When Algorithms Make the Purchase: Marketing's AI Turning Point

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!

Karen and Lenny dive into the wild frontier of marketing tech—where a $60M bet on Cint and Neurons’ bold U.S. move Cint at seismic shifts ahead. We explore how AI is not just streamlining workflows, but reshaping product development, packaging, and the very way consumers make decisions.

Plus, the surprising generational twists in AI adoption and what 90% AI integration really means for effectiveness. Buckle up—this is where the future of insights gets real.

Many thanks to our producer, Karley Dartouzos. 

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Transcript

Lenny Murphy: All right.

Karen Lynch: Here we go.

Lenny Murphy: Here we go. Happy Friday. Happy Friday, everybody.

Karen Lynch: And Lenny, should we disclose that for us it's actually Thursday?

Lenny Murphy: We should, we should. Next week is the 30th anniversary of my wife not killing me. So in reward for that, we are taking a long weekend, which rarely happens. So it's my fault that we're not living, Yeah.

Karen Lynch: Because wedding anniversaries are a thing. Yeah, absolutely.

Lenny Murphy: And yeah, I mean, you guys probably get the sense that I'm a lot, I'm a lot to take. So truly, truly, when I say I should be a saint's wife and 30 years for not killing me, because it's been a near thing a few times, it is worth celebrating. Well, happy anniversary, first of all.

Karen Lynch: Do you know this weekend is also our head of marketing? So just a shout out to Jasmine and her husband as well. So yeah, it's a special weekend, apparently. Very cool. Something has been in the air this weekend in the past. Spring. Spring, yeah.

Lenny Murphy: Very cool.

Karen Lynch: Very cool. Well, happy anniversary.

Lenny Murphy: Congratulations.

Karen Lynch: So we're pre-recording on Thursday. So it is officially April 10th. But for you, it's officially April 11th. So I hope you all have good weekends. But let's get into it, Lenny. Because I think this Sint story, you know, just shows it's another one of those signals, right?

Lenny Murphy: So why don't you tell everybody what's going on with Sint? Yeah, no, Sint raised 60 million US, well, 596 in their local currency. But to enhance our exchange platform and to invest in media measurement and fraud prevention, the media measurement component, I think is the, what really stands out there for me as the, you know, they're building products off of their access to sample themselves. And that just makes a lot of sense as panel companies continue to look for new ways to monetize their core asset, which is connection to consumers and building data products. That media measurement is a data product. Off of that.

Karen Lynch: Surprise, surprise, there's some AI power behind this new functionality also. Once again, investors are confident in AI power.

Lenny Murphy: They are confident in AI power and the need to connect with people for data to feed the AI. So it's kind of both of those components. So yeah, so that's interesting. Hats off. Rising tide floats all boats. So that sends a strong signal to everybody playing in the human data accessibility market, I guess, trying to create a new term that absolutely includes panels or exchanges. Since it's not really a panel, they're the exchange for other people's panels. And access to audiences. So yeah, hats off to them, strong signal. Yeah, a lot of nervousness in the past week or so, so it's good to have signals that say, nope, sky's not falling, we're good. Continue on.

Karen Lynch: We're good. So yeah, another signal of growth is a new office for our friends at Neurons. So Neurons opened its first office in the US in Austin, which is a great hub for innovation . It's like, as soon as I read this, I was a little nostalgic. I was like, oh, Austin, we used to do our IAX there, and now we're gone. So maybe someday we'll be back. But either way, expanding their footprint, always a good indicator, a kind of demand for neuroscience-based testing and the tools that they bring. Office just means further growth.

Lenny Murphy: So another indicator. Absolutely. A private equity-backed company. Thomas Ramsey and his whole team have been growing for years. It's great to see scalability happening there. We say that they were AI before AI was cool. One of their flagship products early on was the visual intensity modeling that was effectively driven by, maybe not LLMs, but certainly neural nets and the earlier version So yeah, good to see growth in anything related to non-conscious measurement. Because that's like behavioral data, that's increasingly becoming important in understanding consumers holistically, utilizing other tools. Yeah. Speaking of, so the trifecta of growth indicators, this next one.

Karen Lynch: I thought this was pretty cool.

Lenny Murphy: I know, it's so cool.

Karen Lynch: So an IQ acquiring sensor. Remodeling firm Gastrograph AI. It's a mouthful. We'll give them a break for that. So this is about predictive insights for CPG brands through flavor, aroma, and texture analysis. So here we go again, right? This ability that Nielsen IQ has kind of now kind of leaned on to model sensory experiences using AI, which was something that you and I were like, well. Right, right.

Lenny Murphy: Sensory is safe. Sensory is safe. People still got to touch it and smell it and taste it.

Karen Lynch: It's fine. Everything's fine. Do they? And I think that's the reality. Anyway, so sorry all you sensory researchers. It is time for you to get up to speed on what these offerings are and to see how AI is being integrated in the work that your kind of competitive set is doing and seeing what you need to also be developing, you know, to stay relevant in that space. Because this moved faster. I mean, it was just a few weeks ago we were talking about just, you know, smell, wasn't it? Like we were talking about being able to simulate smell through computers and all of that stuff.

Lenny Murphy: All of that is true. I think to be somewhat fair, I think this is a very sophisticated normative database that creates predictive models. The NIQ or gastrograph, correct me if I'm wrong, that was my takeaway. But still, it's kind of the version of a synthetic sample. From that standpoint, right, of being able to predict off of some testing measures of what the reaction may be overall. Would it replace the need for sensory testing? I would guess not yet, emphasis on yet, but would it speed up the process in early phase testing, in iteration? Hell yeah.

Karen Lynch: Yeah, and I mean, even the scale if you build profiles for people that have sensory preferences, right? Like, you know, like, you know, a synthetic respondent at, you know, one, two, five, seven, nine, or whatever, they may know like, oh, they have a preference for sweet over savory. If it's food related, they don't like sticky, you know, and all of these sorts of sensory attributes, you know, build a sensory profile of preferences that start to predict, here's this new product, how will a data set respond to it? Well, guess what? It's going to point you in the direction of, anyway, people that you could segment to target. You have to start, I think, when you're trying to wrangle how this all works, start it with a very basic kind of concept and then say, okay, maybe if we expand this out, we can see how it can grow.

Lenny Murphy: And how soon before we get into customization, right? I mean, let's say, eventually we will get to the 3D printing of food. I mean, we already do, it's just not scalable. And I don't know that I'd want to do it yet, but still have that customer based upon your profile, to your point, be able to customize that, you know, oh, yeah, this person likes sweet and savory. Here's the input of these types of databases that drive that.

Karen Lynch: It's- podcast episode with the woman from Estee Lauder, the beauty industry, has been doing sort of augmented reality sensory testing for a while. Like you can get in there and you can do, get in there. All of that. So they've kind of gotten to some of this space already. So they might be ones to study up on if you really want to see how it's all coming together. It's very interesting. And, you know, at one point in our lifetimes, did we think that we'd have, you know, the impossible foods, you know, and, you know, genetically created meat? You know, this is really, this is not that far from that. If we can already create those foods that are not actually, you know, meat, but synthetic meat.

Lenny Murphy: Think about the replicator from Star Trek, right? We've had that conversation. For the Star Trek future. It goes back, the thing we talked about a few weeks ago. I forget the name of the company now, but it was at the molecular level. And that's fundamentally what we talk about with sensory, it is molecules. Is it a reach to think that one day we will be able to like 3D printing at the molecular level to combine things to create that? No, I suspect that's already being done. I mean, hell, if we can de-extinct the dinosaur Wolf, which by the way, I know that wasn't on the agenda. We should throw it out there. Oh, you know, we have dire wolves again. I don't know if people get the wrong message from movies like Jurassic Park. I think it wasn't supposed to be inspirational.

Karen Lynch: But anyway, we're keeping it, keep it wolves. They serve mammoths too.

Lenny Murphy: So we'll see. But I just hope they haven't got a T-Rex somewhere out there. But the point is, we talk about AI and all those other technologies, but other technologies, more kind of fundamental brick and mortar making stuff, continue at a pace as well. And this would be an example of how we may see that, which ultimately gets to the nirvana of ultimate personalization. So we think that we're not gonna see in our lifetime a device in our home that allows us to create something custom based on our tastes off of raw ingredients. I think that's a mistake to assume that we're not going to see that. I think we will probably see it faster than we even think. And this is an example to help drive it. Anyway, All right, geek button.

Karen Lynch: You pushed my geek button on this one. Well, it's all right. It's all right.

Lenny Murphy: It's very omnipresent. I'm just a big geek.

Karen Lynch: Easy to do. Let's talk about some product launches. Because this is where it's like, here we have a lot again to go through. But I just love the name of Ipsos Facto, because it's very clever, well done. So Ipsos unveiled Ipsos Facto, which is an internal AI operating system built for and by researchers, now adopted by over 90% of employees across 86 countries. So enterprise-wide AI integration. We have, and Karley can share the link, kind of this other story about, I think it's much further down, but about the Shopify memo about kind of enterprise-wide integration of AI technology. And I think that's just going to become more and more a thing as everybody has finally said, uncle, you know, uncle, we got to do this.

Lenny Murphy: So, um, well, I did it earlier today, I did a, uh, a webinar with, uh, Ipsos and with GreenMesh and this, we touched on this idea of the, the complexities of, so strong AI. Well, actually there's a hundred different components that fit into this. Um, so it is not, uh, you just, oh, we're now all on Microsoft Office, right? It's not as simple as that for an organization of the scale and scope of an Ipsos that has, you know, a bajillion different vendors and, you know, countries, et cetera, et cetera. So first, hats off, David Zahner, the CTO, talked about this. Hats off to David for spearheading, because I'm sure it was a monumental task from an adoption standpoint. But technologically, it'll be interesting to see because they only did it to increase efficiency and process. So it gives them advantages internally in terms of speed and cost efficiency. So yeah, big example, guys. Yeah. And can I just sidebar real quick also?

Karen Lynch: Yes. It just happened 28 minutes ago. So it's not something that I had digested. And I haven't read the article yet, but heads up, Lenny. Shout out to Will Leach. He posted an article on LinkedIn, and Karley, I'll get you this link, but he had shouted out to us, Lenny, where he said, I listened to an episode on The Exchange with Lenny Murphy and Karen Lynch a few weeks ago, and I can't get this idea of AI for efficiency versus AI for effectiveness. And he says, I'll vote for effectiveness all day long. And I thought, without even reading this article that Will wrote, and you know it's fantastic, it's the concept of, Oh, now that's interesting because we do talk about efficiency all the time. Right, right. And how does that make it better?

Lenny Murphy: How to make it better.

Karen Lynch: Let's focus. Let's stop thinking about the next evolution of our brains and our usages. OK, we're so excited about the efficiency. Let's get to it. And I just love that that will call that out. And it didn't make it into any brief because I just saw it and haven't read it yet.

Lenny Murphy: Well, that's a great point. I mean, let's you know, even as we go through these other product launches, let's keep that in mind, right? You know, efficiency is important for speed and cost competitiveness. Yeah, absolutely. But if we look at the cheaper, faster, better, that's the effectiveness piece. And better, we know this from Grit, better always wins. It can't just be better and be really damn expensive and slow. I mean, sometimes that works, but in most cases, it does have to be some combination of all three, but we can't, the effectiveness component, the impact does need to be factored in. And especially in economic uncertainty or highly fragmented competitive landscapes, that's where we are. So as solutions are being rolled out, I think we're way past the, oh, you get to save 10 minutes. That's not the piece anymore. It's creating more value that answers the business question in a better way. Thank you all for highlighting that.

Karen Lynch: Yeah, it's a really cool thing. For those of you who are like, oh shit, I'm still focused on efficiency.

Lenny Murphy: What should I do?

Karen Lynch: Let's say this is a yes and Yes, it's efficient. And it's effective because I still like the idea of saving time because we're all so time crunched. And that's one use case for it. But when it comes to research applications and your business use cases, I'm not talking about you as a professional. We're talking about the work your teams are doing in insights. That's where effectiveness has to shine over us just doing our work. There's a difference.

Lenny Murphy: Absolutely. Well, that's probably a good segue. Let's talk about the rival tech that launched their multi-agent AI framework to power every stage of the research process. It's pretty cool. It's a pilot, so you can sign on. You don't have to force it down your throat. You can adopt which of these pieces and there's a vision Yeah, I would say that is right. The moment is still an effective focus on integrating and creating more efficiencies across their tool set. It's hugely important, right? Still important. The But if you get into the nitty gritty of from that effectiveness in this case around unstructured data, that does translate pretty quickly into from efficiency to effectiveness right it's easy to see sometimes the lines blur here I think this is an example of that and this and we've been talking for a while about the agentic component now you know we're seeing these frameworks of technology providers like like rival rolling out these agentic systems to help you know streamline and make it more impactful for the users yeah yeah and I think and I I I could be wrong in my recall, but when we were talking about personalization, I think there's also some kind of human user empowerment that happens when you have this sort of multi-agent framework where the user can decide which of these workflows matters to me for this moment, for this project, for these objectives, whatever.

Karen Lynch: So I think that when you have that kind of modular approach that they're building in there, you know, it's not underscoring the humans need to use these, use these tools.

Lenny Murphy: Yes, it's having the whole set of tools that are available as you go. The Swiss Army knife, if you will. Yeah. Yeah. Cool. Yeah. Cool. Cool. Global launched Verified Views, ratings and reviews solutions from real buyers. So, you know, integrating traditional research now with, you know, with ratings and reviews. So think about that from attitude and usage study to easily combine those of here's a survey, but then, you know, here's what people are saying online?

Karen Lynch: And I think they have a verification tool in there for, you know, verified purchasers. So it's helping, helping trust, you know, trust, trust a little bit more what you're seeing. I think that, you know, there's something about real users versus people who are gaming the system or working the system. I think there's something to be said for users who are like, I'll try this product and then share a review. They're using it. They may be giving honest feedback, but they necessarily weren't a user first. So anyway, verified views are working towards this trust that we need, which has been shaky for a while.

Lenny Murphy: Absolutely, absolutely. So cool stuff for them. Sarkana, formerly IRI and MPD, introduced LiquidMix, self-service platform enabling marketers to drive rapid AI-powered marketing mix insights. So like another example of they got a buttload of data, right? That's what they are. And when I think about IRI and MPD, they are data companies, always have been. And now they're given a new solution to be able to do that with a defined business issue. So taking the data, using an AI solution, probably a series of prompts that they've gone through, focus on marketing mix.

Karen Lynch: I think one of the ideas here is that the self-service nature of it leads to faster. So always goes back to the trifecta. Somebody asked me recently, is that trifecta still in place? And I'm like, Yes.

Lenny Murphy: Absolutely. And there is no, no pick two. No, it's just, right. That should point out too, that's interesting about that is like the, uh, kind of like, uh, NIQ. I mean, the focus of those companies really is they have purchased data, right? They are understanding what's happening at the, at the, uh, at the store level, um, uh, as well as overall trends and categories, et cetera, et cetera. So to, take purchase data and now combine that to get in the marketing mix and to look at, you're getting an attribution, right? That connective tissue of, oh, I spent money on this and I saw this lift in product purchasing at this store, so how do I optimize it? Really, you know, kind of nitty gritty meat potato stuff where research shows its value.

Karen Lynch: Yeah, yeah. So speaking of, I love our natural segues when they come up.

Lenny Murphy: Speaking of where you see. You structured it that way.

Karen Lynch: No, I know. I know on some level I did, but also when the conversation just feels anyway, enough, I just feel like that's such an important point because we don't always look at the value of insights in all areas of the supply chain. So that's the, that's the aha that I had. So talking about NIQ again, um, they debuted, debuted GFK Neuron supply chain, AI enabled platform for kind of always on global distributor sales data for techs and durables. And I think that, I think that we don't, we don't often talk about the kind of the insights that are far into a supply chain and in the distribution networks kind of at the manufacturing level. I think that's all really interesting. And It's going to help with insights for supply chain management, which is not necessarily in the insights space. However, insights professionals in many organizations are going to be, especially in sort of tech and durable, are going to be accountable to stakeholders who are doing supply chain management. It all makes perfect sense. I just love that we just got to some depth.

Lenny Murphy: Yeah. Forecasting is absolutely, you know, demand forecasting. Is something that firmly sits within the Insights organization. And, you know, you think about them, so in this current era of changing dynamics of global supply chains manufacturing, hugely important, right? Companies are resetting their supply chains. But even about 2020, this time, five years ago, right? Where's the toilet paper? You know, I mean, the ability to predict and understand the impact of the flows of those things that enable products to be made and delivered to the store, to the shelf for people to buy.

Karen Lynch: Even down at the grocery level. So I was on the phone with a friend of mine who is involved in research at Kroger's e-commerce department. And she was saying the perception of where strawberries come from is fascinating because, by the way, they don't all year long in the US. They don't come all year long from California. They don't grow there all year long. So they are constantly navigating where those strawberries come from. And when there are changes and disruptions to the global economy, even your strawberries are in question here. Like, where are we getting those from? How are we going to be able to get them from somewhere? It's not just eggs. It's not just strawberries, too.

Lenny Murphy: And we talk a lot about eggs.

Karen Lynch: We talk about avocados, maybe. But when she used the strawberry example, I was like, we can't emphasize enough. It's everything that we consume, whether it's food, a beverage, a prepared meal, or a phone. It's all really interesting. And Insights is doing critical work right now. Absolutely. Way to go, Insights professionals. Way to go.

Lenny Murphy: Go, Insights. Yes. I agree. Because that always follows. The business intelligence kind of thing that way, but to effectively get to demand forecasting and, you know, and understand past purchases. Well, you know, all in a Yeah. Yeah. All right.

Karen Lynch: So speaking of shopping behaviors, we kind of touched on the Shopify thing. So, um, you know, we'll, we can always come back to that at the end. Cause there's, there's even more, but, um, but let's talk about this drum article that you found because, um, this was interesting. Marketers adapting to the future of AI agents, of course, but agents in this context making consumer purchase decisions. And we've touched on this before, so I really like the idea that now this is like the call to action for marketers and then insights professionals who are in service to marketers to really rethink how they are influencing consumer decision-making when some of these decisions are happening machine to machine, or agent to agent, or AI to AI. That is a quandary for marketers that more people need to be talking about. And this sort of steps outside of Insights just a little bit. And yet, Insights is going to inform, because the Insights professionals really need to be ahead of consumer behavior on this one.

Lenny Murphy: Right, right. Well, and that does open up interesting components of if the agent is making the shopping and you're doing an attitude and usage study. Do you talk to the person or do you talk to the agent, right? Think about the screening question. Who is the, you know, you are the primary shopper in your home? Well, no, actually my agent is the primary shopper, potentially. So yeah, it brings up just interesting questions on that whole process. What does a path purchase look like when it's agent to agent, right? The decision making. Criteria, so many different things that just the world of buying is changing. Yeah, it really is.

Karen Lynch: I just, as you were talking, I was thinking for a minute about, you know, am I using any agents in my purchase behaviors? And, um, I mean, I'm, I'm doing some integration, some automations, you know, if I look at a recipe in the New York times cooking app, for example, I can send it right over to Instacart and the next thing you know, my cart is populated. So, you know, there is a world where I can just look at a recipe and say, yes, I want to buy all these things, and that can all happen for me. The next thing you know, a bag shows up at my house, and it's just based on me reading a recipe. So I look forward to that evolution, by the way. I'm not afraid of that one, excited about that one. Anyway, think about Amazon. I don't know if anybody ever subscribes to anything. I have my dog's supplements coming regularly. I'm like, make it a no-brainer. The packaging changed recently. And I was a little put off by it. I was like, what happened? What changed? Did it make a decision for me? I had this moment of disconnect with the changed packaging. Now it was still the same product, but the packaging changed. Anyway, I don't know if I would have bought them with this new package. I might've had a pause if I saw the new package design. I might've wondered more, but it just showed up in my house. So I didn't have to really, I didn't have to react to a package design change. They just showed up. So I had to then suddenly trust the efficacy was unchanged, which is one of those things for package design changes, right? Will this affect consumers' perception of this product? If we change the package, that whole decision I didn't have to make because Amazon just did it for me.

Lenny Murphy: And in that world, does packaging even matter?

Karen Lynch: Does packaging even matter?

Lenny Murphy: I mean, I mean, fundamentally, if the visual appeal Is not the because that is fun, you know, that is what packs is optimized for not just but there's a piece of it but if it's really about You know, I want a product that has these ingredients or doesn't have these ingredients and has this cost point, you know It's much more quantitative, I guess. Yeah Yeah, what does that do?

Karen Lynch: Weird world weird world So these two articles I think Karley you can share them in tandem There's two articles. One is a Pew survey that was in Sherwood News, and one of them is an article that was in, I think it's Every.to. Two different articles, both talking about this concept, though, about Americans in particular, and how far along regular mainstream Americans are on the adoption curve. So, you know, there's a big difference between the audience that we're talking to which is this, you know, kind of professional audience, you know, kind of insights professionals in particular, you know, we have a fairly tech forward audience. So there's a difference between those individuals and the mainstream computer consumer who is starting to use AI things, AI platforms, generative AI, it's still pretty small. The good news is if you're on the brand side and you are selling to consumers, if you have any kind of product or if you are out there in the e-commerce or retail sites, you have some time to understand your consumers because they are not as far along on the curve as professionals are. So these two articles kind of give you maybe manage your expectations a little bit, level set where you are with them, but know that as we know about adoption, look how quickly adoption happens in the work world. I don't know how slow it's going to be in the consumer's lives, but two good reads.

Lenny Murphy: Yeah, absolutely. Two good reads. And I think that's actually a really important differentiation to make, right? Because we already talked about efficiency and impact. That's an easy translation to think about in your workflow, right? So no wonder we would have seen adoption in work. But I don't necessarily think about that in my daily life to drive most of my decision-making. I think about easy, kind of system one, system two types of things. Easy familiarity, those types of things. So I think that was important research to come out. We may not be there yet with the consumer-focused AI apps. There's probably going to be a little bit of a trend line there before we need to focus. The focus on business applications of AI technology is probably the right play for the moment because consumer adoption is not quite there yet. Actually, it's pretty damn far away based on these numbers.

Karen Lynch: Although I think I shared, if you think about Gen Z, they're the ones to watch, because they're being exposed to this during their schooling years. My daughter, she's already switched to AskChatGPT. Every time a question comes up, she's like, just AskChatGPT. It is a very easy source of intel for her. She's the one who was doing a lot of her research on TikTok. So 21 years old, and she's switching. So let's pay attention to Gen Z's adoption specifically, because that may look different from everybody else, and we may need to be ahead of that curve.

Lenny Murphy: Right. And Gen Alpha, there indicates that Gen Alpha is actually... Game changing. Well, Gen Alpha is rejecting it.

Karen Lynch: Interesting.

Lenny Murphy: So the Gen Alpha is where we're seeing the flip phone is fine. Yeah. Early, small populations. Early, early, early. But we are adapting generationally. Yeah. Yeah. Technology usage and those things can change by generation. So yeah. Yeah. Cool. Wrap up with these last. Yeah.

Karen Lynch: It's just really just one last article. And this one is a good read. So it's really just recommended reading. We don't even really have to talk too much about it. I don't know if you looked at it, but there's an article that was shared with me and shared prolifically. Yeah, it really was. But it's a good article. So in this Harvard Business Review article Karley shared the link talking about how generative AI is revolutionizing market research. It's not wrong. It is, you know, generative AI is changing things completely. And we all know that, but this is really an interesting framework for kind of how gen AI is going to kind of expand within our function. It's, you know, talking about optimizing functions and how it's going to be kind of shaping decision making in the future. It's just a really interesting read. So it's lengthy. Check it out. And, you know, for me personally, shout out to Jeff Wiener from Evidenza, because he's the one who shared it with me. And he's, you know, his company's in the article. So of course, he's, you know, excited to have gotten such good press, but it's also a solid read. So, um, yeah, I haven't. I haven't pulled up here. One line is the use of digital twins in marketing is mushrooming. And I'm like, Oh, I like that sentence. It's a good way to say that. And I would say, yes, that is true.

Lenny Murphy: Well, I mean, there's a cynical and so what? It's growing in the dark and fed with crap.

Karen Lynch: I mean, there's- I don't know. I don't know. But I think it's growing. And I think if it doesn't have checks and balances, it can grow in the wrong direction. However, there's a whole subculture of people who are really into mushrooms right now. Anyway, that's why he's probably like or everybody's probably like really that's the line she pulls out.

Lenny Murphy: It's just I'll say if you're a regular listener, there's nothing in there that will surprise you but yeah that they go into that it's very wonky and And it's great. If you think of this as the Lenny and Karen echo chamber, that's what I love to see.

Karen Lynch: Like, okay, this is a big industry. Other people are paying attention to it and they're talking about this and they're using different data sources and different perspectives to arrive at the same conclusions, but with a different level of authority maybe than you and I have. What I love about that is also, I just scrolled to the very bottom of it and Andrew Cannon from Outset AI, who's one of our partners in a lot of content, kind of wraps it up. And it says, with that in mind, we'd like to conclude on an optimistic note.

Lenny Murphy: It says, if Gen AI can talk to thousands of people around the world in hundreds of languages every hour, it can instantly draw all sorts of unique high fidelity insights from the data those conversations generate.

Karen Lynch: Then our understanding of one another should deepen. And I'm like, nice. That's great. Really poetic and beautiful.

Lenny Murphy: So nice placement, nice placement outside AI as well.

Karen Lynch: Cool stuff.

Lenny Murphy: And that's from only a human component.

Karen Lynch: Have a great weekend, everybody.

Lenny Murphy: I think that's, I think that's what else.

Karen Lynch: So we'll be back live again next, uh, next Friday.

Lenny Murphy: Good Friday, yes.

Karen Lynch: Yeah, we'll take it from there. So we'll see you all in a week, friends.

Lenny Murphy: Thank you so much for tuning in.

Links from the episode:

Cint raises SEK 596 million ($60M) to enhance its Exchange platform and boost investment in media measurement and fraud prevention. 

Brain Science Firm Neurons Opens US Office 

NIQ signs definitive agreement to acquire Gastrograph AI Further Enhancing CPG Innovation through AI-Driven Data, Platforms, and Capabilities 

Ipsos unveils Ipsos Facto 

Will Leach - LinkedIn 

Rival Technologies launches a Multi-Agent AI Framework to power every stage of the research process 

AMC Global launches VerifiedViews 

Circana introduces Liquid Mix 

NIQ debuts gfknewron Supply Chain 

Shopify CEO Tobi Lütke states that reflexive AI usage is now a baseline expectation across the company. 

AI experts think everyone uses AI all the time. We don’t. 

Tried AI? You Are Not ‘Most Americans’ 

How Gen AI Is Transforming Market Research