AI Adoption: When Behavior Drives or Derails Success w/ Adi Frenkenberg

00:14
Gerardo Pelayo Rubio
Hello. Hello. Welcome to another edition of our In Service podcast here at Service Council. I am Gerardo Pelayo, Chief Research Officer and I’m very glad today to have Adi Frankenberg, she’s a senior customer Success manager with a quant, here with me to talk about a very refreshing angle of a very frequent conversation that we have in the industry which is going to be AI adoption when behavior drives or derails success. Now, before we get into that and we’ll get to know Adi well over the next 25, 30 minutes, I, I want to go through a little bit of housekeeping, reminding everybody that today’s webinars, today’s podcast is going to be recorded so you can go back and access it, revisit it, share it with your friends. We always like you to be not just a listening audience, but a participating audience.

01:05
Gerardo Pelayo Rubio
If you’re watching us on LinkedIn, we see you and you can comment. We’ll try to incorporate those ideas or questions that you might have into the conversation. But getting into the matter, I was thinking why, how to get across the point that this is so important and not be considered on the soft side and which sometimes has some negative connotations and getting a little bit personal. I have a son that’s just shy of a year and a half.

01:40
Gerardo Pelayo Rubio
And it’s interesting, one of the things that you very quickly learn is that no matter how tasty the food is or how nutritious it is, how good it’s gonna be for his health and his well being, if it’s not presented in the right way, if it’s not presented when he’s hungry, if we don’t meet him with the right fork, the right spoon, sometimes the right finger, it’s not going to fly. It’s not going to achieve anything. It’s going to go to waste. And sometimes it’s cheap, but sometimes it could be expensive food, it doesn’t matter. It’s all the same. It ends up in the same place and little by little. One of the ways in which we’ve been able to bridge this gap is by paying attention to the signals, right?

02:30
Gerardo Pelayo Rubio
And trying to listen to not just what he says or mumbles, but how he moves and how he reacts. And when do we anticipate that he’s hungry and try to be around that. And all of that might sound very simple, but yet I know many in the community have kids or have nephews, nieces themselves, and this simple example, I wonder if we’re making a little bit of that mistake, when we think about incorporating technology, incorporating best practices into our day to day, especially when we’re trying to impose those things with the best intentions, but include that for our teams, that’s what we’re going to talk about today. How do we get smarter about that? How do we put a little bit more structure than my very trivial example? But that’s why we have Adi to walk us through that path today.

03:32
Gerardo Pelayo Rubio
I’ve had the pleasure of not just meeting you during the planning sessions, but I’ve been following your content and your insights on LinkedIn. I do recommend the audience to follow her as well. You’ll enjoy it. But Adi, would you mind giving everybody a little bit of background about yourself and what do you do at Aquant?

03:52
Adi Frenkenburg
Yeah, sure. Thanks, Gerardo. And thanks for having me. So I’m Adi Frankenberg. I’m a senior Customer Success Manager at Aquant, where I actually help enterprises and primarily in manufacturing and medical devices and have equipment to navigate AI adoption. And I say navigate deliberately because the technical side of this is rarely organizations is where organizations struggle. Right. So the human side is where success is actually determined. So alongside my work at Aquant, I lecture at Reichman University on the behavioral science of AI adoption. And the combination obviously is intentional. Right. I’ve come to believe that when, that you really need both legs to stand on because the field without theory is just anecdote and theory without fields is just an abstraction. So bringing them together is where the real insights lives.

04:57
Adi Frenkenburg
And I’ve been in this space for about four years now and what keeps me here is something I’ve witnessed repeatedly. So across very different organizations with very different cultures, I see that when adoption truly works, it doesn’t just change processes, it changes people. So the way they see their own expertise, the way they collaborate, the way they show up at work, that’s what drives me. And that’s, I guess, the lens I will be bringing to our conversation today.

05:31
Gerardo Pelayo Rubio
Perfect. That’s a, that’s a great introduction. Thank you very much. And one of the things that probably will surprise you close to nothing is that, but it’s still important to have the data point. We, we go out and ask service leaders at the beginning of every year what’s in their agenda. And 88% of them, essentially all, almost all of them say that they expect the role of AI and digital technology, the enabling role across their service organization to increase during 2026. And I say it’s going to be a little surprised because that’s the talk every Day. Right. But that usually goes around the capabilities that you need. And you raised an important point with these psychological elements, in your opinion, is the greatest challenge when it comes to adoption of these capabilities. Is it technological or is it psychological? And why?

06:35
Adi Frenkenburg
Well, I gotta say, it’s overwhelmingly psychological and I’ll tell you why. And obviously that’s not a throwaway answer. Right? The technology side has come a long way. So the platforms are capable, the models are capable, and integrations now became extremely mature. It’s extremely easy to do. But the remaining challenge in AI adoption isn’t really technical. It’s whether organizations can actually absorb and adapt. And that’s the human layer, and that’s where almost every implementation struggles. What makes it psychological rather than technical is that AI actually asks people to renegotiate things technology usually doesn’t touch. Right? Their expertise, their identity, their sense of what makes them valuable at work. So that’s fundamentally different conversation than please learn this new tool. Right. With the old CRM systems. Which is why I tell organizations really early that this isn’t your typical SaaS implementation.

07:48
Adi Frenkenburg
It requires a three headed creature, someone who understands your data, someone who understands your operations and your people. So all three heads need to be in the room because no single one, not a single one of them, sees the full picture alone. Right. And if you scope those kind of projects only as technological projects, you will solve the easy part, but you’ll skip the hard part.

08:17
Gerardo Pelayo Rubio
Yeah, yeah, I agree, clearly from my example at the top, but maybe a way to just emphasize one of the points that you said, technology is necessary. By no means we’re discarding that, but it’s not sufficient in order to really drive change, to get to the extent of what is possible. And that sufficiency, that’s where the psychological aspect comes in. Now, as I was learning more about you, one of the things that I came across was that you co authored this very interesting article. It’s scary to use it’s scary to refuse it, if I’m quoting it correctly, around the psychological dimensions of a adoption. Now, I’m sure you could feel the whole podcast with the findings of that, but are there any highlights from it that surprised you in particular, any results that you were not anticipating?

09:15
Adi Frenkenburg
Yeah, so it’s a very interesting question. So the article which I co authored with my professor, with my supervisor, Professor Guy Hoffman, came out of something we kept observing, right? People were anxious about using AI, but they were also anxious about not using AI. And I think this is something we can See Also over LinkedIn, people keep posting on their AI usage on a daily basis. So sorry again. So it’s not, it’s. They’re anxious about using it and they’re anxious about not using it. So there’s kind of no neutral ground to it. And that’s the tension. Actually the title is tried to capture and the finding that surprised me most is that AI usage and anxiety aren’t actually linearly related. Right. You would expect that as you continue to use that, the anxiety will probably drop. Right. But we actually found it’s U shaped.

10:16
Adi Frenkenburg
So moderate engagement reduces anxiety and that part we expected, but the high levels of usage. Right. At the both ends of the spectrum, this is where anxiety actually climbs back up. So the lowest anxiety zone isn’t just use it instead of using it as much as possible. This is not a very good recommendation based on what we found. The actual findings should guide us to find a balanced engagement and for field service adoption, that’s a meaningful reframe. Right. Because we tend to measure success by pure usage. And what the data actually suggests is that maximum usage isn’t the healthiest states. Yeah,.

11:06
Gerardo Pelayo Rubio
Go ahead.

11:08
Adi Frenkenburg
Yeah. Just to say that underneath, like the. The whole study is a framework that matters for anyone who is leading this work. Right. So AI anxiety has two layers. This is something that we need to keep in mind. There’s the surface layer. This, this is something that we read in the papers about, right. About job loss, about complexity and stuff. But the deeper layer about the erosion of what makes your expertise yours. For senior field engineers, the resistance we see is rarely about the tool itself. It’s actually about the second layer, what it will do to your expertise and to your professional identity.

11:46
Gerardo Pelayo Rubio
Yeah, that’s very interesting. There’s a lot to unpack there, Georgia. Generating reactions not just in myself, but I see John Carroll, my colleague, also calling out this symbiosis that needs to exist, right. Like the interrelationship between it’s human sand technology, not having to choose one over the other. And just supplementing that comment on how being smart about that co evolution, which I think you’re shining a light on, it’s not a nice to have, but it’s something that is necessary for service organizations to be successful and to even exist in the near and middle term. One of the reactions I had just briefly one of the things you mentioned is because it’s very counterintuitive that the more that you use it, anxiety starts going back up. Right.

12:47
Gerardo Pelayo Rubio
And one of the things that we’ve heard that I’ve heard from the community is also this loss of accountability and the loss of ownership of a process that starts to appear once AI is used in excess. And therefore one might start to wonder, well, where is my value and how am I going to be valuable for the organization in the middle and the long term? So very interesting findings there. And if we take a step back and look not just at the individual’s reaction, but how then they behave within the concept of an organization and the organizational maturity.

13:34
Gerardo Pelayo Rubio
One of the interesting things in the last response that we got from the state of AI and service technology is that a third of the respondents said that the reason for them putting a stop or slowing down their efforts to implement AI with a partner had to do with a feeling that they didn’t have the internal talent to leverage the AI capabilities. And so can you share a little bit about your comments, maybe some experience that you’ve had with the customers on what is it about the organizational nuance that actually matters for the adoption of AI? I mean there’s the, you started talking about the size of the teams, the expertise, what would you say are the key elements there?

14:24
Adi Frenkenburg
So I’m not sure the key elements are necessarily like the size or the team count or the expertise level. Right. This, it matters operationally, but they’re not the real adopt, where the real adoption variants lives. So I think that the nuance that actually matter sits in two levels. So at the individual level. Right. The strongest predictor of how someone will respond to AI isn’t their age, isn’t their title, it’s not even their technical background, even though we would expect that. Right. It’s actually their tenure in their current role. So someone who has been doing the Same job for 15 years has built their professional identity around a specific way of working. Right. They show up every day to work, they expect to do the same thing or something along the same lines.

15:19
Adi Frenkenburg
And AI doesn’t just ask them to learn a new tool, it asks them to renegotiate the value. Exactly like what you just said. That’s a fundamentally different psychological challenge than just learning a software on the other end if you got a six months hire. Right. He hasn’t. He or she obviously hasn’t established any identity to protect. So they’re still figuring out their role. So AI is just part of their landscape. And at the organizational level, the nuance is the composition, I would say, of the team leading this initiative. So not necessarily the size of it, not necessarily the Budget, it’s the composition. So you gotta have like the three, I call it like the three headed creature. Right. So you gotta have those three which I mentioned earlier, which is the data, the operation and the people actually in one room.

16:17
Adi Frenkenburg
Because otherwise if you’re missing one of them, you will struggle, right? But if you’re missing two of them, you will actually fail. Which is why saying we have limited internal AI talent isn’t necessarily a blocker. It’s actually a signal to partner or carefully together.

16:42
Gerardo Pelayo Rubio
I loved your answer. One of the reasons being this different focus from what we see every day on like this more shallow analysis which is everything is determined by the age bracket that you’re in and the generation that you belong and therefore the behaviors that you’re going to have. I also like it because that’s the same result that we got in our Voice of the Frontline research last year where that how long you’ve been at the organization in the role was a much better source of explanation for some of the things that we didn’t even expect. So yes, the quality of the training, the quality of the onboarding, the clarity of the processes.

17:26
Gerardo Pelayo Rubio
But then some of the other more outer elements of the employee experience, like your satisfaction with the salary and the benefits or the influence that data and technology have on your level of mental stress. So the reasons behind that, you’re eliminating some of it as well. But I think this is no minor thing because the service leaders build this learning structures, think about how to reskill and upskill their teams. It’s not just about getting the young ones with the old ones are having separate programs depending on their age bracket. It’s really that nuance that we’re talking about here that should orient those efforts in order to get a better outcome there. Now I mean some might say that we’ve just made the problem bigger that the challenges that service dealers were thinking about. All of a sudden the size of that pie has increased.

18:40
Gerardo Pelayo Rubio
Let’s talk a little bit about how to deal with that larger pie. And in order to put some structure like getting our own collab in this fun conversation. Let’s talk about how do you get started with AI? And one of the elements that I might start to create some segments building on this same research that I just spoke about is roughly a third of the participants define themselves as either slow adopters or laggards when it came to their own organizational capabilities around AI. So for those where that expertise doesn’t may not exist, you might have a loan champion at best. Is there any advice that you would offer these type of service organizations in order to how to get started on this train?

19:39
Adi Frenkenburg
Yeah. So the first thing I tell organizations is you don’t need a perfect setup, right to start, you just need a foothold, something to start with. And what I mean is you should identify like one workflow, right? One moment in the field service engineers day where the friction is real and the friction is actually visible. Not necessarily the most complex problem, but actually the most felt problem. Because when you solve something people actually experience as painful, adoption will follow organically. So they will tell each other word spreads. Word usually tends to spread faster than any formal training initiative. And the second thing is that don’t wait for a dedicated team. In most organizations with limited resources, adoption succeeds because one person decided to own it. And that is usually sufficient. So. And not because you don’t necessarily need a committee to create that.

20:50
Adi Frenkenburg
So when you have that person who is, and that person is usually someone from within the organization, but the actual job is to recognize them, right. And give them the space to lead. And the third thing which I see in many organizations is that. Or what organizations lose the most time is don’t underestimate the fundamentals, right? Manager visibility, like consistent touch points, tracking usage, but as a feedback loop, not just as something that you need to monitor because you implemented AI and learned by that. What’s working, what isn’t, who needs support that doesn’t require any budget, right. It just requires intention.

21:44
Gerardo Pelayo Rubio
Yeah, no, I agree and I’ll double click on that by sort of like taking a little bit of a creative freedom building on your article. But you were calling out this U shape, right? When too little and too much are both represent the problem. And some might say, well, AI again, getting started is going to be very challenging if you have very little resources. But the more resources you have and if you have this strong foundation and group of experts, then life should be easy breezy. I have my own point of view, but I’d like to get your take on it first. Are there challenges as well on the other side of the spectrum and how.

22:35
Gerardo Pelayo Rubio
Same as you just stressed for those that are getting started, how should an organization that has this higher level of maturity, more resources, more expertise, how should they think about starting on this AI experience?

22:51
Adi Frenkenburg
So this is actually one of the most. If you have these experts, AI experts. So how should I say that it’s actually one of the most interesting population to work with, right? If you have those expertise, those AI experts, because the challenge with them are Almost like the opposite of what less mature organizations would face. So the first veteran I see, this is something I came up with recently, is that I think they have the AI powder problem, right? Those teams have real internal fluency and they love the technology, so they want to put AI on everything. Basically, they assume that AI would solve everything. But what gets skipped in that process is the discovery, the research, and the honest analysis of what are the actual pain points that they need to solve.

24:00
Adi Frenkenburg
They fall in love with the solution even before they’ve defined a problem. And AI applied to a wrong problem isn’t fast, it’s just wrong, but actually faster. The second problem is actually more subtle in some ways even more important, I would say. So the internal experts, the people championing AI inside the organization, unintentionally create for them some sort of an echo chamber, right? They talk to each other. It’s kind of like what’s happening on LinkedIn if you follow only AI experts, right? They, they assume their comfort level is the baseline for everybody. And then when time comes to scale to the frontline FSCs, they’re genuinely surprised that field engineers doesn’t relate to the tool the way they do. So the thing these teams need to internalize is that AI fluency is something that you build gradually, right?

25:08
Adi Frenkenburg
You build it through experience, through playing with the tools, through having some success stories or failing with it. You can’t expect the frontline to arrive where experts already are. And trying to force that through training alone is actually one of the fastest way to lose them. So the advice is almost the opposite of what you would expect actually for a mature team. The recommendation would be to slow down on the building and invest more time on the discovery, on understanding the pain points first and then kind of take it from there.

25:50
Gerardo Pelayo Rubio
Yeah, no, I like it how sometimes you got to push people towards the goal and build that confidence and sometimes it’s pulling them and say, hey, let’s take a step back, we got to get this right. And knowing when to do which, that’s the art part of it. I included this comment from John because I think it complements really well what you were just talking about, this habit loop and how once you’re used to something, why it’s so hard to get away from it. I don’t know if you might have an additional thought in there on that, but sort of like a short follow up on this last group.

26:39
Gerardo Pelayo Rubio
I would expect that this build versus buy conversation, that’s it always happens whenever there’s technology that tends to get trickier when you have that talent in house and there’s this perception of, oh, we can do everything right. And that doesn’t need to believed by everybody. But as long as there’s a group that feels that they’re better off keeping everything in house, then that could create a lot of friction for any partnership that is intended. Do you have any advice, any examples on how to either prevent or mitigate this kind of behavior?

27:21
Adi Frenkenburg
Yeah, it’s interesting. It comes pretty often and it’s one of the most important patterns to recognize because the friction is usually not what it looks like on the surface. Right. I can share a story with this one. So were working with a global medical technology organization, highly technical team, extremely sophisticated people. And there was this project manager on the implementation who was keep pushing back at every turn, basically in every meeting saying stuff like the system was too complex, was saying that demonstrating value would be nearly impossible, the conditions weren’t right, stuff like that. So on the surface it looked like an abstraction. But I’ve learned to over the years to listen a bit differently. So over time, through kind of subtle cues from other participants, I realized that apparently they previously attempted to build an internal AI solution. Right.

28:25
Adi Frenkenburg
Right before we came in. So. And it failed, unfortunately. And this project manager was actually been very close to that effort that failed. And it actually unfortunately shaped how I now see every external partner who walked through the door. So once I understood that, my approach completely changed because I realized it’s not like resisting to the tool as a tool. Right. Or the system as a system. So I tried to shift our entire focus to talking about the infrastructure and the foundational requirements first. And you know, the things he had lived through going wrong the last time actually shaped his expectations because eventually he was trying to signal that he’s not willing to repeat the same mistakes that he seen before.

29:29
Adi Frenkenburg
And I, you know, once it clicked for him and I kind of tried to reassure him it’s not going to happen again, he disarmed basically because he realized we’re not going to repeat those mistakes. And you know, that’s usually the way to make sure you have someone to become a committed advocate.

29:57
Gerardo Pelayo Rubio
Yeah, no great example. And scar tissue is a very real thing and I’m sure many here will relate with any bad relationship, it’s hard to trust again. Right. And thank you to Libby Healy one of these comments, highlighting one of the things you said on the criticality of having a true problem to solve, having this, highlighting the success stories A heads up to our listening audience. We will go a couple of minutes over, but it would be criminal of me to cut short. Adi. I want to extract as much as I can while she’s here in the studio, so I’m sure you will appreciate this as well. So you’ve talked a little bit about the differences. Briefly, are there any elements that are common regardless of where the organization set of little resources, a bunch of experts?

30:55
Gerardo Pelayo Rubio
Is there one or two things that are always necessary?

31:00
Adi Frenkenburg
There is one element that matters regardless of where an organization sits on the spectrum. If it’s mature or immature, well resourced or lean, and that’s visible manager behavior, you cannot go without it. And it’s not just support from management, it’s not sponsorship for managers as we love to stay. It’s actually visible behavior from management. And it’s important to distinguish because they get conflated constantly. So, you know, it’s important to remember that one session doesn’t drive adoption, it just drives the illusion of adoption. And I want to give you just a quick example of what real management involvement looks like. So were working with a global medical diagnostic organization. Again, extremely sophisticated team. Most of the companies that we’re working with are relatively sophisticated, I gotta say, and highly skilled people.

32:05
Adi Frenkenburg
So we had deployed the system, we’d done the onboarding run the initial training and everything. And then we started watching the usage data like we usually do, and it was flat, like week after week. We managed to achieve like 5%. And 5% isn’t a technology problem. Right? The platform was working and the data was there and the use cases were very clear. But 5% is actually a human problem. And what changed everything was a director of service who reached a turning point. So he actually took the time to truly understand the system. So not just what it was, but what it could do for his organization.

32:49
Adi Frenkenburg
And when that clicked for him, something so he didn’t send just an email, he didn’t delegate the next training session, he delivered that actually himself, you know, end to end everything like opening remarks and the tone setting and actually live demonstration and working with the system on real time, on screen, with, on screen, with his entire team watching him. Because when your director opens a laptop and uses a tool in front of you, two things happen simultaneously, right? First you can no longer say it’s complicated, he just proved you otherwise. And the second is that you understand that this is not optional. And there’s a concept in psychology called social modeling. Maybe most of you are familiar with it, but it’s taken from Bandora’s social learning theory, which tells us that people form beliefs about what’s possible by watching others. Right.

33:48
Adi Frenkenburg
Especially people they respect. So a memo doesn’t do that, a policy doesn’t do that, but a director, you know, live using the tool that does. So usage didn’t stay at 5% after this one.

34:04
Gerardo Pelayo Rubio
Yeah.

34:05
Adi Frenkenburg
The most powerful thing to remember is that your, like, adoption tool isn’t the technology. Right. It’s your manager opening the laptop and using it first. That’s the actual adoption tool that you need.

34:21
Gerardo Pelayo Rubio
Very powerful example. And just coming behind it with a bit of data that supports what you were saying. One of the interesting things that came out of the Service agenda is 2/3 of respondents saying that they didn’t feel extremely confident. Again, didn’t feel extremely confident about executing their service roadmap. And as we looked at the reasons and the difference between the group that was versus wasn’t the confirming what you talked about visible support from above. But the other one was the horizontal alignment across organizations. The level of importance that took was much higher for those that were extremely confident. And I think what you called out is the key that it needs to be visible.

35:09
Gerardo Pelayo Rubio
We might be on the same page, but if I don’t know that, if I don’t see that reflected in the metrics that you’re using, in the message that you’re communicating to the teams in the data and the collaboration that you’re proactively doing with me, then it’s gonna be very hard that we align across organizational boundaries. So, again, really like that. Two final questions for you. One, we’ve sort of flirted around the idea of engagement as part of the adoption process. And a question we get often is, hey, when and how many people should I engage when thinking about this to be successful? Is it always as early as possible? Is there a sweet spot? What have you learned along the way?

36:02
Adi Frenkenburg
That’s a very interesting question. Actually, there is a sweet spot, and it’s probably not what you would expect, unfortunately. So the instinct in most organizations is to front load everything, right? Engagement ad kickoff, engagement ads, go live immediately. Engagement in the first training and all of that obviously matters. Right. But in my experience, the most important engagement moment comes usually a couple of weeks after Go Live, after users had the chance to actually play with it a little bit, get to know it and not before that. So before Go Live, people are listening to what you say, write about the tool, and after Go Live, they’re still forming their opinion about it. That’s where the real engagement conversation has to happen. Happen because that’s when people have something to react to.

36:59
Adi Frenkenburg
It’s kind of like, you know, having theory exam before start to driving you. You need to start driving a little bit before you actually do that exam. This is like how I see that.

37:13
Gerardo Pelayo Rubio
Yeah. For any teenagers listening out there. If there’s any. Remember she did say a little bit.

37:20
Adi Frenkenburg
A little bit.

37:22
Gerardo Pelayo Rubio
Yeah. Otherwise John’s gonna. Is gonna be mad at me when his daughter uses this as a reference. No, but you’re right. I remember this concept of drinking from a fire hose. And we’ve been getting a lot of that feedback from the voice of the frontline research. The close to 3,000 responses. Field service technicians say it’s very. There’s too much knowledge. The install base has gotten way too complex. Trying to absorb all of that in the two weeks of onboarding, it’s not working. Why are we still trying to do this as if were in the 90s? And I think that applies to how we think about technology as well. It’s a, it’s a mini onboarding scenario.

38:08
Gerardo Pelayo Rubio
And lastly, this thinking about one of these other terms that gets thrown out commonly and I don’t think people always look under the hood of what it actually means. The idea of meeting people where they are. It tends to be used with customers like, oh, we’re gonna meet them where they are and more recently. And I’m glad that the conversation is happening, but we need to put some substance behind it. What does it mean to meet the frontline where they are and how does it connect to the adoption conversation that we’ve been having?

38:46
Adi Frenkenburg
Yeah. So I think meet them where they are is one of those phrases that gets used so often it almost loses its meaning in some cases. But let me try to give you a concrete definition from the field in my perspective. So the first part is actually kind of literal. Right. You cannot understand what the front line actually needs from a conference room. So I’m a big believer in ride alongs. You know, spending a day in the van or on site with the FSC watching where the friction really happens. So what looks like a workflow problem from the executive floor is often like, might be a timing problem or missing knowledge problem or just, you know, my hands are full and I can’t. Time problem. And you can only see that by being there.

39:39
Adi Frenkenburg
And the second part is hearing from every level of the organization. So, you know, pain points don’t just scale up the org chart clearly. What a Field engineer struggles with is often invisible to their manager. And what the manager struggles with is often invisible to the director. Right. So I like to run some roundtables across levels separately because you know, people are more honest in their own peer group. And when you then design agents like what we do in those roundtables, we try to figure out the pain points each level suffers from. And when you then design agents around the real needs that surface adoption is almost automatic. Right. Because people recognize themselves in the tool. But there’s a third layer to that and this is where the psychology comes in and where they’re at isn’t only operational. There’s. It’s actually also psychological.

40:43
Adi Frenkenburg
And there’s a framework I find really useful from a recent how old Business Review article just came out just I don’t know like two weeks ago. And they talk about three psychological needs that shape how people respond to AI. So it’s their sense of competence, their sense of autonomy and the sense of connection to the work. So same AI tool lands very differently on a 25 year veteran rather than a 6 months higher. Not because of age and just because of each of them is being asked to renegotiate about their own role. And meeting them where they are means recognizing that and designing the conversation really around that.

41:30
Gerardo Pelayo Rubio
Yeah, no, I like it and I think I have seen the highlights of that. I’ll go and read that full article. I was going to let you go, but we got a question from Henrik Black. Thank you for that. I believe you talked about this happening this early adopters influencing the rest of the organization. Maybe to expand on that, the added question would be is this something that you see often that like there’s a couple of people that usually start to get the ball rolling and it creates a snowball effect.

42:12
Adi Frenkenburg
Yeah, I do see that often and it’s extremely important when we have like punch pilot that convert to a full organization contract or scope, usually they have a relatively good influence because those people eager to prove right something. So by having those early adopters you manage to create success stories that then are being shared across the entire organization and everybody want to be part of something successful. Right. So those early adopters usually kind of set the level of where everybody else needs to be. And I think it’s really good to have those because they kind of mark the signal or create the signal for all others that could be aligned to them.

43:08
Gerardo Pelayo Rubio
And I would connect it to something you said earlier of how people believe in something that once they see that somebody else is doing it and is succeeding at it, Their own behavior changes and their concept of what’s possible changes. Now it does matter who is it that is this early adopter, when it is, doesn’t have to be the title, but usually when it’s people who are respected within the organization who have this authority not necessarily connected to the title, that tends to carry a lot of weight. So we do have to wrap this up at some point, sadly. But before I let you go, you’ve shared so much about the intersection between the psychological elements and the implementation of technology. Can you tell us a little bit about yourself?

44:09
Gerardo Pelayo Rubio
Maybe some passion that you have outside of this, something that’s coming up in the near term for you that excites you?

44:17
Adi Frenkenburg
Yeah. So I’m actually working on something right now that I’m genuinely excited about. So I started kind of vibe coding an app. Yes. I’m obviously using AI for that. Right. So that should help organizations to diagnose themselves in a way. What’s actually preventing them from getting the full value of the tools that they, the AI tools they’ve chose to adapt. Like I, I try to capture there the cultural factors, the communication patterns and the management behaviors. Because what I’ve learned in four years of doing this work is that most adoption problems aren’t visible on a dashboard. Right. You cannot infer that just by from reading data or trying to analyze data. So they live actually in places that you don’t usually get measured. And honestly, that’s the same reason. I gotta say, I’m excited about something else coming on the calendar. June 10th.

45:22
Adi Frenkenburg
It’s field service edge 2026. So if this country conversation resonates with you should be there. It’s where the industry is coming together for exactly this kind of candid conversations about AI adoption and what’s actually working in the field. We have an exceptional lineup. So Shane Parish, if you’re familiar with him, is providing a keynote. And we will have practitioners from the organizations that we’re working with like Waters, sandfix and ativa, GLJ and others which actually navigate AI adoption real time. I’m planning to moderate this panel on AI adoption. So you should be there if you’re interested in this kind of content.

46:13
Gerardo Pelayo Rubio
Absolutely. That’ll be fun. John Carroll, that you saw in the comments, my colleague, CEO and founder of Service Council, he’s going to be joining the conversations as well. So it’s going to be a very exciting day for sure. Adi, once again, thank you so much. Thank you for listening and participating audience. I’m sure you had as much fun and got as many notes as I was doing over here. Hope to see you in our next event, our full calendar, and have a great rest of the month. See you soon. It.