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Ethnographic Mind – Exploring Ethnographic Thinking in All its FormsEthnographic Mind – Exploring Ethnographic Thinking in All its Forms Menu Ethnographic Mind Exploring Ethnographic Thinking in All its Forms Menu Email Subscribe Get your copy of Ethnographic Thinking on Amazon or Routledge Search for: RECENT POSTS: What Works? Adaptation. What Works? Curiosity. Staying Power Three Questions Read. The. Room. REACH OUT: Contact © 2018-2025 Jay Hasbrouck; All Rights Reserved. All opinions are my own. What Works? Adaptation. The enduring value of adaptation in the age of AI Mar 19, 2025 by jayhasbrouck (Part three in a series of four) Growing up in Florida meant many long days at the beach. We’d spend the whole morning riding waves, playing ‘Marco Polo,’ and chasing down errant frisbees. Then, after a snack-gorging break, we’d shift to more sedate activities like collecting shells and, eventually, settling down to build sand castles. As we grew older, our castle construction methods graduated from standard bucket-and-shovel to more elaborate drip castles and sophisticated molding techniques. Once finished with our creations, we would pause to admire them from different angles, and then force our parents to do the same. We might add a few finishing touches or maybe a tunnel or two, based on ‘feedback.’ Eventually, our attention would stray and we’d stroll down to the pier, or wander off to watch people fish as the tide came in. Oh, wait! The tide’s coming in! A moment of panic would set in as we realized that we’d built our castles too close to the water’s edge (again). Filled with urgency, we raced back to check on our creations. The first order of business was to dig a moat as fast as possible to establish a barrier. Then we’d hastily construct a ‘wall’ to block the incoming force. It would work for a while, but the water would inevitably power through, slowing shifting from broad thin skims to more forceful foamy gushes. But this only boosted our valor. Sometimes ‘re-enforcements’ were called in. But we knew all along that the tide would win. In the final moments, we’d sit and watch each wave slowly take down our creation. We’d collapse, out of breath, and gaze at the destruction with a mix of loss, awe, and fascination as we surrendered our work to forces much larger than us. Inherent Adaptation There are many ways we embody and embrace adaptation as a species. In fact, we willfully and regularly construct contraptions far more substantial than sand castles with full knowledge that time will pass, paradigms will shift, and pillars will fall. Even when we don’t consciously do this, our enduring fascination with both historical shifts and science fiction are there to remind us that what may feel is permanent, is anything but. In her recent article “Can Animals Tell Time?,” evolutionary biologist Heather Heying highlights this point in her consideration of the different ways humans have devised to measure time. There are, we humans have proclaimed, 24 hours in a day. And an hour is now of fixed length, a length that is split into 60 minutes. But in the Middle Ages, this was not always the case. In some parts of Europe during the Middle Ages, it was asserted that those 24 hour were evenly split between day and night, no matter the season. Twelve hours of day. 12 hours of night, all year long. Thus, in the long sunlit days simmer in medieval Europe, the 12 hours of daylight were long hours, far longer than today's standard 60 minutes, and the brief nights had short hours, 12 short hours each of which was shorter than the modern hour. “[Humans have] a tendency to create things, and then let them change.” – Heather Heying In daily life, adaptation can surface as we watch the ‘anchors’ we collectively create shift from feeling like permanent structures to reminders that what’s around them has changed. Shifts in government, holidays, or commemorative objects — and the social functions they perform — are all prime examples. At the genetic level, our bodies evolve through ongoing adaptations that take the form of mutations. There needs to be enough ‘error’ in the ‘printing’ of our DNA to allow our bodies to change in response to different environmental conditions. In that sense, adaptation is truly hard-wired into our physical form and how we exist in the world. Unsurprisingly, adaptation is equally prevalent in the insights industry. The frameworks, strategies, and mappings we create may be perceived as fixed representations of a system; but, in the grander scheme of adaptive change, they function more as snapshots or perhaps catalysts within the flywheels of change. Essentially, they’re tools to drive more informed iteration (not solutions in and of themselves). Ultimately, the value of our work is in guiding iterations, and reminding our colleagues that the ‘right’ solution is rarely the first one, and likely won’t be the last either. Leveraging Adaptation Models So how do we leverage our species’ inherent forms adaptation? How do we facilitate them within cross-functional teams? To follow is an example from work I led that leveraged an adaptive process to inform product development. As part of the central team within a product incubator for social apps, my role was to provide insight into human behavior and culture to help founders and their teams develop products that aligned with the communities they intended to serve. One of the biggest challenges most of the startup teams faced was how to connect people in meaningful ways while also providing ongoing value. Most of the founders tended to approach this challenge by focusing intently on tweaking different features and then testing them with a small group of beta users in tight feedback loops. While critical, what this approach often missed was a deep understanding of what consistently brings people together to form communities with lasting deep bonds. In short, they needed to zoom out, not in. Borrowing from some previous work I led on collective achievement and the optimal ways it can be facilitated on digital platforms, I leveraged Van Gennep’s rite of passage as an inspirational model for how humans form lasting bonds. This was instrumental for the start-up teams within the incubator because it helped them understand the critical, and universal, stages of experience that drive people to form deep associations with a collective. Depending on the team and where they were in their development process, I would start by sharing a mild adaptation of the model itself (below). Then, I added a layer over the model that translated it into the needs of the teams. Part of this involved breaking the model down into three basic stages: trust, bonding, and new identity. I created a goal for each, and then broke the goal down into actions they could take to reach the goal. The actions were rooted in research that ran parallel to rites of passage, most of it focusing on community development practices. These two frameworks were part of a workshop I hosted, which included a set of examples from start-ups and companies that excelled at building products that helped people form bonds. It also included exercises to help teams actualize the steps they would take to apply the principles. While many factors contributed to the success and failure of each team, this application of one way in which humans adapt was instrumental for many of them to create more compelling and enduring experiences for their users. Many of them used the model to frame their product growth metrics across the stages of trust, bonding, and new identity, during their reviews. AI Doesn’t Wander and it Isn’t Embodied It’s worth noting that, even when mediated through an app or other digital experiences like gaming, people experience rites of passage in highly personalized, and often emotional and physical ways. They form trust with others through experiments with vulnerability and humility; they bond through shared struggles in which the give and take of collaboration and support are navigated through relationships within a cohort; and they take on new layers of identity that internalize their own unique mix of the personal, the cohort, and the cultural. While AI models can help us identify patterns across adaptations, and even adapt themselves (listen to two AI agents ‘talk’ to one another here), their capabilities don’t include the forms of embodied experimentation essential to our adaptation. Nor do they involve the emotional connections between humans that might be considered the ‘lubrication’ for that embodied experimentation. In short, we humans see inherent value in the freedom to wander (even aimlessly) through new and different environments with each other; to form bonds through those experiences; and, eventually, reach new embodied understandings (even new physical manifestations) of ourselves. The French situationiste’s understood this well. Wandering explorations of urban environments they called derivé demonstrated how our bodies serve as collective devices through shared experiences across changing conditions. This practice was designed to generate collective meaning-making through “aimless, random drifting through a place, guided by whim and an awareness of how different spaces draw you in or repel you” — to sense the ‘psycho-geographic’ conditions of a location. Applications from derives led to new approaches to architecture and urban planning that integrated what they called the “discovery of unities of ambiance…their principal axes of passage, their exits and their defenses.” The ‘topoanalysis’ of phenomenologists like Gaston Bachelard, brought similar values to light. His examination of intimate spaces such as the house, cellar, attic, drawers, nests, shells, and corners, illustrates how these spaces evoke deep emotional human responses, and even reveries, within us. He argues that these spaces, and our relationship to them, serve as holding bodies for our memories and dreams; and that they play a crucial role in shaping our sense of self. The forms of human adaptation explored in this post, their relationship to our physical environments, and our interactions embedded within them, are largely physical, sensorial, and emotional. They demonstrate how we’re propelled to adapt and interpret and bond, through forces that are uniquely human. Call it intuition, or maybe instinct blended with milieu; but whatever it is, human adaptation doesn’t follow set protocols or adhere wholly to patterns of predictive modeling. As human insight practitioners, we’re in the unique position to leverage the human propensity to wander, to identify symbolic outliers, to sense layers of emotional meaning in our surroundings and interactions. And, we’re experts at sharing these realizations, struggling through interpreting them, bonding through their mutual resonance, and ushering in change (both emotional and physical). I’d like to close with a few prompts: Are you limiting yourself to responding to questions from your team about behavior, or are you helping them see how behaviors sit within human adaptive responses? Are you caught up in solving for an endless string of particulars or are you zooming out to see how they’re connected, and under what paradigms? Are you checking boxes or interrogating the unexpected? Are you shifting focus between process and context? Are you showing up with answers or hosting new understandings? Are you focused on reactions or adaptations? Share this with your friends Tagged with adaptation derivé embodiment psycho-geography rite of passage situationiste topoanalysis Van Gennep Leave a Comment What Works? Curiosity. The Enduring Value of Curiosity in the Age of AI Mar 8, 2025 by jayhasbrouck (Part two in a series of four) Let’s start in the backstreets of Tokyo, with a story from Ethnographic Thinking. My team and I were in the discovery phase of our project, exploring different models for retail experiences. More specifically, we were looking for new and innovative ways that retailers were combining products and services, and Tokyo fit the bill.Our focus was on businesses that had similar offerings to our client: home care, nutrition, and beauty; so our field research included visits to many of Tokyo’s retail hubs and major department stores. In one, we bought a set of tea cups, and observed the intricate packaging practices and customer care rituals of Japanese retail. In another, some team members had skin tests performed at a beauty counter, and received personalized products that matched their skin type. We also visited tea shops, nutritional centers, and took a cooking class.At the end of one particularly long day, having duly completed our research agenda, we wound our way back to the hotel through Tokyo's labyrinth of tiny alleys...and promptly got lost. In our haze, we spotted a shop that looked like it had something to do with shoes, although it didn't look like any cobbler we'd ever seen. In fact, it looked more like a small hotel lobby, with shoes prominently featured in the window. Although this clearly wasn’t on our research plan, we decided to venture in. At the back of the shop, the shopkeeper was putting the final touches on a repair she’d recently completed. We spent some time talking with her about the business — how long she'd been there, what types of repairs she was making, etc. While we were chatting, a customer came in. A clerk appeared out of nowhere to serve the customer, and after some back and forth, pulled out a pair of shoes and showed them to the customer. Then he put them back into a cabinet behind the workspace. They chatted a bit more, and he pulled out another pair — discussed them at length with the customer, and then put those back too. As we stood and watched, we eventually asked the shopkeeper what was happening. After a bit of back and forth, we learned that this was not only a shoe repair shop, but also a shoe hotel, where customers with small apartments could store their shoes when not in use. None of us had ever heard of a shoe hotel before, so we continued to probe. It turned out that in addition to storage, the shop offered ongoing high quality care, and perhaps more importantly, insight and casual banter about the latest trends in footwear — a hub for all things shoe related. This unexpected experience drove whole new directions for our team to innovate. We used it as inspiration for a new concierge model in which experts (not salespeople) offered both product and tailored services or experiences related to it. None of this would have happened had we not stopped at that shop. Having the genuine curiosity to move beyond our plan, and stretch our thinking to be more than just inquisitive, showed us all the value of fully embracing an open mindset. The Universal Appeal of Curiosity You might be thinking that I’m going to call for more curious and exploratory research. Yes, expanding our pool of perspectives and remaining open to unexpected connections in our work clearly has advantages. For one, continually applying a curious mindset increases the odds that new ideas will inspire our work. More importantly, curiosity drives an influx of ideas that eventually cross-pollinate and build on one another. This exposes our organizations to a wider range of perspectives, which stimulates creativity and reduces stifling groupthink — all valuable contributions. But the point I want to make in this post goes beyond that. Across many projects over the years I’ve noticed that curiosity generates the most value when we create opportunities for our cross-functional partners to engage and embody their own curiosity as well. For example, our accidental visit to the Tokyo shoe hotel had such wide-ranging impact in part because the Vice President of Innovation was with us that day in the field, where his own curiosity was activated. In fact, after we got the conversation rolling with the shop owner, he was just as engaged in asking questions as the research team. Facilitating the curiosity of others requires us to shift focus from thinking of ourselves as the only instrument for exploration to serving as hosts for activating the curiosity of others. Your cross-functional partners may be curious about different things than you, but they are curious. This is because the core drivers of human curiosity are deeply intertwined with our evolutionary history and neurological structures. The literature on this topic runs deep, but I think it’s worth highlighting a few insights here. First, like all humans, our collaborators have an intrinsic motivation for novel information and exploration. This fundamental aspect of curiosity has significant evolutionary value. The drive to explore the environment, even without immediate rewards, allows us (and many other animals) to learn about resources, dangers, and opportunities, which enhance our adaptability and chances of survival in challenging environments. Neurologically, curiosity also triggers our dopaminergic system and prefrontal cortex as intrinsic rewards associated with novelty-seeking behaviors. Our responses to surprise are particularly telling in this regard. From an evolutionary perspective, responding to unexpected events is crucial for learning about changes in the environment and avoiding potential threats. But neurologically, experiments suggest that there’s actually a neural link between surprise, memory, and the drive to learn more. Enabling curiosity works because we’re inherently curious as a species. If you can find ways to ignite, engage, and frame your collaborators’ inherent curiosity, they will often become more active contributors to strategic insights and — even better — advocates for those insights across the organization, since their own process of discovery will make those insights more memorable and useful to them. So how exactly do we facilitate and enable curiosity within cross-functional teams? In the following sections, I’ll share three examples from projects in which we leveraged curiosity to accelerate strategic insight and cross-functional ownership. In each, I’ll emphasize a key principle of human curiosity — novelty, information gain, or embodiment — although they each included all three to varying degrees. Immersive Experiences: Hosting Curious Embodiment Let’s start with the last of these — embodiment. In my partnership with Ethnoworks co-founders Soo-Young Chin and Yoon Cho, we developed a series of immersive experiences that were an incredibly powerful means of helping healthcare stakeholders engage their curiosity by experiencing what it’s like to go through life as one of the patients they served. Rooted in ethnography and inspired by street theater, the projects began with ethnographic research into the daily experiences of patients (in one case, uninsured patients, in another, those managing complex health records). Our insights from this work helped us develop a set of personas, each of which served as a role for our stakeholders to assume through a series of scripted scenarios set in real-world locations. Each scenario was designed to directly reflect the challenges of patients from our ethnographic research—and some were quite challenging! One stakeholder fielded calls from his ex-wife (played by one of our researchers) from a golf course as she fretted about managing their daughter’s diabetes diagnosis in light of his recent unemployment. In another, a stakeholder suffered long waits in multiple waiting rooms struggling to track down paperwork to cover breast cancer treatments. In still another, a stakeholder stood on a street corner looking for work alongside day laborers (spoiler alert: a real knife fight broke out!). At key times throughout each immersion, stakeholders were presented with a set of in-the-moment choices to make in response to scenarios and prompts. For example, the stakeholder taking on the role of a day-laborer suddenly ‘suffered a fall’ from a ladder and had to choose between visiting a botanica, navigating care at a local clinic, exploring acupuncture, or ignoring his pain and continuing to send money back to his family in Mexico. After a day spent at various locations, facing often difficult choices and tradeoffs, participants shared their experiences with a broader set of stakeholders across the healthcare industry the following day. This is where curiosity payed off even further. Those who hadn’t played roles in the immersion had the opportunity to question those who did, and, more importantly, to explore their choice logics. They also had the opportunity to view video clips taken directly from our ethnographic research. Breakout groups then served as the venue for stakeholders to brainstorm solutions tailored to (and across) each of the roles and scenarios. In one immersion this brainstorm generated eight new ideas to meet the needs of patients; in another, ten. Results from one included the launch of a healthcare access phone system, and in both cases, solidified new private / non-profit partnerships to meet the needs of patients. Live Model Tests: Getting Curious about Information Gain The second example I’d like to share focuses on ‘live model tests’ – experiments designed to try out a new offering in real-world conditions. I led research for two of these tests, one in Moscow and the other in Toulouse. Both were pop-up experience centers our team built that combined retail sales and sustainable living activities (e.g., cooking classes, yoga, candle making, etc.). The idea was to learn as much as possible in a pre-defined time frame under real-world conditions in markets that had been traditionally challenging for the company. These tests were a fantastic opportunity to gather data about real-world responses to our experimental offerings. We tracked everything from types of products sold, foot traffic, attendance at events, sales agent productivity, and even time and motion studies of customer movements within the stores. So. Much. Data. In fact, the challenge was often to determine what not to track since there were so many opportunities to gather data and derive insights. We spent many long nights after the shops closed, reviewing not only the data we’d gathered, but what we might gather, and what to eliminate. Ultimately, it was our team’s curiosity that helped us all determine the types of information that would be most valuable. Sales results were, of course, critical; but what factors influenced them? What inter-dependencies across various measures were contributing to our understanding of successes and failures for our test? Genuine curiosity helped get us past data overload, and focus on what was most informative and compelling. Overall, the pop-ups served as a vehicles for curiosity, so that we could engage and understand the value of serendipity, unexpected interactions, unanticipated causal chains, and unforeseen interdependencies that drove sales. Signal Scanning: Leveraging the Power of Novelty Much of the work I’ve led over the years has involved pathfinding — a practice that identifies emerging needs and works backwards to develop products that fit, or can adapt toward, those needs. From my days at Intel’s Digital Home Group, where we were tasked with understanding the emerging role of technology in Egypt, Brazil, South Korea, Germany, to more recent focus on generative AI, I’ve deployed a mix of foresight, foundational, exploratory, and secondary research to ensure organizations are investing in products that meet both evolving and enduring customer needs. Early stages of pathfinding often involve a practice known as signal scanning (or horizon scanning) — where researchers explore the margins of a given topic to identify signals of change that demonstrate strong momentum or early signs of growth. The goal is to identify patterns across those signals, and determine if there are strong enough themes that point toward marketplace shifts. A clear understanding of those shifts can help organizations determine whether it makes sense for them to invest in developing products aimed at serving emerging customer needs. While this process includes careful analysis and interpretation of signals, patterns, themes, and shifts, I’ve found that signal scanning itself often offers one the greatest opportunities to engage a team’s curiosity. In fact, more recently I’ve found that gathering and sharing signals of change are in many ways one of the most compelling, participatory, and valuable stages of the pathfinding process. Cross-functional partners with deep expertise in their field are always thirsty for what’s new in their industry. They enjoy the challenge of considering how they might respond to those signals and leverage change in the marketplace. They also enjoy sharing signals of change they find themselves — a way for them to take on the role of curious explorers. Their curiosity about those signals, the different scenarios they may portend, and how the organization might respond, is often more engaging for them than passively receiving reports focused on researchers’ interpretations alone. In short, the novelty of signal discovery, exploration, meaning-making, and debate for our cross-functional partners is often one of the most valuable assets of this work. We just need to provide the platform for them to engage their naturally-occurring curiosity, offer frameworks for interpretation, and get out of the way! AI is Not Curious Returning to the question of the enduring value of our practice for this series of posts; curiosity works as a form of engagement and organizational growth precisely because it’s uniquely human. It’s directly tied to evolutionary benefits like adaptation and survival, as well as neurological reward for learning and remembering. What’s more, across our fellow insights practitioners, among our stakeholders, and throughout the populations and participants who partner with us, curiosity is not only a trait we share, but a bonding force that emerges when we recognize it in each other. A curious colleague or participant is signaling openness, engagement, generosity, and sometimes even empathy. In short, we enjoy learning together. AI models may simulate curiosity by asking questions based on patterns they identify, but ultimately these are prompts that lack the demonstration of commitment and camaraderie that curiosity builds between humans. This contrast is especially evident when you consider how AI tools ask questions of their users. If you’ve ever engaged with a conversational AI, it’s plain to see that they’re mapping patterns of question-making to the contexts of the inputs they receive. In short, they function in ways that might be best characterized as inquisitive, not genuinely curious. Similar to the way we initially held fast to our research plan in Tokyo, AI models never stray from the constraints of the context they’re given — and likely never will, since that’s how they’re designed. In fact, LLM’s, and their prioritization of pattern recognition and reproduction, operate within the realm of the known, even when they are tasked with identifying outliers. Curious humans, however, pick up on odd signs, stray interruptions, absurdities, serendipitous encounters, and other stimuli that trigger new lines of inquiry and increased engagement with each other. We will ask the stupid questions; and the responses have the potential to generate unexpected resonances and collisions with other humans. We will wander and explore without direction; and the result will create alliances and trigger conflicts with other humans. Thank God. So, if we simply want to entertain additional lines of inquiry about a topic, these models can ask productive questions. They can even stimulate, propel, and enrich our innate curiosity. (In fact, the opportunity to surprise and delight here is incredibly promising for education applications.) But, if you’re designing for truly novel, embodied, human-centered offerings, human curiosity (not AI inquisitiveness) rules. More to explore: The psychology and neuroscience of curiosity Merits of curiosity: a simulation study A transdisciplinary view on curiosity beyond linguistic humans: animals, infants, and artificial intelligence Share this with your friends Tagged with curiosty embodiment immersion information gain live model test novelty pathfinding signal scanning Leave a Comment Staying Power Identifying our Enduring Value in an Age of Change Mar 1, 2025 by jayhasbrouck The recent uptick in conversations about the future of applied research has driven many in the industry to pause and reflect. What role will AI play in our practice? How have organizational priorities shifted post-COVID? What counts as ‘actionable insight’ in world where smaller teams can scale rapidly in ‘founder mode’? Which practices and processes can be streamlined? Which ones shouldn’t? These are all valid questions; and while I think it’s important to address them, I’d like to free up some of our collective mind share to get us past the hand-wringing to focus on our foundations. To that end, this post is the first of four that will consider our enduring value in light of relatively recent shifts in the industry. I thought I’d kick it off with a few personal reflections. Timing First, I’ve grown to realize that timing is critical in our industry. Perhaps that’s true in most professions, but it seems particularly pronounced in the world of insights provision. More specifically, the ‘in-the-moment’ value of insights work is indispensable, and I’ve seen it repeatedly accelerate our impact if we take the time to read the signs. It might be tempting to dismiss this as opportunistic. It’s not. It’s about knowing your audience, and tailoring your insights to sync with their momentum, reward structures, and priorities. This shouldn’t be confused with blanket appeasement, which erodes our credibility. Instead, it’s about optimizing our value. I’ve written much more about this recently, but the point I want to make here is that adjusting our deliverables to the context in which our work is received doesn’t mean compromising rigor, which should always be at the foundation of what we do anyway. In fact, this approach allows us to maintain that foundation, while simultaneously freeing us. More specifically it frees us from taking on the weight of thinking we need to solve EVERYTHING, and instead positions our work as more valuable because it is both insightful and contextually-aware — it’s work that’s critically instrumental in lifting everyone up, not just researchers. Facilitation Beyond insights, this also applies critically to the interpretation of data. As research practitioners, we are often trained to think that our interpretations of data are predominant. The same might be said of design thinking, and the inherent ‘it-can-solve-everything’ presumptions it advanced during its heyday. While both are often more informed by theory, years of practice, etc., I’ve got news for you: our interpretive value isn’t always top of mind among our collaborators. If we open our lens a bit, we can see that a huge part of our interpretive value may actually lie in facilitating, guiding, and setting the boundaries for a wide range of interpretations to flourish. If you’re willing to accept this, it too can be remarkably freeing — and powerful. We don’t need to feel responsible for solving “everything, everywhere, all at once.” In fact, it’s often more powerful, and useful, to define the playing field itself. Resilience Finally, a deeper dive into context. Nothing in our training as ethnographers, designers, writers, strategists, or insights practitioners of other breeds, can prepare us for some of the friction we face in the workplace. Your professors weren’t a good gauge for dealing with this; and maybe your coach isn’t either. Interacting (and maybe sometimes ‘wrestling’) with cross-functional peers, over and over again, and being consciously and sincerely empathic about where they’re coming from, gives you two things: a broader lens and thicker skin. We all face a mix of headwinds and tailwinds. It’s up to us to know when to ignore the cynics and when to pause and consider the critical concerns of others; when to build incrementally on institutional orthodoxies and when to advocate for entirely new perspectives. Enduring Value: Intro to the Mini-Series Once we’ve freed ourselves from the burden of solving for everything, and established solid groundwork for facilitating well-timed and thoughtfully-contextualized insights, we’re in a position to work relationally, rather than directionally. That seemingly small shift opens the door to a broader surface for our work; and it is the context in which I want to consider three attributes that serve as what I consider critical foundations for our practice: curiosity, adaptation, and storytelling. In each of the next three posts, I’ll dig deeper into the layers of value that each of these attributes provides, and try to position them in the context of the capabilities of AI. I hope you’ll join me. In the meantime… Share this with your friends Tagged with adaptation curiosity facilitation resilience storytelling timing Leave a Comment Three Questions Leadership in the Age of AI Dec 3, 2024 by jayhasbrouck I’ve had the good fortune to work with a few exceptionally skilled managers over the years. Like a great editor, or a skilled coach, the best managers help you gain perspective and build on your strengths. But more than that, the best managers are authentic, sincere, and invested in ways that make interactions with them feel inspiring, yet challenging. A big part of their job is to get you OUT of your head to frame and direct your energy, which can be a bit destabilizing — and that’s a good thing. Recently, I’ve been thinking about what I appreciate most from exceptional managers, how it plays out in day-to-day interactions, and how both have influenced my own leadership approach. What I see as impactful leadership often arises from a combination of well-timed prompts, moments of insight, and (perhaps more importantly) critical prioritizations, well-informed actions, and keen decision making. At a time when the tools at our disposal are evolving rapidly and becoming much more powerful, these skills are even more essential in the insights industry. In that light, I’ve synthesized my thinking in the form of three driving questions I see good leaders ask as part of productive contributions to teams. What do we need to learn? Our value as insight providers is shifting. Many of us are increasingly asked to go beyond providing insights, recommendations, or developing design principles, to prescribe learning priorities for a team. Leadership that excels in this climate frames conversations with teams around tradeoffs and the respective value of pursuing different lines of inquiry. Operationally, instead of asking “What do we want to learn?” or “What would be interesting to learn?” or especially “What do we need to know that would prove we’re right?” (eek!) these conversations start with organizational priorities, then determine the most critical knowledge gaps, and then understand how team needs fit into both. This is the point at which we can distill strategic research questions, the answers to which satisfy needs across these layers. Strategically, good leadership of this variety helps move beyond simply answering questions for a team and aims toward building core foundational knowledge that helps the organization thrive. It takes deep understandings of the organization’s customer experiences, and situates that knowledge within an outside-in perspective of the organization’s position in the marketplace, to inform a strategy for identifying opportunities the organization is situated best to pursue (both physically and culturally). This is sometimes framed as identifying what’s desirable (do consumers want it?), then determining what’s viable (is it possible to meet those needs in the marketplace?), and finally, defining what’s feasible (is our organization set up to meet those needs in that market?). Each requires a different learning ‘prescription’ offered at the right time and place. What’s the best way learn? It may not be so coincidental that we’re seeing increased value placed in the leadership skills described above. Two major changes are happening in our industry: pace and scale; both driven in large part by large language models (LLM’s). For pace, we’re learning that LLM’s can accelerate many processes in our workflows. Give these models a data set from which to ‘learn,’ and they can outline research or project plans, transcribe interviews, summarize vast amounts of text, identify themes and outliers, jumpstart brainstorms, create prototypes and mockups…all almost instantly. Although not without the risk of hallucination, these models are remarkably effective at pattern recognition and eliminating rote processes, which frees us to spend more time on strategic questions. As for scale, in the very recent past many of us in the industry developed insights through methods that identified patterns from data at scales contained within discrete data sets. We might later draw connections between those insights and other broader themes, but this was most often done by association or inference. While much of this work has long been assisted by computing power, LLM’s now provide the ability to dramatically increase the scale of our work beyond a dataset or association. The scope of LLM’s can span across entire bodies of knowledge, and interpret them via highly tailored prompts, allowing us to mine for insight and identify patterns at previously unimaginable scope and scale. Given these two changes, considerations for how our insights align with organizational strategy become ‘weightier’ in many ways. Where do we go deep? Where do we stretch across? What methods and models make the most sense? Why? With the accelerated pace and unprecedented scope and scale LLM’s offer, the risk of rapidly veering off course is now greater; and, while you could argue that we can now recover from mishaps more rapidly with these tools, we also risk flailing haphazardly without the insightful steer of solid and well-connected leaders that deeply understand and advocate for the right focus for our work. How do we engage? It’s easy to assume that, as insights provisioners, we’re responsible for ‘solving’ team challenges by answering questions. Yet, in the vast majority of cases, our value is far more effective when we sync and parse our insights according to the needs of our teams and the broader organization. There’s much more to consider here, but in this post, I’d like to focus on leadership. As our roles shift within organizations, our strategic value is also changing course. An engagement strategy, with a focus on timing, team alignment, and — above all — a deep integration of organizational priorities, is becoming increasingly important. This means that ‘research reports’ occupy only part of engaging with teams. Instead, we need a mix of deliverables and outreach, applied strategically based on an understanding of the organization and its institutional culture(s). So, there may be times when it’s far more engaging and impactful to present only key pieces of raw data, or conversely, to use LLM’s to generate insights across massive knowledge bases to identify broad patterns. There may also be times when you want to gradually build on the momentum of positive relationships with close colleagues, or, make a big splash with unfamiliar coworkers by introducing a new perspective or deliverable across the organization. A leader brings value by continually gauging the environment and adapting accordingly. When executed well, an engagement strategy can create a North Star that goes beyond ‘solving,’ to accumulate a vision for teams, de-risk bets for the organization, and build momentum around promising new opportunities. It’s rarely perfect, and it often consumes MUCH more time than anticipated. Final Thoughts All of this may feel a bit ‘out of scope’ for some, but without it, we risk operating rudderless (and runaway) projects with new ‘power tools’ we fail to use responsibly. By focusing on the three questions above, skilled leadership helps organizations maintain focus and framing. Are you hearing your leaders ask these questions? Are you finding yourself asking them? Share this with your friends Tagged with engagement leadership learning strategy Leave a Comment Read. The. Room. Leveraging ethnographic thinking to increase resonance with stakeholders Apr 4, 2024 by jayhasbrouck It would be an understatement to say that a lot has changed since 2018, when the first edition of Ethnographic Thinking was published. As the second edition hits the shelves, I’ve been thinking about the context in which this new release is landing. While I believe strongly that ethnographic thinking is more valuable than ever, we’re at a critical moment where strategically (and creatively) positioning that value is essential. This post is a personal reflection on what I’ve seen shifting in the broader world of research / insight since the first edition, and how I’ve tried to leverage ethnographic thinking to respond. As always, your mileage may vary. Let’s start with a couple of high level observations, and then focus on a three-pronged strategy I’ve been using to address them. Hold on a Sec The first observation is simply this: Nobody wants your strategy. While this may be a bit hyperbolic, it’s a healthy premise for framing your approach. Across our stakeholders, many think of themselves as providing strategic insight in one form or another through the lens of their own practice. From their perspective, there’s no reason for them to prioritize your strategic insights simply because you’re a researcher. If you enter into engagements assuming that your insights should take priority, you risk coming off as arrogant — not a very effective strategy for influence (especially in an unsettled work environment). It’s our responsibility as practitioners to build a case and connect the dots to strategy, and research insights are not inherently strategic for many of our stakeholders. So, you may be thinking that if positioning insights to inform strategy is an uphill battle, then you can lean into the role of messenger who conveys customer sentiment. Which brings me to the second observation: The ‘voice of the customer’ is not enough. For this, I’d like to share a brief story about a failure that helps bring the point home. I was working on project focused on understanding the needs of young entrepreneurs. Our team had recently wrapped up initial analysis from a set of field visits across the US, and the company’s leadership was eager to hear what we’d learned. Because we hadn’t fully developed a set of actionable insights, we decided to pull together a set of video clips from field visits with some early theme statements for a screening with the leadership team. On the day of the meeting, we briefly reviewed the background of the project, set the context for the video as a ‘first look’ into lives of our target demographic, and hit play. When the lights went back on, the room was deadly silent. The leadership team’s reaction can be summed up perfectly with just one gif: Clearly, we hadn’t provided either enough of an on-ramp to help them recognize the state and intent of the deliverable, or offered enough interpretation to give them an invitation to engage with the work. We were so steeped in our own process that we failed to recognize where they were in theirs. The takeaway from both of these observations is that your insights won’t resonate with stakeholders if they don’t have a way to understand them from their own perspective—and it’s your job to determine what that is. The reality is that any of your stakeholders who aren’t researchers are working in a completely different headspace most of the day. Your deliverables need to help usher them to your POV. Give them compelling points of entry and fresh interpretive approaches that will lighten their cognitive load and seed their engagement. How? You need to tell a story that resonates with each person in that room so that they internalize your insights. There’s much more that can be said about the craft of storytelling and tailoring narratives to different listener dispositions, but I’ll reserve that for another post. For now, I want to highlight the question of both when stories have the greatest influence with stakeholders and what types of deliverables could help. I bring these two considerations up because I think they’re essential for effective engagement; and, also because I see a professional landscape in which many organizations are more idiosyncratic about the ways they integrate research insights than they were back in 2018. Once companies began building internal teams to bring insight work closer to product work, they also started to understand how that relationship did or did not jive with their approach to innovation, their organizational practices, and their interpretation of what impact means. As insights provisioners operating within this context (in-house or as consultants), navigating how and when orgs ‘digest’ insights is increasingly important. The good news is that doing so involves tapping ethnographic thinking in ways that are very familiar to many who operate in the insights industry. The When and the What So back to the when and what. A while ago, I started to reflect more on these two questions in the context of research deliverables. I decided to go back through key projects and really dissect what worked, what didn’t, and why. I focused on the context of deliverables in particular, so it wasn’t just an assessment of rigor or research quality, but also a consideration of reception, audience, engagement, and demonstrated momentum. What I found overall was that in the vast majority of cases, success hinged on instances where I consciously considered the timing of insights, and de-emphasized efforts to convince my audiences. To follow is a three part strategy I devised in response to this assessment. As I mentioned before, it may not apply to your set of circumstances or work style, but it has proven effective in many of my settings. The strategy relies heavily on turning the ethnographic lens toward your teams or org so that you can understand their values, priorities, behaviors, norms, etc., and adapting your contributions accordingly. It also relies heavily on collaboration with your core team to pull together the right insights, in the right form, at the right time. The starting point is to consider where your stakeholders are in their workflows, and tailor what you have to offer based on their current set of priorities. I’ve always found the catch phrase “meet them where they are” a bit cringey since it leaves little room to challenge the status quo, stimulate new thinking, or evolve ideas; but it comes close to conveying the general approach here, at least initially. So, to begin, ask yourself: Is your team heads-down in their current work streams, operating in get-it-done mode? Or, are they at a pivot point where they need to pause and assess or prioritize? Or, maybe they’re starting something new and are just beginning to give shape to the project goals. Each of these should trigger a different response in the way you frame and engage stakeholders. Just in Time Let’s begin with what is often the most common scenario — a team that’s mid-way through their product development process and is fully immersed in execution. The circumstances here usually include a lot intense, heads-down concentration on building and revising features. Weekly, or sometimes even daily stand-ups, prototype testing, and breakout squads are a common part of the team’s workflow. Do they have time for an extensive research read out, or a mentally-taxing workshop? Likely not. Instead of trying to force the team to disrupt their workflow and adapt to your process, step back, and ask yourself where they are in theirs. Attend their stand-ups, get to know their priorities, their short term and long term goals, try to understand their pain points, their promising bright spots, their motivators, what energized them and provides them with momentum, etc. Then, revisit your insights, break them down into subsets, and determine which ones you can bring back to the team in the form of a ‘just in time’ deliverable (or deliverables) that will help them most. Ask yourself what would help accelerate or fine tune their work best right now? How can you deliver this in ways that dovetail with their workflows so that your work offers added value. This may delay some of the plans you had in place, but guess what? That happens all the time to your stakeholders too. Adaptation equals success in these circumstances, all the way around. Effective research insights that I’ve seen fit into the ‘just in time’ deliverables bucket have been adapted to the needs of the team in a few core ways: they are succinct, directional, and tied to the team’s current priorities and processes. You’re shooting for small-scale adjustments that have potential for outsized impact. Keep your deliverables simple and brief, prioritized for immediate relevance. A useful conceptual model here is something like a newspaper brief — with a tight headline and just enough information for relevance and action. It’s helpful to craft phrases and generate labels for concepts that help people remember key points. Similarly, it’s often useful to illustrate the value of critical takeaways by highlighting one or two key images that help reinforce your message. And, most importantly, keep it short. Outputs that I’ve seen work well in these cases include resources that allow the team to quickly absorb insights, apply their learnings, and then revisit the deliverable when needed. Some examples include: A simple visual diagram / framework that helps define an ecosystem, the flows within it, and current opportunities for action. Brief walkthroughs of competitor products and features to illustrate marketplace context and steer shifts in development that offer product differentiation. Co-creation sessions with stakeholders to quickly iterate on product specifics and prepare ideas for testing. Insight Ignitors It’s not uncommon for researchers to regularly have our own ‘aha moments’ in which we identify valuable connections across our insights and others. As insiders these moments are often exciting realizations, but they aren’t readily apparent for stakeholders who aren’t immersed in research workflows. If you want to highlight the value of these broader themes, you need to bring them on a journey and help them reach the same conclusions you did, and hopefully ignite the same enthusiasm you have along the way. If successful, igniting insight with stakeholders helps them internalize the connections and higher order insights you’re highlighting, and gives them ownership of both so that they can leverage them in their own work. Timing insight ignitors includes two considerations. First, do you have a set of insights along a theme that can lead to a larger POV? This doesn’t have to be a grand thesis. It can be as simple as pattern identification across four or five projects, paired with secondary foundational insights, and possibly a framework that ties them together. What’s most important is that these insights collectively lead to a higher order POV that has direct relevance to product decisions. Second, where are your stakeholders in their own process? Are they open to pausing a bit and joining you on this brief journey? I’ve found that these deliverables are best shared at key milestones within a project’s lifespan — moments where project pivots might be necessary, or where priorities need to be set (or re-set). As for crafting these type of deliverables, you want to guide your stakeholders through a clear and compelling story, with actionable results. You might imagine yourself an attorney who’s building a case for a jury. Set the stage, use (and carefully time) multiple data sources, alternate dense and lightweight content, use visuals to drive home key points, and edit down to just the essentials. Your job is to help them reach the same realization you did. Some forms I’ve seen this take include: A short video to illustrate key themes across research insights and their connection to current product priorities; Competitive landscape analysis that situates your team’s product within the context of current offerings. Residual, Dominant, Emergent analysis that positions your team’s product within industry trajectories spanning from the past, through the present, and into emergent paradigms in the marketplace. Host an Exploration For these deliverables, your goal is to help expand the team’s thinking and guide them toward a ‘North Star.’ You’re not there to convince them of the importance of your work, but to provide the fodder and insights that can stimulate creative, generative thinking. Ask yourself what’s most compelling about the insights you have that can accomplish this. And, further, what sorts of provocations might you offer that spark curiosity and engagement? While there are generally smaller windows of opportunity for these deliverables, they can have outsized impact if they resonate well with stakeholders. In most cases, the ideal setting is when a team has either just finished a project, is starting a new initiative, or is in a strategic planning stage of some sort. You want to catch them while they’re in a reflective mindset, and are pausing to consider critical direction or set key priorities. Sometimes this takes the form of adding to a de-brief or strategy session, or maybe contributing to a vision initiative. Explorations are typically welcome components at of any of these. A useful model here might be think of yourself as a director staging a play. What are the key touch points that help your team connect personally with your insights? How might you offer a variety of content that helps people gravitate toward what interests them most? Have you shared what captivates you most about your insights? Have you left ample room for the team to engage, contribute, and interpret their own views? I’ve found that the following deliverables have worked well when hosting an exploration is the goal: A podcast series or ‘fireside chats,’ followed by an open Q&A, where you invite stakeholders to explore and discuss different facets and considerations on a key strategic topic; A consequences wheel exercise to frame and stimulate long-term thinking and actively integrate stakeholders insights; A microsite or ‘museum’ accompanied by an open forum that collects and curate different POV’s, and gives people the chance to roam, interact with insights, contribute their own perspectives, or bounce ideas off of one another. A Summary To encapsulate the key points of this strategy, and help readers quickly decide where they might leverage these strategies best, I’ve summarized some prompts, models, and approaches for each below. Just in Time — Is your team midstream in product development? What would help accelerate their work best right now? How can you deliver key insights in small ‘bites’ that dovetail with the team’s workflow? Model: Newspaper headline writer Approach: Offer simple statements, images, or summaries that emphasize action and direct connections to current team goals. Ignite Insight — Is your team at a key milestone in their work? What connections across your insights would be most valuable to help your team make better strategic decisions? How can you bring them along to reach the same conclusions you found valuable? Model: Attorney making a case Approach: Usher your team through a clear and compelling series of insights that build on one another. Host an Exploration — Is your team pausing to expand their thinking or find a North Star? Do you have a set of insights that could be configured in ways that stimulate creative thinking? How can you convey engaging, compelling insights that set the stage for generative work? Model: Director staging a play Approach: Curate themes across insights and offer easy ways for the team to explore and dive deeper where interested. Some Final Thoughts Although most of us aren’t operating in the entertainment industry, there are some interesting parallels embedded within this strategy. The following quote has always resonated for me: “You have to have the talent for the art–the music, the acting, the writing, the art–but you also have to have the talent for being in the right place at the right time with the right people with the right approach. I had to become a certain physical person and I had to place myself in certain places in front of, beneath, and around the right people. It’s an art to be noticed; to be necessary; to be needed and desired. Develop it, if you can. If you can’t, then I don’t think any amount of talent will be of any use to you. Talent has to move. Talent has to walk up to people and ask to sit down and talk a bit. Most talent stays at home, and it remains a gift, but it doesn’t get out enough. Someone has to see it in the right context. The context is entirely your job.”Marlon Brando / Interview with James Grissom Photograph of Brando and Marilyn Monroe at the Actors’ Studio benefit screening of Tennessee Williams’ “The Rose Tattoo” Main photo credit: Jay Hasbrouck 2016, Independence Palace, Ho Chi Minh City Share this with your friends Tagged with deliverables insights strategy Leave a Comment AI and the ‘Untouchables’ Considering the limitations of LLM’s Feb 24, 2024 by jayhasbrouck There remain many unknowns about the capabilities of large language models (LLM’s), but their limitations are beginning to reveal some interesting boundaries. More specifically, by accelerating or automating certain functions, their irrelevance in other areas is gradually exposed. In the shadow of all those over-hyped stories about how LLM’s are going to “change everything” there remains an array of human interactions that are ‘untouched.’ By examining what’s both in and out of the purview of these models, this post considers how ‘untouchable’ practices might gradually garner more attention, as well as how their value may shift. Let’s start with three broad use cases for LLM’s that have drawn a lot of media attention: companionship, creativity, and productivity. For the first of these, a whole crop of tools have emerged that are designed to serve as LLM-driven ‘companions.’ From conversations with historical figures (hellohistory.ai, character.ai) to virtual boyfriends/girlfriends (replika.ai, candy.ai) to remarkably personalized advisors (pi.ai), these models have been trained to mimic language patterns that convey familiarity to their human users. Of course, this comes with risk. Some particularly newsworthy instances where these models fall short include at least one suicide as a result of a user’s immersion with an LLM-driven ‘companion.’ This is clearly tragic and unacceptable. However, over time, it does seem possible that the most common patterns of communication that occur within the context of close human relationships, as well as boundaries for safety, could eventually be captured by these models to the point where they can offer familiar and trusted interactions that trigger human responses resembling ‘companionship’ — if we want them. Next, let’s consider models that generate content (images, audio, video, text, etc.), a category of use cases we might label ‘creativity.’ The generative capabilities of tools like Mid-Journey or Dall-E, which can produce images in a vast range of styles, are familiar to many by now. As these models are trained, their capabilities are becoming increasingly more fine-grained and ‘realistic.’ Those following the industry will remember quite vividly how early models had trouble generating images of hands, or inadvertently added extra limbs to figures. But regardless of how much more ‘accomplished’ these models become, ultimately they are incapable of being truly original. They’re locked within the datasets from which they were trained. While they may be able to masterfully iterate on a theme at super-human rates, their outputs are inevitably derivative. Finally, another set of broad use cases for which these models are touted could be classified as ‘productivity,’ which includes LLM capabilities such as summarizing, reformatting, translating, classifying, and automating. This is where we’re starting to see increased attention in the workplace, including a great deal of curiosity among corporate leaders. It’s not difficult to imagine accelerated workplace productivity with these capability-enhancers at our fingertips. However, we also now know that LLM’s are prone to hallucinate, or generate responses that sound feasible, but are factually incorrect. This is because the predictive modeling they enlist prioritizes the most likely next piece of content based on the initial prompt and the set of data from which it was trained — and then iterates. Anyone who’s played with some of these models and tried to ‘correct’ inaccuracies they produce will quickly realize that all versions of the ‘reality’ they produce are treated as equally valid by the model, even if contradictory. You might get wildly different responses from the same prompt, or even within a string of prompts, yet all are presented as equally ‘factual.’ There are many people working on ‘correctives’ (and ‘alignment’) for model hallucination. The jury’s still out on whether they can completely solve this challenge, especially in instances where accuracy is critical. Still, with improvement, it’s not hard to imagine a future in which LLM’s retrieve information, process it (e.g., summarization, translation, etc.), and (re)format it ways that are reliable enough to make them commonplace for non-critical tasks. Use cases like learning, planning, and shopping come to mind. Where does all this lead? The scale and speed at which LLM’s can iterate means that they can offer capabilities that outstrip ours when we need to classify, personalize, reformat, translate, summarize, converse, recommend, generate, or automate digital content. Yet, as LLM’s and other machine learning models proliferate over time, their output will become increasingly common. While compute costs are high now, it will be interesting to see whether tech companies can continue to charge premiums for tools that are inherently designed to endlessly churn out more X and iterate on it at a faster pace — hardly a formula for market scarcity or price stability (unless we start to see demonstrably valuable specialization). Many industry observers have already commented on the AI ‘gold rush’ as a race toward mediocrity. They clearly recognize that iteration is not the same thing as innovation. However, when we drill down on what these models CAN’T do, things get more interesting. The question then becomes, ‘where ISN’T the spotlight shining?’ Here, I’m drawn toward recognizing that ‘analog anything,’ by virtue of its inability to scale or iterate at the pace of LLM’s, seems destined to increase in value. So, instead of ‘replacing’ traditional arts, the capabilities of these models may very well drive the value of things like original paintings or live performances up. This also extends beyond the cluster of ‘creativity’ use cases. Let’s go back to the companionship category. In a future with increasing availability of virtual companionship, ‘real’ companions (especially the exchange of stimulating original thoughts) will only become more valuable — cue the renaissance of salons. Even in use cases focusing on productivity, the risk of hallucination will place increasing value on human interpretation when the stakes are high. I would argue that this is good news for ethnography. After the novelty of these models wears off, and the dust settles from their disruption, the limitations listed above (and likely others) will become increasingly apparent. In the process, understanding and interpreting the consequences of human-to-human interactions and characteristics like intent, morality, motivation, emotion, inspiration, frustration, implication, etc. will become increasingly valuable. While advances in tech may shift this (I’m looking at you, metaverse), they may also contribute even further to increasing the value of non-digitally-mediated human-to-human interactions. Surprise! — these are exactly the realms in which ethnographers thrive. Of course, ethnographers may find LLM’s useful for things like summarization, research planning, or pattern recognition within a data set, but ultimately our focus is on human experiences and interactions, and our HUMAN interpretations of them (original insight). These attributes should increase in value precisely because their unique and non-predictive qualities lie outside the purview of LLM’s. All of this doesn’t preclude the value ethnographers can extend to interpretations of human-AI interactions, including the ways less predictable human characteristics intersect with the ‘logic’ of LLM’s, but I’m focusing more specifically on where we might see unexpected increases value. What types of organizations are likely to benefit most from ethnographers’ unique offerings in this shifting landscape? If digitally-driven experiences and products churned out by LLM’s remain trapped in iterative cycles of mediocrity, demand for live, original, and interactive experiences with other humans may increase. These may include components driven by LLM’s that shape aspects of these offerings (crowd management, interest-matching, adaptive pricing, etc.), but the draw itself would remain focused on human-to-human interaction. In contrast to passive experiences, we could witness significant growth in amusement-park-like offerings, where mutual experience and human interactions are privileged, fostered, and facilitated. (For a somewhat more dystopian view, see Daniel Miessler’s take on how AI might evolve, or watch the clip from AI, below). The skills ethnographers could bring to these settings are those we’ve been offering for more than 100 years. I’ll pull from Ethnographic Thinking here, as a means of summarizing some of those methods and their continued value: Many ethnographers have spent countless hours in the homes, workplaces, and communities of people who are initially strangers to them. Among all the stimuli they encounter in these settings, there is no prescribed set of observations that are always key to forming an understanding of a culture. Instead, ethnographers are continually on the lookout for cues that will help them paint a fuller picture of the culture they’re exploring. While observing, the ethnographer’s aim is to look beyond the obvious and discover the key components that collectively make up an “ecosystem” of observations. These ecosystems are always complex and are made up of many different cues. To demonstrate the wide range and level of their complexity, here’s a sampling of some of the most common observations ethnographers consider: body language, interpersonal interactions, behavioral triggers, contradictions, unspoken priorities, normalized practices, sequences of events, affinities, attachments, repellants, workarounds, social transgressions, implicit hierarchies, priorities, neglected people/places/things, honored people/places/things, displays of comfort (or discomfort), unconscious habits and practices, and interactions with material goods. Each of these finds its way into the ethnographic mind as ethnographers examine the sights, sounds, scents, touches, or tastes of the culture that surrounds them. A core part of this examination of cues is the ability to continually sort and prioritize levels of relevance in situ. This skill is sometimes described as context-awareness, but it also includes visual literacy, layered listening, and the ability to identify and home in on relevant details in order to explore them in more depth. Perhaps someday LLM-driven android ethnographers will take on these tasks — infusing themselves into the very last corners of non-digitally-mediated human experiences, and ushering in a whole new set of moral and existential challenges. Who knows what the human response might be. Photo: 2018, Soo-Young Chin and ‘friend’ in the Changi airport, Singapore Share this with your friends Tagged with AI digital ethnography foresight futures large language model LLMenusen-US1771335241https://ethnographicmind.com

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