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‘Humanizing’ AI

It’s Time to Revisit Universals

(Opinions are my own.)

Preamble

Most children start to take objects apart to see how they work at a very early age. In the field of human development, this type of curiosity is referred to as building a “connection schema,” and typically occurs around the age of two or three. Later, beginning at around four years old, children begin to engage in constructive play, in which they build structures that represent real-world objects, such as roads, furniture, or houses. This is a time when they are developing a sense of control and accomplishment by manipulating their environment to create something new. Toward five years of age, play becomes more complex and cooperative. Here, children begin working with others to build larger, more intricate structures. They also start planning their creations, thinking about the design process and the purpose of the structure, before they start building. This helps them develop good problem-solving skills, as they figure out how to make their creations stable and use their imagination to create more complex, detailed structures. I’d like you to hold this in the back of your mind as you read the rest of this post.

A Brief Diversion into Theory

As many readers of this blog know, the poststructuralist mode in social science argues for the rejection of human universals and objective truths in favor of an emphasis on social constructs, within which we all navigate interactions and ‘intersections.’ Within this view, we live in an ongoing stream of cultural patterns; ‘always already’ being influenced by cultural processes, and in turn, shaping them through our behaviors and interactions. Some of these patterns clash with others, some converge, some spiral around one another, etc.

There are no foundations in this view — just perpetual flows of interactions that host cycles of approximate replication and/or simulacra. Language is one medium through which these patterns transmit, and through it’s deconstruction we learn that there are no ‘truths’ either, only patterns that some of us agree are useful to define our experiences in the world. In a word, the best we hope for is a sort of “truthiness” that varies depending on each person’s own ‘lived experiences,’ affiliations, alliances, identities, etc.

The task most poststructuralists / postmodernists / critical theorists assign themselves is to expose how people are ‘situated’ within these cultural flows, and to deconstruct the institutions or social structures we’ve created within those flows to expose power imbalances. Often, the goal is simply that — to take apart institutions to reveal their underlying workings as ‘oppression.’ Sometimes it’s more political (e.g., “dismantle the hierarchy”), but we’ll leave it there for now, because I want to move on to something more interesting.

LLM’s as the Poststructuralist’s Dream

There are some fascinating similarities between the poststructuralist position and the ways in which large language models (LLM’s) work. For starters, LLM’s don’t naturally assign value to our cultural institutions or social structures, primarily because they don’t integrate the full historical, folk, or biological history of hundreds of thousands of years of human evolution. Instead, they’re trained on ever-changing flows of digitized languages — be they written, visual, symbolic, spoken word, code, or other forms. Of course, they’re particularly adept at identifying patterns within these flows, and they do so at unprecedented scale — but these patterns are not indicative of core human values or operating principles. A good example is ‘common sense,’ a set of valuable anchors humans use everyday to navigate the world, and a phenomenon that largely escapes the grasp of LLM’s.

When we interact with LLM’s, our human queries are used determine a position for identifying patterns across huge data sets. These models then generate an ‘answer’ in the form of ‘new’ content derived from the patterns they identify. They aren’t vested in any truths per se. Instead, their ‘truthiness’ is formed from an amalgamation of patterns. They present their outputs as facts because they were designed to present their findings that way. For them, all of their outputs are equally factual (a pattern or string of relationships shows up often enough to be ‘true’) — even when our real world experience may prove otherwise. This is why their absurd hallucinations are often delivered with such confidence; and it’s also why you hear some people argue that LLM hallucinations are actually a feature, not a bug.

So, here’s what I’d like to ask – If, from the postmodern perspective, our social constructs are all ever-changing amalgamations mediated through language, what does it matter if an LLM taps these patterns and ends up hallucinating? If it makes up quotes, references books that don’t actually exist, cites legal cases that never happened, or advises people to eat glue, aren’t these all just extrapolations of the pattern ‘logics’ within the data it taps? Aren’t all these outputs equally valid, in the same way that everyone’s ‘lived experience’ represents their ‘truth’ within the postmodern condition?

If all of this is so, it would seem to me that LLM’s are in some ways a poststructuralist’s dream; and indeed, we see evidence of fascination with their ‘truthiness’ among both AI-doomers and AI-utopians. From the doomers we hear about nightmares involving AI take-overs of our infrastructures, runaway fraud, and strings of falsehoods built on top of falsehoods driven by those savvy enough to ‘flood the zone’ with their own ‘truths.’ From the utopians, we see product peddlers hype our collective magical / mystical associations with technology, playing into the propensity for human fascination with tech itself — the wondrous tech wizardry that ‘knows’ you and sells pattern identification veiled as sage, magician, or prognosticator. (Did you know that when the telegraph first came onto the scene, people would hold seances around them?)

To techno-utopians in particular, we need to pose a few specific questions. First, if LLM’s postmodern truthiness is, in fact, a virtue, or at the very least, a legitimate reflection of the ‘truths’ these models encounter in their pattern-finding schema, why strive for accuracy, ‘responsible’ AI, or ‘alignment,’ at all? Don’t these attempts at ‘correction’ run counter to their deep faith in pattern identification across increasingly large models and data sets? Even if our utopian friends manage to hold together the contradictions of an appreciation for model truthiness and the prioritization of model responsibility, WHOSE version of responsibility are we talking about within this relativist view? WHOSE ethical standards will hold these models accountable (if that’s even possible)?

Cynics might argue that AI-utopians ultimately don’t care about whether these models are responsible, and even more, whether or not they risk dissolving our cultural institutions with a never-ending plethora of ‘truthiness’ / AI slop. They might also say that some AI-utopians could very well be quiet advocates for deconstructing our cultural institutions anyway (aligned with the postmodern assertion that we’ve falsely attributed power and importance to those institutions); and further, that LLM’s could be instrumental in tearing them down. So, a poststructuralist might argue that the way LLM’s work merely reflects the fluid truthiness of language, and that our assumptions about authenticity or the cultural cornerstones of language were always delusional. Besides, LLM’s ultimately pose little threat to humanity if we’re all hurling toward some form of postmodern trans-humanism that’s free from the burden of cultural institutions, history, folk knowledge, and even biology.

Let’s pause here. I get it — I was in grad school at the turn of the century when poststructuralism was saturating the social sciences — Frankfurt School, Foucault, and all that. But, despite the magnetic pull it has had within academia, I’m asserting that we, as a species, need anchors far more than postmodern ‘fluidity’ or deconstruction — even if those anchors may be imperfect, or if they’re ultimately constructs within ever-changing flows of idiomatic chains.

The Case for Human Universals

The case I want to make here is that current LLM’s inherently undervalue the cumulative, undocumented, emotional, and interdependent expressions we humans bring to our experiences in the world in two important ways. First, they mistakenly privilege language as primary (just as many postmodernists do). Second, that they train on data that’s heavily over-indexed on contemporary linguistic expressions (social media, forums, etc.).

To address the second point, let me introduce you to two people: Donald Brown, an anthropologist; and Manfred Max-Neef, an economist. Although motivated differently, both focused on the value of human universals as foundations, or lenses, through which we reach understanding our place in the world.

For Donald Brown, his drive was to challenge contemporary tendencies within the social sciences to over-index on social constructionism, which he felt exaggerated the range of variance across cultures. Instead, he argued that cultural and linguistic differences are actually products of highly structured human universals. By referencing cross-cultural evidence of hundreds of features (grammar, kinship terms, moral codes, etc.) he demonstrated that our shared cognitive architecture actually sets a finite limit in the range of cultural variations. In fact, despite potentially infinite stylistic elaborations, his research points toward a remarkable convergence on the same essential set of solutions to the human condition.

Max-Neef’s focus on human universals was motivated by his drive to correct the many failures of programs designed to support economic development in the global south. Instead of imposing a system on cultures, he wanted to tie economic development to core human universals that would ensure lasting and deeply rooted success. He argued that conventional development efforts suffered from a conflation of human needs with their satisfiers. Instead, he positioned fundamental human needs (like subsistence, protection, affection, participation, etc.) as finite, few, and constant characteristics of the human experience across time and cultures. How those needs were satisfied (satisfiers) is where he saw variance. Without that critical distinction, he asserted, we run the risk of denying human dignity rooted in our shared fundamental needs, and reducing the person to a malleable object.

LLM’s and Human Universals: What Tools Should we Use?

If we agree to limit ourselves to language in the near term, I want to advance a position on whose version of responsibility we should prioritize as LLM usage spreads, and whose ethics we should use to calibrate our interactions with LLM models. It’s not going to come from Reddit posts; or Wikipedia, or the political whims of the latest US executive branch. While these are all arguably valid training sources, they should be positioned as layers of expression, not core indicators of shared fundamental, and enduring, human needs and values.

We can start with Brown and Max Neef as foundations for training models that are rooted in enduring, human-centered, essentials. A merger of their approaches might look something like this:

Merged CategoryMax-Neef’s Aligned Needs (The Goal)Donald Brown’s Aligned Universals (The Manifestation)Merger Rationale
1. Universal Cognition and ExpressionUnderstandingLanguage and Cognition: Grammar, phonemes, abstraction, classification, logical notions (“and,” “not”).Both agree that complex, structured thought and communication (language) are fundamental and universally present.
2. Social Structure and GovernanceProtection, ParticipationSociety (Law, Sanctions, Rules of membership, Leadership, Conflict, Moral sentiments).Both recognize the universal need for organizing collective life, establishing order, defining rights/wrongs, and mediating inevitable social conflict.
3. Kinship and IntimacyAffection, IdentitySociety (Kin groups, Marriage, Family, Personal names, Collective identities, Sexual jealousy, Generosity admired).Both acknowledge the essential, intimate, and emotionally charged nature of kin relations, love, and group membership for individual identity.
4. Creative ProductionCreation, SubsistenceTechnology (Tools, containers, fire, cooking, work/labor) and Beliefs (Art, music, dance).Both highlight the universal capacity and requirement to transform the environment (tools/technology) and produce symbolic, aesthetic forms (art/music).
5. Well-being and Mental StateIdleness, FreedomBehavior and Psyche: Private inner life, emotion expression, dreams, logical notions, mood-altering substances, feasting/play.Both identify universal psychological states and personal freedoms/activities necessary for mental equilibrium, rest, and imaginative thought.

To this, we might add insights from key evolutionary scientists that explore the links between human foundations and expressions of them:

  • Cultural Group Selection, posits that competition between groups with different cultural traits (norms, institutions, technologies) drives the evolution of large-scale human cooperation and altruism (Joseph Henrich).
  • Memetics, a model for a self-replicating cultural unit (a meme), that creates a simple, powerful analogy for cultural transmission in which ideas, behaviors, and styles are replicators that undergo a form of Darwinian selection (Richard Dawkins).
  • Punctuated Equilibrium (a paleontological pattern consisting of long stasis punctuated by rapid change), has also been used to highlight the role of historical contingencies and the concept of spandrels (non-adaptive byproducts) in human evolution and culture (Stephen Jay Gould).
  • Dual Inheritance Theory, models how genetic evolution shaped the human capacity for culture, and how cultural traits, in turn, influence the selective pressures on genes (e.g., lactose tolerance in dairy-farming cultures), (Robert Boyd & Peter Richerson).
  • Kin Selection theory, based on the concept of Inclusive Fitness, explains that an individual can increase their fitness by helping relatives who share their genes, providing a direct evolutionary basis for family bonds and cooperation (W. D. Hamilton).
  • Sociobiology, systematic application of evolutionary principles (like kin selection and cost-benefit analysis) to explain social behavior, including the development of biological explanations for human social structures, aggression, and gender roles (Edward O. Wilson).

Finally, we could add data sources that encompass an array of ‘satisfiers’ / cultural expressions. The annals of anthropology include diligently recorded, rich archives of human cultures and practices that allow us to identify similarities, differences, and the ways our foundational needs are addressed across time and cultures. Some particularly comprehensive sources we might tap include:

  • Human Relations Area Files (HRAF) – an international consortium of institutions dedicated to promoting comparative study of human culture, society, and language. (Database here).
  • Standard Cross-Cultural Sample (SCCS) & Ethnographic Atlas – datasets developed for quantitative cross-cultural analysis, a subset of the larger Ethnographic Atlas, now housed as part of the World Cultures Journal.
  • D-Place (Database of Places, Language, Culture, and Environment) – an open-access resource that integrates the cultural, linguistic, and environmental data for over 1,400 societies
  • Aarne-Thompson-Uther (ATU) Index – a comprehensive classification system for folktales – related indices and sources can be found here.

While some of these theories may be controversial in certain quarters, they can collectively help us lay a foundation of human universals that’s far more grounded and systematic than online echo chambers or social media pissing matches. If we enlist them in conjunction with a wide range of similar sources, we can provide a training foundation for LLM model pattern identification that prioritizes human interests rooted in deep understandings of human behavior.

The objective here is to establish an initial framework to anchor these models in what I’ll call the moral foundations of human universals. This might be achieved by using the resources above as key components of model context protocols (MCP’s) for consumer-facing LLM’s, or perhaps as content for reinforcement learning from human feedback (RLHF), but there are people much more qualified than me that could best leverage these resources to train LLM’s.

Is Gaming The Next Frontier?

Returning to the first point above, let’s dig a bit deeper, and think about future alternatives in which AI doesn’t treat language as primary (particularly text).

To get started, consider religion. Religion is about far more than scriptures. These are only partial relics of a set of practices, interactions, and emotions that compel us to come together for spiritual fulfillment and bonding. Other critical relics of religion also include the institutions and artifacts that mediate practices: temples, churches, kneeling, bowing, the cloth of priests, the smell of incense burning, etc. Practices and relics together form what I’m calling anchors that are far more rich, layered, historical, and critical to the human experience than LLM’s currently capture by focusing on written language. In fact, there’s MUCH more to be gained by understanding what qualities have made these anchors and related institutions ‘work’ for humans across cultures and time than in deconstructing and re-assembling approximations of language related to them.

So how might that look? There are some pioneers in this space that are beginning to flesh out different approaches that show promise. The most likely near term traction for this approach is within the fields of gaming and entertainment, where world creation has always played a central role. For example, Michael Libby’s Worldbuildr creates digital ‘twins’ of guest experiences (games, theme parks) to help simulate flows, interactions, and physical requirements for players and guests. Or, take Fei-Fei Li‘s World Labs, a “spatial intelligence company, building frontier models that can perceive, generate, reason, and interact with the 3D world.” Their mission to: “transform seeing into doing, understanding into reasoning, and imagining into creating” has recently produced Marble, a multimodal world-building model that allows users to create and edit 3D environments from various inputs like text prompts, images, and videos. Efforts like these, in which the experiential is moved closer to the center of focus appear to leave more room for integrating human universals within the rich range of expressions they take. Perhaps even more promising is the direction Yann LeCun appears to be headed. His departure from LLM’s centers around a vision for building “world models,” or systems that perceive their environments and grasp physical concepts.

Let’s hope these visionaries take the time to build models that don’t stop at integrating principles of physics or human factors, but treat our shared human values as a critical foundation for their work.

Photo: Examination of Easy Listener, by Nick Veasey

Meaningful vs. Compelling

Are we headed toward a post-meaning online world?

Think back to the most recent movie you saw that you really liked. What did you like about it? What sort of impression did it leave?

Now, think back to one of your all time favorite movies — one you always like — a movie you’d watch again and again, anytime. What keeps you coming back? What makes this movie so special to you?

Now let’s consider some key differences between each of these movies.

Which one pops up in your mind’s eye when you’re just moving through your day? Which one resonates more deeply on an emotional level? Which one have you recommended to friends more?

The difference between the ways you responded to these questions surfaces some of the qualities that distinguish meaningful from compelling content; and this difference is becoming increasingly critical in what we now seem to be calling the ‘attention economy.’ Most importantly, this difference is a core part of determining how algorithms are shaped, which plays a critical role in establishing and reinforcing what constitutes ‘common’ knowledge (think content-scraping for training AI models), as well as which content becomes resilient (since the role of memory is an essential part of this difference — more on that later).

To get us started, I’ll explore how both meaningful and compelling content have played distinctly different roles as the web matured. Then, I’ll consider how we interact with each, the value ascribed to them, and how they’ve been (and could be) reconfigured as we head into a new era of interactions in the age of AI.

Breaking it Down

A close look at the ways the tech industry traditionally related to content helps set the stage. Although the history is diverse, the industry originally focused on the informational and the factual. Content was, simply, data. This made sense, given the core nature of digital technology itself, and the way programmers and engineers drove most of how tech products were developed. From strings of zeros and ones to coding logics, these systems were ultimately rooted in binary ‘call-and-response’ interaction models: inputs and outputs. Content functioned technically as a resource that could be referenced or processed. Eventually, the internet took this model to another level. It made information available globally through a robust and decentralized distribution system that had never existed before. Yet, the interaction models within it remained relatively constrained by utilitarian transactions.

Then came social media. While you could argue that it was originally conceived in primarily transactional terms — finding and ‘friending’ others — it quickly became evident that many users found these interactions to be valuable beyond the transactional. They valued the connection itself. As these platforms matured, there were clear indications that people wanted to include a much broader range of expressions, interactions, and co-modifications of content that included qualities like nostalgia, commemoration, fondness, flirting, and humor (to name a few).

It was perhaps no accident that the rapid growth of social media nested comfortably within a parallel rise of reality entertainment (and the decline of traditional media institutions). There’s much more to consider here, including the long (and deep) tradition of seeing ourselves as sources of entertainment (parlor tricks, seances, folk dance, ice bucket challenges, etc.) — more on that in another post. The point I want to make here is that transactional relationships with digital content only went so far. People were seeking deeper engagement, with content and with others. Their impulses were driving them to look for much more than just getting an answer. They were seeking exchanges with other humans, channeled through content that was emotional and authentic. We’re still seeing this play out in our digital lives everyday, of course.

Whether you perceive these emotions and connections as positive influences on our collective social fabric or not, it’s clear that the range of our interactions has expanded, and our shift from transactional to social revolutionized the way we think about digital content. A techno-centric view might posit that technologies like social media ushered in a new wave of behaviors. An anthropological one might argue that those behaviors always existed, and the technology caught up to (and exploited?) them.

“Huh” or “Hell, Yes!”

Let’s return to the differences between meaningful and compelling. While meaningful experiences are typically tangible, useful, and timely, they don’t necessarily fill us with the urge to reach out and share with others. Their value comes in the form of knowledge and understanding. In contrast, compelling interactions are magnetic, personal, and tap shared cultural narratives. Even more importantly, compelling interactions are catalyzing. We’re drawn to them again and again because they resonate so deeply for us. This depth propels ownership, action, and agency.

The table below summarizes some of the core differences I’d like to highlight.

MeaningfulCompelling
Transactional / Informational
Tangible
Useful
Timely		Engaging / Magnetic
Deeply Personal
Culturally Relevant
Catalyzing
Outcome: Knowledge & UnderstandingOutcome: Ownership, Action & Agency

In other words, meaningful content makes you go “huh,” or maybe “I see.” Whereas compelling content makes you go “OMG, did you see THIS?!” Meaningful content helps you feel informed, appreciative, or even satiated, but its function is ultimately utilitarian — then you move on. With compelling content, you feel driven to share, to build on the momentum of your enthusiasm by adding your own energy to it (sometimes repeatedly).

Value and Risk

In terms of our daily lives and interactions with content, we can think of meaningful content as the something that’s often used to resolve small disagreements, to navigate, to make decisions, or to learn something new. Overall, it’s a tool, not a propellant. Compelling content, on the other hand, has a distinctly emotional component. Interacting with it is embedded with qualities we don’t associate with the transactional nature of meaningful content.

Psychiatrist Dr. Goulston argues that when people are engaged in compelling interactions, they feel respected, engaged, and invited. They feel talked with (instead of talked at, or over), and then behave as if they’re choosing to do something. Perhaps most importantly, compelling interactions catalyze the initiation of new interactions from people exposed to content they find deeply engaging and highly resonant. They’re driven to share their experiences with others (remember your all time favorite movie?).

In this light, it’s easy to see how compelling content can set the stage for increased customer loyalty and brand resonance in marketplace settings. Examples include engagements with content that grow organically within a culture or community of customers. Emotional connection, authenticity, and active participation are some of its core characteristics. Here we’ll find superfans (not paid ‘brand ambassadors’) who enthusiastically share content that’s compelling to them. This can be exceptionally powerful and empowering when organizations are savvy enough to include them. They co-create, co-opt, and co-evolve brand identities (think IKEA hacks, cosplay, or fan fiction).

It’s also easy how to see how compelling content can be misused. In Jonathan Haight’s now classic analysis of moral judgments, he uses the metaphor of a rider sitting atop an elephant to illustrate how we reach moral decisions. His argument is that intuition (the elephant moving along) tends to come first, dominating our moral compass. Its nature is to go where it likes (what compels it). Rational thinking, represented by the rider, has some control, but is secondary to the often overpowering drive of the elephant.

Hacking this dynamic by ‘luring’ the elephant with compelling content is the risk I’m referring to here. There’s now ample evidence that conflict draws clicks, as does other content that one could argue has questionable social value. But the intent isn’t always nefarious.

Motivational science also plays on this dynamic to optimize for positive behavior outcomes. For example, in the early days of recycling in the US, it was initially difficult to persuade people to change well-engrained behaviors around waste disposal. Gradually, however, municipalities began to realize that if they tethered their recycling goals to something people cared deeply about — their children — they could influence household habits. Many launched recycling education campaigns in schools, which included both take-home materials and a new environmental consciousness students brought home to their parents, who were then ‘compelled’ to recycle at least in part out of their deeply rooted love and care for their children. Ownership, action, agency.

Beyond Binary: AI

What does this difference between meaningful and compelling mean for the digital world, and especially the web, moving forward? One view is that meaningful content may increasingly be subject to automated and endless reinterpretations via AI’s ability to scrape, summarize, re-word, and translate content. This could lead to commonplace repackaging of meaningful content that personalizes it in ways that give it ‘voice’ (both literally and figuratively). Could this transform meaningful content into compelling content? Will AI essentially merge the two in the form of ‘companions’ (or agents) that time, contextualize, and find personal points of relation and activation of meaningful content for us?

This potential merger (dissolution’?) is something our collective imaginations have only recently entertained. Even in the fairly recent past, our interpretation of how we might interact with AI was constrained well within the bounds of traditional meaningful-content conceptualizations. Take this clip from the film AI: Artificial Intelligence, where AI is portrayed as a font of knowledge largely conceived of as a giant vending machine for human queries. Here we see a future in which meaningful content is simply scaled up, but not reinvented as compelling content. In fact, while Dr. Know is all-knowing, his services are provided purely on his terms — and he’s quite literal!

We’ve come a long way in thinking about how we might interact with computers, in a short amount of time.

If we take the potential dissolution of distinctions between meaningful and compelling interactions and extrapolate it, it’s not hard to conceive of a post-meaning AI-dominant online world, where compelling content and interactions are ALL that matter — and meaning (or accuracy) is served up as a secondary priority embedded within compelling forms, tailored to our interests and preferences. Some would say this is already happening. They point toward common hallucinations and inaccuracies that ‘sound right,’ and are positioned by AI as correct, but are completely fabricated based on familiar patterns the tools have identified. In short, AI models may inherently index toward compelling at the expense of meaning (or accuracy). Another risk is that institutions or organizations generating AI content may use this to leverage our ‘elephants’ in ways that obfuscate the reasoning of our ‘riders.’

In a recent piece that explores the impact of AI hallucinations on our interactions with content, Matteo Wong takes this a step further: “AI products could settle into a liminal zone. They may not be wrong frequently enough to be jettisoned, but they also may not be wrong rarely enough to ever be fully trusted. For now, the technology’s flaws are readily detected and corrected. But as people become more and more accustomed to AI in their life—at school, at work, at home—they may cease to notice. Already, a growing body of research correlates persistent use of AI with a drop in critical thinking; humans become reliant on AI and unwilling, perhaps unable, to verify its work.”

Maybe. Or we could see an erosion of trust in online content (including suspicion that its purveyors are toying with our ‘elephants’) that leads to a rise in reliance on verifiable real life experiences. Events, gatherings, and any other interactions not mediated digitally may grow in importance, as a sort of radical empiricism rises. These gatherings (be they salons, conferences, clubs, debates, or other) may become the only way we feel we can find what we consider sincere, faithful, meaningful interactions, and authentically-expressed enthusiasm for compelling content. This is not to say that AI couldn’t play a useful role in organizing such events, or identifying alignments between them and potential attendees, but that ultimately the value of real life gatherings may lie in our collective appreciation for face-to-face experiences in which meaning is generated through conversations, and compelling interactions are ascertained by looking directly into some one’s eyes.

There’s an important take-away here for Silicon Valley: it’s probably not a good idea to throw AI at every possible interaction. Product-market fit isn’t rooted in what’s technically feasible, but in the value and trust a product builds with its users. At a minimum, considerations for using AI in any product should include the degree to which users are seeking meaningful versus compelling content and interactions, as well as the potential risk for inaccuracies to erode trust. This may not matter much in creative tasks, or conversational interactions; but when we need to rely on content and value its meaningfulness for productivity, for example, the stakes are much higher.

In Closing: A Break from it All

I’ll leave you with the clip below from a recent stay in New York — a glimpse of people engaging in playful acts of joy, scooting around in bumper cars in a rink of giant pink inflated balls. No phones. Probably no meaning. Just deeply compelling fun. And, perhaps most notably, no AI.

What Works? Storytelling.

The enduring value of storytelling in the age of AI

(Part four in a series of four)

Let’s start with a story.

I was once part of a sizable team working on what’s sometimes called a Phase Zero project — the type of project with a brief that begins “The Future of…” The team included a few designers, a historian, an account manager, me, and a crew of other advisors who came in and out as needed.

About halfway through the project, we reached a significant milestone and decided to treat ourselves to some wine to celebrate. We cracked a bottle from a case we’d bought, and sat there in our project room feeling accomplished, welcoming our hard-earned decompression.

The room was situated in a row next to others in a huge loft-like space that was constantly abuzz with activity. It had three walls on which we leaned stacks of foam core boards filled with post-its. There was no door, so our colleagues could easily see the entire contents of the room just by passing by—which got us thinking: anyone could come by and help themselves to our wine! There wasn’t really a problem with theft, but we wanted to preserve our stash for future milestones.

As we sat there and sipped, the conversation drifted toward where we were going to hide our case of wine. The company kitchen was obviously a poor choice; so was the locker and bike rack area—way too much traffic. As we considered various options, the lead designer on the project suddenly stood up, grabbed one of the many Sharpies scattered about, walked over to the case, and scrawled the phrase USER VIDEOS on top of the box.

“There,” he said, “No one will ever touch it!”

As a relatively young researcher, I’m pretty sure I didn’t find his joke very humorous at the time. But in hindsight it was perfectly timed and well-delivered. And, he had a point. The reason video clips of interviews with participants can fall short is that all too often they aren’t situated within a compelling narrative. Talking heads aren’t a story, and simply sharing them as ‘the voice of the consumer’ risks random interpretations of their meaning among your stakeholders. The larger narrative we construct about participants’ experiences IS the insight. It should convey the interpretive value we bring through the positioning and sequencing of stories from ‘the field’ that help our message resonate.

“The difference between giving an example and telling a story is the addition of emotional content and added sensory details in the telling. A story weaves detail, character, and events into a whole that is greater than the sum of its parts.” —Annette Simmons, The Story Factor

With this in mind, I thought it useful to take a deep dive into storytelling from an anthropological perspective, with an emphasis on what gives it lasting value, as well as where AI fits within our long shared history of storytelling. So, let’s start with the basics.

Why we Tell Stories

“Our species thinks in metaphors and learns through stories.” — Mary Catherine Bateson

Anthropologist Mary Catherine Bateson, daughter of Margaret Mead and Gregory Bateson, is recognized for having enriched the discipline in many ways, especially her contributions to intergenerational communication. And like many anthropologists, she leveraged the power of storytelling in two primary ways. First, she saw the value of storytelling among her participants as a means of understanding how they embodied and shared cultural norms in personal ways. Second, she deployed storytelling in her own work to help her audiences see how their experiences within their own culture are echoed in, or contrasted with, those of another. In both cases, the value of storytelling centered much less on conveying facts or convincing others of a particular position, than on sharing narratives that evoked, inspired, connected, and transported people beyond the current moment and their constrained spheres of concern.

“Information has value only for the moment it is new, but storytelling is capable of releasing information even when the story is very old…” —Anne Pellowski, The World of Storytelling

On a personal level, we tell stories for many different reasons: to seek emotional clarity, process feelings, connect with others, achieve affirmation, fill a void, or make sense of the unknown. But above all, we tell stories to share and; if the conditions are right, transform ourselves and others in some way. Social research into the dynamics of live storytelling demonstrates that the process is primarily a co-adaptive interaction, where the storyteller offers up a personal narrative that they entrust with their audience. In return, invested audiences demonstrate empathy and engagement by showing that they care enough to take the time to listen and respond.

This creates a form of trust-building that’s critical to the shared meaning-making that storytelling provides. In short, good storytelling connects. It’s the medium that holds the shared experiences that build empathy through the dynamics between storyteller and audience.

“Stories provide the context to understand the awakening of transformation…This experience is at its most powerful when it occurs at the cusp of an era, a transitional turning point in a person’s life, or in current events, as we experience the world changing before our eyes.” — Bobette Buster, Author of Do/Story

On the biological level, neuroscientists have identified ‘mirror neurons’ that are triggered inside our brains when we watch the actions of others. These neurons, which are also associated with empathy, have been shown to activate when we immerse ourselves in stories. So, when a storyteller expresses an emotion, the neurons that fire in their brains are mirrored in the brain of the listener.

Even more intriguing, neurologist Dr. Uri Hassan‘s experiments have revealed that not only do these neurons fire in the brains of the listener when triggered by a storyteller, but that listeners often anticipate upcoming emotional stimuli, as though they know what will happen next. Even with different stories and different listeners, the pattern persists. Whether or not this can be construed as evidence for storytelling as a hardwired, primal, trait remains up for debate. But clearly it is critical to how we interact with one another and navigate the world around us.

Storytelling Basics

Many social scientists, behaviorists, and even neuroscientists have long argued that all humans are inherent storytellers and that we are continually engaging in different forms of narrative every day. This is evidenced by much of the research into storytelling in the field of human development.

If you’ve spent any time with kids, you know that they have very unique ways of telling stories. The plot (if there is one) often wanders; they tend to ramble on, end abruptly, suddenly shift topics, or connect unrelated ideas. Although the absurdity (and cuteness) of these stories can be entertaining, they can also be challenging to follow. What you’re witnessing in these tales are little experiments with language that eventually build toward full stories.

Human development researchers have identified five main stages of narrative development that map roughly to different age groups. These range from early ‘heaps’ of content that tend to form at around age two, through to ‘true narratives’ that emerge when children are five to seven years old. At each stage, narratives build greater context, reflect increasing awareness of cause and effect, and eventually delve into resolving a problem and connecting motivations of characters to a plot.

Below, listen to a 4 year old share a primitive narrative…

…and here Claire (advanced for her age) shares a focused chain.

Each of these stages are layers we all build upon as we grow; and any layer is accessible to us as we share stories in everyday settings. For example, early stages of brainstorming could be categorized as ‘heaps’ or ‘sequences.’ Some memes might be considered focused chains, where additional knowledge is required to interpret an ending (a story fragment embedded with a ‘wink’ shared among the like-minded). The same can be true of small group conversations between people familiar with one another, where participants tell just enough to be understood by others, who then complete the story on their own based on shared views of a topic. In short, we’re all telling some sort of stories, all the time. They may not always be full-fledged ‘true’ narratives, but they tap different stages of how we learn to tell stories, depending on our audience and contexts.

Putting Storytelling into Practice

Hopefully by now I’ve convinced you of the value and underlying drivers of storytelling. But you may be thinking “I don’t have the time to master this skill.” Well, we don’t all have to be world class storytellers to leverage the connections that storytelling brings. In fact, it can sometimes be enough to engage one of the subsets of storytelling we all learned as children.

For example, if your audience consists of people with very similar shared experiences or close ties of some sort, it may be enough to offer focused chains, in which events follow a theme, but the additional knowledge of the audience is required to interpret an ending. In these cases, you might enlist focused chains to reinforce bonds between the storyteller and people in the audience, as well as between audience members themselves.

Similarly, primitive narratives, in which events follow a theme and there is some indication of cause and effect, could be used as short cuts for storytellers to identify with their audience, without the necessity of constructing a logical sequence of events. Memes often function as very effective primitive narratives. Neither of these require developing full true narratives, but can be very effective ways of leveraging our shared storytelling integrants.

Regardless of your strategy for leveraging different storytelling stages, you’ll want to prioritize a few critical techniques that will always help you tell stories effectively: emotional connection, authenticity, interaction.

  • EMOTIONAL CONNECTION: Expressing your emotions evokes empathy between you and your audience. Center your story on emotions that will trigger connections with each audience. You don’t necessarily need to cry or shout, but letting people know how you feel is incredibly powerful.
  • AUTHENTICITY: Variation, imperfections, and vulnerability help stories feel real and accessible. It’s a myth that you need to “find” your storytelling voice, just speak as if you’re talking to one very understanding friend.
  • INTERACTION: Good stories evolve and adapt based on exchanges between the storyteller and their audience. These interactions draw storytellers and audiences into a stimulating exchange that invites the audience into the creative process. Provide ways to help your audience become part of a co-constructed narrative – ask questions, pose hypotheticals, and leave room for pauses where the audience can digest and reflect.

In addition to these core techniques, Bobette Buster has written an invaluable guide that offers an enlightening set of ‘rules’ for impactful storytelling. Everything from making your story personal, to highlighting one gleaming detail, and emphasizing the senses, are all baked in to tiny book that serves as a critical foundation for constructing stories. She also shows us exactly how storytellers leverage metaphor and passion to move their story from personal to shared meaning. I won’t attempt to summarize her work here, mostly because it’s worth its weight in gold to any student of storytelling (just go buy it, the link is right up there!).

However, it’s worth thinking about how some of her rules map to the core techniques listed above. Below is a rough sketch of how you can use some of Buster’s rules to accomplish this.

Finally, I’d like to expand on a key component that Buster and other storytelling scholars emphasize. Nearly every storytelling expert argues that one of the most critical components of effective storytelling is letting go so that audiences have the space to identify personally with the content by engaging their own imagination and personal references.

Letting go often unfolds in stages as storytellers seek out new audiences in ever-widening circles of safety. This helps them adapt their narrative and find new ways to visualize more satisfactory alternatives. Experts describe the process as one of claiming ownership (comfort and confidence in telling the story), building trust (prioritizing honesty & transparency), and gaining permission (understanding when your audience is ready and willing to hear different parts of your story). They also emphasize that these techniques should be used only after the storyteller has fully processed the experience, and understands its meaning for themselves in order to usher others into it.

The Moth, a storytelling organization that hosts live events, does a great job of helping storytellers both actualize their stories and “let go” to make room for the audience. They not only encourage storytellers to “have some stakes,” in which the teller stands to gain or lose in the telling, but to also “…Play with the details. Enjoy yourself. Imagine you are at a dinner party, not a deposition.”

Storytelling in the Workplace

In workplace settings the goals of storytelling shift a bit. Instead of connecting with a broad audience, you often need to connect with — and influence — a wide variety of stakeholders. While this does make things more complex, tailoring your story to them and their circumstances will go a long way toward connecting with them on a personal level. Your mission, using the tools described above, is to get beyond convincing, so that you can change hearts and minds. In short, you want to evoke, inspire, and transport them (and maybe you); and to do that, it helps to start with an understanding of who they are and what motivates them.

Before heading into any room of stakeholders, remind yourself that each person there is coming from a different experience that day, and more importantly, few of them have likely considered the the role of research in their work. What’s more, many of your stakeholders likely exist in a world of continual context switching, which increases the importance of ‘meeting them where they are’ as you begin to share your stories. This means that the first minute or so of your story is incredibly important, since it sets the tone for the interactions that follow and provides an ‘on ramp’ for them to acclimate and immerse themselves in what you have to say.

Ask yourself where the team is within their development process. Are they early stage? Mid stream? Post-launch? Each of these will have bearing on the how they listen and the degree to which they can absorb additional cognitive load. You’ll often find greater receptivity for stories focused on foundational insight at early stages, or during a de-brief at the end of a project, when their appetite for expanding their thinking is greater. Take the time to assess what forms might work best at these stages as well. Are they looking for a North Star? Do they need a hero’s journey? A common enemy (aka competitive landscape)?

Shorter stories that emphasize action tend to resonate best when the team is fully immersed mid-stream in a project. These shorter stories may later add up to a larger POV, but ask yourself whether or not the team has the capacity or interest to soak in a ‘master’ narrative at this point. Consider collecting these shorter insights to pull together in a larger story that emphasizes sense making and reflection when the team is better situated to receive it.

Finally, ask yourself where else your POV might be useful. Pull your head up and look around, there may be multiple places to land your work. Are there groups who are in a better position to benefit from your insights right now? Are there people who are well-positioned to amplify the story you’d like to tell? Who could advance (and challenge) your thinking? To whom might you reach out for feedback? Where is there mutual benefit for others to champion your work?

AI and Storytelling

By now you might be thinking, “OMG, when is he going to get to AI?!” Your time has come, my friend.

It’s now common to hear that AI models can generate stories. While it is true that they can use predictive language modeling to manufacture texts that include the basic structures of stories, they are inherently limited to patterning narratives after the data on which they’ve been trained. Some have described these models’ attempts at constructing stories as a ‘race to mediocrity’ to describe the quality of the narratives they produce, since they’re inevitably constructed from content that’s been ‘averaged out’ across all of the stories they’ve been fed. They can iterate ad nauseam on a theme, but can’t produce unique or novel ideas (or any actual ideas at all). More importantly, they can’t offer the kinds of connections from one human to another that storytelling provides.

That’s because a huge part of storytelling’s value lies in how it’s experienced. Storytellers learn as they share by experimenting with, and building upon, layers and sequences of metaphor — discovering where they find resonance between their passion and their audience’s engagement each time they share. This interactive exchange between the storyteller and audience responses not only helps the storyteller evolve and refine the narrative, but also (and more importantly) builds the scaffolding for trust between them and their audience.

While large language models may iterate and refine their stories based on human feedback, their results are always already focused on pleasing the user. Real-time feedback in the form of an audience’s laughter, gasps of shock, tears, boos, poignant silences – none of these are inputs that large language models integrate in their process of refinement. And, even if they eventually do, it’s difficult to see how these models might find value in connecting with, and transforming alongside, their audiences.

You could imagine a scenario in which a human prompt engineer develops a storytelling system designed to connect and transform audiences. Maybe it’s a story that adapts itself each time it’s told to a different audience. Maybe it’s delivered by an animatronic robot. Even then, the relationship between the audience and the model is predicated upon the priorities and dispositions laid out by the prompt engineer, who is ultimately (presumably) driven to connect with the audience. The model itself doesn’t care. It isn’t passionate about sharing any story. But a storyteller does care; and, good storytellers are full of passion.

All this is not to say that AI isn’t a valuable tool for honing stories that storytellers craft. I’ve heard of storytellers that use AI to help them bring order random thoughts, to ‘interview’ them about an experience, or even to better express themselves in a voice they’ve established as a signature form of their unique expression. Perhaps we’ll see storytelling evolve over time as these tools are increasingly leveraged by storytellers. In the end, AI’s greatest value won’t come from taking the place of storytellers but to enrich the worlds storytellers co-imagine with their audiences.

Man…is the storytelling animal. Wherever he goes he wants to leave behind not a chaotic wake, not an empty space, but the comforting marker buoys and trail signs of stories. He has to keep on making them up. As long as there’s a story, it’s all right. Even in his last moments, it’s said, in the split second of a fatal fall—or when he’s about to drown—he sees, passing rapidly before him, the story of his whole life. — Graham Swift, Waterland

Sources

What Works? Adaptation.

The enduring value of adaptation in the age of AI

(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?

What Works? Curiosity.

The Enduring Value of Curiosity in the Age of AI

(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:

Staying Power

Identifying our Enduring Value in an Age of Change

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.

Links to series installments: Curiosity, Adaptation, Storytelling

In the meantime…

Three Questions

Leadership in the Age of AI

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?

Read. The. Room.

Leveraging ethnographic thinking to increase resonance with stakeholders

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

AI and the ‘Untouchables’

Considering the limitations of LLM’s

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

Straight Talk for Hard Times

Using ethnographic thinking in interstitial moments

It is by now clear that the field of applied research is experiencing some very dramatic shifts. From mass layoffs, ‘silent’ re-orgs, ‘right-sizing,’ and many unknowns about the impact of AI, the pendulum for insight work has swung to an extreme I’ve never seen before. This is difficult for all of us in the industry, and is particularly challenging for anyone impacted by layoffs, or those who are in the early stages of their careers.

This post is dedicated to anyone facing these recent challenges. It’s mostly a loose collection of thoughts and insights that I might have shared with my younger self; including, most pertinently, questions I wish I’d asked of myself and others. It’s not meant to be comprehensive, and its applicability will vary depending on your circumstances. It is, however, an attempt to help you take advantage of this shift in the industry by pausing and using ethnographic thinking to re-frame your career considerations as you engage in conversations with prospective employers and your fellow travelers in life.

Let’s start with a few ways of looking at job-seeking in this field that often get overlooked. Regardless of industry, these considerations should help you steer around the hazards that could de-rail you and, worse yet, erode your confidence in a tough job market.

How does the company make their money?

This one is FAR more critical than I once realized; and it’s not about examining balance sheets or annual reports. It’s about understanding the core value of what a company does that enables it to exist. For example, a social media company doesn’t succeed unless it can offer a compelling platform for people to share content. Without that, it doesn’t make money. If people don’t come to the platform to share and enjoy content, the company has no clear way to attract either advertisers or memberships — which means they have no business model. So, their research needs encompass things like understanding what motivates people to create, share, and consume content, as well as how to best facilitate momentum for this ‘flywheel.’ If you’re talking with a company that operates in this space, ask yourself whether this is something that truly drives you to produce your best work. If yes, then great, this might just be the type of company where you’ll thrive, because this org will repeatedly look for these types of insights to help the business thrive and grow. If not, there’s no reason to think you’re deficient in some way. But it’s best to identify this early, and preferably well before you start the interview process or accept a position.

Beyond the matter of research focus, you should also consider that the core business model of most companies permeates nearly every interaction within it. When people talk about organizational culture, they often refer to things like whether the org has a ‘flat’ structure (I’ve never seen one that really does), ‘work-life balance’ (you should have your own definition of this), or even perks. These may shape a org’s culture, but they often pale in comparison to the overarching influence that the core business model has on how people prioritize, make decisions, influence change, or get rewarded. Want to know what really drives people and behavior in an org? Follow the money.

Is there room to be you?

To answer this question, you’ll want to think beyond identity categories to consider your own unique attributes and whether or not they’re a match for the org. Start with a solid understanding of what you bring to the table. Self-evaluations like Strengths Finder are one way to do this. However, what I’ve found to be even more useful over the years is to listen carefully to how others perceive your value. Ask them about what stands out in your work for them (good and bad) — maybe even propose a feedback exchange of some sort. Reflect back on things like previous performance reviews, casual conversations with colleagues at the bar, surprise compliments that stuck with you, or random comments about your work that resonated deeply for you for one reason or another. In a phrase: feedback is a gift — unwrap it.

Then, try to understand the org’s dynamics. Start with a focus on processes and practices. For example, are the org’s practices rooted in well worn orthodoxies or are they more flexible and open to change? Ask them about reporting structures and levels, operating procedures, and approval processes that will impact your work (e.g., research planning, budgeting, participant recruiting, collaboration protocols and practices, organizing workshops, reporting out, etc.), and listen for signs of flexibility or ossification. Compare their responses to your own thresholds for structure and process in your work.

You’ll also want to probe into how people are rewarded within the org. Even if you aren’t particularly career-focused, these standards will have bearing on your level of satisfaction in the role and your relationship with your peers. To get started, ask about success stories for people in roles similar to the one you’re considering. More specifically, ask them to tell you about the one thing that person did that stood out most? How were they rewarded? Listen carefully for signs of how the org positions and recognizes value, and make sure you gather as many different views as possible and take detailed notes on each.

Then, ask yourself what type of stories you heard. Are they sharing examples that focus on how this person successfully managed up?; about how they changed hearts and minds?; how brave they were?; how efficient they were? how radical they were? how they rallied colleagues?; how they systematized a practice? Each of these is an indicator of the values of the org. You may find that the greatest insights you glean are from the subtexts of these conversations. Pay close attention to these signals; they’re the interstices and the cracks where company culture actually resides. And those cracks are where you’ll be living…nearly every day. Listen for telling pauses, tone of voice, veiled judgements, elation, joy, team alignment, patterns of conflict, etc. Then, finally, look for themes across your conversations and ask yourself if you see alignment or gaps between your own values and those that you’ve seen signaled. If the latter, how big are those gaps, and are they deal-breakers?

Who’s in charge really?

There’s often a distinct difference between official org charts and actual networks of influence within orgs. Many of us have seen instances where “dotted-line reports” or “advisors” hold far more sway than those listed on formal team rosters. Informal networks will influence your experience in significant ways, since they often set the conditions and tone for how the team communicates, interacts, and makes decisions. To get past the party line, you’ll want to dig a bit deeper than a glance at the org chart. Ask about how the group identifies stakeholders, who they are in relationship to the team, and how they achieve buy-in with them (including examples of both successes and challenges). Then follow that up with “who else is critical in the decision making process” to get a more complete picture. Knowing who you’ll need to convince that your work is valuable, and where they sit in the org, is critical information for understanding where opportunities lie for the role, and whether or not the interactions needed will sync with your strengths, work style, and values. Will you be spending most of your time sharing insights and influencing decisions with designers?; product managers?; executives? Your approach to each needs to be different, and it will shape nearly everything about how you work.

In addition to reporting structure and understanding the network of stakeholders with whom you’d interact, you’ll want to get some understanding of how leadership is performing. For example, are they sending signals that indicate leadership gaps? The obvious first place to look is the job description itself. Read it carefully. Is it clear? Does it feel scoped correctly, given your experience in the field? Signs of leadership gaps are often reflected in job descriptions that are scoped too broadly (they want one person to cover a vast array of responsibilities that stretch across multiple different practices) or too narrowly (caution: micro-manager ahead). In addition, strong leaders have strategic conviction. Ask people to help you understand the top two or three strategic priorities for the year, and how they’re driven. If they give you empty platitudes instead of a strategic vision the includes at some indication of how goals will be accomplished and measured, the odds are high that you’re headed into a rudderless team.

Leadership gaps aren’t necessarily a bad thing, if there are indications that the team is open to new ways of influencing the org. This could even be an opportunity for you to help drive change and make significant impact. Listen for cues from people that demonstrate how change happens within the org. Ask about how someone in this position might shape the role once they’re in it, and whether the org is open to some early ideas you might have.

Finally, dig a bit further into the characteristics and approach of key leaders in the org. Ask about their leadership style, their tenets, what makes them unique? Probe for examples of how they affected change or drove impact, and how the org responded. Do some digging online to see if you can find interviews with those leaders, or guest appearances on podcasts, etc. Try to gather as many of these stories as possible, and then ask yourself one very important question: Given what I’ve seen and heard so far, can I imagine myself as a leader in this org? If the answer is no, this may be an indication of cultural misalignment, which could lead to feelings of indifference toward the company or even resentment. Either way, feeling like this certainly won’t inspire you.

What’s their approach to innovation?

Research is often tied closely to innovation initiatives within an org. However, there are many different ways innovation is approached and positioned within companies. I’ve had the privilege of working with some incredibly talented and experienced people in this space, and two conversations have really stuck with me.

The first conversation I want to share with you is one I had with someone who I sincerely consider a product genius. We were discussing our experiences with the different attempts we’ve seen to activate innovation insights within a company, which I’ll paraphrase here: There tend to be two main modes for integrating innovation within an org — the “looking for friends” approach, and the “shiny thing on a shelf” approach.

In the first of these, an innovation group is tasked with creating great new ideas. They might conduct exploratory research, carefully craft a set of design principles, and even cook up some prototypes. Then, they begin to look around the company to build relationships with teams that might want to take up the mantle and bring these ideas to life. If they find interested teams, they often eventually realize that those teams’ reward structures and workflows are simply not designed to ‘ingest’ thinking that’s so different from what has traditionally worked for them. The innovation teams’ insights eventually just become more work for which they won’t be rewarded. If they do find some alignment with those teams, they often come up against a game of odds: most new ideas fail, and those teams don’t want to be associated with a series of failures.

After a few attempts at this, these innovation groups will often shift to a “service model” where they begin working with another team that has some sort of mandate to innovate, and offer their skills as a means of helping them discover and develop the next big thing. Unfortunately, the objectives for this type of work are often constrained by the mental models and limitations of ‘tried and true’ practices. The team ‘contracting’ the services of the innovation team often frames goals and objectives in incremental terms that don’t align well with the riskier and more creative type of work that drives the innovation team. The result is frustration on all sides; and, often, a re-org or dissolution of the (non-revenue-tied) innovation team.

On the other hand, the “shiny thing on a shelf” approach follows a workflow much closer to that of an incubator. The innovation team is responsible not just for exploring, setting parameters or specs, and generating mock-ups, but for finding product-market-fit, and developing and testing workable prototypes in the marketplace. The idea is that once successes are clearly demonstrated in the ‘real world’ by these teams, they can more easily earn the trust of other teams in the org, who are motivated to integrate their successes and adapt them to their needs. This approach is much more difficult, requires high levels of buy-in from leadership, and often longer timelines — all things that are rare in lean times. However, I’ve seen it work first hand in the form of live model tests, pop-up stores, and new product experiments where teams develop and test offerings in real world settings. Research is critical throughout the process in this approach; and, because it sits much closer to addressing business needs in real-world settings, its value is self-evident.

The second story I want to share is from a conversation I had with a fellow researcher who’s seen his share of innovation practices come and go as a researcher within some very well-known large companies. His take?: a company that launches a dedicated innovation group in the ‘looking for friends’ model described above is likely not fostering the organic innovation that is already occurring within its product teams, and isn’t committed enough to give an innovation team a long enough leash to pursue the ‘shiny thing on a shelf’ approach. So, teams formed under these conditions often have the odds stacked against them right out of the gate.

So, where does this leave you as a researcher looking to find your next role? As you talk with prospective employers and teams, ask them how the org approaches innovation. Is it considered a speciality handled by a dedicated team, or is it an integral part of how products change and evolve? If they have a dedicated team, does that team operate as a discrete unit, a service model for other teams, or more like an incubator? What role does research play in the model they’ve deployed? What are the channels they have in place to cultivate and recognize research-driven innovative initiatives? What are their success stories?; their failures? Then, take the time to consider where your strengths would be most valuable in the context of the model they’ve chosen.

Do they prioritize humility?

This may be the most important assessment you make as you engage in conversations with prospective teams. You will make mistakes in your work. Your colleagues will too. It’s essential to understand the character of interactions within a team to determine whether you’d be entering an environment where teammates learn from those mistakes and offer one another support in the process. I’ve had two employers in the history of my career that made this a priority when recruiting. One did so explicitly (yay!); and, while the other may not have articulated it so directly, it was clear that they held people to standards that privileged authenticity, kindness, humility, and empathy.

It may be difficult to make an assessment like this with what little time you have to get to know a prospective team, but I would say that it’s a combination of gut feeling paired with some lightweight queries. When I talked with people in both of the companies referenced above, I was struck by how warm, sincere, and transparent everyone was. After seeing this across 10-12 different employees, a clear pattern emerged. In addition to looking for these qualities, you might also ask some questions that get at the matter somewhat tangentially. For example, you could inquire about how the team has responded to adversity and then listen for signs of humility, accountability, and openness to learn. Do they share stories about how people rolled up their sleeves and came together, or does their response focus more on internal politics or maybe conflict avoidance?

Another way to assess this is to explicitly demonstrate your own humility and see how they react. After talking about a project and your impact or accomplishments, wrap up with a short summary of gaps, challenges, or shortcomings. Talk about where it went wrong, and what you’d differently if you had a chance to start over. Maybe even prompt them for ideas about how they might have done things differently, just out of curiosity. Does sharing this experience seem to fall on deaf ears, or is this the moment where they engage even more with your work? Either says volumes about how they work and where they invest their energy.

What to do with responses

The job hunt can be a bit of a minefield. Managing a rapidly growing number contacts, context-switching, adapting to changing org needs, ghosting (just weird), etc. If you’ve been through a series of interviews with an org, and you’ve reached the point where you finally receive a response, I’d like to offer what I think is the healthiest response to each.

If they say ‘no,’ in whatever form, many companies aren’t in a position to share why. Feel free to ask them politely for any information they have about your candidacy, but ultimately your best reaction to a ‘no’ is to learn what you can, then let it go and move on.

If they say ‘maybe,’ prepare yourself for a ‘no,’ but try to dig for more detail. What’s delaying the decision? Is it a matter of budget? team match? waiting on a re-org? When will they know more about budget? What are their standards for a team match? How long do re-orgs typically take? Then, be sure to offer more of your time to clarify questions about your work and approach, during which you’ll want to get as much feedback as possible from them. No matter the explanation from their end, your goal is to use the ‘maybe’ response as a way to learn more about the org and their perception of your candidacy. You’ll also want to consider that a ‘maybe’ could also be a delay tactic while they court other candidates or wait from a response from someone they consider their top choice. All that sucks, of course, so try to use the ‘maybe’ as a personal learning and growth opportunity.

Finally, if you get a ‘yes’ response, congratulations, but make sure you have all the clarity you need going in. Do you have a clear understanding of expectations for the role? Have you taken the time to read between the lines of the role’s responsibilities to determine the higher order need they have? Are you seeing signs of humility and integrity among those with whom you’ll work? There’s no such thing as a perfect org, but you’ll want to ensure that you have optimized for your well-being before jumping in.

A few final thoughts

Most careers are non-linear, so give yourself a break and open your perspective to possibilities that aren’t at the top of your wishlist. In my career, I’ve run across projects, clients, and jobs that I initially felt weren’t that interesting for one reason or another, only to find out later that they included some of the greatest growth opportunities I’ve ever experienced. If you’re just getting started, this is particularly important. You may have your heart set on that dream company or job, but you may very well be limiting yourself. This is why I often recommend that younger researchers spend some time working in a consultancy, where they can get exposure to many different industries and types of clients. You’ll learn much more about your strengths and interests this way, and in a much faster timeframe.

Lastly, I realize that people have different levels of interest in, and tolerance for, playing the career ‘game,’ but I’ve never seen anyone who’s both happy and laser-focused on winning that game. In the end, you have far less control over what happens than you might think. Enjoy the ride, the destination is never what you think it’ll be.

Photo credit: Marcel Duchamp – Five-Way Self Portrait (1917)