The Crucial Difference Between AI Efficiency and AI Transformation with Eric Fulwiler

[00:00:00] Eric: Increasingly, the differentiator for what people and companies can do with AI is not gonna be just using the LLM, it is gonna be everything that they build around the LLM. So you say agen system, right? Fundamentally what that means is just how do you build workflows that leverage code, tools and AI to deliver a solution.
[00:00:20] Mike: My name's Mike Lander, and you are listening to Higgle, the B2B Sales Club podcast. Where we bring you actionable insights about sales, RFPs, negotiations, and difficult procurement discussions from sales leaders, brand leaders, and procurement leaders, please subscribe to get updates when new episodes are released.
Eric, thanks ever so much for joining me on Higgle. The B2B sales for podcast. Great job as your guest.
[00:00:48] Eric: Yeah, always fun to chat with you, Mike. And I'm sorry it took us a little while to get this booked in, but I'm really looking forward to it.
[00:00:54] Mike: No pleasure. Uh, you are, uh, very, very busy and we're gonna talk about that kind of busyness and growth as we go through.
How did we meet? I've forgotten now. Someone introduced us. But it's years ago.
[00:01:04] Eric: I should know. I mean, you're one of
[00:01:06] Mike: the so should
[00:01:06] Eric: I people that a while ago graduated from somebody introduced us to, you're now the person who introduces me to people, so I gotta go double check. We gotta, we gotta, we gotta both reach back out to whoever it was and thank them.
[00:01:17] Mike: Yeah. And we should get a coffee in person when you get back in September. So, Eric, without further a. Explain to the audience, who are you, what do you do, and your favorite song and why?
[00:01:27] Eric: So I'm co-founder and CEO at Rival. We are a integrated consultancy that builds AI native marketing systems for blue chip brands.
Our focus is on understanding what challengers do differently. So we spent the last five years, now over 10,000 hours actually researching and documenting the playbook of successful challenger brands, which of course, in large part includes how they are deploying ai. Then we work with blue chip brands like Unilever, burger King, Xerox, JP Morgan, to help them adapt and implement these solutions.
My, I don't know that I have a favorite song right now, but I have been down a deep rabbit hole on like old school, little Wayne recently, like going all the way back to like late nineties, early two thousands. So good. I listened to a lot of hip hop and rap when I'm working, so that's been my loop recently.
[00:02:13] Mike: Brilliant. Any particular reason why that was a loop for you? What was the kind of the foundations, if you draw the thread back? Yeah.
[00:02:20] Eric: So you know what's funny? My oldest daughter is about to turn 15.
[00:02:24] Mike: Okay.
[00:02:24] Eric: And I'm always so fascinated, I learned so much from her just kind of about culture and how the world is changing because.
You know, when I was growing up, and I don't know about you, there was like a genre or maybe a couple genres of music that you listen to, but now you know, she listens to hip hop, she listens to country, she listens to rock, she listens to old music. She listens to new music, and I'm so fascinated. Of course, there's a lot of the work that we do, research and work that we do at Rival with how culture has changed, particularly over the last kinda like five, 10 years and how it's become so fragmented.
I see that in my daughter and I think there's an interesting conversation about kind of, you know, that saying like any business is just kind of either bundling or rebundling what has come before. I think there's a really interesting kind of trend going on that we're gonna do some research on around how brands are now Rebundling culture.
You know, culture has been. Unbundled into Microcultures, largely through the internet and media and now ai. But I think brands have an opportunity to rebundle Microcultures. But my 15-year-old daughter got really into the Carter three probably 'cause she found it on one of my podcasts. And so that's been playing on a loop in my house and that just brought me back to old school Little Wayne, which I've always loved.
[00:03:30] Mike: Brilliant. Very good. So Eric, let's go into the questions. So you distinguish between. I think rightly, wave one, applying AI to the existing model and wave two, rebuilding the model around ai. So kinda what evidence tells you a company has genuinely crossed into wave two, and which legacy constraints stop it from getting there.
I think this is at the heart of the opportunity and the challenge with AI in business currently.
[00:03:56] Eric: Yeah. And I have to give you proper shout out and credit because you've recommended what I think has been the most impactful book for me.
[00:04:03] Mike: Ah.
[00:04:04] Eric: Onm. Thinking about the reshuffling. The stuff. Reshuffle. Reshuffle.
Oh, so good. I'm delighted. Like my copy of it is like more highlights than not. Highlights. So for everybody listening, if Mike has not recommended it before, although I'm guessing you have, definitely go check out Reshuffle. I forget the name of the author, but you'll find it Reshuffle ai. It's really, really good.
[00:04:21] Mike: My guy called Sangi Chowdry. And a quick spoiler, Eric, he's coming on the show.
[00:04:26] Eric: Oh, amazing. I'm definitely, you know what, I went down after I read the book. This is what I do when I kind of find a topic or an author that I'm particularly into. You know, I read the book and then I just go listen to every podcast that they've done.
So I've listened to like all the podcasts, but I guess I, I'll have another one.
[00:04:41] Mike: He's a really talented.
[00:04:43] Eric: I mean in terms of like the value per page, it's like, you know how like most books could have been a blog post? Yeah. And you, it's like, alright, well this is really one thing that you took 300 pages to say.
Exactly. His exactly is the opposite. I feel like almost every page, but certainly every chapter there's kind of like a, oh shit. Like big. Yeah, big takeaway.
[00:05:01] Mike: All of mine have got page turners on virtually every page. Yeah. And then I'm like, well, how do I, how do I work out what all of these things assimilate into?
So, no, I think it's a brilliant book. Thank you.
[00:05:10] Eric: And it's so, you know, as with most things that really matter, it is so simple, but it is so significant. And so he really just this idea of kind of, it's not about the technology, it is about how systems are redesigned around new technologies is, I think gets, you know, gets to the root of what we're gonna talk about and where I think the biggest challenges and also the biggest opportunities are for the industry.
So wave one, wave two are those definitions that people listening will already know, or should I kind of go into that first?
[00:05:38] Mike: Why don't you just like give 'em a quick definition, get one on the
[00:05:40] Eric: same page. Okay, cool. So again, and this has always been a belief of mine and you know, I kind of fell into marketing and technology by accident, but the thing that's always been fascinating to me and what I love about what I do is not necessarily advertising and marketing.
It's understanding how perception and behavior. Changes like people really, and when you look at any technological disruption, ai, mobile, internet, PC going all the way back to like electricity, any of it, the disruption and the opportunities and challenges that it creates are less about the technology and they're more about the fundamental human realities.
Of how we think and how we act and how we work together. Psychology, sociology, economics, capitalism, et cetera. And so understanding those as opposed to understanding the technology itself. Of course you need both, but that's the most important thing because history repeats itself, right?
[00:06:28] Mike: It does.
[00:06:29] Eric: And so these cycles that we go through are the same over time.
They play out slightly differently. And what you need to do specifically is different, but the cycles are the same. The cycle that always happens with significant technological disruption to commerce and communication is wave one adoption of the technology is you take the technology and you apply it to the way that you've always done things, the existing system, the existing operating model.
I think that's where most of the marketing industry is right now. You know, we're recording this end of July, end of June. I was in Cannes, the advertising conference, and it was fascinating. Just my biggest takeaway from being there was that I just feel like the industry stuck, you know, last year at Cannes.
Everybody was talking about boom of AI and all these amazing things that the technology can do. And this year there was a bit of that, but it was more like, all right, well I've given everybody copilot, why isn't the number going up to the right? And that's because technology applied to the way that you do things at best, gives you efficiency.
It allows you to move a little bit faster in some parts of what you do, and it allows that to be a little bit cheaper again, in some parts of what you do.
[00:07:29] Mike: So you get small. Marginal gains, wave one, small marginal gains, and actually also quite a lot of problems around, well, I'll just use for example, ai. Oh, it's produced something.
Well, it's good enough. Well, I'll send it to someone else and see what they think. And you get this kind of mediocrity effect as well. Which is dangerous as we know.
[00:07:51] Eric: Yeah. Especially with this specific technology. Right. Because of course, when we talk about generative ai, which by the way is most of what the industry talks about, but is course only one part of ai.
But generative AI is basically, you know, statistical prediction of what's the most likely thing to come next based on what's come for. So literally you could argue it's about, in a nutshell, about the average. Yeah.
[00:08:09] Mike: Yes.
[00:08:10] Eric: So you need to be extra careful of this, I think, with how you think about applying generative AI within our business.
And for marketing. That is of course, a huge chunk of it. But wave two is when you actually rethink the system around the technology. What's possible now that wasn't possible before, but also what are the constraints, the new constraints that now limit effectiveness in that system? And so the kind of standard example that a lot of people talk about, but I think it's the best one to kind of understand the distinction, is the steam engine and how it was replaced by the electrical engine within factories.
Long time ago. And so what happened is the steam engine, because of the constraints of how you needed to build a steam engine, was a central power source within a factory. And when electricity came in, they replaced the central steam engine with a central electrical engine. But the way the factory was designed and the way the factory operated was still the same, a central power source.
And it took them about 30 years to redesign the system around the technology and what was possible with the new technology that wasn't possible before. Which was essentially, they didn't need a central power source anymore because electricity could be distributed and decentralized across the factory, and so you could design and function and you know, have the factory operate in ways that weren't possible before.
But it took them a while to go from wave one, which gave them efficiency and cost saving to wave two, which completely changed what a factory could do, and drove effectiveness, not just efficiency.
[00:09:28] Mike: Exactly right.
[00:09:29] Eric: And then the second part of your question was like, how do you know if a business is doing it, essentially?
[00:09:34] Mike: Yeah. And also kind of like why did they get stuck? Why are so many companies stuck in wave one with the C-suite of all size of companies getting frustrated with, well, there's a big investment gone in, but I'm just not seeing, uh, to your point, it's not going up and to the right. And they don't understand why.
[00:09:52] Eric: Yeah. Well, I think the first thing is, you know, again, our world is understanding what challenger brands do differently from incumbents. And one of the big advantages that they have is that they are more long-term oriented in how they approach what they're doing, right? So they are thinking about, we wanna build a company for the next 10 years.
We want to give people something that they don't currently have. We want to challenge convention of how this industry operates, whereas most incumbent businesses are, what are the results that I need to deliver in the next 90 days? The more long-term oriented you are or can be, the more you are focused on how do you add value to the customer as opposed to how do you extract value from them?
And that is fundamentally what grows businesses. And so a lot of big companies are just very short-term oriented. And so if you're thinking about the next 90 days, the solution of what AI can do for you in the next 90 days is gonna be wave one. It is gonna be give everybody a copilot license, you know, bring in this new vendor that is promising that they can do X with y.
It is not gonna be, hold on. Like we're doing with our business and, and I was saying before we press record. We completely ripped up our plan for this year and we said, I don't care if we're flat, I don't care if we're not profitable, but we are gonna completely transform and rethink the entire system of how we do what we do with AI at the center and be true wave two by the end of the year.
And most businesses are just not willing to do that.
[00:11:10] Mike: So Eric, here's a great case study and give as much as you feel comfortable giving. Just talk through the kind of the outline steps of once that seeded formed with you as the kind of, obviously one of the principal co-founders of the business. Once that seeded formed, what approach did you take and make it accessible for people listening?
[00:11:29] Eric: So we had hired a chief product officer middle of last year, and he, you know, takes a few months to get up to speed, but then we sat down with him in February. You know, we'd all been playing around with ai, but he was like, Hey, this transition to agentic from kind of prompt and chat-based or task-based AI is gonna completely change everything.
And that was kind of the moment, and you know, we would've gotten there, but I don't know that we would've gotten there as quickly or as clearly without having Costas on board. So I think the first thing was like we hired and invested in technical talent because they're just gonna see and understand things differently than other people will.
And from there, the first thing that we did was not to build technology for the sake of technology, but technology is a tool. It's a means to an end of delivering on a job to be done. And so Costas, and the other thing we did was invested in more engineering. So Costas and his team basically spent months just sitting down with our subject matter experts, strategy, creative media, understand not just what we do, but what is it about how what we do that's different than other agencies.
Because essentially where we've gotten to with rival intelligence is, and where I think the entire industry is gonna be at eventually is the technology that powers the AI is gonna be less the differentiator than the harness that you build around it.
[00:12:50] Mike: Absolutely. Now just explain harness. Because people listening are like, he said, harness.
What does that mean?
[00:12:56] Eric: Yeah. And it's so fascinating. Again, like we were talking before we press record, I don't come from a technical background.
[00:13:00] Mike: Yeah.
[00:13:01] Eric: But I have like, you know, taken the red pill enough. Like I'm like very far deep down a rabbit hole. Because for me it's like I could just learn the headlines, you know, and I could learn the things to say when I come on a podcast.
But both for rival and also to be honest for my career, investing in learning the fundamentals of this stuff. I think is just really, really important, critical, and a really good investment of time for people. Okay, so harness, so basically, you know, let's just take generative ai. Nobody in this industry is building their own LLM.
We are all using the same tools at the end of the day, right? We are using Touch EPT, we're using cloud, we're using Gemini. Maybe we're using an open source model, but for the most. Part, it's the same engine that is powering Mable using And the generation. Yeah.
[00:13:43] Mike: Yeah.
[00:13:43] Eric: But not only that, but like I think that, you know, back to what I was saying before of history repeats itself.
It's not about the technology, it's about the humans and how we interact with each other and interact with the technology around it. If you stretch out the time horizon long enough, the technology becomes less and less the differentiator. Right. Take the internet. 19 97, 19 98, 19 99. If you were an internet agency, yeah.
You were hot shit. Right?
[00:14:06] Mike: You were.
[00:14:07] Eric: And you had digital people, right? Yeah. Now it's just how everybody does everything. And there's no internet agency. There are just agencies that use the internet. And I think it's the same thing with ai, right? There are now AI agencies and quote unquote AI experts, and the adoption of the technology is a differentiator because there's this jagged edge of like some companies using it some ways and some companies using it in other ways, or some companies not using it at all.
But you play it out long enough and the technology just becomes the thing of how we do everything.
[00:14:36] Mike: It's the enabler. Exactly. It's
[00:14:38] Eric: the enabler.
[00:14:38] Mike: Yeah. We,
[00:14:38] Eric: it's
[00:14:39] Mike: the tool that we all use. Exactly. Right.
[00:14:40] Eric: And so where we're at right now with AI is that the models that power the generation of, you know, let's say it's a, some research that we wanna do or a concept that we wanna refine or a paid media plan that we want to develop, those are all relatively similar.
And so if you, you know, used one model or you used a different model on its own, you get back relatively similar answers. And so the differentiator then becomes everything that you build around that model to have it do something differently than somebody else would be able to do. And so the big things for us have been, and the way that I think about it, you know, the constraints of the old model with marketing were time just man and woman hours.
You needed people to do stuff and you didn't have those people internally if you were CMO. Expertise. Expertise. Yep. Exactly. That's the next one. And then also volume. So I need to do a lot of stuff. I can't do a lot of stuff easily, wanna
[00:15:30] Mike: do a hundred campaigns in a quarter. I want it in 20 different languages.
[00:15:33] Eric: Yeah.
[00:15:34] Mike: It's like, okay. That needs people to do it.
[00:15:36] Eric: AI has completely collapsed those constraints. Yes, but there are new constraints that limit effectiveness within that system, which are data to differentiate what you can get out. There's taste, so a little bit different than expertise, although I think they sit very close together.
Taste to me is, you know, the Henry Ford quote of if everybody asked what they wanted, they would've said a faster car. It is the, I know my shit, but also I'm not just gonna do what everybody else does. I have a vision, I have a point of view of what this should be, even if everybody else is saying that it should be something else.
And then there's also technical fluency of knowing what you can do with these tools. 'cause the tech is just a tool at the end of the day.
[00:16:13] Mike: And what governance you up around it so that you know, as the. Tool does its job. You don't just let it run wild. You have checkpoints that allows a human with taste to intervene before it goes to the next step.
Hence, the ag agentic piece is so critical. Don't build all your agents and let them run off randomly on their own. You need some governance around it.
[00:16:36] Eric: Yeah, exactly. And to that point, you know, we've got kind of four first principles of how we're building AI native marketing systems. Which are human led, AI orchestrated, custom built and loop based.
And the human led is intentional. 'cause a lot of people say human in the loop. And I think that's the wrong connotation. Agree. The human should should not be in the loop. The human should be in the lead.
[00:16:55] Mike: In the lead.
[00:16:56] Eric: Exactly. So that's how we think about it. So the harness is basically everything that we build around the ai.
That means that what we get back from it is different and more rival than what somebody else could get back from the same engine.
[00:17:09] Mike: And let me ask you a question. So Eric, around the data. So what I'm through experimentation, starting to realize also is I'm just me in my home office doing a podcast.
There's no big team around me. It's just me. And I use the tools like you do, but I have institutional knowledge now. It's a small knowledge base. It's tacit. Some of it's explicit. But if I keep that in simple terms behind the firewall and I use the models that we all use, I can demonstrate to people that.
I have an edge 'cause I can run an untrained model against a problem statement and I can run my trained model, which hasn't been exposed to the training for the big LLMs and I can see a difference. Is that where you are heading with the data that you've got as rival the institutional knowledge over time because you've got recursive learning loops that starts to become a differentiator.
Is that where you're heading?
[00:18:03] Eric: The way that I would think about it. Is increasingly the differentiator for what people and companies can do with AI is not gonna be just using the LLM, it is gonna be everything that they build around the LLM. So you say ag agentic system, right. And I think that's another term that seems like this like super advanced kind of scientific thing that most people can't understand or can't break down.
Fundamentally what that means is just how do you build workflows that leverage code tools and AI to deliver a solution. So the whole idea of like ag agentic, I think maybe could get talked about in a simpler way.
[00:18:36] Mike: It could. And in fact, Derek, on that point. If I go back to when I was an engineer, as a systems engineer, there's an input, there's a box that does things and there's an output, and then if you feed the output through a function back to the input, you get a recursive learning system, which is in its really simplest form, it is an agent, it has a goal, it has inputs, it has feedback loops.
And it does things.
[00:19:00] Eric: Yeah. And to that point, how you design the system is gonna be what determines whether it's effective or whether it's not effective. And within that system, there's the instructions of what you want it to do. There's the tools that you give it to be able to do it. So access to certain things, but then there's also the knowledge or the data that it is able to access.
To be able to come back with the answer or to do the job. And so to my point of the new constraints are data taste and technical fluency. We've basically taken all of the research that we've done on challenger brands, so 10,000 hours over the last five years, and we have built a database that our system goes through to pull out relevant information for whatever the query or whatever the job is.
But then we've also built specific sets of instructions and routines. Based on how our people go about doing their job. So we have a set of agents, strategy agent, creative agent, media agent, et cetera. Those are specifically designed based on how our chief strategy officer, our chief creative officer, our chief media officer, thinks about doing what they do.
They're not generic, they're differentiated. So when you talk about using AI in rival intelligence, and at the end of the day, again, it's multimodal, but it is either gonna be Gemini or it's gonna be chat GBT. Or it's gonna be Claude that is actually doing the reasoning in the generation. But everything that happens before and everything that happens after is completely different at our company than it would be at a different company.
And I think that's probably the most important thing for people to understand is it is not about the technology. You do need to understand how it works and there is. Better and worse ways that you can use it because it is a tool. But where we are now and what is driving the difference in people that are succeeding and people that aren't, and certainly people that are effective with this technology versus people that are just efficient is everything that they build around the technology in terms of the context and the data and the taste.
[00:20:52] Mike: So let's move on now. Now that you've described how you are doing it for your own business, let's move on to kind of clients. So we've talked about this kind of capability overhang. Everyone's got the tool, but no one's doing much with it, apart from a bit of efficiency. How should A CMO identify the few areas where AI can create real serious commercial advantage?
I love your phrase. Kind of while avoiding a diet of fast, cheap AI junk food, which I think is where quite lots of organizations are.
[00:21:19] Eric: There's so much of that, and I get it. You know, we as humans are wired to want the cheap, fast and easy thing. You know, we wanna be able to do more with less. But I think that the way that I would think about it is essentially, at the end of the day, everything is designed as a system.
The way that you do things. Now, the way that you want to do things in the future and a system if you break it down, is a series of workflows that is basically a series of tasks. And so what we've basically gone through, and so I, you know, I talked about a lot of the process around like the context engineering and the data and the taste and how we kind of bake that into the system.
But the other thing that we've done, 'cause that's really just rival intelligence, that's just the AI orchestration platform. But if we think about the whole system of the human in the loop, the AI orchestration, the custom built and the loop based feedback mechanisms, it's really about breaking down the system of how do you develop a marketing campaign, marketing strategy and marketing campaign into a set of workflows and then a set of tasks, and you put them all on the table and you say, okay, these have all been bundled in a certain way.
Based on the constraints of the old system, which were time, expertise, and volume, those constraints are no longer relevant or as relevant. So if we were gonna think from scratch, and this is why my main podcast is called Scratch, if you were gonna think about from scratch for the world of today, how would you do things differently?
The job to be done is still the same. You have to grow the business and many of those workflows and many of those tasks are still gonna be needed. But how you do them and how they fit together is gonna be very different. So I'm actually, you know, I'm here in Boston, our US offices here, our chief creative officers here, a lot of our creative team is here.
And one of the exercises that we are doing is literally just this. So you think about a traditional creative process. You have the strategy team that briefs in the creative team, they then go look up. Relevant examples, inspiration, competitor, ads, whatever. They sit and ideate, they come back together. The strategist gives them feedback, they iterate, they bring it to client, et cetera.
What we are doing is just on a whiteboard, we're saying, all right, what are all the tasks that need to happen as part of the creative system? And then we split it into what is technology really good at, and what is the human really good at? And then you bundle them back together. To be AI at the center, what's possible now that wasn't possible before, but also managing to the new constraints of do we have data to differentiate the system?
Is there a clear human in the lead to provide taste for whatever the technology is bringing back to you? And then also, are we bringing in engineering to be able to think about what technology could we build? We have the technical fluency in this team to go solve this problem.
[00:23:46] Mike: Precisely. Now also Eric on that point, and it's in Sangeet Chow's book on, uh, reshuffle about, which again really caught my attention was the kind of role unbundling organizations are a set of roles, large or small.
People have jobs, they have a role description, and then they have a job description. There's a bunch of tasks where you've got to think about, well, what are the tasks that get done? Unbundle it from the role description, and then do what you say, which is technology best at, which is the human best at, and then rebundle those tasks into different roles, agentic roles and human roles.
When you do that, I think you get quite a different type of organizational form and a different type of purpose and value that the human brings to the party.
[00:24:31] Eric: Yeah, it should be, I mean, if you think about, you know, and obviously there's a spectrum of people that are bullish or bearish on kind of what AI is gonna mean to this industry, although I'm guessing if they listen to this podcast, they're probably more on one end of the spectrum than the another.
[00:24:43] Mike: I think so. I'd hope so. Yes.
[00:24:45] Eric: We don't know what it's gonna look like and anybody that tells you that they do. You know, they're full of shit. And one of my favorite expressions right now is like, there are no AI experts. There's just people that have spent more time experimenting than you have. But we know it's gonna look different.
Like even just anecdotally, like you can see what this technology can do. And it cannot be that the future of how we work is gonna be the same as the past. Of how we work.
[00:25:07] Mike: It can't be, I mean, for example, we were talking just before the show, and then I'll, I'll do one more question then we'll wrap up.
Thought leadership back in the day, trained by KPMG in the, in the late nineties, thoroughly enjoyed the role. Amazing organization. So as a, a consultant at KPMG, we'd write thought leadership papers. You'd go to the library, you'd have a hypothesis, you'd get the research, you'd do all the work, you'd write the paper, you'd get it moderated, and then you'd send it to clients.
Well, it was a kinda one and done exercise because it was too hard to recreate the next version. While I've now created a thought leadership engine, which is it now I've got a framework, I've got a harness, I've got a framework. I'm using, uh, different AI tools and each week I get signals coming in 'cause it goes off and does a load of tasks, comes back the load of signals.
I then work out which signals are important and then it feeds that back into the thought leadership knowledge base. So I've got a knowledge graph and then every quarter I can produce an update. So it's now a living, breathing piece of genuine thought leadership. And that's the point about it changes the way that we work and it changes the type of output you can produce.
[00:26:09] Eric: I'm gonna send you a podcast that I just listened to with the founder of Morning Brew, who's now doing something else, but he describes his kind of operating system for thought leadership for content, which is similar to yours in the sense that's identifying content themes. But then he's also built out some really interesting things in terms of, he kind of like has a interview panel that will.
Interview him about what the point of view should be. He then has kind of like a critic panel that loops based on a certain benchmark of like, is this good enough and is it like, not AI slop enough?
[00:26:40] Mike: That's a great idea,
[00:26:41] Eric: really interesting. One thing that might be really helpful and very practical and actionable for people is like, you know, when I, I'm in a meeting for rival, or we're considering anything about what we're doing.
My question to everybody and what I'm asking everybody here. To constantly think about is, is this wave one plus or is this true wave two? So are we just kind of adding technology or iteratively evolving how we do things or is this truly thinking from scratch? What's possible? Now, that wasn't possible before.
And I'll give you a quick example 'cause it's my favorite right now and is actually me that fell into the trap of like, oh, oh shit, this is actually just wave one plus thinking slides, slide decks. Like even the term, it literally comes from like. Slide carousels. Yeah. You know, 60 years ago and just the I, and we were trying to think about how Tora intelligence, like do these slides for us.
And then our lead product engineer was like, maybe we don't even need slides. And I was like, oh my God. Yes. Yes. What if we knew nothing about how information was communicated from an agency to a client? What's possible now that wasn't possible before. It probably would not be slide decks. And so now we make artifacts.
That are like interactive dashboards that we send to clients. They can comment on it, we can update them really easily. But that prompt of like, is this wave one plus or is this true wave two? I'm hoping that that can be like the number one thing that people take away from this conversation.
[00:27:54] Mike: Brilliant.
One last piece, Eric, just before you go onto your next meeting. So if I put my bias hat on my old procurement hat on in this world, I'd be very concerned about what do we own? If I'm on the brand side and I'm buying Ag agentic capability, AI capability, I'm like, okay, who owns the data? Who owns the workflow?
Who owns the measurement tools? Who owns decision rights? How do I off board a supplier? How do you think about that? How do you make sure that you have a relationship with a client That's a true partnership, but there is not massive over dependency on you whereby they just can't get off the rival kind of program 'cause it, it's a serious point for a buyer.
[00:28:36] Eric: I think the lines between agency and technology provider and the lines between service and product are blurring. And so I think the business model of how an agency like ours operates should be very different. And actually we're pushing for, we actually have a module within rival intelligence that will generate a bespoke commercial proposal for each engagement.
Because again, what's possible now, it wasn't possible before. It should not be that you. Estimate the number of hours that it's gonna take over the next six months to deliver a project, then it's, it's just like crazy. It was always crazy, but now it's like we can actually, it, there are things that are possible now that weren't possible before.
So I think that is gonna change. And I think if I was on the buyer side, I would absolutely be asking questions about it and figuring out what that means for me on our side. It's a really good thing because we are increasingly, you know, our revenue split between services and product is changing. We are increasingly selling not just technology, but also custom built technology to our clients.
The data one is a little bit trickier, and that's one that we of course have to be really careful of because there is external data. There's also a lot of synthetic data that we are bringing in. But the first party data, of course, there's a lot of regulation around like how that actually needs to be managed.
So I would definitely be aware of it. But the other thing is that. Where I think the industry is going, which will really benefit, I think, both sides, but certain clients is less of an input based pricing model, time hours, timeline, and more of an output based pricing model. What's the value to the business, or ideally, what's the variable impact that this work is gonna have?
This course is really tricky to actually get aligned on. Yeah, yeah. But that's where I think it goes. And then it doesn't matter, is it people, as a technology, if the job gets done, the job gets done. So we're increasingly doing custom fixed and variable pricing models Bespoke. To each engagement. But that does involve us building custom technology that the client is buying access to.
And if they walk away, then they lose access to that. And of course everything can get negotiated. But we are increasingly, and I think the companies that are gonna be leading the wave to change on our side of the industry are increasingly becoming technology and product companies. And so I think the buyers need to start thinking about buying marketing, quote unquote services more like they would a hybrid of services and product solutions.
[00:30:43] Mike: Eric, it's been fantastic. I knew it would. Brilliant. Really, really, really interesting. Very pragmatic. What's your kind of final takeaway for people listening? I think I know what it's gonna be, but what's your final takeaway? It's
[00:30:54] Eric: gonna the, is it wave one plus or is it true Wave two? Yeah,
[00:30:57] Mike: I think that's very good.
[00:30:58] Eric: And then, yeah, I think the other thing is like, and we were talking about this before as well. Like just diving in on the deep end of learning this stuff, hands on. You know the fundamentals, do it, try it. Just pull up the, what I do is I've got chat GBT in a window on my computer and anything I don't know, or anything I come across that I haven't heard of or don't understand.
I just like look it up. It is amazing what and how much you can learn if you're committed to doing. And I think, you know, they say, what is it? Best time to plant a tree? It was a year ago. The second best time is now. I think that's the same with like actually learning about this stuff from like a true hands-on perspective, not just a headline perspective.
[00:31:34] Mike: I'd agree. Eric, it's been amazing. Thanks ever much. Where can people find out more about you?
[00:31:39] Eric: So I'm Eric Foiler on LinkedIn. Our website is we arrival.com and my email is eric@wearrival.com. So I shouldn't be at hard to find.
[00:31:47] Mike: Thank you. It's been a pleasure Eric.
[00:31:49] Eric: Cheers, Mike.
[00:31:50] Mike: Thanks for listening to Higgle the B2B Sales Club podcast series with your host Mike Lander.
Please subscribe so that you'll catch all the next episodes.