The Research and AI Trends You Need to Watch!

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!

 

The market research industry is at a turning point. Strategic mergers and AI-powered tools are streamlining workflows, while verified spending data exposes a troubling gap between what consumers say and actually do. Wikipedia's demand for content payment raises questions about data ownership, and Salesforce's Spindle AI acquisition signals that enterprise AI is now essential.

With collaborative AI potentially disrupting traditional methods, the message is clear: professionals who can leverage AI effectively aren't just advantaged—they're necessary. McKinsey's advice? Two-year roadmaps are the new long-term planning.

Many thanks to our producer, Karley Dartouzos.  

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Transcript

[00:00:01] Lenny Murphy: There we go.

[00:00:03] Karen Lynch: Hi, everybody. Happy Friday.

[00:00:05] Lenny Murphy: Happy Friday. And I had pointed out beforehand, but to our audience, look at that lovely quilt that Karen's mother made. Isn't that gorgeous? That was amazing.

[00:00:16] Karen Lynch: I know. This audience doesn't always see that I work from my parents' house sometimes. So in all of the rooms in my mother's house are beautiful walls of quilts. They're on all the beds. It's everywhere. So she's very talented. As much as I have tried to tell her you should sell this stuff.

[00:00:31] Lenny Murphy: She doesn't so Okay, that's a beautiful, what a great backdrop. So yeah Yeah, well speaking of backdrop, I guess uh Many of you probably uh got The early release of The grit report. Uh this week for participating In uh in The report, so that's out there and on tuesday We have uh our grit forum so we're we're uh are gonna talk about all of this. And this one's gonna be a little more, almost like a group therapy session, I think. This report is impactful, I've been telling you. Really check it out.

[00:01:08] Karen Lynch: It's- And I think what'll be fun is like next, we should just like, you know, plan ahead for next Friday. We should dedicate some time on this call to do some unpacking post-GRIT forum, you know, and be like, you know, for hopefully everybody can make it to The GRIT forum. And if you can't, you know, maybe you and I can have a special love, So what does it mean?

[00:01:28] Lenny Murphy: We will do that. I've been chomping at the bit to, you know, we've hinted at a lot of stuff and we won't get in all of it right this minute, but it is a good frame. It is for me for weeks and weeks now, it has been a frame of reference for everything that we talk about. I'm like, oh, yep, well, that validates that. Oh, we see it, which is probably a segue.

[00:01:50] Karen Lynch: Speaking of validating, I mean, we're kicking things up with some insight, some industry news that, you know, a couple of announcements of partnerships and, you know, the pairing up and merging and adding AI helpers. And, you know, it just keeps manifesting the way we've been discussing it's going on. So why don't you grab The first one, Discuss and Voxco?

[00:02:10] Lenny Murphy: Yeah, Discuss and Voxco, a strategic combination, interesting choice of words there. I don't think it was an acquisition. I think it was more of a merger. But to combine The AF Howard Qual of Discuss with The Quant platform of Voxco, The AnaScribes analytics, put that in there as well. One of the things that we talked about in GRIT, new this year actually, was an investor spotlight. We predicted a whole range of mid-market roll-ups beginning to happen. Here we go.

[00:02:46] Karen Lynch: The goal, smoother end-to-end studies, faster, smoother, more efficient, and all of these kinds of partnerships are allowing that to happen. And I think that's The, you know, let's, instead of some sloppy handoffs from one to The next, let's just integrate it all.

[00:03:02] Lenny Murphy: Built by a partner for efficient workflows. Yeah, you know, that's The, how do you create that end to end? And whether you're a, that's what we've talked about The Genetic, we'll get into all that in a few minutes as well, right, that that's tech to help create that connection, but there's also business cases for just combining The companies and doing it yourselves. So there's one. Now, The partner, our next one. And by the way, shout out to Discuss and Bosco. Discuss was one of The, they entered The competition, gosh, 10 ago, right as they launched. They didn't win that year, but they were certainly one of The finalists. And, you know, Jim Longo and that whole team, I mean, good, good folks, early pioneers in digital qual when you couldn't give it away. And then, a good outcome for them. Caplana and QuestionPro, partnership to make it easier for organizations to collect and analyze customer feedback. QuestionPro, you know, works a lot in CX. And so good, that's a partner. Relationships that I can see where they're kind of combining IP and expertise and eliminating friction in the workflow, right? Yeah, exactly, exactly.

[00:04:24] Karen Lynch: So efficiency and speed to insights, right? That's what all of these kinds of combinations are. They all have that in common, right? We're gaining efficiencies, we're speeding up how quickly the end client can get to insights. So pretty good stuff. And you know, Canovo, is launching Survey Studio, which has agentic AI built into it with Metaform. So helping healthcare teams design and script surveys in minutes, right? So another speed play, right? So like, we're trying to get to things faster. Helpers, let's get into The field faster. So hats off to them as well.

[00:05:03] Lenny Murphy: Yep, absolutely. I recently did an interview with Tal, The CEO, that was in The CEO series coming out. And I think that's particularly interesting, this broader trend now being applied to a very vertically specialized solution, right? That's what, you know, Canovo, it's just healthcare, that's all they focus on. So these trends, these broad trends we see happening at a macro level are now, you know, here's an example where it's being poured into something that is very specific because you can't fight the efficiency gains.

[00:05:33] Karen Lynch: Yeah, yeah. You know, this next one is really interesting because who I don't know, they debuted something called Orchestrator, a super agent and suite of AI agents to automate creative delivery measurement and optimization across its ad platform. Suite of AI agents, so super agent, suite of agents. Anyway, I love the kind of operational efficiency that is being baked into these platforms for ad testing. I just think it's cool and one worth checking out if you're fascinated about how these agents are doing the work that would require step after step after step.

[00:06:18] Lenny Murphy: Yeah, well, I thought what was really interesting about that is that it's an ad platform, it's on The marketing side of The house, but they're baking in measurement and optimization, which are research functions directly into their solution. Because it makes sense for the workflow. Talk about disruption, disintermediation, this idea of having, it's two different things, researching and creative and ad delivery. Not anymore.

[00:06:51] Karen Lynch: Well, and that's what, The promise of agentic work is that exact thing. There's no more of The, The switching from pillar to pillar. If you've been playing at all with ChatGPT's Atlas, which I've been playing with and now use quite a bit, when you let The agent built into that platform take over, and you're switching from communication to task management. So for instance, if I'm in my email ecosystem, and I'm like, I want to add something into my task management system, and I let The agent do that for me, I don't have to switch in my brain, which what I'm doing, am I communicating or am I managing my work? I don't have to do that work, I let the computer do that. And that is very interesting because it is saving a mental load. It's slow, it's not the most efficient right now if I'm worried about speed, but what it does is it gives me a breather in between tasks like that. And I think that's what's interesting to think about if you can watch the way it does it on a personal level, then you can begin to more clearly see how it can work at an enterprise level.

[00:08:08] Lenny Murphy: I think The, yeah, we've talked about ecosystems a lot in The past few months, you know, with The emergence of The, you know, The agentic framework, that's really what it creates. And The, uh, I just encourage everybody to be thinking about that, whether you're on The supplier, you know, The, The service end of The supplier spectrum, Certainly on The tech side of The supplier spectrum, it's easier to think about that, but we are, no man is an island, right? No company or solution is an island. We've got a broader system and everyone's knitting those together now. And we're seeing that demonstrated because why not? The potential benefits are simply too large. So yeah, I thought that was neat.

[00:09:00] Karen Lynch: And we'll, you know, spoiler alert, we'll get into some more kinds of AI developments in a minute, but there's a few different stories that Lenny and I came upon this week that, um, are all, I'm sorry, my phone just keeps dinging. Stop it. And I've turned it off, but it's family. So family dings anyway, everybody. Um, so anyway, um, that, that all has to do with data, right? So we talk a lot about insights and analytics, right? Or whatever, but there's these few stories that have come up about data. And I wanted to pull them out and cluster them together and really kind of look at the millennia to see how they like to connect to one another. Because it looks to me, well, we can just talk about it and then talk about what it looks like, but there's something is shifting in The world right now in our ecosystem about free data and data that exists that I think we all can be mindful of So, you know, one of The articles, for instance, I'll talk about this first one, Restaurant Dive, if you're not reading The industry dive newsletters, but, you know, they talked about, and I think it's a sponsored piece, but verified spend data versus survey data, you know, sort of like, you know, stated versus revealed behavior. There's these like parallels of what's actually happened versus what people say is happening, that say-do gap, and it's fodder where this particular article is fodder for kind of what The dashboards might be missing. So it's an investigation into this verified spending data, which we talk about all the time. All these retail organizations have it versus The survey spends. And what is the truth? And how do we understand the why? So anyway, I just thought that was an interesting Read for those of you who are tracking, like, what is Walmart doing? Or I forget who we were talking about. Instacart is doing all of these retailers. You know, there's shopper data, which is just getting larger and larger and larger versus survey data.

[00:10:53] Lenny Murphy: And, um, I've done Humvee, uh, 8451, The, you know, all of it, but also numerator, you know, uh, companies like curious, uh, lots of, yes. In that synthesis of The two, right. Especially those that say there is a gap. I mean, look, we all know there is a difference between we've seen lots of data that shows what you say, oh yeah, I eat at McDonald's three times a week.

[00:11:21] Karen Lynch: Well, the data doesn't show you eat at McDonald's three times a week. The data shows maybe you're there a little more often, yeah. And yeah, we've stopped drinking soda, really, because your, whatever, your stop and shop receipt sure shows a lot of soda moving through your household. So yeah, I think that's interesting, and as a source of truth and understanding, At a time when survey research is under a microscope because of fraud and data quality issues and concerns, I think that, anyway, we just need to be, those of you who are in that space need to really be watching this kind of verify spend data and, you know, mulling over what that means and how do you, how can you acquire some and integrate some into The work that you're doing in your survey work for these types of brands and or So anyway, just one that makes you go, hmm.

[00:12:16] Lenny Murphy: Yeah, yeah. Well, this Wikipedia one is interesting.

[00:12:20] Karen Lynch: Yeah, so share about that one, because I did not read it. I mean, I read it, but I didn't read the article. I'm like, all right, this is interesting.

[00:12:27] Lenny Murphy: Well, Wikipedia is saying, so if you look at the primary training sets from almost every LLM, publicly available data, Reddit Yeah, which then they're saying no, we got to pay. Yeah, media is number two And now it's PD saying nope you got to pay now that that's interesting because Wikipedia is a foundation and it's people who do it and You know, so there's like some yeah, there's some interesting components of that like we're really You're asking to be paid. But what about I'm The editor on that whatever anyway, we put it all back The point is that The IP content is IP. And when people are producing content, and Wikipedia is an example, people produce it. It is not AI driven. It is human driven. That has value. And they're looking to say, hey, if it has value, then you should pay for it. I mean, every time I go on Wikipedia, I get a little pop-up, please donate. So I guess it's, you know. Somebody must be donating though because you know, yes, you know somebody's donating right it takes that I'm sure they have hefty server expenses no matter what. So we're just gonna see more and more of Data's new oil. Yeah oil has value. Yeah, what does that value exchange? Look like and whether an individual or or business or an organization I mean, here's an example of that, that we are, this ain't settled. You know?

[00:14:11] Karen Lynch: Data is The asset and data is, The training sets that The data is, you know, a part of are valuable because they are feeding intelligence. They're feeding these intelligence systems, if it's good quality data from Wikipedia or from verified spending data sets. That is what is coming our way is a more and more intelligent system based on this type of data that is factual and expensive.

[00:14:58] Lenny Murphy: I mean so anybody I know right? I mean, you know, I use every lip as an example. It's 2017 when we started that yeah with this we knew that there needed to be a system. Yeah this Which is a good segue to old Tim Berners-Lee Who you know widely credited as The is The father of The Internet or at least a godfather The Internet? He's been building a platform for a few years that's based on personal data control and data ownership. It continues to be a timely debate. So I think he started working on this, gosh, five or six years ago that Tim Berners-Lee was talking about, and it is still absolutely relevant.

[00:15:49] Karen Lynch: Yeah, so here we are as individuals, and we give our data just as but is there going to be a time when people will be fairly compensated for their data? Does my aura ring, for example, which, you know, I'm actually paying for The privilege of aura collecting my data. Is there a universe where, you know, aura actually owes me a little bit because they're gaining my data. So maybe that model changes, right, and they start to compensate me for my personal data that they can use for other purposes. And are we getting to that point, or is that just wishful thinking that ethics and integrity will evolve to that point? I'm not sure.

[00:16:30] Lenny Murphy: You know, from The experience trying to build a solution to manage this, at The time, 2017, we were not The only one. There were many companies that were playing with this idea. And almost all of them were using some type of blockchain as a supply chain management mechanism. To facilitate this. Nobody, you got 2017, almost 10 years ago now, nobody stuck The landing. The Measure Protocol is still who won The competition, they're still out there with some variation of it, but now we're seeing The emergence of a whole new set of companies trying to do it again. So what was Early in 2017, sure. But are we going to be too early or is it systemically, it's just not going to work? Because the business models of now, after trillions of dollars being invested into these platforms, they're not built to pay for data. The business models don't accommodate this. No more than Google, or Facebook, or Twitter, or anybody else, or Reddit. Their business models weren't based on this either.

[00:18:02] Karen Lynch: Yeah, well, all of it is interesting to think about, and hopefully this kind of a conversation stimulates some thinking out there of how people that are collecting data, of how end users might be compensated for their data, and what they're ultimately doing with it. I sure would like a world where it all seems to come full circle, and not only is our data protected, but it's valued to the point where it should be.

[00:18:30] Lenny Murphy: Yeah, universal basic income, those types of ideas, what is The one thing that whether you're a tribesman in The Sahara in Africa, or suburbanites in The United States, that we all all generate data, and that data all has value. So we'll see. I took my run at it. I'm not going to run at it again.

[00:18:56] Karen Lynch: We'll be paying attention. We'll be paying attention to the players that are going to be like, yeah, I'm on this.

[00:19:03] Lenny Murphy: So God bless them. If you have an idea and you want to hear about all the pain, I'll show you my scars. But I would wish you the best. You could succeed or I could not.

[00:19:14] Karen Lynch: Well, speaking of success, let's segue back. Let's segue kind of back to, um, The success of Salesforce, uh, just in general. I mean, uh, you know, it's kind of a universally used CRM, right? So Salesforce signed an agreement to acquire spindle AI. So, uh, you know, expect even more, uh, native AI features inside your Salesforce kind of ecosystem. System, tighter integrations across what you might be using it for. I think it's interesting because they had some AI baked into Salesforce already. So, you know, I don't know what The lift to spindle was, you know, better AI, more competent AI, you know, not really sure what The, what The added value is. So anyway, but expect more, right. And I think that that's really The bottom line for me is like in, in your enterprise world, in The work that you use, The tools you use, your ability to collaborate, expect more and more AI baked into what you're doing every day. I mean, we get new messages on Slack all the time, like, hey, you want us to take notes on your Slack huddle, and then you can chat with it afterwards. It's like, all right, I guess I'll start doing that. All right, well, yes, of course I would like to interact with our meeting notes. Whatever it is, like across all of our platforms, we're seeing AI integrations, they just show up, so more and more.

[00:20:44] Lenny Murphy: Yep, and we should point out, one, I think we should pay attention to companies like Salesforce, Microsoft, you know, any of those companies. They don't innovate as much internally now, which is not any shade. They are large enough that they accelerate innovation through external companies, through acquisition. And that's the way that it works. So I think we'll see more of these types of things, where they're acquiring smaller companies and providing scale and efficiency throughout. Because ultimately, it's kind of easier to buy it than it is to build it.

[00:21:23] Karen Lynch: Unless you're a company like OpenAI. So Lenny and I talked on The pre-show about this next news story that we're going to share with you. OpenAI is prepping group chats for ChatGPT, enabling multi-user conversations with custom prompts and responses. So it would allow, for example, if Lenny and I wanted to share this brief in The ChatGPT ecosystem, he and I could potentially chat about it together in a thread, other people in. Like, maybe we say, oh, let's get The team from Disqus in here, potentially, and invite other people to chat about things. So it's a collaborative workspace with shared prompts and, I don't know, potential group workflows, because, of course, there's a Genetic assist built into ChatGPT now. Like, it's a really interesting way of changing the way we work. And for me, the reason I keep speaking slowly about this is I'm wrangling with research on WhatsApp, for example. Like, what are The research implications of an LLM, a tool that was supposed to be just generative AI, you know, suddenly changing, and now we can say we load some reports in there, and then we chat with The reports, and I might have a probe and then let says, yeah, but what about this? Isn't there, is there room for this argument based on this report? You know, it's just fascinating to think about the research applications for data that exists. And then is there a data collection opportunity there? Is there, do you get multiple consumers into a chat with an LLM? And what is possible? I don't know, my brain is kind to go into town on this one. I would love to know your thoughts too, but I just think it's really fascinating.

[00:23:29] Lenny Murphy: Yeah, my first thought was. Virtual focus group, yeah? Yeah, the hybrid, but you know, is it really? I imagine there would be pretty easy to deploy coming to AI moderation within a group chat.

[00:23:45] Karen Lynch: Yeah, you train the custom GPT with your guide and you know The science of moderation. Like it is intelligent enough to learn how to and not to probe. And you get it moderating in there with a group of individuals. And then it's already got preloaded stimuli in there. So really The job of The researcher becomes to train The LLM or train The GPT to initiate a conversation. Then you sit back and watch it happen. Like The observational researcher in me is like, that's kind of cool. Like I'm not necessarily moderating, I'm free to observe and collect my thoughts on what I'm observing, which might actually be significantly more powerful even than qualitative research. It's like when a qualitative researcher would sit in the back room of another qualitative researcher moderating focus groups. Those, to me, those groups were always The best, The most electra, because when you're not in The task of moderating, you're free to pay attention, watch, observe, listen, and make connections that The moderator who's very focused on The question and answer and The process can't quite get to. So those tag teamings were very helpful when you were trying to do qualitative analysis.

[00:24:58] Lenny Murphy: Remember back, so I'm going to talk about this. So at Rockhopper, we used Adobe Connect to do virtual focus groups back in And we figured out how to have The back room through Adobe Connect, right? And we thought we were so smart. And we can give it away to everyone who works with focus groups back then. But, they basically think of the same thing of these, there's standard platforms and technologies that come out. And even back then, creative people were thinking, how do I deploy this for, for, for research? Now, there's not even the barrier of Oh, what digital call or a moderation? That's ubiquitous. So now the potential disruption. And all these companies come out of The woodwork with specialty AI moderated focus groups, chat, and OpenAI comes in and says, yeah, everybody can do it now. What are the trade-offs? Obviously, no shade towards any of the companies that are doing that. I would argue they're better. They're trained, et cetera. But you got a marketer. A small company and they're like, oh, I got to talk to people. We're just going to throw up this Chachapiti group. Somebody probably created this over the weekend.

[00:26:24] Karen Lynch: I know. Well, and especially you think about in the corporate world, some of the work that I used to do would be employee satisfaction or employee research or whatever, and you're having focus groups internally at a company. To talk about what you could do to improve the employee experience. But imagine, you know, they've already got these tools in place. Now they have groups. Now it's like, hey, let's, you know, HR departments, man, like, you know, like bring this in. And the next thing you know, you're doing some live groups with some of your internal teams. So The corporate research world, potentially cheaper, like The ease to get corporate professionals chatting might have just gotten significantly, you know, significantly better because we've just knocked down a bunch of different hurdles. If they've onboarded, you know, ChatGPT, then now we've got a research platform internally in our organization.

[00:27:22] Lenny Murphy: Right, but only if, I go back to The data, so you have an enterprise license so The data is private and not feeding The And my bet is that they'll probably have that, but they're going for The free because they want The data. This will give a whole other form of data input to train on to look at the type of conversations and chats and things like that that occur. And yeah.

[00:27:58] Karen Lynch: Cautionary tale for everybody also, if you have these systems and they do updates and you, you know, you turn on your computer and they're like update available or whatever, just make sure you go in after each update and check your settings to make sure that you're not, because it, it defaults back after a lot of updates I have noticed. So if you don't want training, if you don't want your, um, your conversations to be used for training, make sure you go in every time there's an update, say, let me go back in and check those settings again. Because it talks.

[00:28:29] Lenny Murphy: And on that note, we had three really interesting suggested readings.

[00:28:40] Karen Lynch: Technically four, because there's two from McKinsey.

[00:28:42] Lenny Murphy: Oh, yeah, two from McKinsey. OK, got it.

[00:28:43] Karen Lynch: Technically four. So let's talk about The Wharton one first, this AI adoption report. So Wharton's adoption report and The kind of synthesis of its flag for rising AI integration across certainly market research and customer experience. So adoption is broadening.

[00:29:04] Lenny Murphy: Yep, yep. And it was interesting that all of these still keep predicting implications for human capital in staffing levels. I don't know if it was in, I think it was in The McKinsey or it could have been something separate. Anyway, it was another data point that said, we're not seeing that yet. Yeah, but everybody's predicting it, which I think makes perfect sense. So that was there. But it was a really good and then The McKinsey Yeah, of 2025 AI survey. Yeah, these two are very, you know, they were saying exactly the same thing. So validation, right a little some tackled a few other topics a little more depth, but heavy piloting growing bottom line impact. Yeah, the genetic systems move from experiments to transformation. So I thought that was the most interesting thing we've heard. Oh, nobody's getting, even a few weeks ago, I think we covered, you know, oh, we're not seeing The impact yet. Well, going to the McKinsey survey. Yes, some brands are. Is it widespread? Is everybody just, you know, experiencing amazing ROI? Not yet.

[00:30:16] Karen Lynch: But it's starting to, it's starting to move the needle, right? Starting to move the numbers. And, you know, that's going to be interesting to track this time next year. Let's see what happens in McKinsey's 2026 report, because my guess is that's when we might start to feel The impact. And I think that, you know, everything that we've heard, for everybody listening, right, we've heard it now in a couple of different ways, at Green Book's IAX AI event and in various other places, people saying, you know, AI is not coming for your job. A professional who knows how to use AI is coming for your job. So like The marching orders for everybody who's listening is, yeah, make sure that you're AI savvy. You have to be. It probably, it's not even negotiable right now. You must be AI savvy.

[00:31:10] Lenny Murphy: But you also must be, and I'm glad you brought up, next week we'll talk more about The grit stuff. Because it must say, we absolutely see the shift towards AI-based solutions from traditional solutions happening significantly. It's quantified data, which has a business impact, regardless of staffing. Some companies are losing business to these solutions, which do have obviously staffing applications, you know, in a whole other way. So this, you know, The McKinsey thing, I thought was good about it. It's at a macro level. Yeah, we even if in grid, we have a very specific view that it is happening. We are seeing that happening from a commercial standpoint. Yeah, within The industry, we have quantified data that shows that as well. So maybe they thought we're not there yet. Yeah, we are. It's just not widely dispersed yet.

[00:32:14] Karen Lynch: But yeah, it's going to Well, in this second report from McKinsey, it's not really a report. It's an outline. It's a blueprint. It's a way for CEOs to kind of navigate this age. So it talks about the mindset that CEOs should have, talks about kind of creating a two-year roadmap, which I think is like that's killer, right? A two-year roadmap. Because we've talked internally about how five-year plans are hard right now. But yeah, if we can get to a two-year roadmap in any of our businesses, right? A two-year roadmap. What does that look like, Forget about, you know, five, 10 year planning right now, you can't do it. It's just, it's futile. It's scenario testing at this point, right? So- You can imagine maybe kind of what The end state, you know, maybe, but The process- Yeah, you can put it out there and have it be kind of inspirational, visionary, sure. But your roadmap is gonna be much shorter. So anyway, yeah, I like this piece for that. Kind of with The marching orders to treat AI as company-wide transformation, right? This is what's happening. Our business is transforming. Let's come up with a two-year plan. And I think it was in this report that it's saying, don't treat it as a tool rollout. That will be The wrong mindset to have is let's roll out AI tools. No, how is AI transforming your business, not just your offerings?

[00:33:36] Lenny Murphy: I think that's The- The business model, yes. Yeah, sorry. This is really literally like what I've been talking about all morning. I was on back-to-back calls with CEOs, talking about this very topic around The transformation of The business. And on that note, one more thing, The Anderson Horowitz Marketplaces in The Age of AI. This is more of a think piece, but you don't dismiss Anderson Horowitz and come back saying, yeah, The whole marketplace Yeah, it didn't really work. You got a couple winners, Airbnb and, you know, but nobody really hit scale. But then along comes agentic AI. And their approach was thinking through how technology solves sticking points in marketplace models. So if we think about ecosystems, we're thinking about, you know, agentic powering. If that is what we are going to see and we've talked about this as a research application marketplace, I fully expect to see that. For very automated, for your ad test, concept test, whatever The case may be, it makes sense. How do you function? If you're a service business, that's going to be really tough to navigate. If you're a tech business, you better make sure that you're part of that marketplace. If that happens, but that, you know, as Horowitz says, yeah, we're willing to put a bajillion dollars into this concept if somebody has The right one. So just be prepared, guys, that every level in The transformation is unmistakable.

[00:35:18] Karen Lynch: Every level. Start with that supply chain, that sample supply through research ops.

[00:35:23] Lenny Murphy: 100% through delivery and decision making. Yeah, but interesting times, interesting times.

[00:35:33] Karen Lynch: For sure, for sure.

[00:35:34] Lenny Murphy: All right, we can, 1235.

[00:35:35] Karen Lynch: Yeah, that's pretty good.

[00:35:38] Lenny Murphy: We almost made it.

[00:35:39] Karen Lynch: Almost made it, almost made it. Anyway, so yeah, good stuff. I'm excited for next week. I'm glad The Gritforum thing banner is still up there. Block your calendars. I'll be tuning in and paying attention to myself. So anxious to see how it all comes together.

[00:35:55] Lenny Murphy: You and I both.

[00:35:59] Karen Lynch: You'll be great.

[00:36:01] Lenny Murphy: You know all The reasons why it's always like guys we try many things Lenny proof and occasionally like this one of The examples where we're not making it Lenny proof so You know, it's that we fly by The seat of our pants sometimes.

[00:36:13] Karen Lynch: You'll be great. You'll be great Nope, it's gonna be fantastic, get a lot of rest Yeah, if you can't make it too damn bad so I Anyway, it'll be good.

[00:36:27] Lenny Murphy: Everybody have a wonderful weekend.

[00:36:29] Karen Lynch: Wonderful weekend, everybody. And yeah, we'll see you next week.

[00:36:33] Lenny Murphy: See you next week.

Links from the episode:

Discuss and Voxco announced a strategic combination 

Caplena and QuestionPro formed a partnership 

Konovo launched Survey Studio 

Innovid debuted “Orchestrator” 

Market research meets reality: How verified spend data is transforming restaurant decision-making 

Wikipedia Asks AI Companies to Stop Scraping Data and to Start Paying Up 

Owning our own data is the only way to stop enshittifcation 

Salesforce Signs Definitive Agreement to Acquire Spindle AI 

OpenAI readies ChatGPT Group Chats with custom controls 

2025 Report - Accountable Acceleration: Gen AI Fast-Tracks Into the Enterprise 

The state of AI in 2025: Agents, innovation, and transformation 

The change agent: Goals, decisions, and implications for CEOs in the agentic age 

Marketplaces in the Age of AI, Take Two: Graveyard to Greenfield