IIEX AI Recap: What’s Next for Research with Sarah Snudden

Fresh from IIEX AI, Karen Lynch and Sarah Snudden unpack the ideas that stood out across two packed days of conversations about AI and the future of research. They explore why AI’s biggest opportunity isn’t simply greater speed or productivity, but creating more room for strategic thinking, stronger relationships, and better business decisions.

Sarah and Karen discuss accountability in AI-assisted work, the evolving role of synthetic data and AI-moderated qualitative research, and why human judgment remains essential even as research workflows become increasingly automated. The conversation also underscores a key takeaway from IIEX AI: cognitive offloading can be useful, but cognitive surrender is not. Researchers still need to question AI outputs, apply critical thinking, and remain accountable for the work.

Key Discussion Points

• Moving beyond productivity: Why time saved through AI can be reinvested in strategic thinking, skill building, relationship building, and higher-value business advisory work.
• Keeping humans accountable: Why researchers need to own AI-assisted decisions and outputs rather than fall into “accountability offloading” or cognitive surrender.
• Synthetic data and AI-powered research: Where synthetic data, digital twins, and AI moderation can add value—and why real-world data and thoughtful validation remain important.
• The changing research toolkit: How AI-assisted qualitative research is expanding access to richer conversations while UX, MRX, technology, and cultural insights increasingly converge.
• Developing the next generation of researchers: Why experienced researchers need to model critical thinking and methodological rigor so early-career professionals learn how to recognize, question, and improve AI-generated work.

Resources & Links

IIEX West

You can reach out to Sarah Snudden on LinkedIn.

Many thanks to Sarah Snudden for being our guests. Thanks also to our production team and our editor at Big Bad Audio.

Transcript

[Karen] Hello, everybody. Welcome to another episode of the Greenbook Podcast. I'm so excited. I'm Karen Lynch. I'm excited to be here with you with Sarah Snudden, who is no stranger to these podcasts with us. Sarah, I feel like not only have you been on the Greenbook Podcast before, but you've been a stand-in on The Exchange. Thank you.

[Sarah] It's always good to get exchanged.

[Karen] It's always good to have you here. So thank you for stepping up for this. This was a kind of a Hail Mary pass where I'm like, I need someone to debrief IIEX AI with me. And you raised your hand and said, I'll do it. Thank you so much for that.

[Sarah] I'm a glutton for good content and IIEX AI was really fantastic. It was a lot coming at you. So definitely these will be some very hot takes, but it was hot stuff.

[Karen] For our listeners who don't know your background, because I just assume everybody does at this point, but there are new listeners every week. So just give them the high level of who you are.

[Sarah] Yes. I had, let's just say 20 something years, mostly on the client side, starting at Clorox, working on a lot of Glad and innovation and Clorox laundry and cleaning products. Then over to Seventh Generation, the road less traveled to Vermont, then abruptly to coffee. I like to say that I worked my way up through from the trash bags and laundry stains into coffee, where I stayed with Keurig for about seven years. And then a brief foray into the supplier side, which I loved and may be my next move career wise, but I got my coffee band back together one more time at JDE Peet's. And as they're in the process of merging now, I'm figuring out what will be next.

[Karen] We'll go back into that because I think there was one comment in the back room or one request for an example. And you were like, let's talk basically, let's use coffee as an example. And I was like, she's always got coffee on her mind.

[Sarah] I do. I do. I feel really lucky that I've had so much coffee in my world.

[Karen] I think we've talked about this. When I was a qualitative consultant too, I worked with a large coffee brand too. So that's where you and I have that shared experience. So nothing like qualitative conversations about coffee, man. It's just.

[Sarah] I actually once had a moderator tell me coffee was a functional beverage and there was no emotional component. I was like, it wasn't a moderator that I'd picked, but I was like, I'm not going to pick you again.

[Karen] You do not understand the category.

[Sarah] No.

[Karen] Yeah. That's so funny. Well, we are here everybody to debrief IIEX AI. And just so you know the timing, the event took place on Wednesday and Thursday, September 23rd and 24th. Today, day of recording is Friday, September 25th. And this episode is launching on well, technically, you know, Monday night, September 28th or Tuesday release September 29th. So this is quick turnaround for you. We want to get this debrief into the audience's hands as quickly as possible. So not even a week has gone by by the time you're hearing this from when the event took place. Certainly it's only, it hasn't even been 24 hours since the event wrapped up for Sarah and I to be coming at you live to, or coming at each other live to bring it to you. So these are top of mind debrief responses. And Sarah and I have not connected about it. So what you're getting is unfiltered, unsynthesized, really our top of mind debrief conversation. We both have taken notes. We're going to fire at each other like, you know, hey, what'd you think? And see where we both go with this conversation. So I don't know where to start.

[Sarah] I think we started at the beginning. Let's go. And Abran Maldonado of CreateLabs, I think was a fantastic way to start. And I appreciated his perspective and his thoughtfulness about what it takes to really make a great AI experience in a world where we're all thinking about content that really misses the mark with humans and how to be thoughtful, be inclusive, really bring human perspectives from all different angles to the table. I loved how he teed up culture and tech and culture. And honestly, I was really excited about his examples about making tacos while he did the RFP because that inspired me to make some chicken soup on day two as I was rapidly synthesizing, you know, and listening with my own ears and not the AI. But still, yeah.

[Karen] Yeah. For those of you who missed that session, and we'll talk about how, if you missed these sessions, you can get there. Sarah and I will give you the details. But, you know, one of the points that he made is, you know, with agentic AI, the goal should not be to be more productive with the time savings of AI. It shouldn't be to produce more, to have greater output. The goal should be to use more of our strategic thinking, like allow that time for strategic thinking to come forward, allow more time for skill building or allow more time for relationship building. Like, let's use our time wisely. The time that we gain back, let's use it wisely and not, you know, waste it away. And when we're talking about economies of scale, he's like, yeah, let's, you know, save some time. And he's like, yeah, I'm going to use it actually on relationship building in his own life. Right. And he's going to make, you know, make a good meal or whatever. And I was like, that's actually really great, you know, good way to look at it. Like, what are we doing with our time and not just like, okay, I'll put my agent to work and I'll get more emails done or, you know, something like that. Like, what can I do so that it's not just all about productivity? I was like, brilliant way to look at it.

[Sarah] Yeah. And I feel like that exact theme was articulated from a really different angle in Stephanie Vance's description of the research process and just how the reality is never what you set out for it to be like it always takes longer to get the stakeholder alignment. And then there's the back and forth of, you know, doing it yourself or writing the survey or checking the programming. And I appreciated how in that detailed kind of storytelling way she had of laying out the challenge. Because I think it's easy to kind of skip to the, you know, this gets you to the strategic business advisory part, but that definitely came through the business advisory part as a key theme as well for what we need to do. So, I appreciated the aytm take on that, because I've lived that so acutely and it's painful every time when you think like, okay, I'm going to make this happen fast. And you are making it happen faster than it would if you were, you know, handing it off to someone else who you had to brief and go through the full service, you know, team working on it, but still, it's not the miracle of speed that you want it to be.

[Karen] Yeah. And I think, you know, one thing, you know, as I jumped sort of bookending the event, you know, you start at the beginning and I tend to go to the end. Speed is one of those things that everyone is capable of now. Like, you know, let's just get there. AI is allowing a lot of the people to do a lot of the work faster. So, we know that. But that strategic thinking, right, that ability to really bring judgment, that was talked about in so many sessions, like throughout the event. That ability to kind of use our strategic thinking skills, our critical thinking skills, our professional judgment, our experience to kind of level up and deliver against client objectives or stakeholder objectives and really answer the business questions at hand and pull it all together. That's where the real value is right now. And that's how we are serving in our role as insights professionals. And I thought that was loud and clear.

[Sarah] Definitely loud and clear. Another talk that I really appreciated that had that theme to it, I mean, very clearly from the title, Volume Isn't Value, was Melissa Ramey at Salesforce's talk, which was so rich that I'm definitely going to go back in and really think about that one again. But that had a lot of thinking about how you frame the problem and framing that problem is key in such a big, you know, like if you're really going to be a strategic partner, really listening deeply, almost taking the qualitative skills that many of us have for consumer listening and applying it back to our organizations. And honestly, in that one, one of the other pieces that was kind of an aha for me, and I'll have to think more about it, is I think on kind of the most meta level of insights, consumer packaged goods was kind of like the, you know, the foundational kind of starting point for a lot of market research going back. And as I think about the interaction between what's happening now in the UX world, I almost feel like it's almost going to be more of that yin and yang almost of like old school marketing products, strategy and branding meets like kind of the new UX thinking, because AI is so fundamentally kind of close to that UX kind of world, I feel like, that a lot of her points there made me just sort of say, hmm, there's different muses coming in to our ecosystem that we need to listen to, and pay attention to and just think about from a new angle, like if you've gotten comfortable in your world as a CPG person, or maybe you work in finance, or you work in, you know, some other vertical like healthcare, thinking about the tech space as kind of an increasing muse of how we get our work done is, feels valuable to me.

[Karen] Yeah, yeah. God, it's really interesting. Sidebar, shout out to IIEX West, because that convergence is happening a lot there. And I'm just wondering if you're tapping into like a much larger trend. The agenda for IIEX West is loaded with the convergence of UX and MRX has never been stronger from what I'm seeing on our agendas right now. So a lot of people are talking about that. What those disciplines are learning from each other right now. So super interesting.

[Sarah] Yeah, if I were an early career researcher, I would possibly think really differently about making sure that in the experiences I get, as I'm kind of making my way through a career journey, thinking about how you get closer to that earlier is, is probably a really important, you know, piece that didn't maybe used to stick out in that way.

[Karen] Yeah. Yeah. Another thing I want to get to Melissa brought up again, this is Melissa Ramey at Salesforce. So she was like the second or third session of the day. I think the third, cause we had our, Quilt was in there, Quilt AI, our title sponsor. Thank you so much Quilt. I really appreciate you. And we'll probably bring you up again later, but Melissa touched on something called accountability offloading, which that was probably the biggest, like mind blow moment for me. I had never really heard of that before or framed in that way. It was this whole idea that like, we can't just say, oh yeah, like that, you know, AI did that for me or like that output, yeah, you know what? I used AI for that. And kind of like not be accountable for the work that AI assisted us with and not say, yeah, I know it's long, you know, AI had something to do with that or, you know, not kind of like use AI as an excuse for being, you know, less accountable for the output that it's providing or something like that. She brought that up. Meanwhile, like Dan Wasserman at KJT, he made that point. Sarah Kling at Amazon made that point. She, I think, said like human in the loop only matters if someone actually owns the decision, like you have to own your decision. I think Simpson Carpenter and M&S made that point. I think they said the further an AI agent gets from a human who is accountable, the worse it's going to perform. So throughout that was like another thread that went through this event was, you know, accountability rests with, with the professional. So let's make sure the more reliant we get upon AI, we don't forget that we, the insights professionals, we cannot let go of our responsibility and the work that we're doing. And just kind of hand over the reins, you know, I think Lisa Courtade said it. And what did she say? She said, cognitive offload is fine, but cognitive surrender is not like, we cannot just say, you know, do it for me. We have to be mindfully attached to all of our AI work.

[Sarah] And I feel like the other side of that very same coin was very much this idea of business acumen and making the work flow into real business decisions, which is certainly a theme and certainly something that you see over and over. If you're reading job descriptions in our industry different places kind of put it different ways, but that real understanding of what is your business doing? And it made me reflect back on the reality of framing the problem because it's always thought of as not as fast. There was a time at Clorox when we went through one of those excellently facilitated kind of Myers-Briggs exercises where they have the whole leadership team go through a problem and they had us walk through a Z shape, you know, and at the start there were those of us who spent, you know, more time framing the problem. You had some functions that were just itching to zip, zip, zip along. Sales, I remember that was you. And when I think about the reality of what we need to use that time for and really get right, because if you're really truly trying to drive business action, understanding that frame up, and there were some great examples of really spending the time to see the bigger need versus like the more superficial need that, you know, I think a lot of times business comes to us with like, I just need to know if this copy works or doesn't work, but then the why behind it, we now have more and more access to, and getting to that bigger understanding.

[Karen] Yeah, for real, for real. So, yeah. So what else you got for me? What else mulled around in your brain?

[Sarah] I think one of the themes that I saw come through that I really liked in a world, and it kind of picks up where you left off on the, like, you can't have, what was it? There's cognitive offload and then cognitive checking out or whatever. One of the, you know, like human out of a loop. No, thank you. One of the things that I really appreciated was what I'll call the just do it theme. And it came from really different angles. Pam Forbus, I thought did an amazing job. Talk about someone who just saw an issue and was like, wait, people aren't, you know, validating thoroughly. Who can I work with to really, you know, kick the tires, kick the whole thing around and really check it out. And I, you know, again, with the 24 hour loop since all the sessions ended and, you know, chicken soup to make and all the regular life things. I really want to go and explore her talk more and follow up on some of those threads. But then also Leslie Willis's talk from a really different angle had that sort of like, hey, I'm not a coder, but I just, I saw vibe coding was a thing and I went for it. And I just appreciated from, from those two different angles, you know, people just kind of sharing their work in a world where it does take work to do behavior change and it does take work to figure out how to best appropriate new technology into our lives.

[Karen] Yeah. I think with Pam, the soundbite that's in my head from Pam's talk. And again, this is Pam Forbus from Mondelēz who was our first speaker on Thursday morning. One of the things, the concepts that she brought up was, you know, remember we are, we are the teachers of our AI, not users. So stop thinking of yourself as a user and start thinking of yourself as, you know, you are the teachers of it. And, and that is now how I will be approaching it from now on. Like, like it's so much more of an active role, not a passive role. And I, and I think like that is to me was one of the more brilliant things that I walked away with, like just a paradigm shift in my brain. So I'm so grateful for her for that.

[Sarah] It's almost like maybe teaching at a boarding school, because I feel like there's the care and feeding of these data sets that is, you know, it's not just the the teaching. I mean, that's a super important part of it, but the reality of how you refresh the data and how you keep things current, especially as you get into the synthetic, into the digital twins, like into the whole world where you're trying to get, you know, democratize access to insights in a way that still stays true to how fast the world is moving. One of the things that I liked about Pam Forbus' talk is, you know, thinking about the reality of heritage brands like Mondelēz has a lot of, and the reality of like, you know, the brands that are coming up more, you know, through e-comm, through the social scroll, through all of those things, and how we track that top of mind awareness. Quantilope's talk got at that, I think, really nicely, but that mental availability, but the reality of how quickly that can change in the push and pull and the tension between heritage brands that mean something to some of us, some of the time, and the newer brands that are finding their demand moment are really zooming in on that context where they can activate that mental availability.

[Karen] Yeah, let's, you know, you brought up synthetic, and I feel like we just have to just go there for a minute, because there were obviously many sessions that referenced synthetic data, digital twins, not exactly the same things, subtle nuances, differences, but, you know, we weren't too far from that in a lot of sessions, right? Yes, quantilope was one of them. You know, things I heard, and, you know, tell me if you heard the same thing or different things, like, you know, as I summarize all of them is, you know, obviously we're at the point where we know these exist, where we're starting to figure out where they fit in, you know, what are the right use cases for them? You know, there's, you know, people are saying like, yeah, we're using it early, you know, this is where, you know, it fits in, in certain use cases, like early on, you know, maybe we're doing some, you know, early testing with it. But it really does need real data underneath, right? We have to keep feeding it. We have to keep feeding the machine, we have to keep, you know, it's an ongoing, almost circular data ecosystem. You know, you can run it alongside real studies, but real studies have to keep going. So it's almost like it's just additive. It's not necessarily a substitution, right? It's additive information. So, yeah.

[Sarah] And it may not even be additive, it may be the democratization of access in a lot of ways, in a world where businesses run increasingly lean and speed is increasingly important. If there's a way to kind of amplify that consumer voice, I can get behind that. But for me, I think, and you have to be kind of careful about these words, but there's times in the insight space, when you need a nudge, you need like consumer guidance, I would call it like when you're developing a product or communications, or you're on an, you know, sort of an iterate to greatness path. The reality when you really need to validate, and for me, I'll use validate in this context of, I need to really say, like, I need to be able to go to sales and say, this is how much volume we expect, or I know that this will deliver. That to me is still where I'm a little, you know, I kind of tap the brakes and think about, you know, really what's behind it in a different way, because the guidance is kind of, again, the iterate to greatness, you can always do another iteration, but those moments when you're getting to the final, final, and you've got to be ready to go, that's one where I'm still, you know, where I personally would exercise a lot more caution.

[Karen] Methodologically, you know, I think you and I said this right before we got on, you know, one of the demos I was able to check out was Listen Lab's demo of their, you know, AI moderation platform. And, you know, I'm particularly tuned into that one, you know, thank them as well for their sponsorship. I think that anybody in this industry would be foolish not to be paying attention to AI moderation and AI moderation platforms right now, because there's so much that's happening in that space. And, you know, it's like every time I watch, and this is me as, you know, a thirty-year veteran of qualitative research, you know, like, I wish I could poke more holes in them than I can. And I kind of watch it. And I think, you know, I just I shake my head. And I think I'm really glad my career transitioned a little bit, because I am on board with that for the right use case, not for everything, you know, I'm still a big fan of ethnography, in particular. But I think those demos are all worth watching.

[Sarah] Great demos. And I feel like when it comes to AI assisted qual, and, you know, maybe, you know, thinking about like Bounce and some of the other demos that were there. I feel like the reality with AI assisted qual is that it's taking a lot of share from quant and rightfully, because qual, like classical traditional qual always had trouble with speed. And so it became very precious and very, like, you know, ideally precious, and you'd use it, you'd prioritize it for strategic cases, but a lot of times too often, I feel like as practitioners, we were needing to get something and your chance, your options were like, you don't have time or money to really do traditional qual, as well as you should. So you squeeze something that you really wanted to know the why, but you'll settle for the scale and an open end and really being able to get at the sort of words behind it and really think about that richer, especially if you're asking provocative questions, or, you know, I know, in my own work, one of the ones was like, if we did new products, if you had to sit down with the CEO and explain it, you know, over coffee, what would you tell them and I feel like those richer answers are possible now in a way that that weren't so I, you know, making research the great conversation instead of, you know, like, it's the 9.5 on a 10 point scale, you know.

[Karen] Yeah, yeah, no, I agree. I agree. I think that, going back to what Abran Maldonado said in the very first session. I loved it at the end when he brought up, you know, I should say he had me at Christopher Nolan, right? Because of course, he's so on my mind right now. After, you know, I was a big fan of the Odyssey and a lot of Christopher Nolan movies. But, you know, nobody really questions like what tools he used to make the movie, right? So nobody's really gonna say at the end of the day, if you produce some, you know, some brilliant results from your work, and the outcome is, you know, is speaking for itself, not the output, but the outcome of the work, like he was very clear about that.

[Sarah] I think he had a line, like, nobody praises the plastic and wires, you know, behind it all. Like, yeah, there are the silicon chips or cone chips or whatever.

[Karen] Yeah, yeah, nobody is going to say at the end of the day, what tool did you use? You know, what happened there? And I think that like, the bottom line is, that's what we have to start to do with AI, you know, we have to start to recognize it's one of the tools in our toolbox, right? It's one of the things. And that's, you know, we are creators, we are people doing the work, and the tools are all in our toolbox. And, you know, that's what we are bringing forth. We are the thinkers, we are the thought partners. I think that's another word that was put out there. I think Lisa Courtade had said that we are the thought partners that are hired to do the work and help solve challenges and doesn't matter what tools we're using. So anyway, another kind of soundbite that I really loved when he shared that, or put that slide up, it was brilliant.

[Sarah] And I feel like that tension of the now what, in the sense of like, if we've changed our work patterns, and we've made certain things that were really repetitive, like, you know, the example that Leslie Willis had of the DIRECTV comparison charts, and, you know, we've automated some of the stuff that kind of made us like, not happy to do our job sometimes, you know, because it was just like, yes, everyone should have this. And oh, boy, it's on me again to make these comparisons. And I feel like Abran kind of hit this point, and Stephanie Vance. But I feel like Quilt AI opened up kind of an interesting connection, and Abran Maldonado did too, of like, to me, the culture angles are really interesting. And we're starting to hear culture used more and more in a lot of different ways. And I think for me, when I hear a word like that start to pop up in really different places where it didn't used to be, I think like, what's behind that? And, you know, sometimes it's helpful to come back to Maslow's hierarchy of needs, or what's happening there. But I think it's really belonging, like, how do we find a way to belong in our groupings in our human groupings and use AI to enable that. And it felt like Quilt actually did a nice job of bringing that out, too, in their talk, because I guess I just think about like, what are we going to do with this, you know, supposed new time? And, yeah, it's not just pump out more tests, like, ideally, it should be find some meaningful way to connect. And I feel like maybe that's articulated increasingly as culture, because it could be connect to all sorts of different things, culture of our organization, like culture of, you know, movies and relaxation and cooking and family and, you know, the world around us. Yeah.

[Karen] Well, thank you for that. And because, you know, Quilt AI, again, our title sponsor, so we're so grateful. I mean, we're grateful for all of the sponsors that allow us to put allow us to put on an event for free, first of all. So, you know, thank you for that. Free for attendees, not free for us, but free for attendees. So we're so grateful for their title sponsorship and to all of our sponsors. So thank you for also, for bringing them up in that context too, Sarah.

[Sarah] And the last thought on that is I feel like the role of the anthropologist, which always was sort of like, isn't that cute when business used to be kind of more sharper corners and harsher edges? But really, when you think about brand value, you're trying to get people to pay somewhat more for something that probably, if you blind tested, is a bit more of a commodity. And so we really think about what's behind that. It is kind of an amazing… you’re selling a great intangible a lot of the time with branded products. And that's like that belonging or that sense. And it's always been really hard to get at because it feels kind of maybe not business-y enough and maybe the world is going to be able to get better at pinning that. It's kind of like when, you know, 20 something years ago, when the world was still shifting from like, oh, well, this is a rational product and they’re emotional products. And, you know, it's both, you know, it's always both.

[Karen] It's definitely both.

[Sarah] Sometimes more emotional.

[Karen] Yeah. Yeah. For everyone listening, I want to make sure that before Sarah and I wrap, you all understand that you all can access all of this content. I'm going to read this so that I get it right. For those of you who were there live, but you're wondering about your passes, if you had the free live access pass, you were only able to access it on demand for the first 72 hours. So you might not have that access anymore because by the time this episode is airing, your on-demand access has probably expired. If you purchased the live and on-demand pass, you will have access for the next six months. If you would like, now, anybody can purchase the on-demand pass that is still available to purchase. It's only one hundred and seventy five dollars and you get six months of access to the recordings and downloaded content. Many of the speakers are saying here, take my presentation, which is great because a lot of people were saying that they just kept taking screenshot after screenshot of presentation decks, et cetera, et cetera. So one hundred seventy five dollars to get all of the content for six months. Access to the recordings for six months and downloaded content. So that's how everybody can access all of the stuff that Sarah and I are talking about right now.

[Sarah] I want to just say, when you think about the price of going to a conference and the amount, this was a conference worth of material to be sure. And you're getting it for the price of a couple of dinners in San Francisco. To be fair, there's great burritos and you can do that.

[Karen] Yes, you'll have to do your own Uber Eats.

[Sarah] But really the amount of value you could pull from this and just the continuing education mileage that you might be able to get and maybe your organization covers. It's worth thinking about because there really was a conference worth of material here that you can process much more efficiently than you can process going to a conference in terms of the tradeoff of time and travel and all the things in between.

[Karen] So much. And we have really just kind of touched really the tip of the iceberg there. If there was one more thing for you to say, I don't want to get off this debrief without bringing up this point. Is there something else that you're like, Karen, we're not getting off without bringing up this one topic.

[Sarah] I fought all my points in early and often. I think I learned that on the corporate side.

[Karen] That's great. That's great. Yeah. Right. Very clever. The only thing I want to bring up is, we sort of touched on it, but it feels really critical to me. We talked about the beginning of that accountability offload. Let's stay accountable to our work when we use AI. There was another part of it that came up, I think, in the town hall. Again, I was saying how when you're a moderator, very often when you do the debrief, it's the last thing that stays with you so much. This town hall that I had the very last session with Lisa Courtade and Barry Jennings and Dave Duganne from DIRECTV. Barry Jennings, who's with ESOMAR now, he's ex-Microsoft at this point, and Lisa Courtade from Organon. We were talking about all of these topics, but one of them in particular was this idea that we have to really challenge the results of AI sometimes. We have to use our critical thinking skills, look at what the output is. We have to model this for junior employees who don't have the benefit of the years of experience that we have. They don't necessarily know what they're looking for because they haven't done the work the way we have over 20, 30 years. How will they know what good output is? Because AI output really looks good. It looks good. It makes sense to an untrained eye. It seems logical. The arguments it makes looks logical. If you read it, it seems to make sense. But if you have the experience and the background, you can say, well, wait a minute, something's not right here. You really have to be discerning when you read AI output. We have a responsibility to junior staff members to not only challenge AI because that's what our stakeholders deserve, but also model that for junior employees and say, wait a minute, we have to show them because they don't have the benefit of the years of experience that we have. I just think that that's a responsibility that we have, not only to our stakeholders, but also to junior employees because they're never going to learn it a different way because they are now learning a way of life working with AI. They're not learning research the way we learned research. I just think that's something that everybody should really pay attention to. It's one of those things that my brain, that will keep me up at night thinking about how people in their 20s are learning research right now. It is a different way of learning research. On that sobering note, I think everybody should think about that. I'm not saying it's a bad thing, but I think we have to be really mindful of how will they get the same kind of data analysis experience? How will they get early moderating experience? How will they get those skills if AI is what they're learning it on? How will they know what non-leading questions are? I know that sometimes when I've had AI draft a podcast brief, not for this one, but I've had it draft a podcast brief. I'm like, dude, that's a leading question. We cannot ask them that. It's like, oh, you're right. That is a leading question. I'm like, duh. It doesn't know what a leading question is until it is trained that I will not ask a leading question.

[Sarah] Yeah. Maybe more optimistically, that's where our human eyes and ears and experience all comes back into play. When I think even back to anthropology as a field, part of the power is the watch and listen and learn piece of it. On the data side of insights, I feel like getting really quick. I think one of the talks talked about a potential split between the data side of the house and the more strategic discussion side of the house. I feel like the real power comes in that linkage of making sure that you... I don't know. Sometimes I jokingly call it Rain Man, like the movie. You have to be the one who can kind of... Sadly, you're the one who has to count those toothpicks sometimes and you get a gut knowledge. I think part of what makes me a little... I'm not sure how I feel about synthetic on this front is sometimes I feel like that's literally like when you get a consumer gut built into your leadership team, that inherently is kind of synthetic in the same way that you could get robots or AI to tell you that same thing. It's kind of like muscle memory or it's in you, like Gatorade in the best classic commercial way, advertisement commercial way. You kind of have to soak in your space, I guess. I don't know where it's going, but I feel like that human connector has to kind of be there. Once you do that and roll up your sleeves, again, it's that accountability that I think is where the traction comes from.

[Karen] Yeah, absolutely.

[Sarah] And the credibility.

[Karen] Yeah, for real. Sarah, thank you so much for joining me for this. I really love talking to you and we could talk for hours, but-

[Sarah] And thanks to Greenbook, that IIEX AI, it was a really good amount of content packed into a tight space.

[Karen] Thank you. Well, I appreciate you and I appreciate your attendance and I appreciate your time for this, really. Thank you so much.

[Sarah] Happy to help.

[Karen] And thank you to Jamie at Big Bad Audio. I so appreciate what you do. And Emma for producing. All of our sponsors, once again, and our attendees and all our listeners for the podcast. We'll see you next time for a whole new show. Talk to you later, all. Bye.