100 Conversations That Shaped the Future: The Exchange Milestone

Check out the full episode below! Enjoy The Exchange? Don't forget to tune in live Friday at 12 pm EST on the Greenbook LinkedIn and Youtube Channel!

For our 100th episode of The Exchange, Karen and Lenny reflect on the stories, people, and ideas that have defined the journey so far. They share listener voices, highlight the themes that keep surfacing in our industry, and look ahead to what the next 100 conversations might bring.

From the rise of AI in research to the importance of human storytelling, this milestone episode is both a celebration and a reminder of why these conversations matter.

Many thanks to our producer, Karley Dartouzos. 

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Transcript

Karen Lynch: Maybe, there we go.

Lenny Murphy: All right, here we are.

Karen Lynch: I'm going to have those confetti poppers or something for this exact moment when we go live with our 100th episode. What?

Lenny Murphy: I know, 100 episodes. That's kind of crazy. I didn't stop to even think about it until a few minutes ago. When you think about 52 weeks a year, we take some weeks off.

Karen Lynch: And yet, it's still like we've been doing it. We have been consistently showing up, give or take a week or two, for two years. What? I just don't know how it's possible that time has flown.

Lenny Murphy: I know. And still, the high point of my week. Well, I think it's funny to share with people what led up to it.

Karen Lynch: Like Lenny and I talking and saying, oh, yeah, we should do this. We should give it a try. So the whole idea that this wasn't necessarily something we were convinced was, can we just all take a moment and if you're, wherever you are, like applause that Lenny is wearing the headband.

Lenny Murphy: We're drawing attention to it or next time I'm just going to let it happen. I know.

Karen Lynch: I said to Tim when I was like saying I'm wearing a 100 tiara and I sent Lenny a headband with Jiggly and he goes pretty on brand for you both. Paul's pretty on brands. Anyway, sorry, sorry, sorry. Back to the day, like, do you remember, like, your thoughts about why we were going to do this? Like, I think this is a good time to level up with everybody. Like, what, what do you recall as the, you know, as the thinking that led us to start this?

Lenny Murphy: There was, well, part of it was we, we've never done news organization. Right. Um, but yet we, uh, Read a lot and, and, and share a lot. And the thought is what's unique? Unique USP and it's to make sense of the noise and like well down a newsletter. No, that's kind of dumb Let's do a live show to to separate the signal from the noise and especially with there being so much signal, okay That was it without knowing that that was gonna be something that would be, that anybody would want us to do, or would be even particularly compelling or interesting, and it's been a very pleasant journey to find out that it was. All those things, all those boxes have been checked, so. Yeah, I agree.

Karen Lynch: It's been really cool, and you know, I have seen, you know, some people, you know, they put out their kind of recaps and all that, but it's been wonderful to see. Yeah, I want to get to these in a minute. But my thought was, it's been wonderful to see the value of talking, of having conversations about the news items. Because anybody can read the news story. Other people can go to Search Live or check out somebody's LinkedIn post. You can Read them. But I really do pat ourselves on the back with bringing them to life through conversation. And so Karley's got a few testimonials pulled up. And I think we should share them. Because Carrie, who's been one of the, Yeah, one of my guest hosts, all right, he's saying, now I have to put my glasses on. Glasses and the tiara, I guess that's happening. Anyway, great builds, exciting times. Beyond Botgate, it's really exciting to be here to watch the next iteration of our industry. Reinvention is growth. So that's what we're doing, right? We have front row seats, Lenny and I, to talk about what's happening in the industry and the disruption that AI brought. It's hard to really think about two years ago, right? Because we've come so far. Anyway, yeah, and Jay, this one was one of my favorites that Jay shared recently. Not a coincidence that he and Keri collaborate so frequently and have worked together, but I was out on my bike yesterday listening to Karen and Lenny's latest episode of The Exchange in my helmet speakers. Great episode, by the way. If you haven't heard it yet, give it a spin. We all see what you did there, Jay. 10 out of 10, MRX news always. Thank you, thank you, thank you, both of you.

Lenny Murphy: Lenny, why don't you share this one that came in from Dave? Sure, from Dave McGuinn, the man, the myth, the legend in Asia. Espresso and listening to Karen Lynch and Lenny Murphy and the craziness of the developing market research world. That's my Saturday morning starter.

Karen Lynch: We would not have anticipated anyone saying this.

Lenny Murphy: We would not. We love you because you're insane, Dave. And that statement is clear evidence of that. But, you know, yeah, very cool. And that was part where we did this, too. We knew this was great. And we want people to live here. But we knew that, you know, people would listen and absorb this. And we want to keep it digestible, even though our definition of digestible continues to kind of expand, but I think it will in this episode. All those things are important. And thank you for those commentaries and folks letting us know that you're getting value out of it. Yeah. Yeah.

Karen Lynch: It fills our cup, fills our professional cup. So thank you to our co-hosts that have joined us over the last 100 episodes. Thank you, Will and Tim and Kerry and Susan Griffin. Will Leach, by the way, Kerry Hecht, Susan Griffin, Tim Lynch, specifically. I think those are our, kind of, the four co-hosts. Am I missing anybody, Lenny?

Lenny Murphy: Did anybody else? We've had Dana on, but not as a host.

Karen Lynch: Yes, yes, kind of joined us. Yes, so thank you to all of you. And we have a lot of people behind the scenes at Green Book, Karley largely, who's been with us from day one also. Really, Karley, 100 episodes for you as well. Thank you, thank you. And then just everybody who tunes in, whether you're tuning in live or joining us on YouTube, we have lots of gratitude for you. So yeah, thank you.

Lenny Murphy: Yeah, thank you. So we do have, we think, an interesting way for this particular show to stay current while also having a little bit of a retrospective. Do you want to talk through that? Yeah, exactly.

Karen Lynch: Yeah, yeah, yeah. So here's what we decided to do. So a little bit of backstory. We were trying to figure out, how do we digest 100 episodes? How do we synthesize 100 episodes? And of course, we have a tool at our disposal these days in AI. So Lenny and I each kind of went our separate ways. And we fed all of the episodes into AI and asked it to do some synthesis for us. And we got a few different versions of synthesis, which we then synthesized. And here we are. We came up with six themes that kind of have become apparent across the last 100 episodes. So if you are tuning in today for the first time, this is where you're starting, right? This is your new level set for moving. This is how far we've come and Karley kind of put them into words for us. So let's just start with the theme number one, really from AI buzz to AI infrastructure. I mean, for real, we went to like hype and getting ourselves around generative AI and what does it mean and what will it do? And now it is foundational. It is table stakes. If you're a rest tech platform, what else can we say about it? It is the way of life in two years.

Lenny Murphy: In two years. And still, and not even a mature technology yet, truly, by most metrics. So I think that context is important, that the shift, I mean, it happened, it happened fast, and there is still such a long tail to go.

Karen Lynch: Yeah, and I just saw somebody shared on LinkedIn their AI stack. So traditionally, a lot of organizations have their tech stack, we have our own tech stack here at Green Book, and people are now starting to say, okay, what's your AI stack? Because now it's not just play around with chat GPT, but what do you use for use case A What do you use for use case B? What do you use for use case C? So, you know, maybe you're using gamma for presentations, you know, you're using chat GPT for conversations, you're using perplexity for deeper research, like, you know, what does your stack look like and what other tools rolled into that. I think it's just pretty cool to see the way we're changing.

Lenny Murphy: It is. And the difference between, right, the first wave was kind of an AI wrapper around existing solutions, and now increasingly we're seeing AI native. And that's still kind of the broad AI, and then we're doing this agentic component. So we've talked about before, there's kind of multiple adoption curves happening at once that are overlapping. And yeah. And let's talk about this. Yeah.

Karen Lynch: If you haven't been on two years ago, episodes from two years ago when Lenny and I were beating this poor baby horse, which now, and you might've seen me petting it back then too, wishing it wasn't dead. As you can see, it has healed because we no longer have to beat the dead horse and say, get on the AI bandwagon folks. Do you remember we used to have to tell people to get on the AI bandwagon because, um, anyway, and that was the dead horse we were beating at the time, but now healthy horse, healthy horse that lives on and has a life. Cause you don't need us to beat you into submission. You're all doing it some way, shape or form, or you should be.

Lenny Murphy: Don't make me start hitting that baby. That is a, is it a, uh, bionic horse, a robotic horse yet?

Karen Lynch: Um, it's not, it's a, it's just a friendly little, no, it will be a robotic horse soon enough. It will be a robotic horse. If you're paying attention, it'll have some acronym like K265 or something, and it'll literally follow me around the house doing things I say. That's coming soon, too. It's not coming.

Lenny Murphy: If it were probably in China, it already exists.

Karen Lynch: It probably does. It probably does. Anyway, it's been super fun to talk about. But let's talk about some AI innovations that are happening now, because this first story that we have for you, Carl, you'll share the links as usual, but OpenAI rolled out GPT real-time for live conversational AI. And you might say, all right, well, I've been talking to it or it can talk back. That's, you know, maybe that's not a big deal. But if you really dig into this launch, there's a few things in there to pay attention to, especially if you're integrating voice into your platforms. With technology, these models are now able to speak with intonation, emotion. They're able to speak as you direct it to them. So, for example, speaking quickly or professionally or speaking empathetically in a French accent is one of the things in there specifically. I'm like, OK. It can switch and capture nonverbal cues. And it can adapt mid-sentence. So maybe you want it to start off snappy and professional. But then if somebody says something revealing, you want it to become kind and empathetic. So qualitative platforms in particular, take a look at the improvements with voice. A big deal, and it's a little terrifying to think about how well these platforms are evolving to meet needs, certainly in our industry. It's cool stuff, ain't it?

Lenny Murphy: It is. And to be clear, this is an API, right? I'm sorry.

Karen Lynch: This is just, it keeps coming off. It can come off.

Lenny Murphy: I'm keeping mine on now. I might be wearing it for the rest of the weekend.

Karen Lynch: But I love it.

Lenny Murphy: Hold it up every now and then. I will. Sorry, my head's too round. Things don't go well. Anyway, but it's an API, right? So this is, this is not just something built within chat GPT. This is a solution, any developer can utilize it within their own, just, you know, just discrete applications. So, and the fact that it is real time, emphasize that it's called real time for live conversational AI, it this, you know, that's just the pace that they We got a couple other on this, the AIfication, PureProfile, our friends at PureProfile continue that shipping, and they just keep shipping AI coding option. So with open-ended responses, again, that's kind of one of those no-brainer things that, of course, and now in table stakes now. So if you don't have an AI solution to help with coding, extra data, then what the hell's wrong with you? Right.

Karen Lynch: I know. And even this one, so Ipsos launched their AI-powered insights agent, which, you know, when you read this, it feels so common sense at this point, which is the really interesting thing for me. It's like, yes, it's a seamless approach to collect, connect, and catalyze insights across the organization. Yes, it's made the knowledge management system powerful and actionable. I mean, basically you can, um, instead of saying like, Oh, we probably have done research on this before. Let's check the knowledge management data bank or whatever. It is a way to query it and find out what we know. Here's what we need to act upon. Everything in, um, in those, those hubs become super actionable when you access it with the power of AI. So yeah, it's, it feels so intuitive and, and it's so interesting to me. Yeah.

Lenny Murphy: Well, and on the, The other kind of an extension of that is Comscore. We've seen Nielsen with their marketing cloud, I think it's what it's called, maybe it's a data cloud. Anyway, we see these cloud integrations of data feeds that are complimentary and unlock more value creation. Comscore, of course, with their media ratings and metrics, their AI-powered data partner network, which is interesting, is to turn ID based data sets into scalable privacy first audiences. So they are taking that approach of matching at the individual level, DSPs, et cetera, et cetera, to data. And AI just makes all that much, much easier than it used to be. So we're going to keep seeing these interactions, right? The data marketplaces combined with the primary data sources and the efficiencies and yeah.

Karen Lynch: Yeah. Yeah. And let's use that, that kind of data, data and privacy and segue into integrity and data quality and trust and fraud, because you can not look back at the last two years and not see the, not see the damn, you know, if, if, if that, if this was a whore, if, if I was the dead horse we were beating, this is The drum we are beating, the whole concept of garbage in, garbage out. We talked about both farms. We talked about fraud, large scale fraud brought to the federal level. We've talked about trust, and we've talked about the critical nature of data quality at this point in time being essential.

Lenny Murphy: Just for research, but the more research becomes a primary feed into an LLM, obviously the risk of contamination, but that also applies to any other data source, business intelligence or supply chain, operations, whatever it is. Data's the new oil, you need to produce high-grade gas.

Karen Lynch: High-grade oil, especially because I found this article from AI Magazine, where SAP's chief AI officer highlighted the shift towards AI shaping C-suite strategy. So if your AI is informing strategy at the C level, C-suite level, and you've put garbage in, guess what? You are highly jeopardizing the integrity of those critical boardroom conversations. Like, nope, we cannot have that to maintain business integrity, the data from those LLMs has to be locked up. So not only does this have an impact for the research community, but also any kind of strategic consultancies, anything that is happening at a larger level, you must ensure you're not putting garbage in.

Lenny Murphy: Yep, yep.

Karen Lynch: And that drumbeat is not going I know that's the next thing I need, is I need to get a drum.

Lenny Murphy: My son's a drummer, so we have lots.

Karen Lynch: You get the drum for next time we talk about data quality. Let's talk about, segue from here into our third theme, which was really synthetic research and human versus machine and where we are with that, because we've come a long way in synthetic data, haven't we?

Lenny Murphy: We have. And actually, you know what? So at some point, as an industry, we need to have a definition, because we use this catch-all term, synthetic data. But there really are, the source defines the use case. And so when I'm talking about synthetic data, in my mind, I'm thinking more of purpose-built consumer data, right? You know, live, real profiles, because that's the applications that I see being deployed most effectively in the industry. But other folks are talking about, well, I just run a persona on ChachiBT. That is not purpose-built. That is pulled off of Reddit and, you know, all these other sources. Doesn't mean it's not good. I would argue it's not as good from an accuracy standpoint back to garbage in, garbage out, right? It just doesn't have the detail necessary. But it's not just us noticing that. This every article, you know, somebody's like, yeah, I decided to test all this out on, you know, to replace research and I was getting 70% accuracy.

Karen Lynch: Like research nerds who are like, no offense, research nerds or research geeks who are just researchers at their very core who are like, I think I'm just gonna test this. It's just like, excellent, thank you, well done, you give us data, right? And the data in this was really interesting. They tested 12 models against human behavior and showed 70 to 80% accuracy at a fraction of the cost. And I think the purpose of this, or not the purpose, one of the takeaways of this study is you don't have to pay a lot of money for kind of the higher quality responses, like that's, it can actually get quite inexpensive for quality of responses, depending on how you define quality, that's a separate rabbit hole that we're not going to go down. But for certain use cases, if we're doing this work at a fraction of the cost, early stage testing, or, you know, drafting posts, you know, either, you know, posts to share or, you know, messaging, like very early stage messaging, copy, great use cases for that quick reaction to product to new product ideas, quick screenings, things like that are fine, save your investment for when you know, you're about to spend a lot of money on a launch, you know. But it's testing.

Lenny Murphy: Sorry, I mean, talk to every Karen, what are we gonna say?

Karen Lynch: No, no, it's interesting to think about it and think about it. I think one of the questions posed here is, when is good enough? What's the percentage that we should be seeking? I've seen some studies that are like 90% or 95% accuracy when compared to how a human would respond. This is talking about 70% to 80% accuracy. What's good enough? Do we want to go all the way to 100%? It's got to be 100%. Will we settle for 90%? What's the right stat? I don't know the answer.

Lenny Murphy: I don't either. I do want to point out that even with all of those things, the way it is still, from what people have shared with me that are in the midst of doing these experiments, one or two levels deep, still really good. You go deeper than that to be really wise, for instance. Then we lose fidelity. So I want to be clear, when we're talking about these numbers, actually Simon Chadwick and I were in a debate this time last week on this very topic. Shout out to you, Simon. Those numbers are similar, I always use the example of visual intensity, where the models predict where the eye is going to be drawn. That has very specific use cases and you can't argue against it because it's so damn good. Same thing here, but you want to get deeper, we still need to validate and have real research. But for those use cases, to your point that, It's good enough.

Karen Lynch: Yeah, it's hard to debate, so. Yeah, interesting, interesting. Well, I'm sure we'll continue to debate that, and there's gonna be purists, and there are going to be people who are a little more kind of prone to taking risks, and to saying, eh, it's okay, it's good enough to inform me. It's just the directional input that I need, and that's okay, so.

Lenny Murphy: It is. This out real quick. There are still, there are companies now that are enabling you to build your own digital persona and license that out in some form or fashion to create revenue. There's this whole other bigger question. What does that look like when people are simply creating their own digital twins? It's going to be synthetic, but if it's based on real data, does that mean that's not good? And we're just touching the surface on that, but we're gonna get there really soon.

Karen Lynch: And when we consider a world with AI agents, like my agent's persona might be different from your agent's persona. My agent might know that if it's doing shopping for me, for example, like, oh, you know, Karen as my end user has these preferences, so I'm going to behave this way. Whereas, you know, Lenny's might have these preferences. So our AI agents are going to have different personas. So it's really quite exponential.

Lenny Murphy: It really is. And then you have agents selling agents. It's Rust Ghostbusters the other day. It's cats and dogs, you know, chaos.

Karen Lynch: We're getting ahead of ourselves. We will get to kind of what the industry is going to look like, but let's hover with this synthetic versus human conversation and shift it into just AI versus human. Versus human, this whole high tech, high touch concept as it applies to the industry and where we've been for the last two years. There is no doubt that even in a world where we're shifting to AI agents and synthetic and digital twins and all of that, we are still in strong need for human storytelling, human critical thinking over the synthesis. AI right now is still missing something, which has a large message for res tech providers and any other kind of supplier in our ecosystem. Let's talk about this sub stack article you found. Yeah, it's really time to stop selling a SaaS solution.

Lenny Murphy: Which I think, you know, you step back think in our industry, there's never been a pure SaaS play that has ever truly succeeded. Every, every company has had a type of consultative component, some service element. And I've always argued that that's important because clients aren't, they're not particularly interested in methodology. They're not interested in the how. That's just, you just kind of have to, you got to deal with that stuff. But then what is the answer? And what's the best way to get to the answer? That's the consultative component. And sometimes the answer is just a number and that's fully automated. That's good enough and that's fine. Sometimes it has implications. Sometimes it is implications and then recommendations, which gets on the consultative side. And SAS is not a defensible moat anymore. So what is it? What is expertise? I would argue, and I think that's what the article argues as well. It's not just the, here's a solution, go do that. That works for some things, absolutely. But I don't think that you scale off of that when you are in the problem-solving industry. And that's effectively what we are. Yeah.

Karen Lynch: And I think in this piece, because this one I had a read of, they talk about making your team the guide in some potential customer's journey, not the product or feature that you offer. Like, stop selling your products and features. Make it about your team. We are a team that will make sure you solve your problem. And this tool is our weapon for doing that, is a quote from the article there. So your team. And it's interesting. I have seen a lot of, if I'm looking at social media or LinkedIn specifically, and I'm seeing how people promote, a lot of times people promote what they do as a team socially because the team is really critical to how they operate as coworkers and all that. I'm like, what I need to see is I need to see the strength of your team, the brains of your team, the thought leadership of your team. What is your team doing to show how smart they are and how because I'm going to segue into this next article about what McKinsey, we've talked about this and we'll talk again about Deloitte a little bit later, but if the future of work depends on human-AI partnership, not replacement, McKinsey is making that argument as well.

Lenny Murphy: Of course they would. Of course they would.

Karen Lynch: But the point of all of that is those companies which are now competing in the research space also though, you know, we've talked about this, Accenture and McKinsey and these large consultancies are competing in the instance space, but their differentiator is how freaking smart their consultative teams are. And therefore, what does that tell you? That tells you that that is what we need to do. We need to show the strength of our teams, show the intelligence of our teams, the prowess of our teams, not just our cool tech tools, because because cool tech tools, everyone's got them now, right? Or everyone's able to get them. Or the more API that's available, everybody can make theirs better. Just hire a bunch of really smart developers. Sure, that's cool. But if you don't have that brain and the brains on your team and the ability to act as research partners. Anyway, that's...

Lenny Murphy: It's interesting. I've been thinking with this framework in my head. I'm trying to... Anyway, What the industry looks like, What I'm playing with is this idea of their utilities. Utilities do very specific things and they do them really damn well. There are companies that can be very large utilities. Then there are augmenters slash enablers. They have tools, but there's still the human element, but it makes it better, makes you super powerful. There's problem solvers. Problem solvers, that is primarily human-led. It may have components of all the technology to help create efficiencies from a process standpoint. But what are they ultimately selling? One is utility, one is an enabler, the other is a problem solver. Problem solvers are more valuable because problems cause pain. So everything else is process efficiency, fundamentally. Process efficiencies are economic decisions, and you're foolish not to gain efficiencies that way. But the problem solver, that stuff keeps you up at night. And people will pay lots of money to McKinsey to solve their problems. And they may inform it, make it get all that efficiency, but it's not going to replace it yet. And I think that was my takeaway from that. And it was a great article. And I'm sure they may be biased. But I still think it's right. Yeah, yeah.

Karen Lynch: No, I agree. I agree. So let's segue into our fifth theme. By the way, heads up, everybody. We have two more themes to go, and we're already at our 30 minutes. But did you expect us to have a short episode for our 100th? Not happening. Right.

Lenny Murphy: We promised not to go for 100 minutes. Yeah, no, we're not going to go 100 minutes.

Karen Lynch: Maybe we can be off in the next 10, because we only have two more themes to deal with. So yeah, thank you, Karley, for putting this up. Really, things have changed within the industry, right? It's not just the methods that people are using for research, but it's from soup to nuts, right? It's automation across the board. It's procurement. It's tech stack, AI stack, M&A activity. Things are in flux in the industry. So before we talk about two stories that show some of this, just give some color commentary on your takeaway for this theme.

Lenny Murphy: Yes. Yeah, I'm three. Yeah, there's M&A conversations happening a lot right now.

Karen Lynch: Yeah.

Lenny Murphy: But they're not your usual M&A conversations, right? Even the ones you think about the consolidation, the financial engineering. Yeah, there's this edge of, but you have to transform too. Right. So, and fundamentally, businesses are built to grow. And then you exit in some form or fashion, right? So from a supplier standpoint, that's the deal. So the industry shifts and evolves because there's dynamics around all that. But it's not the old, the old playbooks don't work now. So it used to be you could just put together two or three companies, get some synergies, increase EBITDA, that's not the play.

Karen Lynch: And how many partnerships did we see this year where people are proceeding as business as usual, but in collaboration with a partner that complements what they are doing, because they're like, that may not be our route. Our current route to get through this time right now is to partner with somebody and shore up a skill set that we lack.

Lenny Murphy: That's right. And so building up ecosystems, which we've always been, but it's becoming incredibly clearly defined. And that's a good segue, I think, because there's two fundamental components that came into glaring focus this week. Sonic acquiring prime insights, which many didn't know, Prime Insights is one of the key suppliers in most every marketplace. Yeah. 250 million. Yeah. I don't know how big Prime Insights was. But I'm telling you, that's a big frickin validation. They were that big from what I was reading.

Karen Lynch: They're actually they're, they're only like a two year old startup, right? They're not that. I'm like, all right, you know, so again, remember, we talked about like, these new to the market players. Yes. Are, are, uh, you know, they're, they're getting in the game quickly. They're scaling quickly. And guess what? They're getting money.

Lenny Murphy: A lot of, well, so there's about, this is, this is, this is a supply chain, uh, consolidation. So Ionic, if I understand correctly, that they own game apps and that was a big deal. Part of supply for primary insights, but this wasn't just that they have, they have an asset that is people who engage with their games, right. And they are monetizing that asset, they already were by recruiting the surveys that have it, you know, internally, and they very expressly said they will be selling directly as a data collection and insight solution, not just a sample play. So now we have a company from outside the space, you know, with a very, very bespoke offering based upon monetizing, They worry about the engagement. They engage humans effectively through gamification, right? Then in every reward system, there were internal currencies in doing that, right? And now they are going to make that available, not just to panel companies or even research suppliers, but directly to brands by building solutions to do that. It's the same thing Walmart's doing, right? Basically.

Karen Lynch: Just swap out the retailer.

Lenny Murphy: Any of these things and that is a monumental shift. It's been happening for a while, but we're really seeing that playing out 250 million dollars is nothing to sneeze at. Yeah, right.

Karen Lynch: That's that's a lot of freaking money So yeah, there was another one and it didn't make it up to the brief It didn't even make it into our chat about these things. But you know one of the largest food retailers in Poland is something similar to Walmart Data Ventures where they're doing the same thing. So they're all coming up with the ways of being able to leverage the data that they get, the shopper data that they get. Anyway, it's pretty cool. I liked this story and it just kind of shows you where we're following the money.

Lenny Murphy: That's an interesting money following. Yep, so if that is an example of supply chain, and the Lupio, it's an RFP software with the full portal-based response management solution. It's AI procurement. We've been talking about this, a Genetic procurement system. These are the technologies that are powering that, where the entire RFP process, et cetera, et cetera, all of that is just being done by AI.

Karen Lynch: And you can picture it. In this release about it, Smart Fill, Smart Scan. These are the kind of, you know, branded AI tools that are underneath what's happening with this software, but basically automating the process to responding to these RFPs. So making it easier to respond to an RFP saves procurement time and money, and it saves the submitter time and money. It's just going to be a learning curve, I think, for people to get on it. But if your goal is to stay ahead of your competition, you've got to look into stuff like this too.

Lenny Murphy: I have heard, we've talked before a few weeks ago, that this kind of big shift now from brands requiring this adjective procurement process integration for their suppliers, we're hearing now from suppliers that are requiring that, or sub-suppliers. And where that was a few weeks ago, first brought up kind of a, oh, crap. Yeah. Now, there's no conversations I'm having with anybody. Yeah, that is not okay. Yep. We see that it's happening now. So that is becoming more widespread in its platforms like this. So supply chains are being consolidated and changed. Also, the process of just the basics of how you buy stuff.

Karen Lynch: Yeah, yeah. Well, that's our great segue into consumer transformation, because we do all work in service to an end consumer. But the last kind of theme that we've talked about over the last few years, consumer transformation, marketing transformation, some of the things, these adjacencies to what we do in insights and analytics. Is changing everything. It's changing how people do research. It's changing how marketers prepare to reach an audience, even, I guess, B2B, but also consumers. It's changing decision-making at the shelf. It's changing, I mean, just all of it, right? This is what is in flux right now as we are, AI is, and technology is driving a lot of consumer changes. So two things that we're sharing here. I'm gonna jump to the second one first. We have, we don't have to really talk about it, but we teased about this last week, Anderson Horowitz's Top 100 Gen AI Consumer Apps. Have a Read, check this out. Top 100, I just want you to sit there for a minute. Top 100 AI-based consumer apps. I thought that was astounding to me. So your consumers, your end consumers, end users of products are embracing this new world. ChatGPT might still be at the lead from a Gen AI standpoint, but you should see what else is on this list.

Lenny Murphy: It's super interesting. That's a ton, a ton. And that'll change next year as things come out, the arms race. And we should point out that this has nothing to do with the hardware changes that are imminent as well, right? So we could have had 10 different articles this week on wearables.

Karen Lynch: I know, on wearables alone. So we'll get back to wearables another time. And we have dabbled over the last, it wasn't a primary theme, because Lenny and I have dabbled in conversation about the wearables, the glasses or the pins or the, gosh, what are some other things? We've certainly talked about the rings. We've talked about all of these wearables, but that wasn't the driving force in the last two years. It might be in the next.

Lenny Murphy: Follow the money. Well, and it's interesting because these companies, these AI companies, now let's put us next week when you talk about how these companies are building out their own ecosystems and their higher supply chains. Yeah. Which now includes hardware, software, consulting. I mean, that is where they're going for it all. Yeah. And so all of them are going to have their own flavor of hardware at some point.

Karen Lynch: But let's, Deloitte and 100X, I thought that was really interesting. Speaking of predictive analytics, I think that one of the themes that we've had in this transformative space and insights in general is the rise in the ability to be more predictive. Predictive analytics is having a moment, right? Because now we have enough data that's informing even more predictive modeling. So Deloitte and 100X have entered a moment to year co-innovation agreement to deliver always on, always on predictive consumer insights for real-time business strategies. So predictive, so interesting. So linking to what we were talking about earlier, I think it's important to note that Deloitte is getting into, into this, you know, we, again, we've talked about some of the other consultancies Deloitte as well, which I think, you know, uh, they do a lot of, you know, because they do so much sort of tax and auditing and, and they've kind of sat in this other space, but their consultancy, the, the, uh, kind of strategic arm of what they do is also now looking at predictive analytics and how many providers of predictive analytics software. And, um, you know, we talked, uh, greatly, we talked about, you know, the kind of things that morning consultants are doing, but if the name of the game is trying to get a crystal ball to see consumers are behaving. And we know that a company like Deloitte has entered into an agreement with somebody called 100X, which we've not really known before. Why are they doing that? They're getting into a partnership like this because our ability to have a crystal ball is actually getting strong by the day.

Lenny Murphy: It is. It isn't that foresight. Foresight is a problem. There's a problem. Yeah. And you know, guys, so we, this was a lot of fun. It was, we were going through this analyzing, you know, the last a hundred episodes of content was, was really fun and cool. And, uh, and these big trends, uh, I suspect these will still largely be the defining trends, um, over the course of the next, next hundred episodes. Um, but it's still going to shift. I mean, there's going to be so much more for us to learn about the nuance and there's so many new things and how these things are rolling out. And that's why we'll keep doing this.

Karen Lynch: We will keep doing this. Before we wrap, Lenny and I, you know, we didn't want to start with this, but we do want to acknowledge this. The industry lost a little bit of a legend in the last week. So Steve Cohen, whose legacy is conjoint analysis and marketing science.

Lenny Murphy: Creator of MacStiff.

Karen Lynch: Creator of MacStiff. Lenny I interviewed him on the Green Book podcast not that long ago, just last summer. So, you know, I think that whenever these things happen, it's a moment to take a pause and look at contributions to an industry. So, in remembrance.

Lenny Murphy: In remembrance of Steve. And if you, please listen to that podcast. Steve, I just have to say, Steve's one because I never, I don't know why he decided to like me. Because compared to his accomplishments and his intellect, I mean, I'm a gnat. He was unfailingly kind.

Karen Lynch: Yes, you're setting me up for all sorts of things that I'm not going to say, because I don't want to. You're a gnat?

Lenny Murphy: I was a gnat. I mean, intellectually, compared to him. And that's fine. I accept that. So the point is that he was just, you know, he handled these massive accomplishments, but he's also just a kind, gracious, charismatic man. And, you know, truly we lost a giant, not just from his accomplishments and his contributions to the industry, but just, he was just a good human. I don't, I don't tear up often with those types of things. I did if that, that was, it sucked. I liked Steve awful a lot and the world is lessened by his passing.

Karen Lynch: Yeah, and he's also, he's, you know, he's been in our, in the Green Book community, spoken at an event, I believe last year, and, you know, written for us. Again, he was very active. Imagine being very active until the day. So, a role model for many.

Lenny Murphy: So, yeah.

Karen Lynch: So, in remembrance again.

Lenny Murphy: In remembrance of Steve.

Karen Lynch: So, that's our episode, 100 episodes. So, 45 minutes, not so bad. That's not so bad, right?

Lenny Murphy: That's not so bad. I think we both expected this one would go, and would stretch the limit. We'll start trying to get us back to the under 30 mark.

Karen Lynch: Back to the under 30. Remember, we thought maybe we'd do it in 10.

Lenny Murphy: Yes, I know, Jesus.

Karen Lynch: Our original thought was 10, and I think our first few were like, maybe we got to 15 or 20. And then we were like, we'll just do half an hour. That's our problem. We should have always tried to, anyway, whatever. We tried to keep it shorter. But yes, we'll try to get back to 30. That's our sweet spot. But thank you, everybody, for sticking with us for 100 episodes, for listening today.

Lenny Murphy: I'll put it back on to close. There we go.

Karen Lynch: I was going to say, I'm forgetting my tiara because I plan to continue to wear it for the rest of the weekend. So you can repurpose yours for your kid's 100th day of school or something.

Lenny Murphy: Something like that, yes. Keep it around.

Karen Lynch: I don't know what I'll do with my 100.

Lenny Murphy: By the way, this was all Karen's idea. And thank you, Karen.

Karen Lynch: All right. Oh, Rafa. Oh, we love you, too.

Lenny Murphy: Hey, Rafa. Aw.

Karen Lynch: Thank you so much.

Lenny Murphy: We do love you, Rafa.

Karen Lynch: Yes, we do. All of you. Yeah, I know. It feels congratulatory. So thank you, thank you, thank you. It's nice to see your comments pop up and to see that you're listening from outside of the US. Joy. Abroad.

Lenny Murphy: Yes.

Karen Lynch: We are so grateful for an international audience. We're happy to be here. Here's to the next 100.

Lenny Murphy: Well, I gotta say one thing since Rafa's on, I have to say that. Everything, IAX, everything else, it's his fault. I'll tell you the story one day. But all of this shift, all these things that we did, it was all Rafa Suspede's fault. And I'll tell that story one day. So blame him. It's good to see you.

Karen Lynch: And I'm sure I can think of other people to blame who might be on this call with us as well. But, you know, if we're pointing fingers, I'll tell the story one day. Anyway, everybody had a wonderful weekend.

Lenny Murphy: Take care.

Karen Lynch: We will see you next week for 101, which will not be as festive, but we're so glad that you joined us today. Thanks, everyone.

Lenny Murphy: Bye.

Links from the episode:

Introducing gpt-realtime and Realtime API updates for production voice agents 

Pureprofile adds AI coding 

Unlock Strategic Advantage with Ipsos' New Insights Agent 

Comscore debuts an AI-powered Data Partner Network 

SAP: Will AI Replace Humans For C-Suite Strategy Decisions? 

The Most Human AI Model Is Also One of the Cheapest 

Stop Selling a Saas Solution 

How people and technology can achieve more together 

Aonic acquires Prime Insights for $250M+ to create global rewards and data powerhouse 

Loopio becomes the first RFP software provider to launch a full portal-based response management solution 

The Top 100 Gen AI Consumer Apps 

Behaviorally launches myBehaviorally 

Deloitte and HundredX enter a multi-year co-innovation agreement to deliver always-on predictive consumer insights and real-time business strategy 

Remembering Steve Cohen, co-founder of In4mation Insights 

Remembering Steve Cohen, co-founder of In4mation Insights