic wand, we actually did research on this. So we did research with 1,107 participants, 500 frontline fund, 400 team leaders, 200 execs, and asked them all about how they were using ai, perceiving it being used in their organization, bringing it into their particular part of the organization.
Looked at all of that data through the lens of what are the patterns that are emerging that, that demonstrate the conditions where you get successful AI deployment, because there's a big pile of unsuccessful ones, but there was a pile of successful ones and what are the conditions that really exist there?
So we turned it into a bit of a diagnostic, but if I were to pick a couple of the ones right at the very top, um, uh, leadership and bold AI vision. But what also came outta the research was a balanced vision. So a bold and balanced vision that really articulates how this technology will transform us, but also what we are doing in the near term to try to bring it to fruition.
And I know. Even in some of the conversations with other guests, right. Paul? There's been like, uh, some real specific examples where we're like, we're hearing bold and balanced vision right in the conversation.
[00:09:25] Paul Gonzalez: Yeah. I mean,
[00:09:26] Jeremy Utley: what's, give us an example. What's an example of bold and balanced vision?
[00:09:29] Paul Gonzalez: Well, I think there's a, a couple, first off, when you talk about like code reds, right?
That happened, like, that's like from the top right? Leaders at the top are really saying, okay, this is an urgent moment for us and it's an urgent moment for the, the future of our organization. And we are going to attack it holistically. That means finding ways to use AI internally to, you know, manage our organization more effectively, more efficiently.
That means finding ways to use AI to solve customer problems in ways that we never could have historically, right? That means AI out for our customers, new products, new services, new offerings. So it really needs to be sort of balanced in that approach. It can't be, Hey, we're just gonna use it to like cut out a whole bunch of call center costs, right?
Again, not inspiring. Um, so it really needs to be across that wheel of both internal external use cases and that allows people to sort of internalize, you know, what's in it for me? How might it help my team? How might help my, my department and my organization. And again, I think if you put the customer at the center, assuming you have a, a culture where people are bought into the fact that we are here to serve our customers in a, in a meaningful way.
That's part of what, what bold AI vision looks like. And I think, you know, one of the things SIM brings up is that this is also not, while it is like very specific to AI adoption, in this case, this is not new. You know, strong sponsorship at the top tends to be a leading indicator of transformation success.
You know, that we all sort of know that. But sort of that, that key role that sponsors and leaders play to get their team on, on board and to sort of paint the vision of the future has always been an important part of change. Um, but specifically when it relates to, to AI adoption, there's this couple of different nuances, like this balanced approach, like this bolder vision other than just, Hey, we needed to, to cut costs.
[00:11:10] Jeremy Utley: Can we dive into the word balance for a second? Because I think bold, everybody gets, but. If I, if I, I, I kinda like to think in terms of Goldilocks spectrums, right? Yeah. So balance is kind of in the goldilock spot, I would say a little bit obvious on one end of the spectrum, which is this is all about efficiency.
Yeah. And productivity. And we're gonna do, you know, we're, it's only gonna take 10% of what it used to, right? So we get that. What's on the other end, what should be equally avoided as just leading with productivity.
[00:11:43] Tim Creasey: The other balance. I would, in addition to kind of the content or the topic, I think there's a timeframe dimension around balance.
And we're talking about the near term work we're doing, the wins we're having, the experiments that we're promoting, um, the pilots that we are shutting down intentionally on purpose so that we can put our focus where we need to. And we're also talking about what we're going to look like in three years as an AI infused organization.
Where all of the work that we do in zeros and ones ultimately is up for grabs to be touched or disrupted by this new technology. And so leaders are, from a balanced perspective, are are talking both about opportunity and efficiency, AI and also near term impact and long term, uh, vision.
[00:12:31] Henrik Werdelin: On a very practical level, as you are advising companies that want to do this and we talk about getting the leadership involved.
I get the sense that there's a lot of CEOs and board members that are very big on getting kind of the ai, AI coefficiency up in the organization. Then sometimes that could be, it could be more challenging. Even the senior management, let alone the middle management. As you kind of see successful kind of implementation, how important is it that you have top and then all the way through the organization?
Or can you have like sporadic, kind of like bright lights that you can build on?
[00:13:08] Tim Creasey: I think, and it was interesting, Jeremy asked about like what metric we might use to decide if this organization set up to succeed. And the one that came to my head, and I'll have Paul talk a little bit about it, is exposure hours of the executive team.
If you could give me literally a measure of how many hours executive team has spent with their fingers on the keyboards, not talking about ai, not just listening to ai, but actually pushing the boundaries, pushing the buttons. I would put my money. That might be the metric I would use to decide if this is gonna be a A.
And so Paul exposed me to the notion of exposure hours. But I think when we talk about what's different for AI change and leaders, I think this is a key piece.
[00:13:47] Paul Gonzalez: Yeah, I, I think it matters a lot. And, and to your point, Henrik, you know, we think about how that cascades down the organization. Um, you know, I think there could be a few bright lights in terms of champions and how that plays out.
I do think at some point you end up with a bit of like a Swiss cheese state at the end of this transformation, which is that if there are certain department leaders that are resisting it, certainly if they're openly resisting it in some capacity, that's gonna be a problem for how that then cascades down within their departments.
That is like a, a known challenge. So I do think it starts at the top. It starts at the executive team level. I do think you need to build a strong sponsor coalition across that sort of middle layer of the organization. Um, and you know, champions can carry things, but it's also not their job necessarily to champion this across the whole organization.
Um, and I think to Tim's point, the amount that those leaders are engaged with using AI themselves tends to be a good insight just 'cause they start to see patterns, they start to see. What use cases might be tailored for their teams. To me, that's like some of the leading indicators that you see. So I think you really need to think about holistically enabling your, your leadership level, um, executives and that sort of key sponsor coalition level with at least some AI literacy, AI foundations so they can start to pattern match, right?
How it might apply to, to their team specifically.
[00:15:05] Jeremy Utley: Okay, so we've covered the first one, bold AI vision. Going back to this question of what are the three or four kinda leverage points you're looking for? What's another or, or a key indicator of readiness for transformation? What's the next one?
[00:15:18] Tim Creasey: Next one I think is really around that kind of people side and the experimentation culture.
So the organization encourages employees at all levels to experiment with AI tools, share insights, and learn from small failures. That was kind of the statement that came out of the research. And again, we started to see the success pile was not just decreeing experimentation, but was creating a system that conditions the structure to encourage that rapids learning, sharing kind of what's in and across the folks that are, are really doing the work.
[00:15:49] Jeremy Utley: So how do you build that system? I mean, that feels like. On the one hand you go, it's either present or it's not. You know, pre ai or not. Now, say an organization goes, okay, we need to build an environment where experimentation can be, uh, encouraged and unleashed. Are there, are there ways to establish or to cultivate a, a, a culture cultural experimentation if it's not present?
[00:16:15] Paul Gonzalez: I think there's a, there's a few things that that come to mind for, for me there, Jeremy. First off, I think I was talking to one leader and they were bringing up, um, Toby's like Shopify memo about like how we're gonna move in this direction. And they're like, we need something like that. Like that would not work here.
Like the way you've hired, the way you've built it, the organization is really around a builder's mentality. It's a lot of experimentation baked into the culture. That's not gonna be the, the right like option. You can't just jump to that in an organization. So to me there's like a few things really, Jeremy, that I think about.
First off, it's really disingenuous in my mind to say like, Hey, just start experimenting, right? If you're, if you haven't built that culture within the company, it's not what, what's rewarded. It's not what's incentivized. Hey, take some time to fail or try things like that's just not baked in. I do think you have to carve out some time specifically within teams on very low stakes things, right?
If you say, Hey, let's do a hackathon to solve this one problem, that's an actual problem we have. People are already like, am I gonna get this wrong? Am I gonna get this right? I think it's almost better to, to to find some time that's almost completely unrelated, just so that they can start to use and try out the tools.
I also think you need to, in these moments, take an opportunity to broaden out the tool set that people are trying within AI to sort of see the art of the possible. If there are just sort of enterprise AI tools that they're using, maybe they're not getting exposed to some of the prototyping tool to try things out, right?
Hey, did you even know that you could build your own landing page or build your own app, um, or do deep research on a topic? So I think as you, you start to lower the stakes, um, how they're experimenting with it, give them some space to try things that are maybe not directly even related to like a problem they have.
It starts to create a little bit more psychological safety around trying something crazy, failing a little bit to sort of build that, that culture in.
[00:18:03] Henrik Werdelin: Have you seen other examples? We've talked to a lot of folks that done hackathons. Yeah. We've done a lot of CEOs who have like a session where they do show and tell.
They kind of like share the other team for some of the organization involved. You know, like their loom is being used a lot to kinda like show a different example. In an effort to try to create like I would say, like excitement and in like entertainment. Yeah. Out of like this, have you seen other tools that people are using just to kinda like get the excitement level out so it becomes as much about like, here's a cool thing you can do with it that makes you or your customer kind of having a better experience rather than just Yeah.
Here's the thing that will make your. For you?
[00:18:44] Paul Gonzalez: Yeah, I mean, I think, I mean, just one small example, even internally at Prosci, um, I don't think everyone had known about what you could do in Claude with like artifacts as an example. Someone on our sales team turned in, like, uh, or they were sort of sharing out the pipeline, how that was progressing.
They built like a bit of like Claude Artifact, like small app to showcase that. Like what a great way to actually experiment, try something and then show it to the rest of the team, and they're like, Hey, how'd you do that? How does this play out? So I think there's some of that Henrik that it
[00:19:12] Henrik Werdelin: And that was through Slack, or was that through like an all hands, or what, what's actually kind of like the, so the
[00:19:16] Paul Gonzalez: mechanism, yeah.
Now we have like a weekly email where they're sending stuff out for what's going on with, um, the, the pipeline. And then that email, they all of a sudden I see this cloud artifact. I'm like, oh, what is that? Um, so that's cool. Again, that's something that as an email I would typically read anyway, but when I see this, this new way to expose.
Um, the data, the content, bring it to life in an interesting way. And now I'm going out to that person and saying, Hey, that was interesting. Like, are other people in your team aware of this possibility? How might we sort of bring that that other places? I think there's, like, again, to me that's like a bit of a novel, at least internally, a ProSight was, uh, way to share that out.
[00:19:54] Jeremy Utley: Now, one of the things I wonder is this the, where does experimentation live? One thought is we're all experimenters. Yeah. You know, and everybody's experimenting. Right. Which is, you call it building culture, whatever. Yeah. The other thought is more of there is a team that's dedicated to AI experimentation, I think.
We had Adam and Andy who wrote AI First on recently, and they kinda referenced Ethan's interview with them as in the kind of, you know, the, the last chapter of the book. I read the book after our interview with them, which I enjoyed. And um, one of the things that Ethan talks about is establishing an AI r and d line that every organization needs an AI RD lab.
And to me that actually that shows the way towards a little bit different structure, which is it's not everybody's job to experiment. This lab. I mean, I don't know how you define lab, but can you talk about whether experimentation has to be kind of broadly distributed or centralized?
[00:20:50] Paul Gonzalez: Yeah, I think, I don't think there's a reason to veer off of Ethan's framework too much.
'cause I think it makes sense. And the important part to add to that is that in his framework, it's not just the lab, right? There's leaders, the lab and the crowd that are all working together to create this, this AI transformation. I think the lab is gonna be that area where those are the people that, as GPT five is released, as nano banana is released, are on the frontier seeing what might be possible.
But you still need access to the crowd of like employees broadly to see how that might actually apply to drive outcomes for the business. Right? Like you need to still have access to people who are dealing with problems on a day-to-day basis with customers that are trying to get through a process.
They're trying to build a new, like a, a new solution or something that can then like. Push the learnings from the lab into the crowd to actually see if it's making an impact on the business. So I definitely like the idea of like, I don't expect every employee everywhere to be up to date on the latest and greatest from ai.
And what's possible, I mean, we're, US four are probably in the weeds way more than others, and I don't even know if we can keep up with all the, the change. So it's disingenuous to assume that employees can. So I like in that regard, keeping sort of a centralized lab to experiment with the latest and greatest, but you still need to get that to the edges of the organization to see the impact of how it's gonna actually impact the organization's day to day or, uh, customer outcomes in some new way.
So that,
[00:22:12] Henrik Werdelin: can I ask a little bit? Yeah. On the impact? I mean, like if you look at just. Let's take BarkBox that obviously very involved. We seem to have gone through these like two phases and going into the third phase. The first one is kind of how do you get the coefficient up? How do you get the crowd involved?
How do you get the up and running? All the, I would say the fundamentals. And then I think we've been through a phase where we're like, okay, let's figure out like how do we basically create an ag agentic kind of like, uh, framework for a lot of the different organizations. So how do we take. Um, you know, like everything from supply chain to hr and we create, you know, different agents that can either enhance, uh, what that organ the organization's doing or make it for them, right?
Yeah. The third phase seemed to be this kind of like emerging, kind of like new architecture that almost need to be of the organizational design because. What I don't see is that we're just replacing kind of FTEs with agents. What I'm seeing is that suddenly the designer can do a little bit of the marketeers job because you know they can do something, but they can also write a little bit more, basically like the narrative around like a toy character, for example.
Right? Yeah. Or the supply chain could do a little bit of like the planners, people. So the FTE reduction is probably not gonna be like, and suddenly will just remove people that it is, that there's like this new patchwork kind of like emerging where people can, if they're allowed to do other people's job.
And that would suggest to me that kind of like the third and final maybe phase, at least the third phase, would be that actually like the a little bit static org chart that I guess in many ways was designed for. When we did railroads in the A US in the late 18th century, right? Like is kinda like being in many ways displaced A by this thesis.
And two, how do you feel people start to do the leaps from kind of like phase one to at least phase two, at least phase two to phase three. As we obviously are also seeing like a lot of kind of writing about people who think they're doing a lot of like the phase one stuff, but they're not really seeing the returns and therefore getting increasingly frustrated.
[00:24:25] Tim Creasey: I'm pretty aligned with kind of your, your high premise. I think the very early on I got exposed to a website, it was called, there's an AI for that, and it was essentially just a big library of AI tools, but it was organized around different jobs and when you clicked into a job, it gave the tasks. That that job executed where AI could augment, and that was kind of a moment to me to realize the task.
I think the task is the denominator here. The task is what we pile together to make jobs. We string tasks together to make workflows. We string tasks together in project plans to make projects. I think if we start to actually break down the work an organization does into the tasks, we can start to reassemble and redesign the org.
In really neat new ways, and I think you're right, that AI expertise and capabilities has enabled folks to start to expand, uh, into some different tasks that they might not have, uh, necessarily extended into before. It does take a huge, uh, role in redesigning what work actually means in the organization.
And there's even this, you know, the notion of the work chart, uh, that you're starting to hear talked about above and beyond an org chart, but really looking at. The assembly of tasks to achieve the outcomes that we as an organization are setting out to. And then which of those tasks we wanna keep human, which of those tasks can we identify and automate?
And then which of those tasks do we, do we bring together, uh, in sort of an augmented fashion?
[00:25:54] Henrik Werdelin: And do you think, how far do you think we are from like these new type of organizational designs that are more agentic? I, I seen from the startup side? We seem to be seeing it more and more because you start to see companies that are kind of built from their ai, kinda like from inception.
So when do you think we'll start to see this not just happening in the startup side of things, but like happening in organizations that you advise, for example?
[00:26:19] Paul Gonzalez: Yeah, I think you'll, in the next couple years, I mean, you'll start to see things where let's say like a new product line is spun up. You might take this approach right outta the gate on like a new product.
So as an example, like even in Prosci, we're, we're launching some new products. The, the team stack feels very different as we're starting to invest and build there, just because there's much more of like a ic, super IC sort of focus where people can do more with their, their current skillset and sort of bleed into other things.
I think the bigger change, Henrik, honestly, around that as we move forward will be how do organizations align incentives, align career ladders, align compensation, because there's a lot changing, in my view, this is actually the bigger change for organizations than the technical side over the next two to three years.
'cause it is incredibly disruptive when we start thinking about what's gonna happen, where they're gonna get disrupted from the startup space. Companies that are so small, moving so fast, having such big impact. A lot of that is how they're, they're organized and structured or like in terms of their teams.
I think you'll start to see it in the enterprise in the next year or so around smaller teams, maybe doing smaller pockets of work, launching new products, whatever the case is. But as whole organizations have to shift into this new, new sort of era. I mean that that requires change in compensation trends and reporting structures.
People giving up power. This is like a much slower change and the likelihood that that happens across all, first of all organizations is very low. Right? Ultimately things will sort of, you know, just get disrupted out. But I think you'll see it more spin up for like individual product lines or individual areas to operate in this new way to start to tease out how that'll play out.
And I think that's right in front of us. I think that's, you know, within the next year or so you'll start to see that for sure.
[00:28:00] Jeremy Utley: Are there any examples of organizations that are, um, moving in that direction? More established enterprises who are starting to employ disruptive models or even differential approaches to innovation?
Uh, who, who should we be looking to or learning from?
[00:28:16] Henrik Werdelin: You know, one, one thing that I might throw in there that I've been thinking about is the difference between the US military and the Navy seals. I've been thinking kind of like, I don't have a background in warfare, but like I was thinking about the other day that you kind of seem to have had like a way that battles have changed.
Like you used to have like these like big armies that was very much like these classic kind of companies. Increasingly you have like the SEAL team kind of like approach where you put seven people, you say, Hey you, they report straight in the us The SEAL team reports straight to the president. They get like an objective and they basically have like unlimited resources to figure out how to fix it.
Uhhuh, and it does seem to me that there might be a future where organizational design look more like SEAL teams than on armies because of the change of nature of. Work, but also because that the AI is gonna democratize the ability to create product and services. So you just need to be able to operate at a much higher velocity than you used to, where you could say, Hey, here's an idea.
Let's productize it, then put it on the factory line, and then have people basically create like these different pots of, of the process. You might have to kinda like, yeah, reinvent that.
[00:29:28] Tim Creasey: Yeah.
[00:29:28] Henrik Werdelin: Thesis statement.
[00:29:30] Tim Creasey: I keep thinking about the culture question. I remember reading one time, it takes like half the age of the organization to fundamentally change its culture.
So a 40-year-old company, it's gonna take 20 years to fundamentally shift the culture. And so I think, and it's gonna take intentionality to continue to make the changes that we expect to see in organizations. A lot of org structure was established to support decision making and dissemination of information.
Dissemination of information is such a critical component of all this, and that was why we had to structure that way. And I think through the digitization in the last 10, 15 years, decision making, dissemination of information are things that we now have, you know, at our fingertips. And so I think you're right, we need to rewrite the rules of, you know, organizations create value in a lot of different ways today than they used to.
And we need to start to organize ourselves around, around some of that.
[00:30:25] Jeremy Utley: I want to go back to kind of change management as a practice, as a science, because I feel like we just dove straight into ai, you know, and straight into now. Uh, but Prosci has been doing this for 30 years, so I want to maybe as we kinda wrap the conversation, I wanna zoom out to.
What are the unchanging truths of change management that leaders and organizations can anchor on? What do you know? There are timeless foundational principles that transcend the particular technology that you can say this principle, I, I can, I can land on this.
[00:31:01] Tim Creasey: I, I'd offer up one first, and that's that organizational change ultimately happens One person at a time.
Is individuals doing things differently collectively that actually enables or big organizational shift. And so in the end, it's helping each person who has to more confidently and competently integrate AI into their day-to-day work. It's helping that person through their own personal journey of understanding why and deciding to knowing how to, and really bringing it to life.
In the way that they do their work. So I think that would be one of my principles. Uh, and then my other would be, you know, we've done 12 longitudinal research studies over 25 years, every single time. When we asked about the top contributor to successful change, it was active and visible participation by our senior leaders.
And so that active, invisible role of sponsors, um. Which gets tricky because we're trying to facilitate that experimentation and that grassroots, uh, adoption. But at the same time, we know to get enterprise lift, we're going to need active invisible sponsorship, not just from that single senior leader, but from that coalition of leaders.
And especially as agents, tie the organization more closely together, we're gonna need even stronger alignment around that coalition. So,
[00:32:19] Jeremy Utley: okay. Active, invisible, please pontificate on the paradox.
[00:32:25] Tim Creasey: Of active and visible in today's experimental nature. Uh.
[00:32:29] Jeremy Utley: No. Active and invisible.
[00:32:32] Tim Creasey: Oh, no. Active and visible.
[00:32:35] Jeremy Utley: Oh.
If that was unclear to me, I hope that I helped at least one listener. Oh, Tim is not advocating for active and invisible. Yeah. Leadership. Yeah. That would be the launch. How does the leader achieve that?
Which
[00:32:48] Paul Gonzalez: is a long issue. Yeah. You don't
[00:32:49] Tim Creasey: wanna sign the checks, sign the charter, come back for the pizza party at the end.
Yeah.
[00:32:54] Jeremy Utley: And visible. Okay.
[00:32:56] Tim Creasey: Act that,
[00:32:57] Jeremy Utley: that
[00:32:57] Tim Creasey: makes
[00:32:57] Jeremy Utley: far more sense.
[00:32:59] Henrik Werdelin: What, you guys done a lot of research on these things. What are other kinda like things that people who are listening to this can kind of like, uh, grab from your research?
[00:33:07] Paul Gonzalez: I'm gonna just sort of con uh, it's, it's a great lead in there, Henrik, and I'll tie some of the things Tim just said together for me, I still think one of our biggest contributions to how change happened successfully, again, Tim mentioned happens at the individual level and we always see active invisible sponsorship as one of the key leading indicators.
Ad car. I actually have a book back here with it. Um, but that's sort of the key framework that or, or model that we have, or one of them at least that really helps sort of track how individuals change. Change behavior change within an organization. It's really about do they have awareness for the need for change?
Do they have the desire to participate in that change? Do they have knowledge? Do they have ability, and do we have reinforcement mechanisms built in to keeping this change to to stick? And you kind of see how then active invisible sponsorship might matter, right? Or might tie to those things. Have sponsors laid out a vision, a mission, a goal that that has inspired people to build that awareness and desire.
Have they put enough investment, um, and resources on helping to maintain and, and build knowledge and ability and reinforce it? So that's like when I think about like tactical thing to use. You can view a lot of people side problems through the ad car lens to understand where people might get stuck on, on any given change.
Um, and all the things we've been talking about today around bold vision, um, the culture of experimentation, you could sort of, sort of see how those might map into to ad car specifically. The other key thing that I think is really like pressing in this moment especially, is when we look at sort of the leading people side indicators of hitting those project objectives, we think about.
The metrics in sort of three buckets there is the speed of adoption. How quickly do they adopt the new solution? There is ultimate utilization. Are they sticking with it? And then there is proficiency, like how well are they using the new solution? And I think on one of your actually recent podcast episodes, you were talking about how you were talking to a leader and they're like, we have a hundred percent adoption.
Like everyone's used it. It's like, okay, that might be the speed of adoption, but that's not ultimate utilization or proficiency. It is such like a pressing thing now because proficiency around using generative AI technologies is just a different type of proficiency than could you click through the ERP to generate an invoice or do we know how we're gonna like roll out this new compensation structure?
Um, so if the proficiency matters a lot here, and again, if we support our people through that change, again, viewing it through, through acar, um, you sort of get all of those, we call 'em our sub metrics, speed of adoption, utilization proficiency. You gotta hit all those sub metrics to ultimately get the, the benefits you're looking for.
So those are two things that I think are super useful and tactical, um, that people could take away.
[00:35:51] Jeremy Utley: Can we talk for a second about one of our kind of, um, curiosities is the resistors Hmm. Or the folks who are holdouts just using your s metrics as kind of a, a proxy here. What do you do for the person who is slow, who is low utilization or low proficiency?
How do you think, or how have you learned in regards to change management to deal with that person? How much patience do you give them? How much leash do you give them? And then ultimately, when do you decide if you're not, for example, with the ERP system, if you're a holdout. At some point you just, you're not gonna be successful here.
[00:36:31] Paul Gonzalez: Yeah.
[00:36:31] Jeremy Utley: How do, how do leaders think about the resistant and the slow?
[00:36:36] Paul Gonzalez: Yeah. I think I heard recently, I forget who mentioned this, maybe it was, uh, Brian Belfor from Reforge. Uh, he was talking about AI change specifically, but he was talking about catalysts, converts and anchors. Um, and how do you sort of address each of them, and this maps so well to, as we think about helping people through this change.
I think first and foremost when we think about change management, it's really about how do we, uh, help support each of our employees in a respectful way that helps 'em get to where they need to go. I think you have to provide the right mechanisms, Jeremy, to do that. Right? Are you giving them adequate training support?
Are you giving them the right ability to try it? Or if, if you're just saying, Hey, do this new thing, and that's added onto your existing thing. Is that really a, a fair shot? I think the other important part is like, it doesn't require every single employee all the time to make the change, to get the full benefits.
I do think as, as culture shifts and people move in that direction, they might self-select out. I think at some point you might have to make exit decisions. Of course. But there are some anchors that ultimately, um, as a people manager, as a leader, um, you'll make sure that they have the right tools, mechanisms to, to move in that direction.
But at some point, the natural state is that the current state of the organization and where it's moving is no longer a fit for what they're, what they're looking for in their, in their role. Um, and that's just like one of the, the hard parts of change for sure.
[00:37:59] Tim Creasey: And this is where the ad car model that Paul brought up is also really helpful to truly understand like the root cause of where the resistance is coming from.
'cause not all resistance is created equal, right? So does this person understand why and why now and the risk of not changing, have they made that personal decision? Do they understand what's in it for them and what's in it for the organization and the motivators? Um, have they been provided adequate training and skills?
Uh, the knowledge that they need? Had they been given the space and the coaching to overcome whatever ability, barriers they might have run into and do they believe that if they go through the effort to make the change, we're actually gonna stick with it, uh, and it's worth sticking to and not kind of regressing back to the old way of doing things.
And it helps us isolate and understand where might this change get stuck moving forward. Our research shows that at the top reason identified for resistance and organizational change is that very first building block. A lack of awareness of the need for change. We never made a compelling case, uh, above and beyond this thing is coming, but we didn't connect that into what does it mean to our ability to transform and deliver on our mission, uh, that we couldn't have before trying to make this change.
[00:39:09] Jeremy Utley: And you're saying that's the top reason for resistance, is the person doesn't understand. Why it's so important.
[00:39:16] Tim Creasey: Nobody made a compelling case for why this change is happening. Yeah, and And that's from our historic research. Yeah.
[00:39:21] Henrik Werdelin: I think what's an interesting point though, and I think what you guys brought up earlier, which you know, like it's obvious, but I hadn't thought about it this way before, is that I think CEOs often make a compelling case of why this makes sense for the company.
[00:39:34] Tim Creasey: Correct.
[00:39:34] Henrik Werdelin: Yeah. I think what you mentioned earlier was basically how do you get down to like the individual. Of like, why does this make sense for you? Or maybe even by proxy saying, how does it make sense for the customer? Because I'm sure most staff will, they get excited about that. But what we've heard, Jeremy, I think a lot of these conversation is that people go like, we need this because the world is a nasty place.
Everything is going faster. Like we need to blah, blah. Um, but not, not like at that next level, which I think is an interesting and compelling kind of,
[00:40:05] Paul Gonzalez: yeah. I think an important gap there too, Henrik, is as we think about how we see successful change happen in organizations, part of it's like process. Like how do we approach the change management of this transformation?
But a huge part of it is like activating your leaders throughout the organization. So we talk about the CEO, we talk about those top sort of sponsor coalition members, setting that, that bold vision, having that cascade down through the organization. It needs to cascade down right to people managers, right?
If my manager is not helping personalize that message for our team, and for me, it's very hard for me then to go back to listen up to the what the CEO might have put out there as like the reason why we're changing. So you need to really make sure that your sponsor, sponsor, coalition, people, managers, are all sort of aligned to the messaging and they can help sort of tailor it to the, the individuals as it cascades down the organization.
We tend to see that be a bit of a gap, right? It's, we started here, we know we have to get it to employees. The employees are listening to the CEO's message, but it needs to sort of like come down. Again, Tim can speak to the, the historical research on this, but we talk a lot about preferred senders. Who's the key person that needs to be delivering this message so that the receiver can internalize it and hear it correctly.
This speaks to that. That's sort of a challenge, Henrik, which is that. You really wanna see how it's gonna impact them directly from the people manager of that, of that person. Um, and that requires a whole lot of activation across our organization to make sure people, managers can sort of connect that back.
[00:41:33] Jeremy Utley: What are the hallmarks of a preferred sender? How do you identify the preferred senders in the organization? Yeah,
[00:41:40] Tim Creasey: yeah. So though research we asked in terms of questions around organizational enterprise, business impact, who do you want to hear those messages from? And from the research said, employees wanna hear that message from the people at the top, somebody at the top of the organization sending those strategic level messages.
When it comes to who employees want to hear the messages about how it impacts me and my team, and my day-to-day work, and my own aspirations and goals and challenges and fears, I wanna hear those messages from the person I report to my immediate manager. You end up with these two critical employee facing roles in times of change, the person at the top communicating that enterprise level messaging.
And then as Paul alluded to, really activating the people managers throughout the organization to effectively coach, advocate for, and support their, their direct reports.
[00:42:35] Paul Gonzalez: And this hasn't showed up in our research as Tim and now we're, we're riffing into how this plays out. But I, I feel like, especially in, in today's world.
As we think about that preferred sender and the people manager, I feel like especially in, in AI change as an example, we're talking a lot about these champions or these change agents. There's a bit of like, there are influencers in an organization that just have gravity around certain changes and how the organization's moving.
I think as you identify those and you use those individuals to share how is it impacting their job, how are they doing things differently, um, I think that's like another sort of lever that leaders and organizations can pull to sort of have the, the change stick. At some point it's like. Okay, I heard it from the CEO, I heard it from a manager.
Now I want to hear it from my like influential peer that's actually doing their job differently, more effectively, more efficiently. Um, that is like another important lever that you sort of see and, and it manifests itself in all different ways. You might have people come to town halls or team meetings and sort of do a share out of how they're using sorts of things.
So I think these are other sort of catalysts you can lean on to help move employees in a, in a, a certain direction in order to get that change adopted at scale.
[00:43:40] Jeremy Utley: I wrote down a very nerdy phrase in my notes just now, which is we gotta shrink the attenuation function.
[00:43:46] Paul Gonzalez: Boom.
[00:43:47] Jeremy Utley: And what I mean by that is when you talk about the alignment between whether it's the sponsor and the coalition and the managers, what is the.
Attenuation rate or how much is that message weakened? Yeah. As it, you know, like I'm, I'm thinking back to the old days of Bell Labs, right? And they're trying to get, like, telephone messages across thousands of miles. It really matters, you know, every percentage, you know, decreased over a mile mm-hmm. Has dramatic implications of how clearly the message gets communicated.
I wonder whether leaders are optimizing for attenuation again, to get really nerdy here, but if, if the, the message has to be clear enough that it can be handed off several layers without a reduction in potency, that requires a very, very tight and clear message from the top, which it's one thing, do I get it when the CEO says it?
That's a different question than is this a transferable message that can survive several layers of cascading?
[00:44:49] Henrik Werdelin: I think that's a good point to stop also, because we're running out of time,
[00:44:53] Tim Creasey: that last comment, there's like a good half hour about the difference between biological knowledge and explanatory knowledge.
Like we could get into the notion of generational atrophy, of explanatory knowledge in a really fun way. Uh, that was. Very meaty, uh, way to, way to sign off. That sounds like, but thank you very much.
That
[00:45:11] Henrik Werdelin: sounds like j Jeremy and you kind of conversation.
[00:45:15] Jeremy Utley: He's, I think Tim's just trying to have a more nerdy phrase, generational atrophy.
It's like, that's like a whole other level.
[00:45:21] Tim Creasey: I, I know where I was when I listened to this podcast at one point that talked about biological knowledge and explanatory knowledge. Uh,
[00:45:28] Jeremy Utley: okay. Well give us like, give, give us like the 32nd, just if we don't have hours and some, but somebody's like, wait, do I actually listen to a whole other podcast?
What's the tl? DR Tim, come on.
[00:45:38] Tim Creasey: So biological knowledge lives in your cells. It gets past sell to cell. It's built up over years and it tells your cells how to work. Uh, so it's why Sam and no swim back upstream when it's time to lay their eggs. Explanatory knowledge lives in your brain, and we as humans started to create it so that we could make sense of our physical experiences.
So the first time we looked up in the sky and we saw stars, we had to come up with an explanation for that because otherwise it's too hard to look at that and not, so we made up explanations for what those things were and then. As science and our understanding grows, we begin to have real explanations for a lot of our exploratory experiences.
But humans die like we are driven to have explanatory knowledge. So if we have experience, we can't explain, we gotta come up with a way to fulfill it in. But generational is how biological knowledge gets passed, and that's why you get preferred traits. Explanatory knowledge also gets passed and how well the message got constructed, how effectively it can be internalized.
That enables explanatory knowledge to be passed at a high level of, of fidelity, or one that starts to kind of erode over time, which is exactly what you were starting to talk about when you were talking about the message,
[00:46:54] Jeremy Utley: Tim. What Tim is getting at here is we do not want an AI dark ages. I think that's what Tim is getting at.
[00:47:01] Paul Gonzalez: It's also, there's an insight
to
[00:47:02] Paul Gonzalez: what it's like to work with Tim. Oh
[00:47:05] Jeremy Utley: yeah. Ha. Daily
[00:47:06] Paul Gonzalez: basis. So
[00:47:08] Jeremy Utley: that's it. And folks, if you're interested in applying for a job at Prosci, you can reach out to Tim at, no, I'm kidding. That's great.
[00:47:15] Tim Creasey: Real while we have one more second. Paul exposed me to GPT-3 five on December 2nd, 2022.
And some other time, I'll tell you the story about the video I got from him. Yeah. But how it was a pivotable moment of providing hyper contextuality
[00:47:28] Paul Gonzalez: Yeah.
[00:47:29] Tim Creasey: Of getting to watch what this really means to the work we do. So yeah.
[00:47:32] Jeremy Utley: That's cool. You're saying that Paul designed his message for minimal atrophy?
[00:47:38] Tim Creasey: A hundred percent correct.
Yes.
[00:47:39] Henrik Werdelin: Thank you so much.
[00:47:40] Jeremy Utley: Brilliant gentleman. Really, this is a really cool
[00:47:42] Henrik Werdelin: conversation. Thank you. Really, really appreciate it. Awesome.
[00:47:44] Jeremy Utley: Absolutely. Yeah. Thank
[00:47:45] Paul Gonzalez: you for
[00:47:45] Henrik Werdelin: this
[00:47:45] Paul Gonzalez: time. Thanks Jeremy and Herrick.
[00:47:47] Jeremy Utley: So Andre, what stood out to you, my friend?
[00:47:50] Henrik Werdelin: You know what? There's a lot. I mean, like, it's always intriguing when people do proper research.
'cause obviously they then take all their different insights and kind of package them up in, in these two understand ways. So I thought, uh,
[00:48:02] Jeremy Utley: agar.
[00:48:03] Henrik Werdelin: A lot of it was fascinating, right? I I do think, um, exposure hour was one thing that I thought was interesting. Like as a measurement, we don't talk too much about what is actually things that you can measure to make sure AI get better introduced into your organization.
I thought that was very interesting. I thought, like, as I mentioned also on when we talked to them, that I think a lot of people probably fail to tell staff why does this make sense to you? Why does this make sense to the customer? I think there's a lot of like, we have to do this because it makes sense to the company.
And, you know, being somebody who's customer centric as a founder, like it's just obvious that, uh, that seemed to be like a, a thing that we probably don't do a good enough job as a collective kind of like AI into organization crew.
[00:48:50] Jeremy Utley: Yeah. Yeah.
[00:48:52] Henrik Werdelin: Um, and then, uh, the last one, 'cause there are always three things, like really the bold and balance, right?
This idea that you need like a statement of why this is important, but you also kind of, I, I like the balance approach of like, how do we get going right away. Something to make it a little bit less lofty. Um, I thought was kind of like all also new and interesting.
[00:49:11] Jeremy Utley: Yeah, I, I agree with you there that this, uh, idea of the attenuation function or the atrophy of knowledge, but thinking about the message that is relatable to your point, to the individual.
I, I really like the insight. I think that it resonates with me, especially in the age of ai, that kind of the very, the singular most important. Piece of knowledge about change management is that ultimately it comes down to individuals. That's the first thing, um, that makes a lot of sense to me. And then the insight that the top reason historically about why people resist change is that no one ever made a compelling case for why it's happening.
I think is a really, it's, it's so, in a way, you could say it's obvious. And yet, in a way it's really profound that the people who resist, resist, not because they're malevolent or malicious, but because they don't really, they haven't been convinced this really matters. And I think if a leader said, my job is to make the compelling case.
Why does this matter to this person now if I do my job? And they still resist that. Okay, great. They're voting right. But it's really easy to dismiss resistors rather than take responsibility for I have yet to make the compelling case for why they should no longer resist.
[00:50:39] Henrik Werdelin: Yep. No, I think, I think that is a, a very, very, very good point.
[00:50:44] Jeremy Utley: I'm also just relieved to realize that they were not saying leaders need to take an active, invisible role, because that was so confusing. I was like, I heard
[00:50:54] Henrik Werdelin: invisible too. I was like, is this still storming? Like is this like, you know how to do that? And, and I thought was so
[00:51:00] Jeremy Utley: fascinating. Okay, so I wasn't crazy.
I'm like, what? And then he, he said Active and visible dude and visible. That was, that was funny. Um,
[00:51:09] Henrik Werdelin: I do think, uh, I do think it's also interesting of how important it's to get. I, I think the whole management team involved because to the point about having to explain to each individual by their manager why this is important, you kind of need like the whole chain to kind of like agree that this is important and why, and then make sure that obviously, that they can form the argument in a, in a way that makes it compelling.
But I definitely see a lot of organization where there are different kind of like holes in, in the. In the organization of people. The chain. In the chain. Yeah. Yeah. Where basically like some manager goes like, oh yeah, you know, I'm good enough to not have to bother about this.
[00:51:46] Jeremy Utley: It's interesting. If you think about that kind of attenuation function, any gap in that chain is the, like the, the, the message that I receive as a frontline employee, say there's kind of five levels between me and the ceo, right?
It's a very flat organization. Right. But say there's five levels, if the manager level four doesn't have conviction. The net effect to me in the front line is no conviction, which is to say there could be four layers of high conviction that are totally diluted by a single individual, which lacks it. And so even one way to think about, it's almost interrogating that chain of command.
Or interrogating the, the relay channel to understand where is this message being diluted. It may just be in a single kind of critical communication node that's actually affecting a, uh, unexpectedly big part of the organization. Disproportionate.
[00:52:43] Henrik Werdelin: I think that's it. So do you want to come up with a code word,
[00:52:49] Jeremy Utley: code word, uh, attenuation.
[00:52:52] Henrik Werdelin: I don't even know how to spell it, so I'm hoping somebody else took that.
[00:52:55] Jeremy Utley: Just, just look in the comments, my friend. You're gonna see it all over the place. If you've enjoyed this episode, what should they do, Henrik? If they enjoyed this episode,
[00:53:03] Henrik Werdelin: they should screen it from the top of their lungs. They should.
They should
[00:53:08] Jeremy Utley: find it. Go outside right now. Just go outside and say, on the prompt, get arrested. Is that what they should do? Okay. Yeah, exactly.
[00:53:16] Henrik Werdelin: They should, they should lie. They subscribe, they should share it. They should comment, they should link them. They should do all these different
[00:53:21] Jeremy Utley: things. Scraw it on the jail cell walls.
That's
[00:53:24] Henrik Werdelin: right. No, but you know what some people do that actually means a lot to, they, they write on LinkedIn something that they've heard in the, in the podcast that they really enjoyed. Um, and that makes us incredibly happy. So if you heard something that you thought stood out, write a LinkedIn post and tag us then, uh, it'll make our day
[00:53:42] Jeremy Utley: hearts emojis.
[00:53:44] Henrik Werdelin: Bye
[00:53:45] Jeremy Utley: bye.