AI, Ethics, and the Future of Market Research: Breaking New Ground

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 groundbreaking developments in market research and AI! Discover how Fair Response is revolutionizing online sample quality, and how synthetic data is changing the game with NVIDIA’s big move. We also explore AI-driven experience agents from Qualtrics, Catch's innovative approach to ethical data collection, and a study showing how AI-human hybrids are outperforming traditional research methods.

Plus, we reveal how AI is supercharging personal productivity. Don’t miss out on the future of research and AI!

Many thanks to our producer, Karley Dartouzos. 

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Transcript

Lenny Murphy: And there we go. Yeah. So, all right. Hi everybody. Happy Friday. Happy Friday. Uh, it's a beautiful, almost spring day here in Kentucky. Uh, hopefully you guys are all experiencing that as well. I mean, it technically is spring now, but technically it is spring weather wise.

Karen Lynch: I mean, it's a beautiful blue sky up here in Connecticut, but the wind is, you know, chilling me to the bones this morning. I had to run out and I was like, seriously.

Lenny Murphy: It was cold here at the Red Cross this morning, but it's supposed to warm up this weekend, which means lots of outside work, which I'm sure many of us can relate to. Springtime, we got stuff to do. Yes, it's time.

Karen Lynch: It's time, I suppose. So yeah, I just want to be able to go outside, take those walks without feeling like I want to get back home. That to me is always the, it's just, I want to be out there longer than I'm able to be out there right now. Even my dog, she's like not having it. She's like, you know, Maggie over there. She's like, rather just sit in the sun than take these walks, right?

Lenny Murphy: She's. Yeah. And the older I get, the less intolerant I am of any extreme temperature. I remember my dad, I make fun of my dad. You know, he was like, you know, in the seventies and it's July and he's wearing like three shirts and a sweater. And, you know, it's like, oh God, what, what is wrong with you? And now it's like, I could see that. I could see. I'm either too cold or I'm too hot, but anyway, we digress. Anyway, yes.

Karen Lynch: Well, we'll wish for good weather for everybody this weekend. Hopefully, you know, hopefully it's a, it's a, a nice relaxing weekend for all. Yes.

Lenny Murphy: Yes. So interesting week with a little, little different kind of mix of news this week.

Karen Lynch: Yeah. Yeah. And, um, let's just dig into it because I feel like I said last week that I'm like, okay, you know, like sample quality and data integrity and all that was coming back up, kind of bubbling back up in some talks in North America. And I'm like, Oh, good. We're circling back to this conversation because we didn't solve all the problems. We just stopped talking about it for a minute because AI, you know, popped up its head and said, but wait, there's me. And now people are like, all right, I got you. Let's focus on this issue. And here we are with some startups. So that's cool. Shall we? Let's start with Matt Gershner.

Lenny Murphy: Let's do a big shout out, big shout out to the Gershner. I'm sure everybody, if you've been in the market research industry in the period of time, you know, the name at Gershner and probably John teller are CEO as well. But, but Gershner, you know, and he worked for us as part of Green Book. So we have a distinct fondness for Gershner. As he's known, by the way, this is everybody who knows like Gershner. It's Matt Gershner, but everybody just knows he's Gershner. So yes, launched a fair response to enhance online sample quality and responsive experience. So a new play in that world. So hats off to them. We certainly need it.

Karen Lynch: What's cool about this press release that we're sharing on, you know, Emmer Webb, it's this concept of, you know, kind of going back to the basics, kind of, of building, and, you know, one of the things it says in this press release is, you know, what does it say? It says, oh gosh, I'm not going to find it here, but anyway, but it just says something, oh, oh, here, the overwhelming majority of panel investment has gone to the delivery side of the equation, and they want to go back to the building side and, and kind of start there again. It's all about the supply. I just really, I'm like, all right, I, you know, I think that's a good time for that conversation. So let's make sure our supply's in a really good place. So, so, you know, I wish them, I wish them the best, because, you know, certainly they both collectively have a lot of experience in this space. And, you know, anyway, yeah. So, so cool. Hats off. Good luck.

Lenny Murphy: Well, in sample columns this week, I wasn't there, but I've heard from lots of folks that were there. And the major topic, as you would expect, is SampleCon. And there will be, there's a thread that runs throughout today's, that is related to data quality and engagement, first party data. You'll see the tapestry.

Karen Lynch: All the, yes, we have a little you all the threads.

Lenny Murphy: We will shine a light on all the threads. Yes. Yes. Let's give another shout out, though, to Raj and Jonathan. Raj Manoka and Jonathan Chavez launched a guidance group combining traditional research with AI and machine learning to deliver rapid, actionable insights. You know, Raj was really the driving force behind. Oh, good Lord. I have not had enough coffee yet today. Anyway, they're industry veterans. They know what they're doing. It's a new startup. It's combining, you know, he was with Delvinia and yes, he was.

Karen Lynch: And then they were bought by Schlesinger. Now, thank you.

Lenny Murphy: Methodify is what I was particularly thinking of the Rogers was driving, which was one of the automation plays and now evolving into a So hats off to them. To guidance.

Karen Lynch: But I really- Before we get there, you know, sometimes there's little nuggets in these press releases that I just, I'm a words person, right? So I fixate on these little sentences that I'm like, there's something to that. But there was one in here that, you know, market research has been in dire need of an overhaul by fusing established methods with cutting edge technology. And I'm like, isn't that like the age old thing, like, let's take it's tried and true and give it some gusto, you know, like, I just think that's a great innovation model in general is like, you don't have to recreate the wheel completely. Right. Just make a better wheel, you know, just make a better wheel. And so anyway, so good luck to them as well.

Lenny Murphy: Um, you know, it's cool to see startups, you know, we're big fans, big fans of startups, big fans of startups. Yes. The, uh, all right. Well, this is our putting the, uh, the tapestry together a little bit, the weaving of NVIDIA acquiring a synthetic data firm, Gretel, for AI training data. Now, what's interesting is, as we always say, it's garbage in, garbage out. So they're going to be leaning into the output of using synthetic data as an output for training sets, but the foundation that goes into that is real data. So, and yeah, that's pretty big, it was a pretty big investment. And for a company like NVIDIA, particularly, that should be really indicative of what the value of core foundational data is. We're beginning to see that now. We're really beginning to see the investments of companies in realizing they need to engage with data assets to drive the next generation development of AI for business use cases, right? In their case, it's pretty broad, right? But the people who buy Nvidia chips or whatever, it's not broad. Going to be very specific. So it starts this foundational layer of data. So that's a theme that we're going to see throughout everything we're talking about today. And also, well, I'll go ahead and say it. I mean, there is so much money circling around that idea right now. Really interesting people and investors, and I'm sorry, I can't talk about specifics on any of this, but on recognizing that the next generation of the AI era is driven by first-party permissioned data. Yeah, yeah. Which is, we've been saying for years, was where we were going to get to, and within the next three months, six months, there will be very big deals being announced that make that very, very apparent.

Karen Lynch: Well, and the other part of this tapestry is it's in this release that Karley just shared or in this Wired Story, an article about driving home the point about privacy data and privacy protection, et cetera, of data is relieved when you are dealing with synthetic data sets. And it's an appealing option for healthcare providers, banks, and government agencies. And the idea that we're talking about privacy issues, when we go back to the big thing of like, yes, data quality, integrity, blah, blah, privacy, all of these data conversations that we've had that we stopped having for a while. Now we're talking about synthetic data. And when you think about whether you can start to build synthetic data sets that have that anonymity, therefore the people are protected, you know, and yet the results are still usable. Like that to me was just kind of a big way of getting to a different level in my brain about the importance of synthetic data, as opposed to the threat of synthetic data. Right.

Lenny Murphy: But the foundation is still based on, so at some point, somebody has to deal with the issues around privacy, et cetera, et cetera, which we'll get to the story here in a minute.

Karen Lynch: We have something. We have something.

Lenny Murphy: We have something. Because that is still the foundational element, right? And now the synthetic kind of depersonalize this, anonymize this, you know. But the foundational inputs are still first-party data that must be permissioned and must be high-quality. And that, that is the world, go back to what, to fair response, right? Building, you know, we talk about building supply, which means just building a data asset, which means access to human-derived data, that, and then the mechanisms to make that useful in a variety of cases.

Karen Lynch: And well, and, and I don't know if it's in which article it's in. But we'll get there. But the idea that it can't only be synthetic, right? Otherwise, you're going to just start to have, you know, synthetic data, teaching the LLMs and more synthetic data is corrupt, right corrupt. So, this hot, we have to embrace the hybrid model and the balance of synthetic and real synthetic and real. And that's that to me is, you know, like, yes, it's like, yes. And so for all the insights, professionals listening and thinking about synthetic data, it's a yes and situation. We have to look at the hybrid models. That is how we will have a sustainable future.

Lenny Murphy: That's right. There must be both.

Karen Lynch: It is not a takeover. It must be both.

Lenny Murphy: There must be fresh inputs. And last week, we talked about the Cantor sale process. I think it was last week that pointed out Look what they're leading with they're leaving with the data asset, right? The first party data asset numerator and world panel as the value the real valuable component of that that business is what you know, whatever bajillion dollars the What does that tell you yeah, and it's not just there's the traditional research Business use cases that they have but there's also that those data sets drive the development of synthetic, et cetera, et cetera, for these new kind of AI-first solutions. And there's other things happening. Fingers crossed, at least from the ones that I know of, but that are playing in exactly the same way with interest, again, from companies like NVIDIA. Yeah. I know.

Karen Lynch: That's key, right? It's important to recognize who's talking about this. But you know, connecting some other dots, Anish is sharing in the chat also, hovering on this, the very last sentence, I think synthetic data will make the panel companies more accountable now. And I'm sitting here thinking like, I hope Matt and John are listening, because, right? Because if they're going to the source, right? If they're going to the start of the supply, let's supply some really good quality, you know, Intel.

Lenny Murphy: Yes, I mean, yeah, my, my crude but effective analogy is, you know, unfortunately, most of the panel supply today is, is, you know, it's pissing in the well, it's contaminated, it's contaminated supply. So the, so yes, we, and when you do one project, you could deal with that. But when it is now feeding into an AI system, you know, that's enterprise wide and, and driving lots of different things, you cannot have that level of contamination. So it has to be real and valid. So that's a great point, Aneesh.

Karen Lynch: Cool, cool.

Lenny Murphy: Well, let's talk about the other big thing that we've got.

Karen Lynch: Yeah, you know, like there's agents of data quality agents. Exactly, exactly. And we'll and we'll start with this. We'll start with this because obviously this week, obviously, maybe not obviously to everybody this week was the Qualtrics X4 Summit. And I did not go this year because I needed to spend some time with my daughter on her break from school. And you didn't want to take her to see Gwen Stefani?

Lenny Murphy: I did, actually.

Karen Lynch: So I pulled from the event right before Gwen was announced. And I'm like, damn. I mean, I've had a good run because I saw the Killers and I saw Backstreet Boys. That was fun. But Gwen would have been awesome. But no, I had already pulled the plug instead. My wife is such a huge Gwen Stefani fan.

Lenny Murphy: So she flew to Vegas to see Gwen Stefani. So anyway, go ahead.

Karen Lynch: No, I would have enjoyed that very much. Much. But again, I had, I was being beckoned to have some leisure time in a different way. So, but anyway, my point is, I missed the event, I will get some recaps from some friends. And we will have some, you know, kind of great news about Qualtrics joining us at IEX North America, which is really quite exciting, but they unveiled their AI experience agents at the event this week. You know, leveraging generative AI but also kind of that automation for some really great autonomous agents. And I mean, if you, if you, if you're not sure, like how that, how that all works, like what's different about the way they're using AI is, you know, picture, picture, you know, the call center gets a call and then there's a customer complaint in there. And then they, you know, they tap into a kind of a customer service department and customer service then does some investigation, maybe ties it to a few other data points or whatever, and then recommends a solution. Now, the next thing you know, your marketing team's involved. So like all of this stuff happening behind the scenes to kind of close the customer feedback loop fully, that's what they're talking about with their agent, right? It's all about customer experience and how to close that loop or employee experience, you know, so.

Lenny Murphy: Individualized level, right? Rather one size fits all. It's very individualized to the circumstance and the specifics.

Karen Lynch: Yeah, yeah. And I mean, for an organization of that scope, this could, this also can integrate with research, right? They have their Qualtrics Edge, their research, the research arm that, you know, could tie, say it's, say, say it's a B2B customer that happens to also be a big brand, they can also tie that with your brand tracker and, you know, kind of figure out what's the research we have along this, like they can, they're, they can piece together a lot of different things to, to move forward to action. So that's, I think, why they're, their particular agent is so exciting for Qualtrics customers. There's a lot going on there.

Lenny Murphy: Absolutely. And imagine an individualized satisfaction survey based upon your actual experience. So instead of the one size fits all, the net promoter, everything fits into one bucket and that's it. That is the power of what they're moving towards. We certainly can see that that's a possibility.

Karen Lynch: So yeah, and they are coming to North America and they will have, you know, I'm sure they'll be talking about Qualtrics Edge, because that's the most relevant for insights and analytics professionals. But you can most definitely talk to them about their AI agents when you're there.

Lenny Murphy: I am certain of that, that they will say, sure, let's talk about that if that's your curiosity.

Karen Lynch: So yeah.

Lenny Murphy: Well, some Qualtrics, the other big, another big, big monkey.

Karen Lynch: Oh, thanks, Karley. Here, here. We just talked about North America. Can I show it? How do I show it? There you go. Before we start to go into SurveyMonkey, yes, Karley shared the link for North America. It's in the feed right there. Go ahead and register. We can talk more about it in a little bit. But yeah, really excited about what's coming up in just a couple of weeks at this point.

Lenny Murphy: Yeah, right around the corner. The SurveyMonkey Connect and NoCode Action Library. For integration. So another, you know, kind of another approach to create interoperability and connections across from their platform and all of the others. They don't call out agents in there, but I'm betting if there is and if there's not. I think they are.

Karen Lynch: I think they're it's that level of automation that I think is agents, but they're they've evolved past the name, right? They're like, we don't need to call out what the technology is. Because there's some examples in the Business Wire article that Karley just shared. An HR manager can feed employee feedback directly into her team's Slack channel so that the right person can follow up. A marketer can ensure every post webinar survey response is updated into an Excel doc.

Lenny Murphy: So there are these kinds of things happening.

Karen Lynch: Yes. Zapier kind of, you know, integrations that can just happen. So that's what they're doing, you know, it's, it's similar, just different types of integrations based on serving monkey input. So cool stuff, right?

Lenny Murphy: It was cool, both of them. This, you know, they also reinforce the importance of, of understanding customer experience and data across the enterprise. So, so making that, you know, breaking down the silos of, you know, it's, we got our monthly, uh, PowerPoint with the, uh, no, that's making it really useful, uh, across organizations. So that's cool. Yeah. Um, let's talk about this sketch. Uh, cause that's really interesting.

Karen Lynch: I love it too. I'm glad you found it. Um, progress. So catch, I'll do the headline. Then you can talk more about your kind of top of mind thoughts. Catch introduces progressive consent to revolutionize data collection ethics. So again, here's where we're back into our tapestry, right. And, you know, and weaving here. So real time context aware consent experiences for users. So I thought it was really interesting. And obviously the implications for anybody who puts a survey out there and needs to get, you know, consent. I think that's almost everybody, right? It's cool. Yeah, for it.

Lenny Murphy: Well, I think it goes, it goes even deeper. Of your loyalty program, right? And that data has lots of use cases to trigger offers and research opportunities based upon your purchase behavior or whatever the case may be, right? But as you're navigating through digital and real life, interacting with tech, What Catch is saying is, yeah, that's great, but what we have not done well in the past is factored in, well, do I want this connection? Do I want to enable you to utilize my data? So they're building that ecosystem to do that. They have been around, around the same time that we started Variglyph, a similar concept. They were coming up with the same idea. Obviously, you know every look we didn't pull it off catch has and that's fantastic because the need to empower consumers. Yeah, to be able to permission and manage that on their terms is hugely important and that's a piece of what they were doing.

Karen Lynch: So I'm yeah, and they share that It's kind of like overturning the traditional mechanisms of like accept or ignore where users just kind of are disengaged and they're just, yeah, yeah, whatever. And they're not mindful about what they're doing. They're not mindfully consenting. They're just, you know, trying to get past that as quickly as possible. So I think that's what's really cool about it is the whole idea, like that empowerment to what you're saying, but the idea that it's also driving kind of relevancy at the right time and not just as a, hey, here's a roadblock that you have to click just to get past this. But somehow or another making it so that there's a what's in it for me. Anyway, yeah, cool to think about.

Lenny Murphy: Well, let's put a border around our little tapestry to keep.

Karen Lynch: Yes. I don't have an example. Anyway, we all know what a tapestry looks like,

Lenny Murphy: Anyway, we're beating a horse too. So this, if the data asset, quality data asset is shifting more towards human derived, which ultimately it all is, no matter how we think about it, right? Then companies like Catch are making that connective tissue for the human to recognize. Ultimately it is their asset, right? Now they may be, it may be being monetized. It may not be it may be yeah, I'm happy to do that because you're gonna send me a reward offer or whatever, you know Yeah, I get a funny cat video in my algorithm. The right point is the consumers I think will increasingly become aware that it really is their data that drives the whole damn economy from a technology standpoint and this is a step towards Really doing that where we are not the product We are co-owners as individuals and how that is used. So cool stuff. And then thinking about how that was used, why don't you talk about this new study that was like, ah, I don't know if I like this or not.

Karen Lynch: I don't know if I like this or not. This one, so okay. So new research from the Wisconsin School of Business. That's where the study came out of because I really dug into this one in any way. An issue of the Journal of Marketing is basically saying that through this academic research that they conducted, AI-human hybrid methods outperform traditional qualitative research. And they partnered with this. I know, I know. And I didn't even want to read it, right? Because I'm like, oh man. But I promise you, it's not even really cooking bait. It's important to read this. They partnered with a Fortune 500 food company. So, you know, a lot of times if there's an academic study about research, I'm thinking it's like a social science thing, and it's not really, you know, in our world of insights and analytics that kind of serves, you know, many, many consumer faces and businesses, but it's social science. But I'm like, oh, no, if they partnered with a food company, that's okay, that's relevant. So again, you read on, and they were talking about Friendsgiving. With something a lot of people can relate to in this kind of audience, most likely, is we have our Thanksgiving celebrations. Friendsgiving is when you go together with friends and not your family. Anyway, they were asked questions around a Friendsgiving celebration. So a very exploratory topic. Could be very helpful for a food company that might be thinking about marketing programs or packaging or even new product development or whatever. So these were moderated. Depth interviews and, you know, the results were that the LLMs did a really great job with both data generation and then the synthesis. Basically saying that the LLM generated data was actually quite insightful. And if you think about what I would say at the beginning of any study is, what is our objective? And very often, exploratory studies are to stimulate ideas or innovation and innovative thinking. And you are literally exploring a category or a topic or whatever to get fodder for your next move as possibilities. This isn't like, you know, this isn't, you know, we're at the fine-tuning copy, you know, concept testing or whatever. This is You're not making a decision yet.

Lenny Murphy: Right. Exactly.

Karen Lynch: Exactly. But the synthetic respondents were able to shine and really move the research forward. And I think that's important because it's fit for purpose. Now, the second study in this thing was more quantitative in feel and had much more cynicism around it, but it served its purpose well from a qualitative perspective. That is something I think that the world of qualitative researchers and insights professionals who conduct qualitative research should really think about their purpose. And if it is exploratory, that is a perfect space to explore synthetic moderation and synthetic respondents in.

Lenny Murphy: It's a little bit upsetting and disturbing, but also, you know. About it. So I get that. I was also like, but we've seen similar things like this before, but we've been saying for a while that the qualitative researchers are actually in a better position in this era because they are good at asking questions and making sense of things on the fly.

Karen Lynch: Yes.

Lenny Murphy: So if that is the value of the moderator of qualitative research is probing and asking questions and that creates it is a creative process, really, from the standpoint.

Karen Lynch: Yeah.

Lenny Murphy: Then the other issue is the source. Now, if I understood this correctly, the LLM was trained on real human data, on real human service. Yeah. So which we've been talking about all throughout You know, this, uh, this show. So in that world, okay. You've, you've amassed a thousand, a million data points on people, and now you've developed 10 personas and, uh, but it's based off of real data. And now you are probing as a moderator to your point, exploring early stage concepts, you know, exploration with. An LLM that is trained off of real humans. I mean, is that so bad? Right?

Karen Lynch: Well, that's exactly it. It's not so bad. I, it depends on what you're wearing, right? Is it so bad if you are, if you are the, you know, the large fortune 500 food company who, um, you know, is in need of, of Intel in a different way of getting it and really, really fast, faster,

Lenny Murphy: Better, cheaper. Oh, I'm going to do a focus group.

Karen Lynch: It's fantastic for them. I think it's really great and really affirming. Fit for purpose, of course. If it meets your objective, go for it. Is that great for researchers? I mean, I would have been the first to say, that's my favorite kind of research, just exploratory research. I don't think I ever talked about Friendsgiving specifically, but I had a lot of conversations around random holidays and entertaining for some food companies that I worked for, like we explored entertaining. At one point, I felt like I could be an expert in entertaining just because I've talked to so many consumers about entertaining. Entertaining family, entertaining friends. So I have done this kind of work. It's very interesting. That is something that if that is how you earn your money as a qualitative researcher, I think that it might be time for a value add-on because, you know, I used to tie it into facilitation and very quickly went into facilitating ideation sessions. So we do exploratory, then the next layer was, let's take all of that and turn it into an ideation session. And then I'd facilitate and lead into a stage gate process. So I had a different method to take all those insights into an innovation pipeline. So maybe that's something qualitative researchers should explore. Or what kind of concept writing comes out of this? What type of A-B testing do you need to do down the road? Or, you know, whatever the build on is, this is a good time to think about your add-ons.

Lenny Murphy: If you are an explorer I think it comes down to, we've been talking about it over and over again, everything, now we're putting a fringe around our border. I can't stress the analogy anymore. Fundamentally, the value drivers of our industry are data, people, access to people, and getting data. The ability to extract that data, data collection, whether it's qualitative or quantitative, right, the mechanisms to do that, and then strategy on the right questions to ask, and then what the hell does it mean, right? Those are the three pillars of the industry. And fundamentally, even though we're talking about these transformational components, what I think it really means, to your point, is if I'm a field services company, right? I better start thinking about the value of still needing to recruit people. Yeah, I still need it because I need the data. I need to enable that data to be fed into systems. Yeah, you know, that empower researchers to ask the smart questions and, and drive value out of it, ultimately, to drive the business decision. Yeah. And I think if we are as scary as the ways we go, you know, all change. But the fundamentals are still the same. It has huge implications for business models, right? Revenue streams and all that good stuff. Yeah, but, but we can navigate that.

Karen Lynch: So let's segue into this user report that Mays, Mays had, Charlie can share that link. It's the link to download this report. User Research Platform Mays surveyed 800 product professionals and published their user report and some of the findings in here kind of talks about how they're, I think it's like their second big finding or whatever, how are you using AI tools to conduct research studies? So I think that's really important to anybody tracking that, you know, 74% of the participants of the 800, you know, product, products, professionals are using it to analyze user research, right? That's pretty big. So it's happening in the product research space. And, you know, 38% of them are inspired by AI-generated ideas. It is helping people think. So it's not just doing the grunt work, but it is also helping people think. And I think that's, anyway, a good study to kind of also take a look on as you are thinking about all of it, the whole ecosystem right now, right? Absolutely. My personal experimentation with that line continues, right? I have now got to a point where I officially think of these systems as my research and writing assistants. I've been saying this to my friend, Chad. Yes. I was a laggard. I was scared. I was scared, just like we were talking about. Like, no, it's going to devalue me. And it hasn't. It's given me a superpower. No, it's so much. Anyway, after, I'll tell you one of my favorite personal uses. We just won't talk about it now. All right. Well, on that list, there's two other interesting things. So let's talk about agents, and let's just start there. And these are, I think, good to end on, because people can research them at their own pace, right? So go ahead and- Yeah, Zapier explains AI agents and their automation capabilities. So it's kind of a how-to guide, take a look at that. And then if you need it even more foundational, there's a good Forbes article, well, what is an AI agent? So good combo. The agentification, it will be the theme for the next year because it's the next pragmatic application of these technologies to your point earlier around business use case specific. So get acquainted with these concepts. The last line in this Forbes article gives some advice, which I thought super interesting. It says, start small, pick one repetitive task you dislike and deploy an agent to handle it. So I was looking at that and I was like, you know what, I'm gonna, I'm actually gonna do that. I'm going to think about what is a task that I don't like and I'm gonna bring in an agent for that. Like, because it's more than just a task that Chachi, I need to, I need to think that whole thing through. Cause I don't, you know, yeah, yeah, yeah. I've been doing that with just, you know, with, you know, generative AI. I've been doing that, but it's the idea of what's the next step it can also do for me. That's what I need to think about. See, the problem is the task that I dislike the most is responding to emails. And that's the one that I can't, I don't think I can have an agent to answer for me because I have to give- There are agents to help you prioritize though. There are agents to help you look at your inbox and prioritize which ones you respond to and maybe suggest some language. It may be. And I've, yes. So I still, I am, I am training a virtual Lenny. I'm not there yet either, but I don't, I don't, you know, I'm a proponent of inbox zero. So I like, I take my, I take my inbox very seriously and I get myself down regularly. Cause I'm like, yeah, I don't like having that big old inbox. So, Oh, I'm right there with, well, I don't like having that big old inbox. I'm like, I don't like having that big old inbox. Well, it's a big old inbox, but it's more of the flagged items that I must respond to. I like to keep that small. Anyway. Anyway, we digress into email pitfalls. But we are so over time. It's already 36 minutes. We are. What? Well, we had a little more. It was a little more relaxed today. We were a little more lackadaisical going through and having some thoughtful conversations. Hopefully, our audience appreciates that as well. And so it's kind of frenetic, let's go. Yes. Yes. Caffeinated So What happens when I take a few days and I disappear for a while like that's right you come back you're all just All right, all right friends we will see you next week and Lenny have a great weekend We'll talk to everybody soon. Thank you again Karley. Yep. Thank you to everybody. Take care.

Links from the episode:

New Firm Fair Response Goes Back to Panel Basics 

Launch for Rapid Insights Firm Gydence Group

Nvidia Bets Big on Synthetic Data

Qualtrics unveils AI Experience Agents

SurveyMonkey launches Connect, a no-code action library 

Ketch introduces ‘Progressive Consent’ 

Large Language Models Perform as Strong Collaborators 

The Future of User Research Report 2025

Zapier: What are AI agents? A comprehensive guide

What Is An AI Agent? 5 Practical Ways To Use Them In Your Business