Dave Drodge, Lecturer and Digital Transformation Leader, on AI adoption, Behaviour Change and the Importance of Harnessing your Champions.

David, thank you for joining me this morning for me, but this afternoon for you.

Great. Yeah. Thanks for Neil for having me. Really glad to be here.

So you are in Switzerland right now.

Yes.

And you are originally from the East Coast of Canada. So you've covered off our two major geographic regions in one guest.

Yes, exactly. Yeah. No, actually I have not lived in Canada for over 25 years, but it's a case that, yeah, I grew up there, went to University of the Whole Works, had my first job there.

Yeah. Well, and always an East Coaster, right? Like that's deep in the DNA. I want to, for a second, situate you in the kind of mid to late 90s. So it's it's 1996. You are uh starting a role as the European Digital Communications Manager for Sony Europe. Uh 1996, um, digital communications uh was very, very new, right? That often meant like building the first website. So you've been at this for a long time and you've seen a lot of change. And you have been through cycles of transformation that each time those cycles have happened, right? Digital, mobile, social, there's a lot of prognostication about the impact and how things will never be the same. And here again, we sit with likely the most transformative of all of those technologies, AI, which you've been spending a lot of time with. How do you feel that things are different now as we are in the early days of the AI transformation than they were then, as you were uh very much at the forefront of a digital transformation?

Yeah, that's a that's a great question, actually. And I remember that I had just graduated from a master's degree in Dublin, came over to Cologne, Germany, and uh around that time Radiohead had the uh album out Okay Computer. And I could not imagine today how different things are than what I had thought it would be back then, or how things have just evolved. And again, you know, like for instance, all those waves that you mentioned, I mean, I've always been trying to keep up with them. But AI has been compared to more like electricity. Um, and it's a case that uh a lot of people are saying it's not just a GPT um as you know, the technology, but it's also in economic terms, a general purpose technology. And you know, like electricity. I mean, like for instance, I mean, it took decades for it to come through and actually for us to really understand, apply it, and not just say we're motorizing um something or we're we're we're just kind of you know using the old technology to the new technology to do something that we did before. And I think that's what we are right now as well. But the change is so rapid, it's exponential change, and it's very difficult for any of us humans actually to keep up with it.

It's interesting because you know, 96, it was the tail end of uh my university. I graduated in 94, and my oldest son is in first year right now, and I compare sort of the, you know, the technological infrastructure of uh my time at university uh to his time at university. I remember my roommate saying, Oh, if you come with me to the library, there's a terminal there where you can set up an email address and then you can email your friends, the very few number of friends uh that are at other universities who also have email addresses. And at the time I was like, there could be no more friction in this. Like, I need to go to the library to open up this computer in the hopes that one of my, you know, three other friends of an email address have also gone to the library and have sent me an email. Like there just, you know, that was the sort of cutting edge of technology. And you fast forward to now, and it's just wildly different. And to your point about pace of change, that took a very, very, very long time. And what we're seeing now is, you know, we're measuring things in product release cycles, right? Not in uh in generations. So how are you keeping up with this? Like I feel like there's a small group of people, um, and I think you are included in that group of people who are being called upon to help uh Shepherd entire organizations through this change. There are only so many hours in the day. How are you staying on top of this? How are you figuring out how to leverage this new technology for both you and for the people that you have been made responsible for in terms of adoption?

Yeah, so I mean, you know, going back to your point about you know, having an email and then maybe only three people that you know have them. I mean, we're in this situation right now where, I mean, you know, social media platforms in general, but you know, professionally LinkedIn is just this, it's not as filtered as we probably would want. But it's a case that, you know, this rush of information or data coming at us almost. And um, I think that that's that's that's helpful. And also I think that you know, we need to step back and be a bit humble to say we can't know it all. Yeah, we can't really um you know say we're an expert at everything, we need to pick our battles from that point of view, but then find those allies that can actually help us when we've got a question about the latest that's happening in agentic AI, or uh, but also to take a deep breath as well and say, look, you you you just can't you can't take it all in. Um, and you really just have to be uh selective about that. So I think you know, surrounding yourself with other people that are interested in this and actually can can bring new ideas in and having conversations about them and you know, even meeting up in meet space and actually having a coffee or something helps to have those conversations so that we go together uh forward. Uh, but it's uh it is difficult. And it's something where I, like I said, if anyone says they know it all, I kind of go, I don't know.

Yeah. Well, and also by the time they finish that sentence, they don't know it all because things are changing so quickly. So started at Sony. Um, most recently you were at Rush and you were really charged with helping to guide AI rollout there. Can you talk a little bit about what that experience was looked like? You were the you were the group lead of digital transformation, uh, AI and digital marketing, which is that's like a business card in the old days would have had to fold out because that's a big, that's a that's a meaty title. Um what did what did that mean? And and what was it like to kind of help shepherd that organization through digital the AI change?

Yeah, so I think that one of the interesting things right now is that, and not only, you know, where I was before, but you know, other organizations, even ones that I volunteer in, um, it's a case that unlike digital, like you know, you you mentioned those waves, and it was always a lot of convincing, you know, management, especially, uh, your colleagues, this kind of stuff. And at least the leadership side of things with AI, it feels like they were like, yeah, we we need to do this and we need to do it right. Um, and and that's the something where I haven't seen that kind of level of appetite for the other waves of digital change. I mean, it's a case that, you know, you, you know, you you run a little experiment here or there, and you come back with some facts and you kind of say, I think this is the gonna way to go. We should invest in this. For for AI, it was it was a lot, um, you know, people got a lot faster. Now, having said that, I mean, you know, Chat GPT is is the most recent part of that, but I mean, AI goes back a lot further than that, and and for the organization as well. And so, therefore, I think that's maybe part of the help that was there was that you know, we had gone through other waves of uh AI that were actually already, you know, proving business value. And therefore, they could kind of say, okay, well, this is something that's just really exploding, and we really now see that this is something we need to get uh on top of. And and and again, and and I think a lot, there's been a lot of studies about this as well, where you know, leadership seems to be gotten on this very quickly, and a lot of people are a little bit like, oh, is this really you know gonna be the transformation that our leaders are saying? It is, are they giving me the right tools? Are they giving me a time to try this or you know, upskill in this area and all those type of things as well, which are super important components to adoption and really the focus should be on people. And I've been lucky enough in organizations that have been very people focused, which is great.

I find with AI, given that it, you know, to your earlier points like a general purpose technology, it is very different downloading Chat GPT than it is downloading Google Maps, right? Google Maps, it's a you know, it's a very, very, very specific set of use cases. And sure, like some people are like super maps users and they've got, you know, uh saved places and they've got all this stuff every time they go to a different city, they've, you know, their whole life is embedded in maps. But for the most part, like you put an address in and it works, right? Um, Chat GPT, as you think about a business application, it doesn't feel quite as frictionless and it doesn't feel quite as purposeful. Have you found that as you're helping to lead rollouts in organizations that this is sort of like it can be anything to anyone, and so therefore sometimes like it's nothing to no one?

Yeah, I think that's a great point. So, I mean, again, you know, if you went out, you know, like for instance, when electricity was really starting to take off, it's a case like, for instance, be like, okay, yeah, that's cool, but how do I use it? And I think that's exactly it. You really need to figure out what's relevant for the individual that you're talking to or the group that you're talking to. Um, and and also, especially with management, expectation setting, because you know, they think, oh, you know, general purpose technology like electricity, great. We're gonna, you know, just give this to people and they're gonna figure it out and we're gonna save so much time, and people are gonna do be able to do so many more innovative things. Well, it doesn't work that way. I mean, it's a case that the technology is the small part of it. I think BCG came out with something and said 10% uh the algorithm, 20%, you know, maybe more the the the uh the technology, for instance, or maybe the I'm not getting it right now, maybe the um the process, and then 70% of it is people. And again, it's something where you need to set them up for success. And there's lots of studies that show that when you know the leadership gets on board with this and they really role model it, where they actually say, This is what we think it's useful for, and we're gonna give you time to experiment with it, we're gonna give you some skills um and and time to kind of figure that out, that's when it really takes off. But again, it's it's it is a bit like a Swiss Army knife, but it's a case that every tool is not gonna be the best of class. So if you've got a specific use case, I can think of lots of them marketing, you're better off to have that specialized tool. But you know, it is a good Swiss Army knife, and the Swiss Army knife is getting better and better all the time, and there's more features that are coming out with it.

So maybe, you know, in a way that doesn't reveal kind of like state secrets and you're gonna get in trouble for. Yeah. Can you maybe tell me some of your implementation stories, some of those implementation battle scars, the I thought this would work, but it turns out that uh I actually needed to do it a little bit differently, or wow, I was uh particularly surprised at how effective it was when the team did X. Like maybe talk me through some of what your experiences have been as you've done like really meaningful large-scale rollouts.

Yeah, so I can give a I think of a couple of examples, and I won't name names, but it was something where one thing that I've seen, and again, I'm I'm probably banging away at this too much, but you know, like for instance, if you look at the typical IT rollout, technology's here, this is how you use it, you take an hour course, and there you go, go for it, kind of thing. And that kind of bottom-up approach, uh, or I should say that IT approach is something where that will only get you so far. And um I've seen it in organizations where exactly this is it. You know, they give the tools and then they say, okay, let's go, you go. And I think again, there's some good studies out about this. Um, thinking about one from BCG, and they say basically, like, for instance, that you know, you need to have the leaders on board, they need to role model it. You need to support people with things like training. I think they've actually said about five hours of training is actually what you want people to do to actually get the most benefits out of it. Um, and on that one, you know, we're not just talking about traditional kind of like, for instance, like training online just by itself, but really hands-on stuff where you get actually people roll up their sleeves and try to do things that they're doing in their day-to-day work. Also give them examples where it's useful for at home and stuff like this. I mean, sometimes that's where you get that wow factor and you kind of light a fire under someone to really get in there and kind of go, well, yeah, if this is working for me at home, then you know I can do this at work for this. Or you hear a story from someone else that maybe is a bit further along in a team. And I think that that again, it goes back to humans. I mean, like for instance, when we do things socially, we move faster together. And I think that that's one of the key lessons that I've taken from uh digital adoption, just generally, but specifically and especially AI, because it's something where, yeah, I mean, it can be a bit overwhelming, and you know, if people are not up on it or interested in it, they're just like they might just think, oh my goodness, this is just too much. And you really want to keep it grounded and for people to actually have things that they go away from a hands-on session with a colleague to the next day going, yeah, I can try this and I want to try it.

So it sounds like training is a big part of it. When you've done some of your implementations in the past, how is the technology sort of packaged up and delivered to the people who were going to use it? And I think about maybe frankly, two ends of the spectrum, right? So one end of the spectrum is we had uh Kareem from Bayer on the show uh last year at some point, and he was talking about, you know, Bayer did a really amazing rollout of kind of a purpose-built AI platform for the team that was kind of centrally managed and trained. And the rollout was really about uh you know, was primarily focused on sort of that platform, right? And then you hear other people where it's a, you know, we've got ChatGPT enterprise, everybody in the company's got access to it. And what we're really looking to do is try to get uh sort of adoption and usage of our ChatGPT enterprise um licenses. You know, those are two very, very different kind of ends of the spectrum. When you're thinking about uh getting people trained up, has your experience been it in both of those categories? Like let's get people trained up on what is essentially sort of a you know commercially available product, and or let's get people trained up on a very highly specialized, bespoke platform that's been built kind of by and for the business?

Yeah, and it's actually really interesting because uh literally last night I actually listened to that podcast. So this is a perfect question. And so everyone knows there was no money under the table. This is like completely spontaneous. But having said that, I think, and and I've heard this from other pharma companies as well. I was to an event uh last year, and when they were talking about it, it sounded, you know, I could see a lot of similarities. I think a lot of companies in general, they have taken the different packages available. Because when he was talking about it, he was talking about you know different models from different providers, putting them into a package where it was really like, hey, this is your entry point into the world of generative AI and large language models. And if you feel you want to do a little bit of tweaking under the hood, you say, Yeah, I really want to use Claude because it's really conversational and gives me great text, or another one is better at um, let's say, for instance, like, you know, data analysis or whatever else. You have those choices, but it's a case that we've brought them to you so that you can use them in an easy way. And I think that that's something where that's uh super useful. And then the other side of it is more the co-pilots of the world, the Google Gemini uh for workspace, that kind of thing, where it's kind of embedded everywhere. And it's just a matter of, you know, kind of having a little bit of that reflex of, hey, I can go there. And when I'm in my normal workflow uh for general productivity, I can ask it something, and then it's gonna help me along. And I think both of those are super useful. And what I've seen anyway in organizations um is that the first one, which is like, for instance, hey, there's one interface to go to. You can pick your model if you want or and do things there, but it's something where it's just a general chat thing. And then as organizations have got comfortable with that, then uh sometimes, like they said, they brought along a co-pilot or um a general workspace and embedded that, and then they go into the next level, which is actually, okay, well, how do we actually find the use cases that are really high value? This is applicable to, and is a real pain point for us right now, and go beyond those things and actually deliver real return on investment for the organization. And I think that's the next level up, which is really getting into the specific use cases and taking a step back and saying, okay, we have this technology, we have these pain points, and we can change how we do business, those processes, so that we actually do it in a better way. Because a lot of the processes, especially in FARBA, I mean, we've got an SOP that's been there for decades, and it's a that uh this stuff can actually help us to do it in a much better way, make sure that we actually are um respecting people's time so that we can actually get uh patients' uh medicine in a faster way or diagnostics in a faster way.

And so how have you found it to be most effective to develop those use cases? Like there's there's this funny, you know, there's a technology adoption curve in any organization. You know, you hear a lot about the sort of like AI champions, right? So the people who've just like they're off eating lunch and like they're you know, they've got an agent talking to an agent producing something while they're eating their pizza, and it just it seems like a very different world than someone who's using whatever AI to tidy up an email or you know, do something like that, right? So the folks who I imagine are sort of the power users, their use cases may be uh, you know, sort of almost out of reach for the folks who are lower on the on the curve. And the person people who are lower on the curve, their use cases uh may be so kind of rudimentary as to not feel like it's kind of delivering on those expectations of the senior folks, like you were saying. So, how do you find that balance of here are use cases that make sense? Who's in the room when that happens? Is there a combo of you know, marketing and IT and even like HR sometimes when you talk about the LD folks? Like, how do you find the right mix of people to say this is a good use case to roll out for a brand, a division, a company?

Yeah, that's a really good question. And I think that um maybe at the most broad level, you know, those people that are the ones that are really willing to roll up their sleeves and and you know, give this a shot, experiment, those champions are ones that leaders should elevate and say, hey, you know, you know, Joe Bloggs has actually, you know, been you know working away with this technology. This is an example. I've learned something from that, and now I do this in my way. And I think that you know, that's that's that's super important to make it very relevant to the team, you know, not only like for instance, the from a bottom up that you know, we have champions, but then the leaders are also role modeling and saying, hey, I'm using it for this, and therefore I'm finding that super useful, or I discovered this, and really bringing it to the table on a regular basis so that people are aware of it, and you know, then they start to go, oh yeah, okay, this is this is interesting, and I I can use this. Um and I think then again, you have these bigger things, which is really making sure all those people you mentioned are in the room to design a program where you're actually looking at and saying, this is a big thing for us, this is super important, a pain point, and you know, we can do this better, and finding ways to kind of bring that about. And I think that as long as the the mass of employees in an organization are are along that journey, you've got the champions kind of highlighting quick wins, things that are important, and then you're saying we're reconstructing some of the bigger processes, uh, again, with the leadership on board and really role modeling what you're doing, then it happens a lot more naturally. And I think that that's exactly the momentum. That you want. You really want people to be in a room and go, okay, I need to take this seriously. This is not just one other change program that we've you know had before and it's gone away because you know some per some person high up was really behind this and then they left.

Yeah. That change program is now over at Novartis because someone went there and that's yeah. I won't name names, but it's something very um talk me through uh what you've seen maybe practically that's been impactful when it comes to getting over some of the hurdles of adoption. Because the more people that I I talk to about this, I think organizations again are are struggling to figure out when and how and and where they can sort of move the needle, I think, on the impact of adoption. I think at this point, uh to your point, you you you mentioned kind of like home and work, right? Like almost kind of anybody that's remotely digitally savvy, my mom's using Chat GPT, you know, at home for for stuff, right? So I think that that kind of adoption I I'm not talking about, right? I'm not talking about like you need to download this, you know, software. But you know, I I think that that where you where I see people struggling, um, maybe when I say people, I mean me, but but when I see people struggling, there is this awareness, I think, of the promise of what mass AI literacy can do to an organization. And I think often it is around creation. Like it will take you way less time to create your PowerPoints and way less time to create your marketing assets and way less time to do these things. And I think that where I see lots of adoption is helping to maybe accelerate some of those inputs, but not necessarily, but you know, by virtue of that, accelerating the output, but not necessarily being quite as transformative in terms of the output broadly, right? Like I see it in like, you know, um design or strategy, you know, for sure. And so I think that in that adoption or that training, there's hurdles to get kind of mass literacy where people can really start to see gains. And I don't know if if you've seen that, if that's just maybe my own kind of lens on things, but you've you've done this at scale. And have there been aha moments for you when you're looking to get the sort of gains at scale, if that makes sense.

Yeah, no, no, it makes a lot of sense. I mean, and I think it goes back to the bottom-up approach that I've seen, where IT has got a couple of courses and they do a little bit here and there. And, you know, people then kind of go, oh, okay, I've got that training done now, you know, and they don't use it, uh, they don't adopt it. And that's where, again, I'll go back to this BCG report. Like, for instance, like five hours, I mean, for a big organization is a big commitment. For sure. Um, and it's a case that um, you know, if you think about all the change managed programs that you've seen in your organization the last three years, very few have that kind of commitment for every employee. And when you do that, that's when you have those breakthrough moments. When you have people coming together in small groups, you know, teams, and sharing their stories about what's working and what's not working and asking questions in an open way, that's where you start to get the breakthroughs because then you have the people that are a little bit ahead of the curve. You also hear what are the pain points for people. And then again, you know, if it's set up this way, and I think it's a good way to have AI champions, those AI champions can kind of come back and say, going back to that community of uh practice and say, guys, this is the kind of stuff that's really bugging my uh my teammates, my peers. How about you? And trading those stories and everything else. So they're learning from each other, they're able to bring new things. And for the organizations I've been in, that's super valuable because really is something where you see some parts of the organization of, you know, they're they're way ahead. Um, and it's a case that uh, you know, those stories as well can help the art other parts of the organization to catch up. And you know, it's always gonna be different speeds within an organization. I mean, some parts of the organization are also usually the laggers. They kind of sit back and say, okay, let's see if this actually really takes hold or not. Uh, and I and I think that it's like something where if you just put a little bit of effort in, um, you're not gonna get the bigger returns that you would want. You really have to kind of go, um, I would almost say, all in and really go for it. And it's gonna evolve, it's gonna change, the tools are gonna change. Um, again, going back to that example where you've got a chat interface uh for the company, and then you know, it it the model picks which model it's gonna use, for instance, an orchestrator, or you pick it yourself. It's a case that these things are gonna change. But you know, the good thing about this technology is that you know, moving from a ChatGPT to cloud to Gemini, okay, they have all the particularities, some are better things than others, but it's a case that the technology is pretty straightforward. Um, and I think this is also one of the things that's different than other technology waves, is that um it's more getting in there and practicing it with it and understanding how it works for you, your team, and the organization than it is do X, Y, Z, and then you get the output that you want in a very um let's say let's call it reliable way, because one of the things about this as well is that uh it is um not always the same output. And as users, as individuals that are using this technology, you need to think about that as well. And that's the other thing, too, as well, about the training is like is also to not be over reliant on it, to always question it, you know, like for instance, and even not to treat it like this Google bar that's open for any text. It's really this idea that it's a conversation that goes back and forth versus just this is the answer, I leave it at that.

So, you know, you've talked about training and you've acknowledged in a large organization, sort of you know, five hours per person, and I know you're just sort of picking a number, that's a material commitment. You also earlier in the conversation mentioned sort of executive expectations. And so, you know, where have you seen folks put and again being sensitive to sort of what you can and can't say? I have to imagine there's some kind of KPI or metric that starts to get set. We're gonna invest five hours per person, we're gonna spend a bunch of money on these platforms. That is a notional investment of X, and we would like to see Y come out of that. Are you seeing larger organizations start to put either very, very high level or individually specific metrics against folks to say we are expecting uh this to come out of our investment, to be the result of our investment? Or is it is it a little too sort of ethereal right now to start to put actual metrics against it? How have you seen organizations deal with the um these are the um hard numbers that represent our expectations?

Yeah, and I think that that's a great point because that's where you go from yeah, really a scarcity mentality to an abundance mentality. And what I mean there is that you don't want to set it too strongly against the efficiency gains and the things that are easy to measure. On the other hand, it's a case that it is an investment of uh people's time, especially, and then technology and trading and all those things that need to bring together to actually make it successful. And to say, well, we're doing this, we expect these types of things. Again, I would be more aspirational about this means that we will be able to do more of the things that really matter, and we will be able to be faster at the things that are a bit more um rote, you know, like for instance, and and automatize some things, they make them automatic. Um, but it's something where to have that mix, and um, and that's where you know it's important, I think, to set metrics, but it's also to it's uh to have those quantitative and qualitative. So some of them are just surveys, like for instance, I mean, you know, especially if there's tools available in the organization already, how often are you using them? You know, how how are you saving any time? What are they good for? And then uh to measure that throughout your process as you go through. And if uh possible, and and this is you know generally the the case, it's a case that to also get the metrics of actually, well, are people really using it? Um and have those and to be clear about the fact that you know, as an organization, and I think it's probably better that it's more at a team level or you know, kind of you know, business unit or whatever else, we're going to be looking at those analytics to kind of see what's working, what's not working. Um, and then I'm you know, I'm based in Switzerland, and it's a case that, you know, here in Europe, you know, it's a bit um, there's a lot of you know, you know, protection for workers, so therefore, you know, it's maybe more difficult to actually get to the person level, but it's a case that at least then you can start to see, okay, this seems to be working in in Team X. What are they doing different than team Y? And and to be able to bring those things to together to try to actually make it work. And if it's not working as well, um, to kind of you know acknowledge that and try to figure, okay, well, you know, is this the right thing for that part of the organization? Uh, but my experience has been that if you treat it that way, um, you're not trying to just uh look at for the uh easy things that are to measure, um, especially the efficiency gains, that it's a case that uh you know people get the right intent out of it as well, that we're trying to actually do a better job in general. And it's not just an efficiency gain. Because I think as soon as you start going down that road, you know, then you really get people scared. Um, and also you then people start to have these kind of like, okay, that's not for me uh because I'm afraid of my job and other things. And and those are real fears that are, you know, kind of come up. And it's again up to management to say that's actually not what we're looking for. If they're not, if they are, well then they should be honest about it as well.

It's interesting to contemplate the possibility uh that we're gonna move from the carrot to the stick zone, right? So the hey, we've got five hours of training and we're gonna support you. And here's the champions like these are all kind of carrot tactics, right? We're gonna help you along. Now, in knowledge work and and in creative uh you know, creative work and brand work, and I guess maybe sort of luckily or not so luckily, you know, we're not measured. Uh yeah, the effects of our work is sometimes measured in market. Frankly, sometimes it's not even measured in market. Um, so it's it's there's there's not maybe as many hard metrics, but I do wonder if if there's going to be a move to the sort of stick category where individual metrics or team metrics or usage stats are are gonna start to become more of a these are the things that you're gonna get bonused on, or these are the things that that are you're gonna be evaluated on when it comes time for your annual review, where it where it becomes much more of a maybe a slightly heavy hand in terms of tracking usage of this kind of technology.

Yeah, no, no. And I I think as well, again, it goes back to what the organization is trying to do. So last year, um, pretty much a year ago, I was on a course um at Wharton um about AI or leadership in the age of AI. And um there were a lot of voices in the room, and they were very, very focused on efficiency. And uh, like I said, I can honestly say that um I think that uh you know you get to a certain part of that adoption curve where you start to go, okay, the Lagards, you need to start thinking about the fact that this is actually important for our business. This is something that we really need to do. But I think the bigger value is to actually have, you know, a very open starting and therefore to build from that. But there gets to a certain point where then it's like, well, it's now proven. And you know, if you're not using it, you're not being as uh producing as much or you know, able to do as much as your colleague, then that starts to become a different conversation. But I think that the key thing is that you know the everything has been, you know, the tools have been given and the like, so that actually, you know, people can pick it up, use it and and perform uh like that. But it's a great point. And I think that this is one of the key things is that as the the leadership of the organization, I think they need to be clear about that going into it and be very uh honest about that. Otherwise, then you get to this to that endpoint where it's like, well, you know, you said this was optional, and now you're you're actually coming back with the with the stick and saying, okay, they have to do this.

Yeah, yeah. Not optional, not really optional at all.

And and and and and also this well, you know, as human beings, it's like, for instance, I mean, the intrinsic learning process is something where comes a lot more natural to some of us. And I was on an innovation project a couple years ago, and it was the exact thing is we we got to a certain point where it did really well from the point of view of an organizational adoption point of view, but you had gotten over that curve. And, you know, in this case, it was something where it wasn't mandatory and it wasn't never going to be mandatory, and you just have to kind of say, okay, we've done a great job, but it's not gonna reach everyone, and everyone's not gonna want to use it, but that's okay. And and and you know, having those expectations going into it, and of course, correcting, of course, is super important.

Um I feel like I I I I haven't perfected it by any stretch of the imagination, but I feel like there's a a uh uh the pharma two-phone analogy where uh in pharma and uh you know in banking and uh you know all of my clients uh in those industries typically wander around the office when they're in the office with two phones, right? They've got the the the mandated you must use this phone company phone, and then they've got the uh actually I'm gonna also have this other phone with me. And I and I I I feel like there's something that we're gonna start to get into these mandates that um, you know, there's gonna be the the sort of uh here is my company mandated platforms, and then I've got my other stuff on the side. Uh, you know, I'm using I'm using my version of these tools on my phone and other versions of the tools on the company phone. Um, I think it's just gonna be interesting as as we get more locked down and more prescriptive. Um I've seen example after example of where I think find a way to both clear and adhere into what is prescriptive kind of individual access to the other tool. I'm gonna keep I'm gonna keep looking we've got some issues.

I mean, like for instance, like you know, like you know, uh intellectual property rights, uh, you know, kind of uh privacy, all kinds of really important stuff. And and this is exactly why people can't go into this just with the uh the attitude of that here are the tools, do what you will with it. Um and but having said that, it's something where that's a thing to tap into as well. What are the tools that people are really finding useful and try to see if you can bring those in? And those platforms that are again, you know, using the the uh the multi-platform, multi-model kind of nut uh situation, it's the case that's a great point where, like, for instance, you just new things that are coming in and say, well, let's try to incorporate those in so that we actually anticipate and give people the tools that are the best for what they do.

So I'm gonna switch gears, and we've been talking about uh change over time. You've been doing this for a long time. Uh you know, what you've been doing in terms of transformational and organizational change. Yeah, change for you personally, is that uh I think you mentioned that you're just about to start uh something new. Um necessity, but you're you're starting up as a lecturer.

But I'm very happy that happened. And so I'm gonna be uh Can you talk a little bit about that with the focuses and Western University in Switzerland? And it's a case. Give me a give me a sense of super excited about that because um, you know, it's it's one of these things where to get out there and actually talk about the work that you know I've been doing, but also to kind of figure out as well, you know, how does that land with the students, what are they seeing, and you know, again, it it's it's an opportunity to learn as well. So I'm super excited about that and uh so grateful that uh that that's that's going ahead in uh autumn this year.

Well, such an interesting like it's such an interesting time to be thinking about you know, digital marketing one, I mean, there's just such a fragmentation of of the communications landscape. Um there's such a there's such a um you know, I think there's there's this sort of like gravitational pull for uh uh people who are younger uh to be kind of creators and uh uh be working independently. I'm an advisor for a a program here in uh in Toronto. And when I was growing this new business of mine, I was thinking, I think I'd I really need a um you know a sort of fresh perspective, a a talented generalist, right? Someone who can come in and kind of do lots of things earlier in their career. Um and it was a huge struggle because there was there was this sense of like, I don't really want to go work for somebody. And if I am going to create, like I'm gonna create my stuff because that is the path to like that's the path to to fame and fortune. Um and it's just such an interesting, I think it'll be a very interesting time to to be teaching. Now, again, this this program is a bit more of a hands-on type of program than I'm talking about versus what you're gonna be doing at Northwestern. So I think that what do you anticipate you know, um it's it's worked out. What do you anticipate that that having the focus being on AI as you go into teaching digital perspective uh in 2026?

You know, where is it going and how can students leverage it? And it's gonna be a changing landscape. And it's like, for instance, I'm so glad I have a bit of time to think about exactly what we'll put in there because exactly it is something where it's just changing so quickly. But I think that one of the things is that there's you know, there's the building blocks that they need to have, but then they need to think about okay, whatever I learn now, you know, it's evolving super quick, and therefore, how do I adapt to that? And how do I keep adapting to the the new possibilities? And that that's uh yeah, so I it's great for me as well because it's something I go back to kind of go, okay, you know, these things you need to know, but then these are the things that are changing, these are the things that uh people need to uh be on top of and and keep adapting to as they go through their careers and especially as they're starting. Yeah, exactly.

Yeah, I I think it it is it's about teaching like resiliency uh as much as as much as any kind of specifics, right? Um So to end off, I'm gonna I'm gonna um I'm gonna ask you something that's that's uh maybe slightly adjacent to what uh we've been chatting about. Um and that's uh you know in in a healthcare context, I've been thinking a lot about how um you know how a patient's journey and how the the way uh a patient interacts with a brand and the amount of control those brands have over the interactions um is uh you know on the cusp of or for those who are comfortable with tools has has dramatically changed, right? Um uh the the the conversational nature of um of of of chat as a uh second opinion, uh, I think is going to quickly become um the the first opinion, the physician may be the second opinion. Um uh the ability to control how your brand shows up um in conversational AI is is a is a you know it it there's some alchemy to it right now for sure. Um uh even physicians and how they interact with with uh you know with reps and brands and where they get their information is changing radically can you give a bit of a sense of of where do you think the most needed and impactful changes are as we've gone through these digital waves a patient journey especially you know Dr.

Google came and you know every everyone had to thermally entrenched what's happening. And uh we're at that stage right now as well with with AI. So the AI overviews that are on Google for instance from certain searches going directly into the chatbots themselves. And organizations need to really think about that. I mean uh one of the terms is uh generative engine optimization instead of search engine optimization and uh I like how you put it it's to me it feels more like alchemy right now but there's some you know actions that you know brands can do to actually make sure that um at least they're preparing the way for um making sure that their brands are showing up in in as a good way as they can right now. And again it's such a fast moving landscape that's very difficult. And there's a lot of people uh or I should say a lot of vendors you know promising a lot in that area. And I think it's also to be very critical about your thinking about that as well. But I think it's something where for me that's the biggest one that everyone needs to be thinking about making some actions on experimenting with and then really seeing how much of an impact that's having and I again you know where I'm working globally different markets work in very different ways and you need to really um as well you know rely on your local teams to kind of bring back the feedback and say yeah we've been doing these things these things are working well and that's one thing that I really absolutely love about working globally is that a lot of times you see some really interesting things come from the uh the the countries, the affiliates and there's sometimes where you know you discover something as well globally that can really have a big impact locally as well. So it's so important to have that doll going and that back and forth.

Yeah no and Neil thank you very much really great well speaking of dialogue and back and forth I very very much appreciate our dialogue uh today and thank you so much for having the insights that you've shared and um really appreciate the time that you spent thanks Dave