Customerland
Customerland is a podcast about …. Customers. How to get more of them. How to keep them. What makes them tick. We talk to the experts, the technologies and occasionally, actual people – you know, customers – to find out what they’re all about.So if you’re a CX pro, a loyalty marketer, a brand owner, an agency planner … if you’re a CRM & personalization geek, if you’re a customer service / CSAT / NPS nerd – you finally have a home.
Customerland
Skepticism, air cover, and the new playbook for insights teams
Curiosity got faster—and a lot more practical. We sit down with Tim Lawton of SightX and Russell Evans of ZS to unpack a partnership that blends expert humans with integrated AI to rethink how insights teams generate ideas, validate concepts, and influence big bets. No bolt-ons, no buzzwords—just a clearer path from question to decision.
We start with the reality that researchers live under a microscope, then show how automation can remove the drudgery without losing the judgment that matters. Tim explains how SightX streamlines end-to-end survey workflows, analytics, and dashboards so small teams can do more with less. Russell shares why ZS sought a partner that treats AI as the core engine, not a layer, and how the combo is helping brands compress months of work into weeks while improving the odds of success.
You’ll hear a concrete CPG case where data-driven concept generation plus rapid, integrated validation shaved time and lifted purchase intent by 29% in a human-in-the-loop process. We break down what separates pilot purgatory from real adoption: leadership “air cover,” incentives that reward experimentation, and new roles that scan the market and align tools to strategy. We also get candid about risk, governance, and where AI belongs in the stack—use it where it accelerates insight and keeps humans focused on meaning and action.
We close with a sharp take on synthetic data: explore it for low-stakes ideation, but mine real, underused signals first—reviews, long-form social, and contact center data—when the decision matters. If you’re ready to scale consumer insights, speed up concept testing, and make better calls with AI you can trust, hit play and tell us where you want to move faster next. Subscribe, share with a teammate who needs air cover, and leave a review to help others find the conversation.
I I hope I'm not offending anyone uh by by saying this, but I think insights professionals are among the biggest skeptics in any organization because they're under intense scrutiny. You know, the entire business in many cases is picking apart the things they produce. And oh, I, you know, I I don't agree with that, or I thought it was this. Last week you told me that. And so I think over the years they have built a very um understandable shell and and and and and that has been a barrier to adopting new uh approaches and techniques because they know that if they do, they're gonna get a lot of heat if it doesn't, you know, meet the expectation.
SPEAKER_00:Today on Customer Land, after many, many months in the making, I have the honor and pleasure of speaking with Tim Lawton of Cytex and Russell Evans of ZS. Um, we've been trying to put this conversation together for I mean, a long time. It's been a long time. I want to say, since before the first of the year, which sounds ridiculous, but but the space has been evolving. You guys have done all kinds of things that are really interesting. And I think, you know, we just said, what the heck, we just have to finally do it. So with that, thank you. I really appreciate your both being here. Thanks for having us.
SPEAKER_01:Yeah, my pleasure. This is a topic I can geek out about all day. So happy to uh to chat today.
SPEAKER_00:We have found your forum. So um just by way of introductions, um, if you could, Tim, why don't you just tell tell us about you, your role at Citex, what Citex does, and then we'll jump to you, Russell, and what happens at ZS.
SPEAKER_02:Sure. Uh thank you. Uh and uh thanks, Mike, and for putting it together and pleasure to be here. Um, I don't know about me being the exciting piece, but uh I am one of the the co-founders and co-CEOs of Citex. Uh Citex is a uh an AI-powered uh end-to-end consumer research platform. Um our uh kind of differentiators of space if you're able to kind of uh kind of put a fine point on what that really means is really um survey-based platform research platform uh that kind of automates a workflow, if you will, for consumer research and market research teams. Um, but our kind of real reason for being is automation of the analytics and the dashboards reporting around all that um and the the kind of the level and depth of types of projects you can do to really meet just about any quant use case uh you can have. And um and look forward to talking more about that and how we work with with Z across the board.
SPEAKER_00:I was um I think my first introduction to Cytex was at as at an event somewhere, I can't even recall where, but I saw your tagline, which is still one of the best taglines. I don't even know if you're still using it, but you know, something to do with automating curiosity, yeah, which for recovering creative director is pretty intriguing.
SPEAKER_02:Um yeah, I I remember it was one of our uh we still use it occasionally, but uh, but yeah, and honestly, it uh it it does speak to really, you know, this and kind of the the present state and present day of of this type of technology in the work is really one of the things that that um kind of inspired that tagline is really we're all curious, right? I'm curious about uh you know getting it to into this specifically, but um, what are consumers thinking? What are they wanting? What do they desire? What are their behaviors? And it's it's automating the ability to ask those questions and get the answers faster, easier. Um, that kind of inspired kind of the kind of encompasses the work we try to do and the things we try to do at SideX, the features and the user interface and things. Like how do we make asking those questions easier? And more importantly, kind of unearthing some of the data, uh, the insights within the data faster.
SPEAKER_00:I I want to I want to get to that and and really kind of spend some time on that idea, but but first, um, Russell, I want to give you your do here. So so please, you know, tell us about yourself and ZS.
SPEAKER_01:You didn't just want a third voice randomly chiming in later. Uh yeah, that does happen though for uh for having uh me here as well. So uh I'm a a partner at ZS. Uh ZS is a global uh professional services firm. Uh I usually describe us as a data-driven decision-making company. Most of the work that we do helps uh helps our clients uh better use data and analytics to answer their questions and find growth. And so obviously, that has taken on a lot of uh AI-driven kind of manifestations these days. Um and so that's been one of the hats I've worn here in the last few years is to help figure out how brands should really think about capturing uh and using human insight in the age of AI. Uh, what are the types of tools they should be using? How does that affect the people and the processes that they have? What's possible now that they couldn't uh couldn't do in the past?
SPEAKER_00:So, my big challenge here in this conversation is gonna be to not let it unravel into multiple directions and you know, and feel like it's it's just worthless. Instead, though, I want to focus on your partnership, which is freshly announced. Uh, and I think you're you're actually in Mark and getting traction with each other, um, what that looks like, what's behind it, and um then a little bit more on how you're working together. So I don't know, Russell, if you want to take the the why the partnership idea.
SPEAKER_01:Yeah, happy to. And I'm sure Tim will uh keep me honest if I miss anything. But you know, I I think we are entering or have entered really a period of uh really once in a generation disruption in the human insights space. And it's uh creating a window to rethink how brands get to know their customers and what to do with that information. Um, but it's also going to take a different uh set of capabilities, um, human as well as technology. And so we've been on that mission at ZS for a few years now. Um, have uh quickly realized that uh the answer will never just be, you know, one partner, one tool, one technology. And it's it's really going to take an ecosystem of things that have to come together. So we were on this mission to try to find partners who could augment what we do well as humans with domain expertise and who have been in the insight space for a long time. Uh, what are the right tools and technologies that either we should be building with or building or partnering with to help bring that vision to life? And that's how we met SiteX as well, and felt like they complemented us uh with a really powerful technology that can help enable the you know smart expert humans that ZS brings to the equation. So that kind of dynamic, the the humans augmented by technology that is increasingly AI powered is kind of the spirit of the partnership and what we've put into the market.
SPEAKER_00:Were there anything, any specific I mean, I don't get too deep into the weeds, but were there any specific use cases or utilities about Cytex that made ZS start thinking, hey, we should we should pursue this on a more on a more deep level than just you know client vendor?
SPEAKER_01:Yeah. Um so you know, I've been on the the conference circuit here for a few years, and the amount of technologies in the insight space that have popped up almost hard to keep track of. Um and so have have encountered a lot. I think Cydex stood out to us as one of the few uh platforms that has done a really clever job of integrating AI, not just as a kind of bolt-on to an existing technology, but as really deeply integrated into the platform in an end-to-end way. So helping uh lay people who are not, you know, decades in the insights industry craft research, field research, and then, you know, most importantly, do the analysis on the back end and help synthesize that. And and it's really uh done in a in a pretty you know seamless, uh user-friendly way in in ways that we just hadn't seen on a lot of other tools. And that's the kind of value proposition that we've been hearing from our clients. You know, I want tools that don't force me to go get a degree in how to use a new tool. Um, it should just be dead simple. It should just work. Um, it should take on more and more of the drudgery that I don't want to be doing myself. And uh and we felt like Citex really checked a lot of those boxes.
SPEAKER_00:Yeah. I mean, I I feel like um Citex could have said we automate drudgery, but that's just not a great tagline.
SPEAKER_01:Not quite as sexy, yeah. Uh maybe for the future. Who knows?
SPEAKER_00:Yeah. Next case. So yeah, Tim, I don't know if you wanted to add anything to that, you know, reasons for the partnership, you know, how you guys felt going into it, where you see it going.
SPEAKER_02:Yeah, no, I think one of the key things that Russia is really the human insights piece. Um, and I think maybe we'll talk about this in a little while, but the the kind of the it it makes so much more sense now to me, like today, this year, this month versus a year, two, three years ago, where uh you know AI, we've had AI in the platform since since inception. I sometimes refer to as old school AI versus the the gen ad that came on the scene, you know, too long ago. But when that happened, it was hair on fire, we got to adopt it, AI, AI for AI's sake. And then about this year, you know, conferences and talking to customers and so forth, it was well, okay, but how and and who and does it work and is it replacing my job and those sort of things, where yes and no, and it it was the the kind of using somebody has to use the technology, it has to be implemented and used for the right reasons in the right way, not you know, doing it for doing its sake, you know, can be a bit of a fool's errand. And I think when we started the conversation with ZS, it was that perfect complement of domain sector expertise uh with the technology and the combination of that, where uh, you know, even regardless of who who's using the technology, it it unearths important things in the data and trends and and so forth, but you still need the human, at least today, uh, to understand what is insightful to my business, relevant to the context of my customer, my consumer. Uh, and I think that's the that kind of perfect compliment that really is the is the big value add, is is the uh using the technology in the right way at the right time for the right reasons.
SPEAKER_00:I mean, I I have to tell you, from from my standpoint, uh third party looking in from the sidelines, uh I love seeing these kinds of partnerships emerge where the synergies just seem so kind of natural. Yeah. This this is just one of those perfect fits that just seems to work. So congrats on that level.
SPEAKER_01:Um To that point, I mean, it actually turned out after the fact that we were even working with some of the same organizations. And so I think it was it's been a seamless partnership in that sense too, where it's it's now an added value to our you know mutual clients to say, well, now two of your partners are figuring out how to work that much more closely together um and and bringing you know that advantage to you. So it it it was a little added bonus on the back end.
SPEAKER_02:And so scale up and down that value chain too, where you know, there's the day-to-day, there's a quick turn, quote unquote agile stuff. But then, you know, we again as Russell just mentioned, they get questions about that, and we get questions about some of the strategic stuff. And you know, we have a research services team that can help, but we're not the big time consultants and the strategic thinkers and that sort of thing. Um, it's just not what we've kind of designed and built the company for. And in that sense, that that full spectrum of uh nowadays there's all site types of projects and probably more so on the you know, the tech enabled piece of it, but people still want you know, strategic partner and thinker to again to adapt some of those insights. And you know, I think across the board, across industries, um timelines are being condensed, budgets are being, you know, having more and more pressure on them. So the more that we can come to market and say, no problem, you can scale up, you can scale down, but you can still adapt to the needs of your changing organization and more importantly, changing consumer of today.
SPEAKER_00:So I'm just curious when when you found those uh those mutual clients, um were the efficient, was it just a matter of, oh hey, look, you know, um I've got chocolate, you've got peanut butter, you know, to abuse the analogy. But or or did it actually speed things up in the kind of inquiry development and solution process? I mean, were there was it a speed thing, was it a robustness of the of the effort thing, or what really came of that of those realizations? Because I'm sure there was something.
SPEAKER_01:I mean, uh Tim, I'm I'm thinking, speaking of chocolate and peanut butter coming together, I'm I'm thinking of our mutual client in the confection space. But um and then just as a good example, uh uh you know, in this instance, ZS is using some of our capabilities to help generate new food concepts. What is the next product that this organization should put onto the shelf? Historically, that process has taken months and months and months and was actually wildly unsuccessful. A lot of uh you know, consumer goods products actually fail uh in the first couple of years. Um and so to take you know 12 months plus to put a product on shelf that fails is a big waste of resources. And so we were able to uh partner then with Cydex to say, well, if ZS generates a lot of ideas that we believe are now more data-driven and more compelling, can we also shortcut the testing and uh validation of those ideas on Cytex's platform? And that combination of generating ideas that are better in the first place and also developed faster, and then testing them without myriad rounds of stage-gating processes and conversations and ideation sessions has saved this organization enormous amounts of time and has really helped them rethink a pretty critical process for them. Uh and so that's what you know gets us excited, and that's what drew us to Citex because it it, I won't speak for Tim, but it gets them excited. Um, not just, okay, well, now you had two partners, we're already there and now they know each other, but we're actually doing new things together. We're we're using technology to do things that weren't previously possible. And it's really only because we're collaborating pretty closely that we can sort of identify those you know synergies.
SPEAKER_00:Just curious, how long did it take for for that kind of um, you know, for for both entities to try and figure each other out? Or had that already happened by the time the partnership was was formed?
SPEAKER_02:I think a little bit was well, obviously this makes sense. So there's sort of baseline assumptions of these things were going to be kind of unearthed, but I think luckily for you know those of us who were kind of key to putting this partnership together on paper, that the the case study that Russell was just mentioning was kind of in the first couple of days or early days, early weeks of the partnership, where it kind of immediately proved that that proof point with through mutual conversations of that that customer was. Oh, wait, are you working with Sunsa? Oh my you're working with Sonsa? I'm working with Sosa. And then it was like, oh let's just get on the same call then. You know, that was like the one kind of eye opener. So it's a little bit some sometimes it is the sometimes literal peanut butter and chocolate. Um, and then you know, other times I think it was yeah, a few months after that, whereas uh, you know, a customer of ours was asking, but hey, can you guys help with this thing? And it's I know it's not part of the platform, it's a bigger kind of initiative and this segmentation piece. And we're like, well, good thing he asked. Let's get our partners on the phone because that's what they specialize in. So sometimes it's a little bit of the both that runs in that spectrum. And um, you know, your point about kind of going to market now. We're, you know, earnestly going out and having more of those conversations and finding the ones that were maybe kicking over the rocks that were already existing versus the you know, going out to our respective parties and say, hey, we can offer this now, right? We're we have the the ability in our partnership to be able to offer you these things um that are that can scale up and down that spectrum for all sorts of different use cases and things.
SPEAKER_00:Interesting. I feel like it'd be a big miss if we didn't just suggest that this unnamed confectioner company, if they need to compensate us for surfacing their insights and that you know, we're totally open to that. Just just saying.
SPEAKER_01:Sadly, Mike, I think the idea of combining chocolate and peanut butter has already been on their radar for quite some time. So uh that may not be the way.
SPEAKER_00:It's not original, is what you're saying. If if we start taking on more here, uh if now if you tell me that you came up with that idea, I'll really be impressed.
SPEAKER_01:Uh yeah, my my last name is in fact peanut butter, so it's an old family uh legacy.
SPEAKER_00:No, it'd be like that's my nickname, but that's a whole different thing. So um, so let's let's shut this conversation just a little bit. Um, I'm really interested in both of your perspectives on the idea of the current state, if you will, of AI in the human insights space. Because I you said it a minute ago, Tim, like you know, it was last year, maybe the year before, everybody had to have AI for AI's sake because you needed to say AI. Yeah, just if you had to say it, you had to be the guy. How to say it. Um, but there's been a whole evolution, continues to be, and how that gets gets brought to market, deployed, and utilized. So, you know, current state, what are we looking at? How is it being deployed? And then and then let's look out a year or two. What do you see?
SPEAKER_02:Yeah. Um, I say from my perspective in the you know, conversations around the the tech itself and the potation and the the kind of user enablement from our side, it was um uh you know, again, we've had you know elements of it, and then the kind of Gen I piece as we released you know our kind of feature ADA in there, it we were kind of seeing that trend of you know, but pushing it on, not pushing it on people, but so you know we have the app and do you want it, you want to turn it on, and going through the the IT reviews that were layered upon IT reviews for this sort of thing, and and kind of seeing the different, you know, some companies were check the box, good, we need it, we want it. Some were don't even mention it because our top-down management doesn't want us touching it, and there's all sorts of security issues and so forth. Within actually one company where that conversation was a real, like, we're not even allowed to think about it. It was six months later, had some executive changes. Then it was a fire hose, we got to do it, we got to do it. Um, and I think that was kind of early days over the course of the, you know, over maybe two or three years ago. And now it's kind of dust has settled a little bit where it's like cautious, let's put our toe in it, let's you know, you know, ease into these types of things. Um, uh, you know, again, from the you know, primary research perspective, but also in those conversations around like what else are you doing, how you're implementing it, um, and just hearing some anecdotes for for other types of use cases. So I think we're in this current state of, you know, similar to the pendulum of we got to do it, we got to do it, versus wait a minute, what are we doing? That was at the higher levels of the kind of management, uh, it feels like, you know, large corporations to if we don't do it, we're gonna be left behind, but we got to do it smartly, let's, you know, cautiously, let's, you know, in some more kind of cautious uh than others. Um, but I think it's there's not many, if any, that I can think of that I know of that are saying a hard no. It's we don't want to be left behind, we have to do it. And there's clear uh implications of of the benefits of uh, you know, whether you are adopting it or not. You know, there's a lot of case studies out there anyway, and from anything, uh to kind of support the reasons to do it. And uh, but again, who knows how fast that'll take off in a year or two. So uh, you know, I I think that the pace of adoption will will pick up pretty quickly. Um but obviously, you know, tough to say how you know it'll it'll take one kind of bad example or or you know, some data breach or something to kind of put a halt to things. But I think it's a cautious but forward momentum approach for a lot of a lot of companies.
SPEAKER_00:Russell, what are your thoughts?
SPEAKER_01:Yeah, well, echoing a lot of what Tim said, I I I think we've seen fairly mass adoption among the kind of insights world of what I would call the personal productivity use cases. Just individuals in many cases realizing, well, hey, these tools are quite helpful for me to generate ideas, to do some quick synthesis. Um, the the the drudgery, you know, that that we talked about before, uh a lot of that has been a little bit under the table as organizations were discouraging folks from using these platforms, but of course, people have have found ways around that. And that seems to be pretty ubiquitous. Um we're we're probably then so that that phase I think is is well in well in here. I think the phase of let's do a lot of pilots to figure out what this really is and if it's useful to us. I I think we are, you know, well within certainly most have done a slew of pilots, most larger organizations. I think we're seeing now some organizations, particularly the more progressive or larger ones, moving out of piloting phase and into more, we have a strategy for how we're gonna do this, and we're starting to implement that. And in some cases, that's organizations trying to build their own tools and capabilities internally. Maybe that makes sense for some with more resources or larger teams with those types of capabilities. I think for others, that's solidifying maybe a new partner set uh of folks that they have found that meet their needs and complement whatever they're trying to do. And so I I you know I think we're starting to see that emerge where it's it's now no longer a theory, it's it's organizations putting it into practice. And and I'll sort of allude to your second question, which we can maybe unpack as well. But in addition to just new use cases, new tools, new partners, where we have now seen a realization is oh, wait, it's not just tools. Um we've now piloted and we've now maybe even implemented tools, and we need to rethink the people and process elements of this as well to even use those tools. And so to me, that's probably the next big thing on the agenda for even the most progressive organizations who are starting to put these technologies uh into practice.
SPEAKER_00:I think that's that's maybe the the topic for the next conversation we have because it's a big one and it's I think it's universal. So I'm I'm asking, I'm gonna ask a question that has no real answer. Sorry, but I don't know how to ask this, but but but both of you will have a perspective here that I think is is pretty valuable. So I'm making up a 10-point scale, and at the bottom of the scale are companies that said, no way, we're not doing this AI thing, it's crazy. Most of which we think have graduated from that. And at the top end of the scale at 10 points are companies that are full on. They're they've got teams uh evaluating, deploying, testing, piloting, and even rolling out. Um, you know, within certain verticals, is it possible to identify kind of where those industries are on that 10-point scale of maturity? Is that even a real question?
SPEAKER_01:Certainly, yeah. I I I mean, you know, ZS has done work here in tech maturity in general and AI in particular. Um, and and it tends to look like a bell curve, you know, and there are a lot of folks in that sort of middle zone. Um, some industries skew a little bit higher than others. Um, consumer goods and CPG, for example, in the realm of human insights, has faced budget pressure for quite some time. And, you know, margins have always been pretty razor-thin. And so a lot of them have been at the forefront of uh AI within that space. Um other industries, I think, are now starting to grow there. Maybe they had even previously deprioritized human insight because those traditional insights approaches, your quant research and your qual research just didn't fit the speed of their business, but now they're seeing we can go back and do more of that with some of these tools. And so they may be a half-step behind. But but I I think certainly there is a maturity spectrum there for sure. I don't know that anyone is actually still really in the uh you know zero to one end of your scale. I think that ship has probably sailed. And the pace, the acceleration I've seen, I don't know, Tim, if you would agree, but in in our clients and just meeting folks at conferences in an industry, it's it's accelerating quite quickly. I think there was a lot of skepticism two years ago, even a year ago, but in the last you know, six months or so, it's it's now a we're behind, we have to hurry, we have to do more. And so it seems to be picking up.
SPEAKER_02:Yeah. I'd say the I think the the key of is like across industries and in use case right. I think the ones that were faster to adopt were less human-facing. I think that's why the consumer goods, it's like if you you know screw up a product launch, you can have a bigger impact on the top bottom line than if a machine in a factory breaks down because it's you know ad powered or whatever. So I think that and I think that those use cases have sped up the improvement of the technology across the board, just you know, just in general. Um but I think and feel like the you know, as Russell's saying, the the adoption, at least in the consumer facing companies that we typically deal with, you know, for the most part, are was cautiously optimistic and it was like kind of slow and steady, I feel like, versus uh just jumping in head first. Um and I think it's I think across the board, though, it's the um learning through execution rather than kind of excessive planning. It's like, let's just do it and try to figure it out, but maybe not put it at the front line where it actually may uh you know affect uh customer outcome or product or an actual product launch or something like that, but kind of slowly adopt it to um uh you know, unearthing some insights that are kind of behind the scenes that a human definitely needs to step in front of and kind of adjust versus uh yet again, three, four, five years from now, who knows what uh what the kind of machines and the insights that are applied will will be. But um I think for again, for where we sit, who we deal with, it's that human piece of it that at least certainly now, and I think for the foreseeable future, but I mean today's world that's could be six months or six years. Um, the human still needs to be there. Uh we're dealing with humans, you're still need to have that kind of human interaction. But it's more, I think the adoption is going to be more on the, yeah, but sure, how do I scale myself, right? My if I only have a team of two, three, four people. And I mean, honestly, I'm sometimes still surprised to this day of you know, talking to Fortune 50, Fortune 100 companies, and you know, granted, there's certain kind of brand lines and teams that are um, you know, their own size and shape, but it's like, wait, you're only two, three people? Like that brand that you work on is like a global thing. Like, you know, they're being asked to do so much more um with seemingly, at least from the outside, a lot less, a less fewer resources and and being asked to do it faster. Um, and I think being able to scale their time uh and in power within the organization. I think that market research, consumer insights professionals in general, their seat of the table is getting bigger in recent years. Their insights and their actions are having more of a strategic impact than uh than I think in you know, in recent years and five, 10 years ago, it was heavily on the the strictly data side, the marketing versus market research uh side of the house. But I think that market research side of the house has become much more of a strategic uh initiative um than it has certainly has been in the past.
SPEAKER_00:You know, we we've touched on this in various ways throughout this conversation already, but the the fact is that AI adoption within human insights is a is a change management challenge. I mean, you've got the great technologies can do all kinds of things, but getting the humans to recognize that, to adapt, to adopt them, one, to adapt to the possibilities that they offer, and then the organizational change, and maybe even the organizational changes first that go along with that are huge challenges. And I I you know, I know that you both see this in your respective worlds and probably the the you know the world you coincide in, but I'd love to get your thoughts on, you know, what is what does it take? It's not even a fair question. What does it look like? What are the earmarks of a company that's doing this kind of cultural transformation well versus because I know you've probably seen this as well, companies that just aren't aren't pulling it off, you know, for for all of the will in the world, and they just can't seem to do it. Change management is hard, it's complicated, there's people which you know ruin everything. And you know, you've you've got to get them on board. So so what are you seeing in your world that uh that people listening to this facing their own uh AI adoption challenges could take away that you see that you like to like to kind of give them? What insights can you give them?
SPEAKER_01:A lot to unpack in that question. Um I I hope I'm not offending anyone uh by by saying this, but I think insights professionals are among the biggest skeptics in any organization because they're under intense scrutiny. You know, the entire business in many cases is picking apart the things they produce. And oh, I, you know, I don't agree with that, or I thought it was this. Last week you told me that. And so I think over the years they have built a very um understandable shell and and and and and that has been a barrier to adopting new uh approaches and techniques because they know that if they do, they're gonna get a lot of heat if it doesn't meet the expectations of of a lot of uh uh eyes in their in their organizations. And so to me, the the the things that can help most are um air cover, uh things that don't often get talked about, but for example, incentives. Um and I don't mean just literally how much someone is paid, but how do we measure success of different teams? Um, how do we encourage folks to spend time here versus there? Uh a lot of companies have built processes over many decades that are built around um an approach or a methodology that may be not the best approach or methodology anymore. And until you change the way that we're gonna hold teams accountable, well, there's very little incentive for someone to say, yeah, let's switch and let's do something with a new tool or a new approach. Um because why would they? You know, they're not gonna put themselves at risk if if um if they're being held accountable for something. And so incentives is an underlooked sort of lever, I think, that that organizations can play here, creating freedom for teams to go and try and experiment, knowing that in some cases they'll fail. We shouldn't be putting people's jobs at risk because they were trying a new tool or technology that should be encouraged within reason. So those types of things are big. I think there are new roles that likely need to exist that many organizations have started to put in place. Things like who's out there scouring the market for all these new tools, technologies, and approaches. And it's not just a vendor management role that has existed forever, but it's uh using discretion. I I know where my business is trying to go, and I now understand these approaches well enough to find the intersection two years from now. We should be using this now because it's going to help us two years from now. And that should be someone's role, if not multiple someone's these days. Um and so increasingly there are new roles that don't typically exist uh uh to kind of you know grease the skids, if you will, for these new approaches, new technologies.
SPEAKER_00:Are there are there things you look for in clients that you can kind of just identify up front that would say, okay, these people are ready to have a substantive conversation about AI transformation?
SPEAKER_01:Um uh yes, and I think the tenor of the conversations we have had, Tim, I'm curious if you would agree, has changed very considerably in the last, I would say, six months, even. Um a lot of this is interesting, but and you know, I have to go convince someone else, or we'd have to free up the budget for this, or um we already have an approach, you know, there was a lot of of that before. And I think the hallmarks of a conversation that seems to be going in a productive direction these days is much more of aware conversation. It's uh I see value in this, and here's where we can use it, and here's how I can make it happen. And hey, maybe we don't even have budget, but can we do a pilot to make the case and can you help build that case? And so that was always a good hallmark, but also the the number of conversations that are more along those lines these days these days seems to have increased uh pretty considerably.
SPEAKER_02:Yeah, I can I can second that. One thing that uh you said a few minutes ago, Russell, the air cover. Um back to the the fruit snack brand we worked with, and the the CMO there was probably the the key of that air cover, you know, and he had brought up in conversations that it was the you know, there's a chicken egg, there's the the culture and uh or AI piece. And for this organization, it was a little bit of the I think I think in most cases and and perhaps was the AI was there and it came and it was, oh, well, how do we adopt it? Well, okay, now we got to fix a culture to do. And I think, you know, after enough of that, some companies are catching up to do the culture first piece of it. But to some extent, um, you know, I use culture in a slightly kind of narrower sense than a large organization, but that air cover to, yeah, let's experiment, let's run a you know, a case study or a proof of concept or something, or hey, we're already kind of dabbling in it anyway where you know our interest has grown in it, like let's just bring it on, maybe start to start small and not for every use case or research project we have, but for these ones where it's kind of de-risk it, de-risked to an extent. Um, and I think that's grown uh to you know, really to again to Russell point. The more of those conversations are now we're interested, we've we've done it, we've dabbled into it, ready to to grow or do more, or bring on new tools for other applications and things like that. So I think the the awareness one is is certainly grown considerably. Um, you know, it was at a very personal level at the you know, the your personal um, you know, applications of of uh you know day-to-day work, but kind of grown into the you know larger companies and the adoption of bigger platforms and and across teams and things like that uh has grown. So I think just the the interest of of doing it, I think more and more people are realizing, well, if we don't do it, we're gonna be left behind. So let's let's do it. I think there's been a de-risking of the last like certainly year or two anyway, um, at least in our space of some of the the applications of it.
SPEAKER_00:Is that, I mean, is it because of the emergence of better AI governance policies that are kind of a little bit more universal or ubiquitous now? Or is it just a kind of a general recognition like we better do something here, you know? Um, I guess I guess the question I I'm trying to ask is the structural readiness for AI adoption has to be real. Um you know, these are these are significant shifts. And um the cultural shift has to be supported by systems. And you know, uh it's it's my view, um and I'd love to hear yours, which is much more appropriate and and valid, that the systems tend to be the last things in place for uh for sizable change. You know, everything else seems to happen first. Well, the tech often leads, and then the people get kind of squeezed, shuffled, or reconfigured, and then systems are built to kind of support that. But um it seems to me in this particular case here now, with the magnitude of the shifts that we're seeing, that that maybe it's time for system first um adoption. I don't know. I'd love to hear your thoughts on that.
SPEAKER_02:Yeah, I think the uh, you know, again, for kind of the broader AI for AI's sake, you know, that could be, you know, large corporate global implementation on-site type of thing. But for us, and I think the the benefit of our partnership is it's we're we've just by our the nature of we're outside of your organization, we're automatically de-risking it for you because we're using the AI for you. I mean, we have you know, users can log in and do a thing, but it's not like an on-prem platform that Sidex is cloud hosted, that sort of thing. And you know, ZS has some tools that are um uh you know kind of managed for clients as well. So some of that is like, hey, we have the AI tools, you just have to get permission to log in, use them. Any of that's where some of that's you know, the data security and things, but um, and that's easy enough to kind of turn off you know where data is stored and that sort of thing to um to alleviate some of the risk on you know for some of the the corporate strategic, you know, security type of reviews. But um, I think the benefit of us saying we've built the platforms, you don't need to. We can come in here and we can use it for you, we can implement it for you. Uh, but I think at least in our use cases in our world, um, we're not, you know, we're not building an instance that kind of uh integrates throughout an organization necessarily versus just kind of log in. So, you know, your usability of a software interface doesn't change. You're just getting a heck of a lot more for your dollar uh and can do a lot more uh than you ever had before because we have the technology that we're kind of offering you access to versus implementing insights or AI kind of platforms throughout it versus um uh you know, it's not necessarily another kind of element that de-risk it is it's not always, you know, every touch point, every project is not AI first, AI last. It's use it when you need it, pick and choose, and implement it here for this thing to expedite this, you know, analysis or this type of reporting. Uh, and I think that at least gives people peace of mind is a right, right phrase, but um makes it easier to digest and adapt into such, oh it's not uh, you know, it's not AI always, that's AI when I need it, but the the platform, the strategic uh kind of uh application of at least our partnership. Um I think the benefit is that is like, well, it's our stuff. We're letting you get access to it and use it. So it's less, far less risk for your organization uh than it is to just, you know, we're just helping you do things way faster, way more powerful than than ever before.
SPEAKER_01:I'm not uh kind of building on Tim's uh thought there, I'm not sure it was even clear. I'll I'll say to me. I won't, I won't uh presume to know what other people were thinking, but even clear to me, having been in this space for quite a while, the extent to which systems needed to evolve in light of some of these tools and technologies. And so it it seems to me that we had to go through this uh phase of piloting to even actually understand oh, it's not just a new tool. This actually enables us to operate entirely differently. And I'll give you an example that's all just built like the one we talked about before in the confection space. You know, the feedback that we received from that particular client was the outputs of this new process are amazing, but all of us miss the ability to sit in that room and ideate and kick these ideas around. We all recognize that was actually not producing good ideas in many cases, but that was the part of the job that we liked the most. And and so, you know, they they wanted to revamp their whole process, but in a way that that kept the parts that they actually liked. And I don't think that actually would have been evident until we all sort of went through that process together. And so I I think, Mike, to your point from a few minutes ago, like, yes, it would be lovely to take a system-first approach here, but it may not always be evident which systems are most impacted and and where and how they need to change until you you test your way into it, given the potential for such dramatic transformation from what we're seeing now.
SPEAKER_00:Yeah, kind of it kind of brings me back to the what was my original kind of thought process when we first started planning this conversation out, which was the interaction between this very cool AI-enabled tool set and the humans that ultimately will be you know using them and um and what that takes. And I think uh kind of the more mature version of that very basic question for me has to do with um your crazy good tagline um from way back when. Because look, it was just a great tagline uh at that point, but um as a heavy, heavy uh chat GPT user, I can tell you that it has enabled a level of curiosity that I just couldn't afford before. I just couldn't afford it. And I'm I'm so I'm wondering how does that have you seen material changes in the way the tools are used by the people who are using them because of this?
SPEAKER_02:Yeah. I I would say for absolutely, yes, from our standpoint, I think over the years as we've built and added on you know more advanced methodologies or analytical capabilities and so forth, it became oh wait, I can I can do this now, I can do this myself. I don't have to wait uh two months or six weeks to get this done and and have it, you know, come back to me in Excel sheets and this and that. It's just on this dashboard, it's clean, it's easy, and it's efficient. It's just giving people that power to do more, basically, to own more of the process um and and kind of get to that end state faster. You know, a lot of you know, kind of old school processes will take you from A to B all the way to Z, and it'll you actually have to get through every letter to get there, versus what we've tried to do is go from A, B, C to Z and skip over the stuff where you have to do data cleaning and manually export, import, and you know, bring other tools together to have one final output. Um, to the extent possible, always try to integrate the that workflow uh to give the power back into the humans' hands or the the insights professionals' hands where they can just focus on the what's really important, which is uh the the work and the process isn't what's important. You know, people may enjoy that to Russell's point, which is fine. Um, but it's you know that's the least impact, least important part of it. It's more here's what's important about the data, the outcomes, the uh, the process, and all right, how do I implement it? Now, you know, you get more time back in your day and your week to think about what's important to the business, what's more strategically important. And I think that over time in recent years has really been one of the driving factors and you know, not just solely due to sidex, but I think you know, across the board, across the industry, is uh more time to get more impactful results from the same work, right? The traditional methods aren't changing. We're just doing them in a more efficient, faster way and getting that power in the hands of more people. And I think that collectively, my opinion is that more people having more of the those the answers, the results can ideate faster and and implement things faster or better uh or differently, perhaps. And uh, and I think that ultimately is is one of the large driving factors of this industry and these roles having a bigger um presence and a bigger important uh factor in you know it comes to any and all you know decisions that are under their realm.
SPEAKER_00:I I think it'd be it would be really interesting. I don't even know if this is possible to um to see an index of how um the the speed of ideation has changed with these kinds of tools. I don't know what your thoughts are on that, Russell, if you've seen you know changes in the ways the humans are actually using them and what those efficiencies might be doing for their for their worlds.
SPEAKER_01:Oh yeah. I'm uh I it's uh you could throw a dart in a lot of directions and probably hit a good example of that. Um, I think Tim spoke to some some where he's seeing that happen at an individual level, people who are now able to do more than they were before, and that's you know, sparking better ideas for them or letting them do more with with limited time. We've also seen that organizationally. Um you know, whereas I may not have had time to go do a full round of qualitative research to really unpack uh, you know, a deep insight about an audience. Well, now there are tools that let you do that very cost effectively and in a matter of, you know, hours and days, not weeks and months. And so whole new exercises that were previously not happening can now happen to find a much more comprehensive and holistic understanding of who your audiences are, what makes them tick, how you can better serve them. And, you know, I we're seeing that inspire better products in market, you know, services, concepts of loyalty that can move beyond just transactional sort of notions of it and really understanding audiences on a more comprehensive level to figure out how you can build deeper relationships with them. So it's it's powerful at a macro level as well as an individual level too.
SPEAKER_02:In the in to that point, the the thing of the case study that we're finalizing, uh Russell, where the I think it's a perfect example of the uh humans using the technology, right? So, you know, uh my chat GPT user, put in your prompt, you you get some sort of output. Um, but taking that, you know, kind of at scale and kind of implementing it towards this, where um we ran a side-by-side study of uh you know concept test that was one concept test was designed, run through the kind of the same process, and one was um AI, but you know, human interacted and kind of using the tools, implementing some of the changes and not just kind of default to press a button and then here's your output from everything from the innovation brief to the development, designing the uh the concept, kind of optimizing things like that, and actually led to a 29% purchase intent increase um uh in the results for for the you know for the end, you know, the survey-based kind of respondents uh evaluating the concepts, the one that was run through the the human insights led process uh had a 29% higher uh purchase intent on it.
SPEAKER_00:Wow. Significant. Okay, I I just have I have one question. You need to settle this once and for all. I'm I'm I'm counting on you, and this is a raging debate in everybody in my world. Synthetic data for human insights, yes or no? Please, please discuss.
SPEAKER_01:Oh wow. And you have 35 minutes for the answer, correct?
SPEAKER_00:There's a reason I left this to be the last question.
SPEAKER_01:I'll happily share my hot take here. And Tim, you can you know agree or disagree, but I think there is a lot that can still be done to use AI to unlock real data that has been underutilized before we need to go and create synthetic data. Um, there are troves of information online in the form of things like product reviews and you know, long-form social media and data that brands have been capturing for years and years in their contact centers and through other touch points that have really been under under-leveraged that AI lets you unlock at scale. And and to me, there's gold in them hills that I would tap far before going too far down the synthetic path currently.
SPEAKER_02:Yeah. I would say, I don't know if it's really yes or no. I'll say uh yes with a lowercase and uh and an asterisk. Um, I think there can be some usefulness out of it, but to Russell's point, it's I mean, it's made up, right? Like even just think, you know, six, seven months ago, at least you know, consumer sentiment here in the US, if you had uh it will well, two things. One, it's it's you know, retroactive, it's uh you know lagging indicator at best, but it's you know, you look at take some of the data, you know, historical. Let's say you create a synthetic audience six, seven months ago based off of data and then scaled it up, and you're effectively just weighting that data, which you know, because in the the academic community is is debated for um for for quite a while the usefulness of that. But fast forward a month, two, three months, you know, Q1 of this year, you that is maybe not completely wasted or not useful, but not really as useful. It was literally weeks before that, because you know, things fell off a cliff uh for for a number of different reasons. Uh, you know, those things are it's old, it's stale data. But again, I think there can be some applications of it to you know to to supplement, to add to it, but it's still you know, effectively weighted, made up data. And I'm sure there'll be a lot of people who disagree and we'll debate the other side. But I think at least for now, it's yeah, it's interesting. But uh I wouldn't make a business decision that actually had any weight to it, uh, or wouldn't we not suggest that anyway?
SPEAKER_01:We we we kick around a little. Uh you know, we're a consulting firm. So of course there's a two by two that decides you know all decisions we make. And so the one that we have often used here is you know, on one axis, sort of the the the the degree of of removal of how far removed you are from the original data, and on the other axis is how important is this decision? And as you as you get into you know an AI that is farther and farther from the original source data and a decision that is more and more critical, that is, you know, pretty pretty big risk to be using synthetic data in those situations. If it's something to kick around and help me brainstorm and it's not gonna go anywhere other than informing me, then sure, you know, those are interesting use cases.
SPEAKER_00:All right. There it is. Money. That's the money shot right there. That's the one we're all waiting for. Well, Tim and Russell, thanks both of you for your time and insights. And uh, I'll just say before we even edit this conversation, it was worth the wait. Um, and look forward to the next one we we can get on our schedules.
SPEAKER_02:Absolutely. Thanks again, Mike. Really appreciate it. Yeah, it's been a pleasure.
SPEAKER_01:Thanks, Mike.
