Preeti 0:00 So the SUSHI conveyor belt was a way to visualize balancing, like you said, the speed of insights and then the durability of insights. In early stage research, that's really hard because you wanna move fast, but you also wanna make sure that if the team happens to pivot or change direction, that your insights are so relevant and they're not suddenly going to go stale. So if you think about your research insights as a meal that you're serving, typically, when we have, like, evergreen, durable insights, we think of those as coming from large foundational pieces of work that are sort of naturally slower to produce. And for those pieces of work, researchers, I think, slower to produce. And for those pieces of work, researchers, I think, tend to deliver those insights like an all you can eat buffet. There's, like, these lengthy, dense reports that come out all at once, go underwater for four months, and you show back up. I think so that is changing with the use of AI now, but I think traditionally that's how people do that research.

Molly 0:51 Hello, fellow insight seekers. I'm your host, Molly, and welcome to the Curiosity Current. We're so glad to have

Stephanie 0:57 you here. And I'm your host, Stephanie. We're here to dive into the fast moving waters of market research where curiosity isn't just encouraged, it's essential. Each episode, we'll explore what's shaping the

Molly 1:10 world of consumer behavior from fresh trends and new tech to the stories behind the data.

Stephanie 1:16 From bold innovations to the human quirks that move markets, we'll explore how curiosity fuels smarter research and sharper insights.

Molly 1:25 So whether you're deep into the data or just here for the fun of discovery,

Stephanie 1:29 grab your life vest and join us as we ride the Curiosity Current. Today on the Curiosity Current, we are joined by Priti Talwai, a product researcher and strategy leader whose career has focused on helping organizations understand emerging technologies long before they become everyday products.

Molly 1:50 Priti's path into research is anything but conventional. She studied architecture before moving into technology research and has spent more than a decade helping teams explore AI and other emerging capabilities long before they became fully formed products.

Stephanie 2:04 Throughout that work, Priti has developed a unique philosophy of research. The further into the future you go, the less your work is actually about the technology and the more it's about understanding timeless human needs.

Molly 2:17 So today, we're exploring what it really means to research products that don't yet exist, how researchers can influence decisions even when those products never launch, and why human centered thinking may be the most important capability in the AI era.

Stephanie 2:31 Priti, welcome to the show.

Preeti 2:33 Thank you. It's so great to be here. means to research products that don't yet exist, how researchers can influence decisions even when those products never launch, and why human centered thinking may be the most important capability in the AI era.

Stephanie 2:31 Priti, welcome to the show.

Preeti 2:33 Thank you. It's so great to be here.

Stephanie 2:35 We're very happy to have you. As Molly mentioned, your path into research started in architecture, not marketing or human computer interaction or even psychology. Looking back, can you connect the study of physical spaces to the way that you understand people today?

Preeti 2:52 I studied architecture, and my background in the built environment and architectural theory and research has really deeply shaped how I think not just about people, but also about technology. And I think it's done that in two main ways. I think first is that architecture is really about systems in larger context. And one of the things that I've brought to UX research is understanding people's needs and their use of technology holistically, really zooming out to think about the whole ecosystem, not just one feature or one task or one product. In architecture, we talk about things like thresholds and movement and liminal spaces, and it's a very, like, alive and active research. And translating that into tech research, I think I've always tried to make sure the teams I'm on are thinking about people's broader goals and how a technology fits into the complexity and the messiness of someone's real life, the seams and the transitions between products and features, which I think is not often done in industry just because of the way that organizational structures are often siloed. And so so one way is really zooming out and understanding messy, holistic, real lives. And then I think the second way that it's shaped me is by training me to think in terms of language and frameworks and taxonomies, which make up a lot of what the contributions of scholarship in the humanities are. If you read theory in any of the humanities, um, and architectural theory in particular is very multidisciplinary. So it takes from anthropology and sociology and philosophy and making sense, again, a lot of that complexity of life. And so a lot of my own contributions over the nine years that I was at Google were really going into these deeply nascent and ambiguous spaces and helping them feel tangible through a framework or through a vocabulary, and that has certainly been shaped by the academic training that I had.

Stephanie 4:43 No. That makes a ton of sense. And especially, I think, when you think that, of course, architecture is a human endeavor. And I love that line about seams and transitions that really got my brain turning. It's really fascinating. Well, let's switch gears a ped by the academic training that I had. No. That makes a ton of sense. And especially, I think, when you think that, of course, architecture is a human endeavor. And I love that line about seams and transitions that really got my brain turning. It's really fascinating. Well, let's switch gears a

Molly 4:56 little bit to talk about your anticipatory research, which is always so fascinating to me. Like, what's the research crystal ball we're all looking into? And so you've described that your work is researching products that don't really exist or don't exist yet. What makes this type of research so fundamentally different from traditional UX or product research?

Preeti 5:17 Yes. This is a good question. So I think there are two categories of differences I think about. The first is difference is in research approaches, like how you actually do the research. And there's also differences in how you navigate the organization in doing that sort of research. Sort of at a high level, in traditional product research, you're typically working on something that exists that has users you can probably ask directly about, and it probably has some sort of road map or plan that you can align your research to. With futures research, I think a big difference is that your users may not have any mental model for the technology or the product that you are thinking of designing. They may have folk theories that might be informed by sci fi or increasingly informed by social media. Right? But they don't really have a good grasp on it, and none of us have a really good grasp on it. Right? It's new. So change aversion is a really big thing as well as anchoring in the present. It's really hard for all of us to sort of think beyond our present frame of reference. So that takes sort of creativity to navigate around. And then, of course, when there isn't a product or even sometimes a fully functioning technology yet, you can't get direct reactions to it. You end up focusing a lot more on the needs of people and the experience, and your methods need to be resilient to a lot of organizational change because these early stage products move really fast and they pivot fast. And so you have to be not just aligning to the roadmap, but you have to be part of charting what that road map even is and driving with your insights and be really comfortable, like, being in the driver's seat. And then I think in terms of navigating an organization, it's also a lot less straightforward. It's often hard to get buy in for something that may not show immediate impact. I have heard many times in my career the notorious faster horses quote. Is that are you guys familiar with that?

Stephanie 7:03 Do you wanna repeat it just for the audience, though? rganization, it's also a lot less straightforward. It's often hard to get buy in for something that may not show immediate impact. I have heard many times in my career the notorious faster horses quote. Is that are you guys familiar with that? Do you wanna repeat it just for the audience, though?

Preeti 7:06 So faster horses is a quote that I think is actually misattributed to Henry Ford, but it's the idea that I have heard that it's there's no actual proof that it's him, so I'm not sure. But that's often attributed to him. And the idea is that when you're building a car and you ask people what they want, they would have told you they want faster horses. I have many pet peeves with that quote because underlying that, if a researcher ever did that, which they wouldn't because we don't ask people what they want, but if they were to do that, the underlying sentiment there is that people want speed. That's a good takeaway, and that's what cars provide. But it's often used I have often had it thrown at me as a way to say, hey. We early stage research isn't really viable or is not really trustworthy because people don't know what they want. That is something that a lot of education with stakeholders and creativity did to navigate that is something that you need. And I also think, you know, it's often hard to get a seat at the table when people feel like it's not time for research yet, which is also that skepticism around until we have something we don't really know if there even is something to research, what would you even do? It's really to me about building trust with your stakeholders as a research leader. I feel like doing Betts research is a very human and collaborative endeavor. You really need to have deep trust with your stakeholders, your leadership so that you can do what you wanna do and you can, like, go out and be a little bit of a research cowboy because that's what this sort of research is. And I think that you also need to be more conversant in the technical side of things with emerging technology than traditional UX research. So you can bring people along the journey and they you can both speak the same language. You need to be a storyteller to help people understand the human impacts of these technologies. You need to be an advocate for responsible technology. I think the theme here is that I think of emerging technology or futures research as being a lot broader than you might think of traditional UX research being. You end up wearing lots of different hats like a strategist, an heritologist, a researcher, a critic, and all of that sort of wrapped into one.

Stephanie 9:05 Priti, you started talking about this a little bit already, but I'm curious if you could dive in a little more. When there isn't a product or an interface or even a clear use case yet, what do you anchor the research around? I mean, you mentioned needs, I think, is one area, but what other anchors do you use in research in these situations where what are we evaluating? Probably nothing. urious if you could dive in a little more. When there isn't a product or an interface or even a clear use case yet, what do you anchor the research around? I mean, you mentioned needs, I think, is one area, but what other anchors do you use in research in these situations where what are we evaluating? Probably nothing.

Preeti 9:24 So I think there's a couple things. Like you said, first, human needs. Um, and I think going a little bit deeper into that, I think needs tend to be really durable and unchanging over time. And you wanna look at also, I think, the gaps where existing technology is failing these core needs. So as one example, a core need that I found in my research, like, eight years ago is the need for people to be able to recover from curve balls or unexpected changes in exceptions to the routine. Routines are easy, but exceptions are hard. So live in an age where I can, like, vibe code an app in an hour, but it's still super manual to let's say, you miss a flight to have to find a new flight and then, like, reschedule your entire day. That is still a really stressful process even though that task is sort of more contained than, like, by coding an app. Right? And so same thing if you're, like, trying to recover from a financial emergency or, like, modify my diet when I'm traveling. Those are still not really addressed needs. And so there's often, no matter what domain you're looking at, I think probably these deep pockets of unaddressed needs and helping point a team towards the sorts of problems they should be solving, I think, is one thing to definitely anchor on. The other thing is that I often anchor on is the experience. So, like, what will the user's life be like as a result of using this technology, and can you simulate that? So a good example that I like is if you're trying to do research on consumer home robots and you wanna understand what might people wanna use robots in the house for. You can do a lot of that research without ever having a working robot prototype by just simulating the end product of what a robot would do. For example, let's say they're loading the dishwasher or the robot's folding your clothes or whatever the task is. Right? So what does it feel like to come home to the breakfast dishes that are totally done, but maybe a cup is chipped or broken? Maybe that's what it would be like if a robot did it. What does it feel like to come home to your laundry folded and put away in your closet? Even if in a study, it's just a human who's doing that, who's simulating the robot, you can learn a lot of things about these use cases. You can learn about people's expectations of performance and quality. You can learn about their need for things, control and feedback. Right? You might learn, hey, actually, it's super important for people to give instructions about where clothes go in the closet or load the dishwasher a really specific way, and you'll learn about what delights them and what concerns them. And that can inform how you do build that technology even if zero robot technology exists at that point. And I would even go further to say that even when you do have a working prototype, I think it's really wise to separate out studying how people react to the experience and how people react to the technology. So you don't actually wanna conflate those two things so that way people react to the resulting experience tells you whether you're solving a real need. Do people even want their laundry folded and put away? And the way people react to the technology tells you how they feel about, like, that enabling mechanism. Like, how do they feel a robot doing that thing? You could love the idea of your clothes put away and be really creeped out by the idea of a robot in your house or vice versa. Right? And so I think actually not immediately having the technology be part of a study or part of the research is actually a really crucial part of doing emerging technology research. t thing? You could love the idea of your clothes put away and be really creeped out by the idea of a robot in your house or vice versa. Right? And so I think actually not immediately having the technology be part of a study or part of the research is actually a really crucial part of doing emerging technology research.

Stephanie 12:49 No. That makes a ton of sense. And then it I can just imagine that, you know, in these scenarios where I love the experience, but the enabling mechanism is creeping me out that it helped you focus on what you have to overcome to make this a viable solution.

Preeti 13:03 Exactly. Exactly. And I think a lot of times for all of us, when we encounter something new, it's not always obvious to us why we're reacting a certain way. Right? It's not obvious to us if I were to see a robot putting away my dishes or my clothes. I might say, oh, I don't like that, but I may not even know why I don't like that, what part of it. Right? I can't even tease that out myself. It has to be teased apart for me, like, in the structure of a study or the way that you do that research. But, yeah, exactly. It helps you know where you need to focus and what you need to improve without mushing it all together in one thing.

Stephanie 13:36 Makes a lot of sense.

Molly 13:37 Yeah. It's fascinating the context of the solution as well. Because especially as, like, a mom a busy working mom of a young kid, I would love my laundry put away, but a giant robot in my house? I don't know. I fall into that camp for sure.

Preeti 13:50 Yeah. Exactly. And so interesting. Right? Like, we don't even need to have anything working or a prototype to engage with. Right? But it's interesting if this were the feedback from a study that you did, you might be like, oh, well, people maybe it's not the use case that we need to focus as much on. We really need to think about the form factor of this robot. It evokes something that's big. So what if actual was

Stephanie 14:09 you need a little cute robot, Molly?

Molly 14:11 I also anthropomorphize my robots. I have a little robot vacuum, and he's got a name and he's got a little personality. And despite my son loves watching him go by, we call it a him. I don't know. Maybe that's me.

Preeti 14:24 That's fascinating. Yeah. It's like so much to learn without, um, without needing the technology in front of you.

Molly 14:30 Yeah. Let's talk about teams that wanna move fast on products. Especially in product evolution and wanting to capitalize on trends, teams often feel like they have to choose between getting the stuff done and then also having research that lasts and that is actually going to be meaningful long term. And you've argued that that's actually a false trade off. So I wanted to talk a little bit about something that you've mentioned before, which is interesting, about your sushi conveyor belt approach to delivering insights. e and then also having research that lasts and that is actually going to be meaningful long term. And you've argued that that's actually a false trade off. So I wanted to talk a little bit about something that you've mentioned before, which is interesting, about your sushi conveyor belt approach to delivering insights.

Preeti 15:00 Sure. So the sushi conveyor belt was a way to visualize balancing, like you said, the speed of insights and then the durability of insights. Um, in early stage research, that's really hard because you wanna move fast, but you also wanna make sure that if the team happens to pivot or change direction, that your insights are still relevant and they're not suddenly going to go stale. Um, so if you think about your research insights as a meal that you're serving, typically, when we have evergreen, durable insights, we think of those as coming from large foundational pieces of work that are sort of naturally slower to produce. And for those sorts of pieces of work, researchers, I think, tend to deliver those insights like an all you can eat buffet. There's, like, these lengthy, dense reports that come out all at once, like, go underwater for four months, and you show back up. I think so that is changing with the use of AI now, but but I think traditionally that's how people do that research. And I think also a little bit why stakeholders are skeptical or a little worried about those kinds of research because they're like, it's not it's not gonna be on our timeline. Right? But instead, I like to think about delivering insights on a sushi conveyor belt. I use that analogy because each piece of sushi on a conveyor belt is composed, it's complete, and it's satisfying. Right? So it's not like a half baked idea, but it is also bite sized and it's digestible, and it's produced at a reasonable and consistent and reliable pace. And so I think for research and practice, that looks a menu of different deliverables that are shared sequentially, not all at once. And you might start with something really low fidelity and low confidence like a video reel or a couple quotes, um, or maybe using an AI tool to generate some highlights. Um, and again, low confidence, low fidelity. And then you might move to a top line report, which is high confidence, but still low fidelity. It just has a couple of the main bullets, lacks the details. And then you can move to the traditional insights deck, which is that high confidence and high fidelity. And finally, you can have things like workshops or playbooks or things to, like, really activate the insights and make them stick, um, and that can come even later. So I think even if the project itself actually takes a while, has many phases, is more ambitious, I think the perception of speed is actually the delivery of insights. It's not really, like, how long is your research project. I think it's more how frequently do you deliver something that's useful and insightful. And so I think that sort of sort of sushi can vary about or multi course meal, however you think of it, can help that the team move faster even when you are doing a more durable foundational sort of research study. research project. I think it's more how frequently do you deliver something that's useful and insightful. And so I think that sort of sort of sushi can vary about or multi course meal, however you think of it, can help that the team move faster even when you are doing a more durable foundational sort of research study.

Molly 17:31 That's a fascinating way to think about, instead of this big sort of shift in what the work is, it's more of the workflow and the delivery of the products at a certain cadence, but it's not changing fundamentally the work. Exactly.

Preeti 17:45 And I think, obviously, there's times where the research itself needs to be more scrappy or we're not doing these big projects. But again, I think in emerging tech world, it really is about getting to those core needs. It really is about finding something that's quite deep and driving the product at a really foundational level, not a surface or a tactical level. And so I think, again, I know these days in sort of our AI era, it's all about, like, moving fast. And I think often we think that means, well, let's change our methods or, like, let's use AI to make everything go faster. But actually, I think it's shifting the perception of speed, and there's a lot of things we can do to shift the perception of speed without actually being faster in the in the research itself. That's such

Stephanie 18:25 a good point. Switching gears again, Preeti, some of the products that you've informed have taken years to emerge and I'm sure that there are probably some that have never launched. In that kind of context, how do you measure the impact of research when success isn't necessarily tied to like a shipped product? phanie: a good point. Switching gears again, Preeti, some of the products that you've informed have taken years to emerge and I'm sure that there are probably some that have never launched. In that kind of context, how do you measure the impact of research when success isn't necessarily tied to like a shipped product?

Preeti 18:42 So I think about research impact as a tangible and ideally a quantifiable shift in an organization that a research directly brought about. So as in you can say, you know, before this research, before this research, the state of things was a b c. And then after we did the research, the state of things was x y z. And I think the good news is that you don't have to have launched a product to have orchestrated the shift. And so to get more tactical about it, I think there's four types of shifts I like to think about when I do, especially with emerging tech research. The first shift is definition. So that an example of that is saying before this research, there was no defined user or use case. And after the research, we had defined x user groups and y use cases, and we're really defining what the team is going after. So I think that's like one sort of shift or impact. Another one I think is prioritization. So it's common that when you do early stage research, there's 40 different concepts or something like that. Right? And there's not really defined work streams. It's very messy. And so if you're able to say, hey. We came in and did this research that actually resulted in a framework that helped us pick the top three ideas to pursue that resulted in these three different work streams with 10 people each working on them. Right? And that's a very quantifiable shift that you can talk about about how you went from many ideas to few ideas and putting numbers to that. The third one is direction. So I think that is saying before this research, the team was going in one direction, and then you do this research, and now the team is moving in this other user focused strategic direction. So helping a team pivot or helping a team accelerate in the direction that they're already going in. And then finally, inspiration. That's someone being able to say, as a result of doing this research, we identified, let's say, a particular need, and that might inspire a certain feature that didn't exist before, an idea that didn't exist before and got put on a road map. And now we have a team of 20 cross functional people working on it or whatever that is. And so as you can see, like, in each of those cases, definition, prioritization, direction, and inspiration, even if the projects you worked on, like, never saw the light of day, you still had tangible and quantifiable impact with your research. And I think that the key for me has been to focus on the organization more so than the product as a recipient of that impact. So as long as you can identify the before and after there, you can show impact regardless of what happens to the product, and you don't have to be so precious about that. act with your research. And I think that the key for me has been to focus on the organization more so than the product as a recipient of that impact. So as long as you can identify the before and after there, you can show impact regardless of what happens to the product, and you don't have to be so precious about that.

Molly 21:07 I think that's an important framework to attribute because there's ongoing conversations all the time about researchers having to justify their own work. Because when research is done on a product or a business decision, it can typically be that nothing actually happens. Right? Like, the product goes well. The launch happens. Everything's great. But there's no ROI on the research, but it's just because it prevented a costly mistake. So how you actually attribute in return on investment for research is always a constant challenge.

Preeti 21:40 Yeah. And I would add to that too that made me think of the flip side of this, which is to say that just because something launches doesn't always mean you had impact. If you did research that contributed to a launch, but it didn't fundamentally change there was no, like, before and after that you can point to where you're like, the research actually made the shift happen. I would argue that that is not super impactful research. Right? You sort of got swept in the current of the launch and you did some stuff, but no one can really point to that versus you said, Molly, if you prevented something, that actually is a shift in direction that can be attributed to research. And that product may or may not be launched, but it it's really about those shifts and less about less about the launch.

Molly 22:22 That's super fascinating. You mentioned this a little bit, uh, at the beginning, and we we have to talk about AI. Is there an example of maybe a conversation where someone reacted strongly to the idea of AI, but then you went in and you shifted the discussion towards the human need, which is a lot of what your research is about? Is there a moment where then the insights or the approach change completely? we have to talk about AI. Is there an example of maybe a conversation where someone reacted strongly to the idea of AI, but then you went in and you shifted the discussion towards the human need, which is a lot of what your research is about? Is there a moment where then the insights or the approach change completely?

Preeti 22:46 Yes. I'd say there's probably a particular category of conversation that comes to mind here, which is around the value proposition of using AI in a product. What is the thing the AI is good at is good for, I mean, and should do or should not do in a particular product? And so one conversation that comes to mind is there was a group of engineers that I was working with who really wanted to use AI to do, like, the technically hardest thing that was possible to, like, achieve the technical feat. It's a research organization. They wanna publish a research paper. They wanna do something that's really, really technically hard to achieve. And I was able to show with some of the research that I did, the human centered research, what are the real priorities for people and where are the gaps and needs that we could address. And it's not to say we shouldn't pursue this technology, but it's rather to say let's pursue an application of the technology that is something that aligns with a real gap for humans. And also that this thing that they were interested in doing is actually not something that users were really caring about or excited by because although it was technically difficult, the end experience was not something that was that compelling compared to other use cases that could have been pursuing. And so I think it's really about just, like, making sure that we aren't just enamored with AI and sprinkling AI into everything because we can, but going back to the basics and, like, really focusing on the reality of people's lives and making sure that our value propositions for what AI is used for are aligned with that and having a really strong sense of what that is.

Molly 24:20 Yeah. Having some intentionality behind the application of a thing.

Preeti 24:24 Exactly. And if you look at how we have LLMs today, it's like it's just out there in the world and we're figuring out what is it good for, what is it not good for, and where can it be actually really dangerous? And I think that it's quite common to encounter a very tech first approach. And so a lot of I think the coming in with human insights when people have a strong opinion about AI is coming with a human first approach. Totally.

Stephanie 24:50 Well, Priti, we have segments on every episode called The Current one zero one where we ask all of our guests the same question. So the first is, in your experience, what is one trend or practice in market research or UX research that you would like to see stop, and what is one thing that you would like to see more of? Well, Priti, we have segments on every episode called The Current one zero one where we ask all of our guests the same question. So the first is, in your experience, what is one trend or practice in market research or UX research that you would like to see stop, and what is one thing that you would like to see more of?

Preeti 25:08 Yeah. So something I would like to see stop is the trend of AI powered tools that market themselves as ways to do qualitative research faster and at scale. And the reason for that is I think that there's a misconception here that qualitative research is unnecessarily slow and that it's unnecessarily constrained by the limits of the human researcher. There's this assumption underlying a lot of his marketing, I guess, that it's better to be faster and it's better to always better to talk to more participants, but that's not true. You know, the thing is we know, qualitative research has fewer participants by design because you don't need 500 participants or 5,000 participants to find patterns. And in-depth interviews and immersives and deep conversations with people, I think, are few deeply human touchpoints left in the tech industry. My belief is that if you're on a team where the qualitative research feels too slow to your stakeholders, I think the issue is often that you're not designing a research program well enough or sharing useful insights fast enough, kind of going back to the Sushi conveyor belt. Right? I think it's like the perception of speed, there's an issue there. And just using an AI tool to make the methods go faster, I feel is like a band aid solution. Right? And I think there's a place for things like quality scale. There's a place for synthetic personas just like any other method. But I think what I really wanna see stop is the suggestion that AI should be used to shortcut the qualitative process. The human process is the point, and if it's like too slow, find a better human process for that work. So that's one thing I wanna see stop. And then one thing I wanna see more of is immersives and creative field research. I think that was really big pre pandemic. I remember Google, for a number of years, had this research van that would drive out to different places, and the lab contained within the van, which was really awesome. I think post pandemic in the industry at large, I think field research never quite recovered, but I am seeing now a resurgence. A lot of it is in response to these AI tools, a resurgence of really getting back out there and doing old school. That is a space I actually get really excited about AI use in because I'm like, what can AI tools now give us? How can they augment field research? How can they augment being really immersive with people? And wearables or ARVR or translation or, you know, whatever that is, what can the tech do to field research? So I'm excited to see that resurgence and where that goes.

Stephanie 27:33 I love those. Those are great. us? How can they augment field research? How can they augment being really immersive with people? And wearables or ARVR or translation or, you know, whatever that is, what can the tech do to field research? So I'm excited to see that resurgence and where that goes. I love those. Those are great.

Molly 27:35 Yeah. And I think it's having that discipline to know what can be sped up and what still needs to remain with the researcher. That judgment is always, I feel, gonna be there no matter the technology.

Preeti 27:47 And I think that there's, like, two categories of things you mentioned as a set of research tasks where you're on autopilot, where, like, I know exactly what I need to do, and I just, like, gotta go do the motions. And it's, like, great.

Molly 27:58 Competitive. Same thing.

Preeti 28:01 And where you're not really like, the process of doing it isn't teaching you something, but then there are many other things. And some of them are very rote, like note taking, for example, is an example of something I think is, like, very rote, and it is you are doing so much processing while that's happening that you're learning a lot through that process. And so it's very tempting to have AI notetaker, but you process things differently when you don't take your own notes or don't have a person who's you're collaborating with taking those notes. Right? And so I like to think of, like, learning by doing tasks and autopilot tasks and, like, yes, automate the autopilot stuff, but we're learning by doing it even if it is rote, even if it does take longer, find ways to speed other stuff up and keep that stuff really human.

Stephanie 28:41 Well and I love that example in particular because to me, that's an area where the answer is both. Right? Like, I totally agree with you, and I I've seen all the studies on, like, the importance of taking notes yourself. But, boy, it's sure handy to have that recording or that transcript to return to as well. Right? So it's sort of like it's augmenting something that still should sit firmly in the world of of the human researcher.

Preeti 29:04 Totally. And I think it comes down to what I have found in my practice with AI tools is that it requires breaking down your tasks into more granular subtasks than maybe I did before because it'd be like this specific part of this task, the transcript, right, It's fantastic to have really accurate AI transcription. I remember, like, ten years ago, really struggling with maybe the transcription, the automatic transcription would capture every third word correctly or something. And today, it's amazing. Right? But it's not letting go of still some of those practices taking notes as well that keep you engaged in the work that you're doing.

Molly 29:39 And there's studies too about handwriting notes, the mind and connection. I'm the same way. I have, like, my writable tablet that I still always use for everything. ight? But it's not letting go of still some of those practices taking notes as well that keep you engaged in the work that you're doing. And there's studies too about handwriting notes, the mind and connection. I'm the same way. I have, like, my writable tablet that I still always use for everything.

Preeti 29:49 Yeah. And I think and I, ultimately, it's the value proposition for AI. I feel like even outside of use and research, just like in the world is often speed. I feel like that's the number one thing that is the AI's market is, like, making you faster. And I think, yeah, I think there's a lot of work to be done about really examining what is the benefit of slowness and what is the benefit of speed and where is that beneficial?

Stephanie 30:11 If you don't mind, like in a world where speed is not the most compelling value proposition, what is it? Just I mean, and I know it's gonna vary by use case, but when we think broadly about consumer use of AI, is it like the mental load, the reduction of the mental load? Like, what do you find resonates more than speed? f you don't mind, like in a world where speed is not the most compelling value proposition, what is it? Just I mean, and I know it's gonna vary by use case, but when we think broadly about consumer use of AI, is it like the mental load, the reduction of the mental load? Like, what do you find resonates more than speed?

Preeti 30:27 Yeah. It's a good question. I think well, it's funny using mental load because I think that AI often increases mental load. Right? I also think it's hard to say definitively, this is what AI should be used for as a broad bucket because it also depends on the maturity of the technology and what it should be used for. I think the way that I think about it is supercharging yourself to do things that you may not have like, that you would never have even done before or you you would have wanted to do but couldn't do because you were sort of done before or you would have wanted to do but couldn't do because you were sort of limited in a certain way. And I and sometimes that might be speed. I'm not saying speed is useless. Sometimes that might be speed. But I think that, you know, when it comes to research in particular, I think a lot about times where many years ago, I'd sacrificed things like being able to show illustrations or concepts to users because a designer didn't have bandwidth to do that and that was not priority thing. Right? And so it's like, oh, it would have been really nice to do this, but I actually have to move forward in this kind of suboptimal way. And so I think, like, that's a really great space that now researchers can actually use AI to generate something. It doesn't mean take the designer out of the equation, but it's doing some of what they may have considered that busy work away. Right? Same thing with presenting your insights in much more compelling ways by creating a website for them or creating something interactive. Right? It's maybe not the most eloquent answer, but I think that that there are ways where we can supercharge the things that you wouldn't have had access to otherwise. And I think that's really compelling. But I think when it comes to any use case of AI, you have to think about what's lost and what's gained, and I think you have to sort of do that calculus a bit. I think in the most ideal sense, I think that the optimistic view for me about what I think AI could do for people generally regardless of product is really help people with identifying patterns and identifying and, like, help working with unexpected change. Like, I think that that's so needed, and I feel like AI is actually really good at identifying patterns that humans can't identify and predicting things that humans may not be able to predict and seeing things before we can because of this access to this huge corpus of data. And I feel like that's really underutilized in products right now because this thing of, like, you can just do more and, like, generate so much more is being pushed. I think we aren't focusing as much and we should be focusing more on where does AI have superpowers that people don't have. And some of the stuff that is happening in the AI worlds of, like, things like drug discovery and things like that is quite interesting to me because I feel like we've moved beyond. Literally, humans can't do it. It's not like generating a LinkedIn post for you. It's something that humans can't do and something that AI is, like, uniquely good at. And I think about AI as a material and not a tool. Like, just you would build with concrete or you would make a shirt out of cotton, right? Like these materials behave in different ways and they have unique strengths and limitations, right? You're not gonna building out of porous material and then be sad when the rain leaks through it, right? You're just like, don't build with that. Don't do that. th concrete or you would make a shirt out of cotton, right? Like these materials behave in different ways and they have unique strengths and limitations, right? You're not gonna building out of porous material and then be sad when the rain leaks through it, right? You're just like, don't build with that. Don't do that.

Stephanie 33:31 The architect talking to us, I love.

Preeti 33:33 I think there's parallels there. So you think about AI as a material, you end up thinking about what can this thing uniquely do. What is it really good at? And, like, what is it not meant to do? And, like, let's not fight that thing. Let's just go do the thing that it's uniquely good at. And so I'm all for those applications of of AI.

Molly 33:50 Well, to wrap this up into, I think, one piece of advice, maybe that might be a little difficult. But for somebody who's trying to build a career in researching technologies that don't exist, in a world that's changing, a world that prioritizes AI use, a world that prioritizes speed, and a lot of tension points there. What's one principle that you hope these aspiring researchers never lose sight of?

Preeti 34:14 Yeah. So I think maybe in echoing something I said previously when we were talking, but I really think it's that, you know, new technologies are all of us emerging, but human needs are enduring. And the most fundamental needs of many people, even though the needs are basic, they're very complex and tied up in really complex systems, and they are not fully addressed. And so I think that helping build a bridge between those new capabilities and fundamental needs in really actionable ways, like deeply understanding technology and deeply understanding the human, is really a superpower if you wanna be in this career. Like, I think it's important to not lose sight of the fact that some really basic things for us in our lives are not solved problems. You don't need to be going after the next flashy thing. There are lots of really, quote unquote boring but fundamental things that are very broken that we need to think about how new capabilities might intersect with.

Molly 35:08 That's an amazing and honestly refreshing mindset. Instead of just chasing the shiny things, worry about what makes humans fundamentally humans. I love that.

Stephanie 35:17 Truly.

Molly 35:18 Well, this has been an amazing conversation, Priti. Thank you so much for joining us today and sharing your your amazing insight on AI and foresight research and all of the above. It's been a great conversation.

Preeti 35:29 Thanks. Yeah. This was really great.

Molly 35:30 Priti, this was such an engaging conversation and there's a lot that I'm gonna take away. But one of the one things that's that's sticking with me is to remember that the further that we look into the future, it becomes even more important to consciously recognize that at the end of the day, we're still doing research to understand people. an engaging conversation and there's a lot that I'm gonna take away. But one of the one things that's that's sticking with me is to remember that the further that we look into the future, it becomes even more important to consciously recognize that at the end of the day, we're still doing research to understand people.

Stephanie 35:48 Absolutely. I also love the reminder that great foresight research isn't about guessing what's coming next. It's about identifying the human needs that will matter no matter how or in what ways technology evolves.

Molly 36:03 Yeah. And your point about measuring impact of research is also very important because sometimes the most valuable research isn't just for the product that launches, it's the way in which the research changes how teams may think about even before they figure out exactly what they're going to build.

Stephanie 36:21 For sure. Prithi, thank you so much for sharing such a thoughtful perspective on anticipatory research, human centered AI, and what it takes to study a future that's still being created.

Molly 36:32 And to everyone listening, thank you so much for being part of the Curiosity Current. We'll see you next

Stephanie 36:39 time. The Curiosity Current is brought to you by AYTM. To find out how AYTM helps brands connect with consumers and bring insights to life, visit aytm.com. And to make sure you never miss an episode, subscribe to the Curiosity Current on Apple, Spotify, YouTube, or wherever you get your podcasts. Thanks for joining us, and we'll see you next time.