Adam Bai of Panoplai on Shaping the Future of Insights

In this episode of the Greenbook Podcast, Karen Lynch sits down with Adam Bai, Chief Strategy Officer and Chief Client Officer at Panoplai, winner of Greenbook’s inaugural Industry Impact Award at IIEX North America. Adam shares how his background in anthropology, innovation, and enterprise transformation shapes Panoplai’s approach to modern insights. The conversation explores digital twins, synthetic data, the importance of validated human data, and why AI should enhance—not replace—the role of insights professionals.

Adam also discusses what it means to build for both today’s market needs and the future of research, emphasizing the need for better standards, smarter workflows, and human judgment in increasingly automated systems.

Key Discussion Points:

• Panoplai and Greenbook’s Industry Impact Award
• How digital twins and synthetic data fit into the insights toolkit
• The importance of grounding AI tools in validated human data
• Why insights professionals must become “shepherds of the right questions”
• Lessons from Panoplai’s evolution from Glimpse to a modern insights platform

Resources & Links:

• Panoplai
• IIEX Europe Event

You can reach out to Adam Bai on LinkedIn.

Many thanks to Adam Bai for being our guest. Thanks also to our production team and our editor at Big Bad Audio.

Transcript

Karen Lynch: Hello, everybody. Welcome to another episode of the Green Book Podcast. Yay, I think you're the first person that's actually waived. That's so great. I am Karen Lynch. I'm happy to be hosting today, gosh, with Adam Bai from Panel Play. He is the chief strategy officer and the chief client officer. We're gonna talk about the duality of those two roles because they're both huge roles. So excited to talk about that. 

But first, let me tell you why Adam is here, not just because he's a fantastic person and really easy to talk to. So I'm excited to be hosting him today. But he's here representing the company that won the Industry Impact Award at IIEX North America this year. And this award was new as part of the Insight Innovation Competition that we have hosted every year.

But this award was a new addition to the mix. every year as, you know, there's a there's a competition where one winner has taken all of a you know ten thousand dollar check and they they get a spot in next year's event, a speaking spot. And we started to think last year about something being missing from the mix. we also had done some crowdsourcing and sometimes the crowdsourcing we didn't necessarily think was

Pulling up necessarily the most commercially viable product per se. So we wanted to expand upon that. And then we also thought they were missing something else too, which is what we think is industry experts, we at Green Book. So we expanded it. We'll talk a little bit more about that. And we'll talk about the Industry Impact Award, which was given to Panoplay. That's what we're going to unpack today.

Before I get too much more into that, and again, we'll talk about this specific ward, I just want to stop, pause, and say, Adam, welcome to the Green Book Podcast.

Adam Bai: Thank you. Thank you for welcoming to the pod me to the podcast and thank you for the award and thank you for giving me the opportunity.

Karen Lynch: Gosh, well I'm just so glad you're here. So again, we will dig into all of that stuff that I talked about by way of introduction. But first, let's take a step back, tell the audience a little bit about you and your company and kind of level set with an introduction.

 Adam Bai: Yeah. So I have a slightly weird background. although I'm told everyone in the insights space has a weird background, so maybe I'm at home. I I started off as an academic anthropologist, always thought that was gonna be my career, did my PhD research mostly in China, and then did a postdoc and the whole thing. And the I guess a through line in my career from academic to corporate world has always been 

Karen Lynch: Pretty much.

Adam Bai: The adoption of science and technology and innovation and everything aside from the technology itself that actually determines whether or not technology will be useful. So I always found the cultural and processed and bureaucratic aspects of of tech innovation super fascinating. So that's what I did for my academic research. I had a little bit of like a I don't know one-third life crisis and

Decided I didn't only want to be an academic, although I still do some teaching and research. And you know, maybe I'll end up heading back to that years from now, who knows? the first startup I helped get off the ground is called nomadic learning. It's since been acquired, but the idea was that we were gonna fix digital learning, which is, as probably all the listeners know, viewers, a terrible experience most of the time, not all the time.

And the our question was, okay, instead of replacing the instructor, how do we focus on the experience of peer-to-peer communication in small groups, but at massive scale? So we helped transform the marketing function and the or the sales function or HR sometimes of a lot of big companies like AB InBev or PepsiCo or IBM and you know, like at one time IBM had like fifty thousand licenses in every single IBM salesperson we were teaching. 

And it was my job to go in and and help figure out what was broken, kind of like a consultant, and then teach everyone at massive scale through content how to fix the thing. and I think that that experience has something to do with why I was so excited about Panoplay from the beginning. 

Karen Lynch: Well, one of the reasons I want to dig into that too, one of the reasons why we selected you for this particular award. So the Industry Impact Award, it is it is it is awarded. We we pow out about it after we saw all the talks on stage and and you know, we knew the judges were were looking at it for something different. We wanted to really choose the company that we thought represented where the industry is headed, what we think we we stand for the future of insights, what we truly believe is kind of representative of.

The future of insights, where we believe we're going, and what is kind of a model company for others in the industry to look at and say, they're tapped into something. others in the company oth others in the industry need to look at them and learn something because it's important. There's something important to tune into. So not that we want everyone to copy you and mimic you, right? You you but but you are a market leader in what you are doing and we think it's noteworthy. So that's why we chose the Industry Impact Award. We chose you for that particular award.

Adam Bai: Yeah.

Karen Lynch: This inaugural year. So tell people what you're doing there, because I think it's really important and worth pointing out specifically.

Adam Bai: Yeah. So so Panoplai itself is I guess we'd call it like a a modern insights platform, which sounds really boring unless you actually think about what we mean by modern. And I think this also dovetails with with your question. So we believe in a lot of things, and I personally believe in these things. I believe in human data collection and human insights, I believe in really textured ethnographic analysis and in-depth interviews and all of that good stuff. I mean, that's the thing I believe most in actually as an anthropologist. I also believe in quant research, you know, properly applied. but our thought was maybe, maybe getting those two things actually isn't enough right now. maybe we need something else. So it's like, what else do we need?

And I think to figure out the missing ingredients, I really went back to my experience helping transform marketing functions and sales functions within big companies. And Neil Dixit, who's the founder and CEO of Panoplai, was creating at the time, about four years ago, was creating this tool along with the developer. I think it had a lot of potential. I think Neil also wanted to help transform the insights industry industry through the application of technology. And at that time anyway, before I started attending things like Greenbook IIEX conferences and you know, getting to know every single person in the insights industry. I was a bit of an outsider, right? I'd I'd been a researcher. I'd consumed insights from insights teams to help marketing teams and to help with my consulting practice, but I hadn't actually like been in the trenches. so so Neil and I were thinking together, okay, where where's the insights industry going? But more importantly, where's the insights function going within brands? You know, of course also within agencies to some degree who are who are ultimately serving brands and, you know, trying to

Karen Lynch: Hmm. Yeah.

Adam Bai: Accumulate budget you know from brands. So it's it's really brands at the forefront. And our answer was that it feels like there are a lot of things that are I don't want to say broken, but but sort of legacy remainders of a of an older time, right? And things that felt important to kind of you could say transform, but we're not consultants. So so provide a platform that that people, practitioners could stand on to direct their own transformation rather than telling them what to do. And

So a few things. I mean, number one, we saw again, everyone uses this term, but we saw incredibly siloed flows of data. and I know everyone's been saying, okay, we have to get rid of siloed data for for 30, 40 years, but we meant that in a very specific way, which is why at a big company is there a team that's looking at social data, a team that's looking at CRM and CR CX data, a team that's looking at quant data, a team doing sometimes qual stuff.

And ultimately it's like the product owner or the executives that have to sift through not different underlying data sets, but radically different ways of perceiving the world, differently structured projects, different sets of tools, different insights. And that seemed like maybe not a good idea. And then the other thing that seemed like maybe not a good idea is just how fractured, call it horizontally, the insights world was. That is, why am I going to go hire Ipsos to do the world's most complex you know, segmentation study, but then I'm not actually pulling through all of the nuance in that study anywhere, loto let alone all the way to targeting. So there were all these like missed opportunities that had to do with the handoffs along that process. And then finally, I think we could see that insights world was changing in both a a challenging way to insights professionals and a a a way that was sort of pregnant with opportunity. By which I mean that 

Karen Lynch: Yeah. 

Adam Bai: We can see insights functions are sort of like to some degree dissolving and reconstituting themselves. It's not that people are getting rid of insights professionals, but the team itself as a kind of order taking and like Instacart type delivery service for insights is is disappearing. I think that's mostly a good thing. but as insights functions come to be more embedded in teams that also contain frontline decision makers.

Karen Lynch: Yeah. Yeah.

Adam Bai: As insights teams start to be merged with big data, data analytics kinds of teams, all that stuff is happening. The question is what kind of tool stack and what kind of approach is actually going to help them be even more effective and do their jobs more effectively? And so we went to the drawing board and we started coming up with Panoplay. And so just really quickly, because I know I know I never actually said this, by by complete modern insights platform, we mean human data collection, we mean the ability to ingest data from any source, whether it's

Opinion data from a different platform, survey platform, or whether it's ethnographic field notes or an interview transcript or a segmentation deck or whatever, just to make that really easy, to put it all into one place, but then to say, hey, that's kind of been the promise for the last 20 years. This is not about putting things in one trash can. This is about actually activating the value of different data sources across those data sources in a really smart and kind of holistic way.

Karen Lynch: Yeah. 

Adam Bai: And then on top of that, what can we do? Okay, cool, like chat with your data in a really intuitive way. I think that's great. But we wanted to go beyond. And on that basis, for each enterprise client, we wanted to build really powerful, nuanced digital twins and synthetic data sets, which is kind of what we've become known for recently. But the one thing I'd I'd emphasize is that none of that is possible in a validated, high quality, predictive way without that underlying commitment to human data and commitment to the role of insights professionals within organizations. 

Karen Lynch: Yeah, and I think that that's what differentiates it from just like a knowledge management center that that many corporations may have, right? They may have they may may have already consolidated much of the data that they have, but they haven't gone that extra layer then to to build build the digital tunes based on it, right? Which requires a separate level of of expertise. so I think that's what you know, it kind of it kind of just went one step further with it, which is, by the way, now we can query it in a very different way. for specific purposes, right? So that's some of the some of the conversations that we certainly had at IAX North America, which is, you know, the debate about digital twins. And I think where I net it out, where a lot of people net out, is, you know, fit for purpose. and and you know, it it has a place on in the researcher's toolbox, which I imagine that's where it is for you. And you seem to have, you seem to have a similar point of view that I have, which is there are many methodologies that that, you know, you have an objective and you find the one that is the right the right one for what you're trying to achieve. Am I right? That's where you stand. Yeah.

Adam Bai: Yeah, tot totally. And you know, I think that for those of us, and I include other companies as well in the industry that are that have a sense of the the work, the day to day work of insights professionals, we're much more interested in in providing a kind of like super highway that improves like the surface of the road and accelerates and adds all kinds of new value to what people are already trying to accomplish, not necessarily current processes, but commitment to rigorous methodology, commitment to human data and human signals, et cetera, et cetera. rather than a real like exit to the future where we imagine that we can kind of like jump over huge, you know, swaths of land by going directly to pure simulation, for instance, which again, I think has has its value. It's something that we also offer, but I don't think that it's a replacement for the entire infrastructure

Karen Lynch: Yeah. 

Adam Bai: Of human insights within within an organization. So our question was, how do we provide a toolbox where it it's not full of you know a million hammers where you know we're we're we're showing people how to hammer screws and nails and everything else, but instead, you know, like the saw and the hammer and the in the wrench plus the power drill, plus the AI powered thing. So you have this toolbox and as an insights professional, you're empowered to to match the right tool to the right task across research as a process.

Because I think too many platforms, too many tools imagine themselves episodically and they forget that research when done well, it should not be like a one and done thing. It's about getting smarter over time, it's about sharpening your focus, sharpening the audience that you're interested in. So over that sort of like long haul, how do you choose the right tool for the right moment to provide value to internal stakeholders, yourself, whatever.

Karen Lynch: And not forgetting the learnings from the past, right? Not forgetting, not forgetting what what you know, if you're working on something currently, not forgetting what you might have learned just a few months ago that is very relevant still. or forgetting you know, the information that the previous researcher in your position learned that you may not have been privy to, but it is a part of the system. I think that historical knowledge is something that.

Adam Bai: Yeah. Yeah.

Karen Lynch: Should always be brought forward and in a system like your building, and again, part of why, you know, part of what we saw in this, in what you've built is the ability for that to be brought forward and not get lost, not get lost or forgotten.

Adam Bai: Yeah, yeah, yeah. Yeah, yeah. And I'd say you if you know, like like whatever theorists of organizational knowledge exchange might call the most valuable type of learning double loop learning, which is to say, instead of pure optimization, where say you have a campaign and you're trying to create content that's gonna do, you know, three percent better, it's gonna engage a few more people, you're actually looking at the results of the campaign.

Along with your fundamental assumptions about the audience that you're trying to target. And then you're trying to think, okay, the next time I do that, the next time I enter into a planning s cycle, which is should really truly be a cycle, how do I get smarter so that the whole cycle is more effective relative to my real goals, as opposed to just like suboptimizing for how many people are going to click on the pink one versus the blue one. And I'm a little concerned that some AI-based solutions will over-index on kind of sub-optimizing type solutions, like how do we get a little more efficient at this or a little better at this and not actually enable users to get smarter about their audiences so that the next time they have a campaign or the next time they have an innovation journey, their assumptions might be different. And then on the other hand, I you know I I I worry about current approaches where if you have like a survey platform that's totally separate from the performance of the thing that you're putting out into the world, you know, the inner the the commercial team might say like, hey, that thing that you told us to launch, it didn't do that well. But that's kind of about the the the extent of the feedback loop. But it what if you could get much more nuanced data about the performance of the thing, feed it back into the system, and then from the very beginning of the process provide strategic guidance to the the the product innovation team and say like, Hey, maybe think about this audience next time, or maybe worry about that feature.

And I just think that that vision is so powerful. And of course, our platform doesn't like automatically give you that vision. It requires some rethinking and perhaps some transformation, new ways of communicating. But we like to think of our platform as enabling that vision of the future more than whatever people are currently using in their tool stack. 

Karen Lynch: Yeah, yeah. So so as you're as you're sitting here talking, I'm thinking, all right, so n you go back and forth. This is where I bring up your two titles, right? You definitely go back and forth between like, you know, chief client officer, which makes sense to me, right? You're serving your clients to this chief strategy officer. I wanna really tease that out because I'm I think everybody again, like we understand what a what a chief client officer does. But tell me what a chief strategy officer does at a company like yours. Because

Adam Bai: Yeah. Huh. 

Karen Lynch: That's very interesting to me, especially right now. You know, I I get a lot of feedback. Obviously, you know, I'm kind of in charge of all the event programming. I get a lot of feedback and a lot of people, you know, I shouldn't say everybody and I shouldn't say a lot, but we do get some pe feedback that people are like, you know, it's too much AI, right? They can't wrap their heads around the the amount of talking.

That we do around this particular technology right now because it's it's too much. It's overwhelming for a lot of people, even though some people are able to say different AI, different AI, different AI. Not everybody can do that, right? And and so you know, I sit here and I think strategically, what are you working on with that title on?

Adam Bai: Yeah, yeah. so I I always joke. Like it in the last startup, I also called myself Chief Strategy Officer. And I think part of the reason is is I love not having a PL I'm responsible for. But but that's that's just that's not the real answer. I also, you know, of course I'm helping to sell stuff and and all that. the I think the reason is that. 

Karen Lynch: Excellent. Of course.

Adam Bai: You know, the cliche like the world's changing. I think the the the conditions of change themselves are kind of like unclear. and I think a a strategist I think you know it really is just two things, right? It's it's it's like investigating the signals that will matter in in data, not just quantitative data certainly, but also just, you know, observation, interactions with people, etc. etc.

Investigating those signals, identifying the signals that are most important, and then making connections to your goals in ways that maybe others not that others can't, but others haven't yet. so so it's it's actually slowing down the rate of execution. I don't mean like going to the dev team and saying like stop working on things, but I mean like to do my job well I actually have to exit the the day to day process of of of selling stuff and building a machine that can sell stuff and you know the the dev process that creates new features and and and and and ask questions like wait why are we doing that? Who are we serving? This seems to be working now, but is this where the industry is really going? Okay, our competitors, not necessarily direct competitors, but but you know firms that are sort of competing for share of wallet, what are what are they seeing? Is what is what we're offering really differentiated

Karen Lynch: Yeah.

Adam Bai: And then how do we tell a story about the relationship between what we're doing and that future that I'm seeing emerge in such a way that it can be heard by people and, you know, give you another totally overused quote, but but the William Gibson quote about like the future's already here, it's just unevenly distributed is one that I really like because I think people confuse strategic foresight with the ability to predict the future. I would say it's almost the opposite. It's kind of like predicting the present.

Karen Lynch: Yeah. Mm-hmm. Yeah.

Adam Bai: By seeing signals, listening to signals that are still a little bit quiet, not visible to the to to most, and then understanding which of them will scale and emerge in ways that shape organizational and individual behavior and which which won't. You know, number one, and then number two is like not being too har too far ahead of of the rest of the market. So so intersecting the market, but not necessarily I mean

You know, we used to always say we don't want to just listen to every single insights professional who wants a certain kind of feature in a survey platform. because we think a lot of those methodologies are good, but we think that a lot of them aren't essential to the future. and so we don't want to be sort of sucked in that direction. At the same time, we don't wanna listen to the futurists and the VCs and just say, like, forget human data, we're going to simulation, because we think.

Karen Lynch: Yeah.

Adam Bai: Not only is the market not there now, but in the future that those forms of of investigation will become really important. So it's it's kind of like not getting the balance right, but serving the needs of the present while anticipating the future and building towards the future. And I guess that's how I like to think of very long answer, like to think of what it means to be a strategist.

Karen Lynch: Just think those two again, the reason why I you know, I even wanted to talk about that is when like those are two like I keep do thinking about like stepping on the gas, stepping on the break, stepping on the gas, stepping on the brake. Like those are very the it's not necessarily that way, but but it those seem to be almost like polarities. Like you are you are really using different different parts of your brain. There's no real great analogy for me here. But they are different skill sets on some level. They feed one another perhaps they serve one another perhaps, but

Adam Bai: Well yeah. Yeah. Yeah. Yeah.

Karen Lynch: But definitely different thinking hats. so I applaud you for being able to do both. I think it's I I think it's fascinating.

Adam Bai: Yeah, yeah. Yeah, yeah. And I mean, I have to say one thing I notice in the industry is that many of my colleagues who are on the agency side or the the you know, the the platform side don't have that much experience selling to brands. Or even if they're used by brands, there's not much understanding of what happens to insights once they leave the insights room and how they're applied and how they relate to a P and L of a business and so on.

Karen Lynch: Yeah. Yeah. Yeah. 

Adam Bai: So to me the future of Insights is largely rooted in understanding of the challenges that brands are facing. Or I mean, you know, political candidates or advocacy organizations, you know, whatever. and so in that sense, the part of my brain that's focusing on what some of our bigger enterprise clients want to accomplish with our platform, I think fits really well with the strategist part of my brain.

But again, those are signals as well from clients. And sometimes the signals I am confident match where the where the rest of the market's gonna end up if they're kind of at the leading edge of change. Other times I hear about frustrations about change that I think are also important to listen to. It's like, let's not assume that change is gonna happen in one direction. Let's not assume that it's gonna happen in two seconds. You know, organizations are slow moving beasts and we have to help them.

Karen Lynch: Yeah.

Adam Bai: Actually transform if we want to have a future, if we want to have a market. So yeah, it's about it's about, I'd say rather than striking the right balance, it's almost like oscillating between an empathetic view of where like an organizational insights or marketing or product innovation or you know strategic foresight professional is and then where I think that we need to get to as an organization and the technology that we're offering. 

Karen Lynch: Yeah. Now was there a moment when you and maybe it was deliberate from the beginning, and we'll talk about kind of the journey of the company when it was glimpse to now, but was there a moment when you were like, This is more than just kind of a tool or a platform? This is transformative. Like talk to me about that.

Adam Bai: Yeah. Yeah. Yeah, yeah. Well, I think that Neil, if I can speak for him, had a very good idea, which is like why does coming from Survey Land, you know, Taluna, Lucid Synth, you kind of two two different sides of Survey Land, why why is it that surveys are so complicated? Why is it that there's very little sense of of how survey data is used or applied within the organization. Can't we s just simplify things? and then also flip side of that is can we collect more unstructured data, so human language and emotion, and can we code it more effectively? I mean, it it that's almost table stakes now, although it's not that easy to build a a good sort of coding open end engine. But tons of companies do it, but at the time there actually weren't very many at all. It was a real cutting edge thing to do before generative AI to develop

Karen Lynch: Yeah. Yeah. 

Adam Bai: A natural language processing engine that would reliably code open ended data. And there were tons of companies, as you know, that specialized in that, and that was their competitive advantage and so on. So so Neil's idea was let's simplify the instrument and the methodology, but render more complex and nuanced and so on the the kinds of data that we're getting in in in return. And I really like that idea. But then I think both of us together started thinking about where where that would fit.

In an insights team or an insights you know human data gathering or analysis process. And it's like, okay, that's a couple signals, but then what do you do with them? And how do you read them alongside other signals or the longer survey or the brand tracker or the whatever? And then your mind starts kind of exploding because you're like, I don't know. Like that's that's kind of what insights teams do. But then but then the next step is to say, well can we can can we actually simplify that landscape?

 And make it much more effective from the point of view of like an insights professional or someone you know creating insights without that title, whether they're a marketer or a product person. And so so then the next thing we started thinking about is can we analyze data in a more compelling way? And this is sort of just before like chat GPT became a big thing, we were investing a lot of time and money in exploring the possibilities there. So I don't know, three plus years ago, three and half years ago. And 

It's like, okay, chat with your data, that's good, because that gets us closer to hooking into real decision making processes. And it maybe makes real that kind of eternal ambition of platforms to democratize data and access to data and insights. Because you don't necessarily need to understand MacDiff in order to ask questions ask questions about one, you know, in a in a simple interface. And then we started thinking, okay, but we have we're starting to like

Karen Lynch: Yeah.

Adam Bai: Play around with the outputs and the decision-making processes and the way we're hooking into them, but what about the inputs? Shouldn't we actually like input the sources of data that matter at any given moment to that decision-making process? So then we started layering in the ability to ingest surveys and we realized, whoa, actually this is complicated because there are all these different platforms. Surveys are complexly coded.

And we just as a matter of principle don't believe in ingesting like summaries of surveys. we need to be ingesting the raw data. And weirdly, that's still a fairly unique thing that we do in the industry because it's so hard. you know, just as an aside, Gen AI is really good at ingesting like a deck. It's it's not good at ingesting raw data from a survey. But if you want to look across data sets, imagine you have a deck that a shopper team's created and you have a deck that, you know.

Karen Lynch: Yeah. 

Adam Bai: Marketing communications team is created, the segmentations, the goals are totally different. And then you have summaries of the findings in each. They're not commensurable, right? They're they're not translatable across. So you end up with teams who can't even talk to each other or make decisions together. But the underlying data, much of it, you can actually bring into dialogue, but you have to have it, right? The quant data and the qual data and you have to have it configured properly.

And then maybe you're starting to see certain kinds of overlaps and and actionable decisions that that can come out of radically different ways of looking at the world. So that was the other thing, is like we have to be able to ingest quant data and qual data, but the quant data has to be ingested properly. 

Karen Lynch: Yeah. Yeah. So to me that sounds like a very kind of iterative journey, right? Of like, but you know, what about this? What of this? What about this? What of this? So how long would you say it kind of took for this this journey to take place? Like was it three and a half years, or was it did it happen quickly or did it happen like, you know, slowly then all at once?

Adam Bai: Yeah. Yeah, I I'd say it was we were toggling between a kind of long term strategic vision about where we thought the world was going and the best way to to serve the the the world. and then a kind of product led vision where we were layering on functionality that we thought was was moving us in the right direction. And maybe, you know, say it took like a year, year and a half to kind of fundamentally transform our focus from

Karen Lynch: Yeah. Yeah.

Adam Bai: We're gonna be a cool little survey platform to we're gonna be a kind of enterprise engine for ins insights and the power of insights. and y you know, that was one of the reasons why we changed the name from glimpse to panoplai, which is that glimp glimpse implied a kind of a glimpse of the current moment, like a glimpse under underneath the covers underneath the hood, whatever. whereas Panaplai, we like that it it had a different well, it doesn't mean anything 'cause it's a fake word, but

Karen Lynch: Yeah. 

Adam Bai: It it it you know, it it it I think I think most people think of Panopla as as related to panoramic, which is right. to to Panoplai, which is also right, like a collection of things, and then Neil's favorite is in I think in ancient Greek Panoplia was like a collection of armor that you would wear into battle. Like it it protects you. so that's where the name came from. between us and all the listeners, a big other reason was that Glimpse was a SEO nightmare because there were really big glimpses in adjacent industries. but let's just say it it was all because we we wanted to change our our strategic focus.

Karen Lynch: Yeah. Well, what I like about it is I like the bravery, right? I like a name change that's like, you know what, we can do this. And and I think that a lot of companies, when they're starting out, they like they're like, no, we've done this. And then if they have second thoughts about it or if they've like I don't know that they pivot, you know, and and I think that I think that a pivot is one of those valuable lessons in a startup, you know, that that pivoting is okay. It anyway, so that that to me is just something that that can be out there as a lesson for an entrepreneur. You know, like everything is okay.

Adam Bai: Yeah, yeah. pivoting pivoting is okay. And I would say that there are kind of two types of pivots. and I feel like in a in a lot of ways we've stayed really consistent when it comes to the primary type of potential pivot, which is that we still imagine serving roughly the same set of people, and we still imagine helping them achieve roughly the same set of goals, and we still have a really consistent vision of where we think the industry is going. But the way we got there, included a lot of of sort of local or tactical pivots, especially in terms of platform features and and things like that. And you know, at a certain time there were there was enough change that it felt like there was like this qualitative shift. Like really our center of gravity is something else now, but we got there along a road that stayed pretty straight, I guess I would say.

Karen Lynch: Yeah. So what are some other lessons kind of learned on your journey so far?

Adam Bai: Yeah. well, I mean, a million. I guess I guess I I I guess I guess I would say I mean to be really honest. I mean I think I think in a lot of ways we were too early to the industry with the idea of digital twins. we knew that they'd become important. We didn't know the term digital twin would catch on as it has. You know, we thought maybe AI persona or or or something else would. 

Karen Lynch: Ha ha ha. Mm. Mm.

Adam Bai: At the beginning we had super early adopters who were interested in pilots. Now, of course, some of those super early adopters, big brands, have become enterprise customers, which is great. but nobody was running around asking for digital twins unless they happened to hear one of my talks and somehow agreed with something I was saying. synthetic data, also I think when we started creating synthetic data functionality features, was misunderstood.

Too easily dismissed. I think a lot of industry insiders thought it would just kind of disappear. we knew that it it wouldn't, but we didn't at the same time want to enter into that all or nothing debate because we actually we don't believe in it. We weren't out there saying like forget real respondents. And you know, there are platforms, really prominent ones, that that that was their marketing and messaging, right? Like, hey, forget human data, here's the future. We would never say that 'cause we don't believe in it, but

Nevertheless, like when I was out there, you know, early on at at Green Book, I think it was twenty twenty three, speaking with HubSpot, one of our our actually our earliest client and still a big client, and it's such a cool use case and everyone, you know, tons of people jammed in to listen to it, but not that many people were like, Cool, let's do enterprise digital twins right now. They were like, I'll keep my eye on that. That sounds interesting, or my boss asked about it.

Maybe we can use you for surveys first and then layer on some of the AI analysis capabilities and then and then maybe we'll consider it. And now I would say everyone wants to at least try digital twins. There are tons of platforms that have started business in the last three months, six months where they're they sort they're sort of like digital twin native or they think that's what they're doing. some I think do a good job, some it's really like a crazy, a crazy job that isn't so good for the industry. and so my lesson is

Adam Bai: You want to be too early rather than too late, but you have to structure your business around the fact that you're a bit too early. so I think we learned a lot about what that meant in terms of revenue streams and you know, f to some degree fundraising to keep ourselves going. And so it was a hard won lesson, but the result of it is that we could choose to be profitable, you know, any given month and we know we're gonna be here forever.

And we know that we're able to expand what we're doing without additional investment if we choose. we know that we're really good at you know, project management around surveys and and the sort of nuts and bolts of running an insights company. and I think that had we not kind of gone our own way then and still maintained the talk track and the the feature development around things like digital twins, but also kind of enhanced the the the bread and butter stuff.

We wouldn't be as as complete a platform as we are today. But maybe maybe the lesson was a little harder early on than it needed to be. And I think that if entrepreneurs can keep that balance in mind, like we're building for the near future, but we also have to serve the needs of today. And then we have to think about like where our primary sources of revenue are today versus in the near future, and to just just calibrate a bit. So that's it, that's a big one. And then

Karen Lynch: Yeah, yeah, yeah.

Adam Bai: I that was a really long one. I'll give you a short one too, which is the thing the thing that I've always focused on as an academic and as an entrepreneur. And that is how do people use the thing you're creating? And again, it's like kind of eye roll, like of course, but also of course, as everyone knows, very few platforms or agencies actually listen carefully, think creatively, and try to find solutions that can provide value given where clients are.

Where they want to get and also across different kinds of sets of processes within an organization. So I'm not gonna you know, it's it's part of it is thinking ahead of your customers, but part of it is just like listening. And by listening I mean watching, asking questions, you know, of course using Panoplay to ask clients or prospects questions, things like that. But but really it's just like having those kind of empathetic conversations like, hey, we're  where do you think your job's going? how do you feel about that merger of your function with the with the big data function? Hmm, you don't like that you're gonna be running all the surveys now and instead you're gonna be doing a little bit more enablement of other functions. Why don't you like that? What are you worried about? wait, you know, I think that's cool. Other people but we're afraid of what people will do. They don't, they're not trained like we are. they're gonna get crazy outputs, crazy responses, and based strategy on it.

Our role in the organization is to take a step back and look kind of panoramically and provide really sound objective strategic advice. So it's like, okay, cool. How can we help you do that with our platform rather than, hey, it's the future man like that, you know, marketing's just gonna use a LLM to replace you. so I think I think in listening not only to organizations but also to individuals, we were able to think about the kinds of features and functionality that would serve the the deep underlying needs of those.

Karen Lynch: Yeah.

Adam Bai: Professionals. So that's the second lesson.

Karen Lynch: I mean, I think, yeah. I I think listen I think research companies in general need to do a really good job listening before they build, or while they're building throughout the through and after they've built, let's keep going back to the listening. you know, I I I really I love how much we're talking about kind of the future of insights just kind of woven throughout this because you know, obviously that's our tagline. the future of insights.

Adam Bai: Yep. Yeah.

Karen Lynch: What do you think is next out there? How long do you think we're gonna be living in this kind of AI world at world of disruption? W have you kind of speculated as to what else is coming? What other technologies are coming or or are we settled? Do you believe we're settling in this? I think I think it was Jordan Harper at at Qualtrics had said to me, like, settle in. We're we're we're with AI disruption for, you know, at least the next decade. Like it's gonna be a while. are you are you imagining something else coming our way in the next couple of years, or you think we're settled in for a while?

Adam Bai: Yeah. Yeah. Yeah. I mean I think it i it it depends on who you are and where you're situated in the economy. from a brand point of view, that's probably true. wholesale transformation around AI, such that AI sort of disappears into the fabric of what the company's doing for the next 10 years. I think for platforms like our like ours or agencies, there's gonna be a little bit more compressed timeline. 

Karen Lynch: Yeah. Yeah. Mm.

Adam Bai: And it and it's not the case that after two or three years we're gonna stop innovating. But I th I do think that things will stabilize a bit, as they they've already sort of started to around concepts like digital twins or synthetic data. Not b very beginning, because I you know, one thing I always say is I think that there's an enormous standards validation and and and you know crisis, quality crisis around synthetic data and digital twins because nobody knows what a what a good one is. And

Not only that, but there aren't even clear standards that you can use to evaluate evaluate how good one is, you know, across different use cases. And it's a wild west of claims that platforms are making, which it you know, and let me be clear, it's not that I'm accusing like other platforms of lying or whatever, but it's the the statistics and things gets get that get cited sometimes apply only to one like narrow use case. And then what happens when you take the thing and apply it over here? Like how do you know how accurate it's gonna be, how useful it is, all that stuff.

So so that's one thing. I mean, I think that from the point of view of providers and platforms, the next maybe two years is gonna be incredibly intense change. And then there'll be a bit of a stabilization because that's how these markets tend to work. I think from the point of view of brands, there's gonna be a long haul transformation around these technologies. I mean, if you look at the movement to cloud or digital transformation, whatever that meant, that was like twenty years. This one's gonna be faster, but

Karen Lynch: Yeah.

Adam Bai: Organizations, I think for some really good reasons, don't don't change that quickly. so so yeah, we'll see that. the other thing, it's funny, like I I think it was another podcast. Somebody asked me like in five years or something, what what are what's a good a pl a good platform gonna help you do? and I started thinking about it and I was thinking, huh, actually I give the opposite answer I would have given five years ago. So the answer I would have given five years ago is create more seamless

Karen Lynch: Hmm.

Adam Bai: Processes and more efficiencies and more speed. I think to some degree those things, you know, they'll they'll be hard within enterprises, but those things will become sort of table sticks with more agentic workflows and so on. The ability to look across different forms of data. I think what's going to be at a premium is the ability to apply human judgment to those seamless agentic workflows at the right time in the right way.

Karen Lynch: Yeah. Mm-hmm. 

Adam Bai: So I think the platforms that are gonna be winning five years ago are actually gonna be injecting some degree of friction into the process on behalf of somebody like an insights professional that's able to exercise judgment and make sure that the workflow isn't optimizing for something that they don't want. and I always I you know, early on when I when I started talking to like C suites and things about AI because it was I was interested and it's a thing I'd studied, there there's an early ChatGPT story from

Karen Lynch: Yeah.

Adam Bai: Years ago that I always liked to share. And of course LLMs are much more sophisticated now. But the story was that they were using one of the early LLMs to win a boat racing game, like a video game. And it seemed to humans that the goal was to get to the finish line quickly and win a lot of points along the way. But actually the game was programmed such that you never actually had to get to the finish line. All you had to do is go around in circles winning points. So if you set your own boat on fire and you went in the circle and won points, you'd win the game.

Karen Lynch: Hmm.

Adam Bai: So, of course, that's what the large language model did. Seamlessly, quickly, without any human intervention, the boat got set on fire. So I think we need a new generation of insights professionals who can take a step back and ask the question: hey, do we really want to set our boats on fire? Or maybe we want to get to the finish line? Huh, what is the finish line? Hmm, maybe our systems aren't actually helping us get there. Or maybe our systems are revealing a new way to imagine the finish line as a result of AI.

Karen Lynch: Yeah.

Adam Bai: Led plus human research. And so to me, that's kind of like what I would like the core role of the Insights professional to be in the future. The person who can apply human judgment by injecting some friction into a process that's otherwise automated, otherwise AI powered, both in terms of the data that's being gathered and in terms of the decisions that are being made. So when I think about Panoplai, where are we going to be in five years, I like to think

Karen Lynch: Yeah. Yeah. Yeah.

Adam Bai: Hey, we're gonna we're gonna be throwing some wrenches in in all of the seamless stuff because we're gonna propose to our clients, hey, we think that you should be injecting yourself here in that like generative AI, you know, agentic feedback loop. And we're gonna give you the tool the tools to do it. And you're gonna be the hero in the organization that actually says, Hey, we don't need to stop the automation, but we need to rethink it for these very human and very strategic reasons. so so I you know. Anyway, that that's a much longer version of the the answer I gave.

Karen Lynch: Cool. No, it's very it's no, it's cool. It's cool to think about. it's kind of a a more in-depth way of saying, like, are we even asking the right question? Right. Which is a very simple way, a simple way of of stating that. Like when it comes to the research questions that we're asking, are we even are we even doing it the right way?

Adam Bai: And and and yeah, yeah, and t sor totally and and I'm sorry to speak over you. I was gonna add, so so yeah, so it's like it's like number one, are we asking the right questions? And number two, are we truly empowered to ask the leadership if they're asking the right questions? that is I want inside professionals to be like the shepherds of the right questions. You know, if you imagine a bunch of like sheep that you have to like find and herd in the right direction, it's like

Karen Lynch: No, no, no, you're good, you're good. Yeah. Yeah.

Adam Bai: You know, and historically for all kinds of reasons, I think all too often insights professionals were not asking whether the leadership was asking the right questions. They were answering the questions that were asked. and I think that it's even more imperative now for them to be empowered, for us to be empowered to do that. and and, you know, I'd like to build a platform that helps helps them, helps us do that.

Karen Lynch: Yeah. Yeah. Yeah. Yeah, yeah. Good stuff, Adam. Good stuff. Thank you so much. two questions for you. One, and then we're gonna wrap with the last one. So, you know, I am sure there was a moment when you were on when you when we were calling you on stage for this award that you were thinking, damn, I really wish I won $10,000. So I'm not even gonna pretend that that didn't happen. But tell me what it means that the award you did win was industry impact. What did that mean to you? Industry Impact. 

Adam Bai: Yeah, yeah, yeah. Well firstly I'll say I hate checking baggage so much that winning the giant check would have been a big source of frustration for me. So I'm s I'm so I'm so happy we I didn't win that ten thousand dollars for Panoplai. No, I'm just I'm I'm just kidding. I'll you d any anyone can write me a giant check and I'll I'll take it. yeah, industry impact, I mean I think it's because

Karen Lynch: Okay. All right then. Yeah. Yeah.

Adam Bai: Well, I mean, we think of ourselves as intersecting the trajectory of a changing industry effectively. And we think of ourselves as understanding that trajectory, not not in a you one and done type way, but in an evolving way, perhaps better than other platforms. I'd like to I'd like to think that's one of our competitive advantages. And so to be recognized for anticipating where things are going and seeing clearly where they are now.

And building a business and a platform to meet the needs of the present and the future felt really, really good. And you know, I this is not just to, you know, blow smoke or or to praise you for b because you're you're you're on the podcast with me. I mean, I think that Green Book largely is is trying to do the same thing, which is of course to serve the present needs of of of you know constituents and members and have great conferences and so on. But Green Book succeeds in so far as it can

See the future maybe a little, a few, a few minutes ahead of everyone else, and can design the world, design experiences to help introduce its members to the future that's emerging. And so for an organization like Green Book to say, Hey, we think that you're you're you're like a fellow traveler, like you you kind of get it. The conversations that we're having with our membership, with the rest of the leadership of Green Book, it what you're doing resonates. That felt really, really meaningful to me.

I I was up on stage ultimately with with Kelsey and Courtney who are with me who are who are really amazing Panoplai leaders and Kelsey who is a CS leader but also is thinking about product and and and a million other things for us, she said, you know, I'm happiest we won this one. And she didn't mean it as like don't worry about the big tech kind of thing.

She she said, like the this one actually, this award, this industry award, actually feels like the most validation for what we've tried to accomplish. And I said, you know what? You know, I I agree. And actually, like if you go back and not that anyone would want to, but if you go back and look at my five-minute nomination speech, which of course, in classic Adam fashion, as I'm sure everyone will will understand after listening to this podcast, I can sometimes talk too much. I I went over time. But but in that five minutes, really I talked about where we thought the industry was going in our vision for the industry. And I thought I talked about empowering and enabling insights professionals wherever they end up sitting in whatever organizations of the future. And that's the thing that's kind of like our North Star. So to be recognized for saying those things, but also for for doing them. And you know, I know Neil, like again, not to speak for him, but the guy's had like 20 plus years, some people have lied longer, but his whole career has been within research and insights world.

And he's helped lead a lot of technological innovations for companies like like Tuluna, Lucid Synth, et cetera. I know Neil sees this as his opportunity to provide a platform that's kind of like on on our terms, on his terms, of course, for clients to use, but it's like, here's kind of what we think insights could be, and here's a platform to help insights get there. so that's why it was such a meaningful award, and that's why it was so meaningful coming from from Green Book and from you in particular, and why I'm so happy to be on the podcast.

Karen Lynch: Well well, I'm so happy to have you here and I'm so glad. And you know, I think that I will not forget when you when I you know I congratulated you and you you said the words to me like, you know, I feel seen. and and it it was

Adam Bai: Yeah yeah yeah that's so funny. I d I did. I felt like I felt like like listened to and seen kind of like by the industry. Yeah, it was like a big moment for me. Yeah.

Karen Lynch: And and that just brought me so much joy. And you know, when we sat when we sat in the room as a leadership team, you know, afterwards and discussed it, it was it was clear to us because we do share that vision and and we do want to usher in the future of insights because we really care about the industry. So so thank you so very much for all that you're doing and congratulations on on on the award, but also on on the future that we know and are confident that you will have. So last question.

How can our listeners learn more about you? How can they reach you, learn more, read up? What what what are all the ways?

Adam Bai: Yeah, yeah.Yeah, yeah. So I mean, the main way, of course, is just to go to the website at Panaplay.com. We have a lot of case studies and all all the rest there. You can learn about our offerings. reach out for a conversation with me. I mean, I I'm always told that I need to make myself less available, but I don't I don't actually like doing that. So if if if if listeners or viewers have a a big question about what I've been talking about, I'll try my best to

To respond or to set some time to chat. Of course, you can, if you're interested in using the platform to solve some of your business challenges and you're listening, feel free to reach out for a demo, you know, with some of our wonderful SDRs. And then I think that there are a couple other places you could go. So one is it's a slightly outdated already, which is crazy because it's only like five months old. But we we published a huge white paper that was written by a kind of industry.

Insider from a slightly more objective point of view about our vision for the future of digital twins and synthetic data, enterprise adoption and validation. and a lot of our ideas are at greater length in there and intertwined with case studies and real data points that people can can dive into. So that that that white paper is downloadable from the the website. And believe me, I'm not usually like a you'll love the white paper type guy, but it's pretty it's pretty interesting as far as white papers go.

And then the last thing is I think both Neil and I and increasingly other members of our team like Kelsey are trying to speak and write widely. so there are articles from, you know, Harvard Business Review to Newsweek to Ad Week, to AdAge, where over the years we've set forth parts of this vision relative to where we think the industry is. and you know

Go ahead go ahead and and and and take a look. There are links both on my LinkedIn page and on on the the Panoplai website and we'd love to just keep the conversation going.

Karen Lynch: Well, thank you so much for sharing all of that and for joining me today, Adam. What a pleasure to have you here.

Adam Bai: Yeah, yeah, no, it's really it's really cool to represent Panoplay in this context. It's really cool to be part of the podcast and of course it was very cool to win the award. So yeah, thanks to Green Book, thanks to you.

Karen Lynch: All right, well, what a pleasure. And thank you to you, all of our listeners. you know, I say this every week, but but we show up time and time again because of you. So thanks for tuning in. Thank you to our editor, Big Bad Audio. We love what you do for us. and I, you know, I cannot thank you enough. And of course, to to Bridget and to Emma, who will be taking over in the producer role. Thank you guys so much. We will see you next time on the Green Book Podcast. Bye-bye, everyone. There's that wave. I love that atom waves too.