Beauty Intelligence: Kalindi Mehta of Estee Lauder Companies on AI & Trends

In this episode of the Greenbook Podcast, Karen Lynch sits down with Kalindi Mehta, Global VP of Consumer Foresight, Strategy, and Predictive Analytics at Estée Lauder Companies. Kalindi shares how the beauty industry’s emotional, cultural, and highly personalized nature requires real-time consumer insight and constant innovation.

She discusses how AI is transforming the end-to-end insights process—from sensing trends and matching products to co-creating with influencers and optimizing business results. Kalindi also dives into the skills and mindset insights professionals need to thrive in an AI-driven future, and offers practical leadership advice for driving transformation within organizations. A must-listen for anyone navigating the evolving intersection of data, creativity, and consumer foresight.

Key Discussion Points:

• How Estée Lauder Companies uses AI to drive consumer foresight and innovation
• The cultural and emotional complexity of beauty consumers
• Building insights capabilities that scale across global teams
• Why AI enhances—rather than replaces—human expertise
• Skills and leadership strategies for thriving in AI-powered insights work

Resources & Links:

• Estée Lauder Companies

You can reach out to Kalindi Mehta on LinkedIn.

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

Transcript

Karen: Hello, everybody. Welcome to another episode of the Greenbook Podcast. I’m Karen Lynch. Happy to be hosting this episode today, and I’m very excited to be speaking with our guest. Today, we have Kalindi Mehta from the Estée Lauder Companies. She is the Global Vice President, Consumer Foresight Strategy and Predictive Analytics. Kalindi, welcome to the show.

Kalindi: Hi, Karen. I’m very excited to be here and to be a part of this podcast.

Karen: Well, we’re excited to have you. I know that your role spans insights, and analytics, and global strategy, and innovation, and brand building. We’ve talked a lot about AI, which we’ll get into. You’ve also been an advisory board member and a startup mentor, so you’re bringing a wealth of experience to the show. But before we dive into some of the topics, what can you tell your audience about what your role is like these days?

Kalindi: So, I lead the corporate consumer insights, foresight, trends, predictive analytics team at Estée Lauder Companies. It really entails leading consumer insights and the foresight’s capability, the function, enabling Estée Lauder to win in this fast-changing, ever-evolving digital and AI world. In my past 20 years prior to Estée Lauder, I worked with Colgate Palmolive. Again, all in the world of insights and analytics in various parts of the world, starting in India, a little bit in Philippines, in Asia Pacific, Hong Kong, North America, global teams. So, really building a global perspective to consumer insighting.

Karen: What drew you to the beauty industry? What was it about it that drew you right in and is filling you up today?

Kalindi: The dynamism of the beauty industry, the rapid adaptability, constant change, and the need to evolve with that change is what sets the beauty industry apart from others. And as a result, you can’t just think today; you have to constantly anticipate the future. In fact, while I lead the corporate consumer insights team, but it’s called consumer foresight because all insight and beauty must be foresight. You must be constantly anticipating what’s coming next, what the future is, and changing along with it. Another thing is the pioneering use of digital technology and data analytics. The beauty sector stands out because it not only adopts, but is at the forefront of all things digital, of digital innovation, of real-time consumer engagement. And, you know, the category itself, beauty, we all know, we ourselves feel that consumers and we are so involved in beauty, there’s so much to say about beauty, whether it’s about skin care, or makeup, or hair care, or fragrance, the depth of conversation with consumers is so rich in the beauty industry is really unparalleled. So, it’s exciting to work in the world of consumer insights with this kind of richness of conversation and insight. And lastly, I think the most important thing, which I think it’s not just about data. Unlike many utilitarian and functional categories, beauty products are deeply intertwined with personal aesthetics, with culture, with cultural trends, making them very uniquely personal and emotionally charged as a category. So, it’s very exciting to kind of leverage all that exciting data, but to delve deeply into understanding the emotional and cultural dimensions of it.

Karen: I was going to ask you sort of what is different in terms of consumer behavior in the insight, in the beauty space compared to the other industries, but I know there’s a lot there. So, [laugh] what can you share from a consumer behavior standpoint that makes it also dynamic and interesting?

Kalindi: Consumers are so much more involved in beauty than, say, for example, when I worked on personal care or oral care. It’s not just autopilot shopping every month. The beauty enthusiasts discuss what they want, what their needs are, with so many people. They ask people about brands and products. They’re constantly searching. They’re reading product reviews. They are on multiple platforms, researching, having conversations, sharing. But they’re also in stores, you know? They’re going in and trying different products and talking to beauty advisors, and then they’re deciding. And the decision, therefore, is truly omni-channel. So, that’s very different from some of the functional categories that I worked on in the past. And consumers love this category. As I said, it’s not just autopilot; for many, it’s a passion, it’s a hobby. They’re very knowledgeable about the category. We often call our consumers skintellectuals. They know so much about the category. And in many other categories that I’ve worked on, people don’t think about it until they run out of the product. But beauty category, you don’t just buy what you need or when you run out of a product. It’s expandable consumption. There’s so much of impulse, unplanned purchases. You have a great product, you have a great brand, you create the need, and consumers will want to try it. The other very, very important aspect of beauty—which is unique—is, it is so very personal. If you work on oral care or home care or some soap categories, you’re talking to the household; they’re family decisions. In beauty, every individual has their own specific need, their own desire. It’s so personal. It’s so specific, not just to that individual. It’s to that individual, to their life stage, to their lifestyle, to their skin type, to the season, to the time of the day. So, the level of granularity and specificity is this incredible in beauty.

Karen: Like that, right, there, you just said so much. First of all, I love the phrase skin intellectuals. I’m probably going to be thinking of that every time I have a conversation with my daughter or my daughter-in-law or my nieces about skincare products—which comes up more often than I ever would have anticipated, but we talk about it all the time, and they are so smart about all of it—this next generation, I’ll say—they’re so smart about it. But I’m curious as to how you balance learning about individuals who are also very heavily influenced by social media channels, or influencers, or even just rapid trends that are happening out there. How do you track that from an insights and analytics perspective, everything that’s rapidly changing in the environment?

Kalindi: I mean, insights teams, when you work in beauty, must expect and anticipate change and constantly pivot. So, we’ve learned to react in a very agile way, but as well as be proactive in terms of how we build our insights. We are heavily reliant on real-time data monitoring, and social listening tools, and search tools, and ratings and reviews tools. We have amazing, very simple, easy to use, always on tools for insighting. And it’s not just, like, text-based insighting, it’s visual insighting, it’s image insighting, it’s semiotics, it’s cultural understanding. It’s ongoing, and you have to track it on a regular basis in order to stay relevant. And if you see because online is such a big source of real behavioral insights, it pushes the insights community to find new ways and to go to the next level in terms of this, kind of, real-time listening and real-time tools, and using AI in far more advanced ways than you can imagine, bringing in not just text-based AI, but human, visual, cultural insighting online. And so, it’s exciting and it’s phenomenal. And as you think about creating products, innovation, you have to—as you said, your daughter is so involved and is following influencers, et cetera—so you have to create, not just with consumers, but you have to create with these micro-influencers, with the content creators. And so, as you think about innovation in a typical FMCG world, you’re co-creating with consumers, you’re getting their feedback. We are optimizing our innovation and new products, not just consumer feedback, but influencer feedback, content-creator feedback, so that we can be successful when they’re out there and sharing their point of view.

Karen: So, now I’m thinking about all this real-time, kind of real-time intel that you’re getting, and I’m thinking about the foresight part of your functioning, and trying to get predictive in the future. And so, I’m wondering how—without sharing anything proprietary, please—but how you kind of get ahead in terms of being able to have some foresight into what’s coming. Like, are there things that you are doing that are fairly common practice for your organization, perhaps, but kind of help people understand how you do that, how you predict a future that is changing so rapidly?

Kalindi: So, we’ve adopted an ambidextrous approach, I would say: responding adeptly to immediate challenges and to immediate trends, while also at the same time, possessing and building the foresight to anticipate and shape the future. So, we’ve implemented a scaled approach that utilizes AI to enhance our responsiveness to social trends and short-term immediate changes and cultural evolutions that are happening across categories and brands, enabling really quick agile reaction, which is very important to be successful in beauty. But at the same time, we are harnessing ongoing, like real-time data, deep human-insighting, cultural shifts, technology advancements, ingredient trends. We are bringing all of that together, leveraging AI, leveraging advanced tools and capabilities to anticipate and innovate for the future. And so, we have three different streams of work: one, let’s react today; two, let’s anticipate the future on an ongoing basis using many, many different sources of insights and data; and three, is really create the future with our R&D teams on an ongoing basis.

Karen: It’s a vast amount of work that you are doing, not just at your company, but even on your team and in the space where you are operating. So, thank you for sharing all that. You mentioned AI, and I want to share a loose statistic that you shared with me on our pre-call. And you had said that generative AI hit the scene a couple of years ago, and then all of a sudden we were here. But about—you would say—estimating 70% of your role can be AI-focused at this point, which, whether it’s 70, whether it’s 65, whether it’s 75, I’m not holding you to the stat. I’m just thinking about how much of your work is now integrated with AI functionality. And I think that’s something to say, right? That’s a sizable amount of a workload. So, talk to us a little bit about that evolution from we’re not really using AI products to now having AI products, or features, or platforms be such a large part of what you’re doing.

Kalindi: You know, if you think about my insights career or any insights leader’s career, say 25 years, the only thing constant in my career has been change. It’s evolved. At every point of my career, I’ve had to pivot, change, evolve, learn, do things differently. And so, now’s the time where AI is changing the way I’m working, what I’m doing, how we are transforming insights and marketing. AI is a real game changer for insights folks, right? It’s helping us achieve what we always wanted to do. Our goal has not changed. It’s the same destination, just a new journey. So, what was our destination, what was our goal—and it still continues—is to build human, consumer insights, deep insights, that impact and drive business results at scale to drive decision-making that’s consumer-first, that’s data-based, and to build insights that really have very strong ROI. And so, that destination remains the same. It’s now we have AI in order to help us do that even better than we’ve ever done. So, if you think about that, the destination has not changed. We have a new way of getting about what we always wanted to do. So, there are three areas, right, where we’re trying to transform insights, and build those capabilities, and deploy those capabilities. One is making sure that all decisions across the organization are based on insights. So, democratizing the access to insights so that it’s easily available to all the decision makers around the world across functions. So, to have insights synthesized, easy to access is a big game changer so that you can have it on your fingertips when you’re making the decision. Second is to do things faster. Beauty is changing so fast. You have to keep up, you know, the new brands, new content, new trends. And so, AI is helping us still remain consumer-centric, but do things faster, more agile, and still keep the consumer at the center. And lastly, insights, you’re not just identifying an insight. The insights function is evolving to an end-to-end function where you’re not just creating insights, but you’re also creating action from those insights, whether you’re creating concepts, or ads, or packaging, or very clear strategic recommendations. But AI is helping you do that so that the ROI from your insight is going up, and you’re delivering not just an insight, but actual business value. And so, as we think about this evolution and this new journey, what’s really basically changed in terms of what we do, what my responsibilities are, and evolution in terms of approach. It’s interesting, right, that while we are creating this next generation AI tools, and capabilities, and working closely with technology teams and vendors, I’m going back to the fundamentals of insight, market research, and data more than I have in a long time. And how is that the case is, as we build these AI tools and capabilities, we have to go back to the fundamentals. What is a good insight? What is a good concept? So, that we can train the AI to generate that good insight that we believe is going to help the business, that deeper insight. We can generate those concepts that are truly insights-driven, so that we can build synthetic data concept testing or ad testing solutions that reflect our traditional, rigorous, thorough approaches and methodologies. So, you’re really going back to the fundamentals and getting that right so that you can train the AI to do that and to understand that. So, while we’re moving really fast and going to the next generation, we’re also going back and spending more time on our basics and fundamentals in order to build and deploy that AI. Another area is really collaboration. In the past, we used to collaborate a lot with our business teams, marketing teams, innovation teams, market research vendors. And we’re still doing that, but I think the stakeholders that we’re collaborating with is also changing dramatically. I’m working more with the IT team now than I have in a long time. I’m not just working with typical insights vendors. I’m working with enterprise, like tech giants like Microsoft and Google. I’m working with startup tech companies so much more than I have before. And so, the way you work with these companies is different from the traditional stakeholders and companies that you’ve worked with, so you really have to broaden your horizon as you expand your understanding of different functions and different kinds of companies as you do that.

Karen: That’s great. Thank you. And such great tips for people listening in to this. First of all, I’m just going to take pause and reiterate the fact that you are training the AI that you are working with, and so training it in the fundamentals that it needs in order to provide the output that you’re looking for, and that alone, I think is one of the most important things is you just can’t work with one of these platforms out here that hasn’t had that kind of a training. So, kudos to you for making sure that’s happening internally.

Kalindi: Yeah, and one more just to add there. And I think as we think about that evolution and you’re building the AI capabilities, but as you also deploy it and as you’re training your insights teams to use it, I think the change that you’re also driving is this from you and your team being an identifier of insight, you’re an enabler of insight. You’re enabling the organization to come up with the right insights, the right answers, versus you doing it all for them. You may not be the smartest person in the room now having the brightest insight, but you’re ensuring that the AI generates that insight that you want people to use for their decision-making. So, it’s a slightly evolution of the role of insights.

Karen: I love that. It seems like, kind of, the insights professional that is going to be successful right now is not really in it to be the smartest person in the room per se and have the smartest insight, but be able to really help drive the business forward. And it’s less about them, right? And more about the business and the organization’s goals. And requires probably a little bit of humility and acceptance that this is actually the smartest way to work. So interesting, very interesting. So, let’s just take a step back and think about you, as somebody who has adopted some of these practices, what are some of the challenges that you overcame along the way to getting to this level of adoption? There must have been some challenges in your journey that you’ve overcome that people could learn from.

Kalindi: You know, this journey, I have loved it. It’s exciting, I enjoy it and it’s really very motivating, and I like this ambiguity, and the creative problem solving that you need. At the same time, I’m leveraging my strengths of fundamental insights and empathy to build some of the capabilities and deploy them. I think the biggest challenge really is, it’s less technical, it’s more change management, right? Transitioning to AI-driven insights is bringing people along with you in that journey, changing habit, changing behavior. That is the toughest part. And so, you know, a couple of key things there, right? You have to change and get support right from the top, right? Your leadership team has to be walking the talk, and getting the leadership team on board is super critical because you can’t do this on your own. And so, building that business case and, you know, and most leadership, like our leadership team at Estée Lauder is there. You know, they are so supportive of driving AI, they’re personally involved, it’s a high priority for them, it’s got their attention, they’re very motivated to drive AI. And so, that’s great I have that support, right? And so, the other area is building all the other insights folks in your organization to embrace AI-driven insights. And there are some who will raise their hands and say, “I’m going to do it. I’m going to use it. I’m going to help, and this, I believe, is the future.” But not everyone’s quite there, and respecting people who have years of experience and who know so much and understanding their concerns and understanding their mindset is super important. And bringing them along with you, with your vision, with you on your journey, building that credibility and driving that change of behavior is the real challenge and the real opportunity. It’s not just about building capabilities and truly deploying, embedding, getting them to use the capabilities.

Karen: Yeah. So, leadership walking the walk, and kind of, you know, modeling from the top that this is what we’re doing, and then, you know, kind of leveraging the team members that are on board, and then helping those that are not a little bit along [laugh].

Kalindi: Correct.

Karen: Yeah, very cool. Thank you. So, let’s get into some details if we can, some of the ways that AI is being used. Again, I don’t ask you to share anything proprietary, but if there’s anything that you can speak to in terms of how you’re using AI, like, what are some of the actual applications you’re using it for?

Kalindi: So, you know, I don’t think about AI as, you know, just one little tool or one little app for one specific, you know, just use, really thinking of it as a broader use case and an end-to-end value creation. For example, we want to react to, you know, trends on social media super quickly. And so, there’s an entire end-to-end use case for AI there. We use AI to sense trends, and discover trends, and articulate the trends. Second, then once we’ve articulated it, we match the right product, the right SKUs, the right colors. Just imagine our categories, right, whether it’s skin care or makeup or hair care. We have 25 brands, we have hundreds of variants, formats, colors, subcategories, and so for that trend, what’s the right product to match? So, we use AI to match that product against the trend that we’ve sensed. Then once we’ve matched the product, we can create concepts or, you know, even briefs, you know, like creative briefs or influencer briefs using AI, using the insight, using the trend, and using the product that we’ve matched. And then you can help support building those assets using Adobe, you know, using different AI tools so that now we have the asset. And then finally, when we launch it, we’ve created capabilities to measure success and, you know, whether this is working or not working, and whether we need to scale it or not scale it. And so, this end-to-end process is where we are leveraging AI to unlock that value at each stage so that in the end, we are getting real, you know, incremental business value and it’s driving sales. And not just thinking about one little tool which is, oh, sensing or matching. And so, there are other use cases that we are driving this, end to end, whether it’s innovation, it’s shopper, and really building AI capabilities that help you drive that incremental value for the business.

Karen: So, I know there’s people listening who are sitting there thinking that’s a lot of AI, especially when you consider it, you know, end to end like that. So, how are you balancing, you know, what some people might say is highly valuable, but the role of humans, human intuition, human creative thinking, and all that human expertise into the decision-making that AI is greatly assisting you with?

Kalindi: You know, this end-to-end process in the end is led by humans. You know, it’s to assist humans to move faster, to do things at scale, and to do things better because there's more data and more insight here than there’s ever been before, and it’s hard for human being to manage all of that quickly and at scale. And so, AI is there to help the human. As we design these end-to-end capabilities, we are designing it keeping the human in mind, keeping the human at the center, keeping the fact that we are helping the human do their job better, right? So, there’s this exponential power when this—which I’m sure you’ve heard in other places—when this human intelligence is coupled with the power of AI. You know, we’re very clearly seeing is one is not displacing or diminishing the other; you know, they’re just elevating each other. And the way we’re also looking at it, right, our competitive advantage comes from this virtuous cycle of math and magic, where the math or the AI helps focus the creativity where it matters, right? This math is using that sophisticated AI for managing all the insights, and all the data, and all the trends. And then the magic, you know, is really fostering that culture of creativity as we go along.

Karen: So, when you say all that, I’m thinking about these people, right, and I’m thinking about how they know there’s value in that, in the creativity and all of that type of thinking, and that human intuition. And I’m thinking, yet they still need skills to be able to integrate AI and lean into the change management that—or lean into the change that the organization needs to manage, of course. So, if you were to give some advice to folks, folks that are working to integrate a little bit further AI into their work, what suggestions would you have? What skills would they need to develop? Or what kind of mindsets would they need to embrace? What advice would you have for insights professionals?

Kalindi: You know, talking specifically about insights professionals, right? I think there are some fundamentals from the technical side that you got to just understand. You don’t have to be an expert. You know, just data literacy, right? Understanding all the different data sources, what is good quality data, what are different data structures, structured, unstructured data. Just having working knowledge of that is good, you know? Working with these large data sets and how to clean, manage, kind of interact with these large data sets is something insights professionals need to get comfortable with. You don’t have to become an expert. Again, you need to just have comfortable working knowledge with it. And just familiarity with AI and ML, you know, machine learning concepts. I do think in the end, it’s about harnessing your skills as an insight, as an insight that you’ve built over the years, the fundamental understanding of the business that you’ve built that you need to bring into the AI world. So, in the end, my knowledge about what is a good insight helps me create the right AI tools and the capabilities that help the marketers. And so—or the mind understanding of how do you do a concept test or how do you do a pack test helps me create those AI capabilities, you know? So, I think leveraging your years of expertise and knowledge to build, to enhance those AI capabilities is going to be super important. And so really, you know, getting comfortable with some of the fundamentals, doubling down on your own expertise in AI and teaching AI, deploy—you know, building AI is something that has been very helpful to me in that case. And then beyond this technical data knowledge and capabilities, in the end, it’s all about soft skills. Soft skills are the power skills, I would say. You know, it’s really empathy, right, to design well for the right use case. It’s understanding what the business problem is so that you can deploy the right AI so that you can build the right AI. It’s problem solving and creativity. So, because you’re doing things that you’ve never done before, you know, it’s managing that ambiguity and that curiosity. Because AI world is constantly changing. Every month, there’s something new or different, you know, so having that ability to change and learn. And then of course, a lot of it as you’re dealing and using the AI tools yourself is asking those right questions, which is really a strength of most insights people. We are so good at doing that. So, in the end, you know, you are leveraging your own strengths that you’ve built over the years, while you brush up some of the newer fundamentals and technical concepts.

Karen: You know, as you’re sharing, I’m thinking, the role is quite active. I remember having a conversation with somebody internally—more than once—about, you know, they’re concerned that they were going to be lazy if they started to use AI too much. And I just kept saying, nope. That’s not what’s going to happen because our brains have to be actively involved and really critically thinking at each step. It won’t be lazy; it’s just a different thought process that goes into really maximizing what we can get out of AI in our work worlds. So, you are very well able to articulate what I had been thinking all along, which is how many skills are involved with actually bringing the full potential out of the tools that you’re using. Let me ask you this, Kalindi. Another question is then about leadership. What would you say, you know, would be advice for leaders who want to lead this type of evolution in their own companies? Because your leadership team clearly embraced it if you’ve gotten to where you are now. But what advice do you think, whether it’s change management advice or whether it’s just skill-building advice, what advice do you have for leaders?

Kalindi: Yeah, I think first is, don’t do AI for the sake of doing AI. This is not about FOMO. Just because all companies are doing AI, everybody’s talking about AI, that’s not the reason to do it. Take a measured approach. GenAI requires a thought-through strategy and not FOMO. I would say three fundamental principles as we built our AI strategy and plans, right, focus on use cases that deliver value and don’t risk brand trust. Blend human intelligence with AI, which we spoke about, right? In the end, it’s augmented intelligence. So, even as you’re building your use cases, you’re building your capabilities, build it in a way that you know it’s for the human being. You know, build it along with the human being. And if you do that, only then will you be able to successfully deploy and use AI, if you’re thinking about it from a human-first perspective. Thirdly, watch very closely regulations and ethical use so that you maintain brand integrity and consumer trust. That is super critical. I have spent more time with, you know, legal teams understanding, you know, what’s the right thing to do? What’s the ethical use? And so, I think bringing that in into your strategy is super important. Another area is, really articulate a business case and a business problem. There’s so many cool tools out there, but don’t start with a solution first, you know? Oh, here is this cool, interesting tool, and let me find a problem. But GenAI should always be—if you want it to be successfully used across the organization and drive decision-making and business results, you know, it should be problem first. What are the biggest problems your teams are facing, and find the right tools, the right agents to support you with that problem, right? For example, for us, one of the biggest challenges was driving that agile trends to action and reaction. And so, it was just such a pain point. And since we leveraged a GenAI for that, people embraced it more easily, people use it more easily, it became easier to deploy, and we started seeing ROI and value super quickly. And so, find the biggest problems that your company, your business, your market, your brand is trying to solve, and then go after that, right? And there’s so many things that you need to do. You know, if you’re starting on your AI journey, prioritize some low hanging fruit first. Get that high ROI which is low risk, show the value and then move on to bigger approaches. You know, and then if you think about approaches, right, there’s so many different vendors, there’s so many different systems. You can do it internally, you can do it externally. As you think about driving the transformation, it’s not this or that, you know, as you think about the approaches. Embrace internally building stuff; embrace bringing things in externally, right? Embrace enterprise partners; embrace niche startups. You know, we have a multi-pronged approach which includes building internal capabilities, but at the same time, partnering with large companies who have done an incredible job in building some of the capabilities, or with small companies who are super specialized and agile. And so, having that right combination is important to driving that, you know, end-to-end transformation and change.

Karen: Great advice. Solid advice. And I’m going to ask you, actually, as a leader in this foresight space, to kind of take us home with some thinking about the future. You know, one of the things we say here at Greenbook, we have a tagline, which is ‘the future of insight.’ So, we’re always interested in talking to people, especially those who do some work in foresight or predictive analytics or scenario testing, anything like that. But looking ahead—you have a crystal ball, imagine [laugh]—how do you see the insights and analytics function continuing to evolve, say, in the next three to five years? Knowing where we are now, what do you see coming?

Kalindi: AI will be at the center of everything we do, right? And it’s not necessarily five years from now; one year from now, two years from now. You know, the end destination is not going to change. It’s still about keep the consumer at the center of all decision-making in order to drive business results, but AI will help us do that. You know, it’s such a tremendous opportunity for the insights function to achieve what they always wanted to do. AI is the golden ticket, you know? We will be able to impact more decisions, drive more action, get more ROI than we’ve ever done before, using AI. You know, insights will move from being a function that identifies an insight to a function that drives end-to-end business results. And so, insights teams will no longer just be accountable for, oh, let’s find the right insight, but you will be accountable for business results and action. And so, we will be getting a lot more out of all the insights work that we’re doing. And so, I would say three things also, right? One, all the insights that we do will be used a lot more around the organization. There will be more data that we’ll be able to manage, it’ll be more connected and there’ll be more use of insights. Second, we’ll be building insights faster than we can imagine. You know, so we’ll be competing—we will be building insights at the speed of light. Third, it’s not just faster and more; it’ll be deeper. Things which are, you know, basic, fundamental insighting can be done leveraging AI, and we, our insights teams, will be focused on doing a lot more deeper human insighting, complex insighting, anthropological, ethnographic, you know, just deep, you know, insighting that we need to do, while AI will be doing all the basic fundamental insighting a lot faster through the board. And so, it fundamentally changes, you know, the delivery of insights, what we’re doing, how we’re doing, and what we’re accountable for at the end.

Karen: I think that’s a really important perspective and makes so much sense as you’re talking about it, that the pressure is on, on some level, right? Like, okay, like, this was [laugh]—what we’ve been doing for the last, you know, several decades has been a warm up to what’s about to come because the game just got, you know—the game just got much, much more critical to inform all those business decisions. And, you know I have been seeing that trend play out, talking to a lot of people who are, you know, sitting at the table in the C-suite, you know, grateful that insights now has, you know, that type of an audience at the highest level of an organization, and yet now the pressure is going to be on to say, hey, use everything that you’re disposed of to really impact business decision-making and business growth. I think that’s really key for people to think about. So, thank you for sharing that. Tell me a little bit about what you’re most excited for. What are some of the innovations coming just in your space, in your field, in your role, beyond what we’ve talked about so far?

Kalindi: There is innovation in every aspect of insights, every aspect of the business. What I am most excited by is, you know, bringing deep human insighting and anthropology and data science together. And there’s a lot of work that’s being done in terms of visual and cultural AI that helps us get deeper using data. And so, you’re not compromising on the depth, and the humanness, and the cultural aspect of insighting, which is exciting. Another area is, you know, the ability to replicate shopper decisions with precision, you know, creating virtual shopper agents that, you know, that you can replicate product preferences, buying behavior, you know, similar to the real life counterparts. And actually replicating actual buying patterns with unparalleled accuracy, assortment, pricing decisions, that is going to be a game changer in our commercial decision-making process. And then, you know, what’s very interesting is, you know, how do you bring more creativity into AI? And so, you know, typically, insights teams run innovation workshops at some time, right? And we’re bringing in all these innovation and ideation and brainstorming techniques that make your ideas more creative at the end, and disruptive, and breakthrough. And so, there are these AI initiatives that we are trying to replicate that, you know, the kind of exercises you would do in an innovation workshop using AI so that you can get, you know, even more disruptive ideas coming out of AI, which is very exciting, you know, to see what that could lead to.

Karen: It’s all about training, right? So, in my—pre-Greenbook, when I was a qualitative researcher, moderator, and also a facilitator, I sort of specialized in a lot of innovation work and new product development, and I facilitated quite a few fantastic innovation sessions to help companies, you know, kind of, you know, generate some ideas, then do some rapid prototyping, ultimately going into a stage-gate process for some great CPG companies in particular. But one of the things I have entertained lately is, you know, wow, to be able to train AI in all of those tools and techniques that a facilitator of those innovation sessions just has at their disposal because they’ve, you know, had a career building them, I’m like, that’s what we need the AI to be able to do. And I bet it’ll get there because it’s just a matter of training, as you were saying earlier. Like, you have to train it well, and go back to the basics, and do some of that learning and train the technology. So anyway, really great stuff. I want to be mindful of time. We’re really close here, but is there anything that I didn’t ask you that you wish I had, that we’ve gotten through so much of what we hope to talk about?

Kalindi: Maybe a couple of things, right? One is, so as I think about established market research companies specifically, right, I’ve been working with so many amazing insights companies, research companies for years, you know, and I love them, their capabilities. They’re phenomenal, but you know, what happens in the world of AI? Just so some advice to them, right? So, one is, embrace day-one mentality. Perpetually live in this day one. This is the Amazon Jeff Bezos’ day-one mentality, you know, where day two is stasis followed by irrelevance. So, I think we, as a market research industry, as an insights industry, we need to live in that day-one mentality, which means prioritizing your end business user and business results, making swift decisions, adapting quickly to external changes, and always driving towards innovation. You know, it’s a call to avoid this complacency and inertia, which, you know, which really is the beginning of the end. And so, how do we disrupt ourselves before others do? You know, the greatest enemy of tomorrow’s success is today’s success. And so, be ready to disrupt ourselves, to make ourselves irrelevant because that’s the only way we can stay relevant. And so, constantly disrupt ourselves is the way to set ourselves up for the future.

Karen: I love that. Thank you. And that’s just kind of great advice across every level, whether it’s AI or anything else that’s innovative in your workplace. Just disrupt yourself. That’s where breakthrough happens. So, Kalindi, thank you so much for a wonderful conversation. It was such a pleasure to have you today.

Kalindi: Thank you, Karen.

Karen: Question: if people wanted to find you on LinkedIn, is there kind of a shortcut where they can see you and track some of the work you’re doing?

Kalindi: Yeah. I mean, I am on LinkedIn. You can look for me, Kalindi Mehta. I don’t think there are too many Kalindi Mehtas, so look for me. I’m there. I’m happy to connect, and please follow me. Yeah.

Karen: All right. Thank you so much. And to our editor, Big Bad Audio, thank you for doing what you do to, kind of, bring these shows to the airwaves. I appreciate you. Of course, to you again, Kalindi, and to all of our listeners, thank you for tuning in, week after week. It’s a pleasure to host these conversations to bring some useful and usable and inspiring information to you. So, have a great week, everybody—great two weeks—‘till the next time. We’ll see you soon. Thank you.