A Modest Method for Investigating What Matters

How observation, research, AI, and pilot design have helped me stay with a question long enough for a more truthful version of it to emerge.

Abraham Espinosa — Assemble Studio

June, 2026.

A Note Before You Begin

This is a way of working that treats complexity as an invitation to pay closer attention.

It emerged through practice, not theory. It will ask you to sit with uncertainty rather than resolve it prematurely. It will offer orientation, not instruction.

This document is for people investigating questions that do not yet have good answers. Founders. Researchers. Designers. Social entrepreneurs. Strategists. Policy people. Institutional builders. The category is less important than the condition: you are working on something that resists easy framing, and you suspect that moving too quickly toward a solution may be part of the problem.

Over the last two years, I have dedicated thousands of hours to exploring questions related to aging, literacy, civic participation, economic instability, organizational foresight, and institutional change. These investigations did not begin with business opportunities, a brief, or budgets. They began with observations of the world around me that sparked my curiosity and refused to let go.

Along the way, AI became essential to this work, and in an unexpected way. Not as a tool for accelerating conclusions, but as a companion for resisting them. What AI made possible was staying in the question longer than would otherwise have been comfortable. That, it turned out, was its most valuable contribution. 

What follows is simply an attempt to describe that practice.

The Underlying Premise

A recurring pattern across these investigations has been this: we tend to move toward solutions before we have fully understood the systems producing the problems we hope to change.

This is understandable. Organizations reward decisiveness. Funders reward action. The pressure to produce is real and constant.

But action without understanding can easily reinforce the very systems it seeks to improve. And the cost of this tends to compound quietly, in initiatives that work technically but change nothing, in programs that address symptoms while leaving structures intact, in strategies that solve the stated problem while missing the real one.

The method described here does not advocate for endless deliberation. It advocates for a different kind of discipline: the discipline of understanding first, acting with precision second.

There is an image I borrowed from Christian Madsbjerg that has stayed with me: a peregrine falcon, which spends far more time observing than hunting. It studies the environment, waits, learns, conserves energy. When the moment arrives, it acts with remarkable precision and speed. Long preparation. Brief, decisive action. This method has something of that character.

Design is the underlying lens, and here it is worth being precise about what that means. Not design as the creation of products or interfaces. Design as a practice of observation, synthesis, pattern recognition, reframing, and model building. A discipline that develops sensitivity to what is often overlooked, sits with contradiction long enough for its meaning to emerge, and seeks understanding before building anything else.

The Questions Behind the Work

Before describing the process, it is worth showing the kind of questions it was applied to. Not to explain what emerged from them, but to demonstrate the quality of attention they required.

Each began with an observation that felt important, unresolved, and difficult to explain.

Aging. The world is aging rapidly. But the older adults of the future will not resemble the older adults of today. Longer lives, changing family structures, economic uncertainty, and a lifetime spanning the transition from analogue to digital society are reshaping what it means to grow older. I became curious about the people who are not yet old but are actively moving toward later stages of life, their needs, emotions, fears, and aspirations, and why one of the most significant transitions in human life receives so little attention before it arrives.

Literacy. Many people struggle to retain, understand, and articulate complex ideas — not only children, but adults. At the same time, information has become abundant while attention has become fragmented. I became curious about the relationship between reading, understanding, memory, and expression, and in particular about reading aloud, a practice once central to how texts were experienced that has largely faded from everyday life.

Civic participation and economic instability. Economic insecurity is growing in many societies, while traditional mechanisms of mutual support are quietly eroding. Everyday life is becoming increasingly cashless. Yet people remain willing to help one another. I became curious about what happens to solidarity when the mechanisms through which it was historically expressed begin to change, and what new forms of participation might become possible, or more difficult, as a result.

The democratization of foresight. Large organizations often have strategy teams, researchers, foresight practitioners, and the resources to hire external consultants. Smaller organizations — which face the same uncertainty in an unpredictable world, typically do not. I became curious about why systematic thinking about the future remains concentrated among those with the greatest resources, and what might change if it became more accessible.

Enterprise transformation. Many organizations seem to function remarkably well, until something changes. A merger, a new strategy, a new technology, a period of growth. These moments often expose communication gaps, conflicting incentives, informal power structures, and arrangements held together by habit and institutional memory. I became curious about why change exposes weaknesses that were previously invisible, and what these moments reveal about how organizations actually function beneath their formal structures.

In each case, the objective was not to begin with a solution. It was to better understand the observation itself. Over time, the domains changed. The process remained remarkably consistent.

The Inquiry Cycle

The process is not perfectly linear. Stages overlap, repeat, and occasionally appear in different orders depending on the nature of the question. But looking across multiple investigations, a recurring cycle has become visible.

1. Learn to Look

Every meaningful inquiry requires attention, care, and time.

Not simply seeing, but learning to observe with all available senses, listening, sensing, noticing, comparing, questioning. Paying attention to tensions, absences, contradictions, behaviors, and systems that are usually taken for granted. Many of my most productive investigations in this work began not with a research question, but with something that felt slightly out of place — a pattern that didn't fit the expected explanation, a problem that kept reappearing despite apparent solutions, a silence where there should have been noise.

What distinguishes this stage in practice is duration. Most innovation processes move through observation quickly, treating it as a warm-up for the real work. Here, it is the real work. Staying in this stage longer than feels professionally comfortable — weeks, sometimes months — is what allows less obvious patterns to surface. The ones that emerge slowly are often the ones that matter most.

This stage is about developing sensitivity to what is actually happening. It requires slowing down in a professional culture that rewards speed. It requires treating discomfort, ambiguity, and contradiction not as problems to be resolved immediately, but as signals worth following.

Any worthwhile observation process is slow, singular, and nonlinear. You will bring your own peculiarities to it. You will need to grow comfortable with doubting and drifting. The reward is not efficiency. It is the kind of understanding that makes everything that follows more precise.

The good news is that this can be learned. The catch is that no one can teach you exactly what to notice. Observation is not a formula. It develops through practice, curiosity, lived experience, and time. Over time, you begin to recognize the kinds of questions that repeatedly draw you in. The process becomes less about finding the right observation and more about learning to trust the ones that refuse to leave.

2. Educate Yourself

Build context around the initial observation.

This means reading across disciplines, not to become an expert in each, but to avoid the shallow conclusions that come from staying too close to a single perspective. Anthropology, history, philosophy, sociology, economics, neuroscience, systems theory, technology, governance, and design may all become relevant depending on the nature of the question.

What becomes interesting in practice is that learning rarely follows a linear path. A book recommendation, a newspaper interview, a passing comment, a conference encounter, or an unexpected conversation can open an entirely new dimension of inquiry. Over time, seemingly unrelated references begin to connect. Concepts from one discipline illuminate questions in another. Ideas that initially appeared peripheral sometimes become central.

The discipline here is not breadth for its own sake, but the willingness to follow curiosity wherever it leads. If the reading only confirms what you already believe, you are probably not learning enough.

3. Scan the Environment

Reading alone is insufficient.

At some point, the inquiry needs to leave the desk and encounter the world directly: through long walks, public spaces, events, organizational visits, casual conversations, and direct encounters with the people, institutions, and systems connected to the question.

This stage tests whether an initial curiosity is isolated or part of a larger pattern. It reveals who else is paying attention, what has already been attempted, and where meaningful tensions exist in practice rather than in theory.

Many questions begin to change once they encounter the world outside books and reports. A question that seemed clear on paper becomes more complex when you speak with the people living inside the system it describes. Assumptions surface that were invisible before. Realities emerge that do not fit the narrative.

The objective is not validation. It is exposure. The inquiry must encounter enough reality that reality can begin to push back.

This is often where the most important corrections occur.

4. Identify a Tension

At some point, a tension begins to appear.

It may take the form of a coordination problem, a contradiction, an unmet need, an institutional gap, or a mismatch between lived experience and the systems intended to support it. What matters is that the tension keeps returning. The more context you gather, the more interesting it becomes. Instead of disappearing, it deepens.

The tension is a signal that something worth investigating is present beneath the surface, not yet a problem statement, and definitely not yet a solution.

For example, during an investigation into civic participation and economic instability, one tension kept reappearing. Most societies have built institutions, charities, public programs, and support systems intended to assist people facing financial hardship. Yet many individuals continue to struggle to access timely, flexible support. The tension was not whether support existed. It was why support and need so often remained disconnected despite institutions designed to bridge that gap.

Staying with the tension rather than resolving it prematurely is one of the most difficult disciplines in this work. The pull toward solutions is strong. Organizations are comfortable with problems and solutions; they are less comfortable with tensions, which are by definition unresolved. Yet the premature resolution of a tension is often where the most important understanding gets lost.

The tension becomes the reason to continue.

5. Construct an Inquiry

The tension is then translated into an inquiry.

An inquiry differs from a problem statement. A problem statement assumes the nature of the problem is already understood and that a solution exists somewhere within reach. An inquiry remains open. It creates direction without pretending to know the answer.

More importantly, a well-constructed inquiry often changes the subject entirely.

A question about aging may become a question about demographic transition. A question about literacy may become a question about understanding. A question about civic participation may become a question about social coordination. A question about organizational transformation may become a question about how institutions adapt to change.

This is often the most important shift in the entire process. The inquiry reframes attention. It changes what becomes visible. And how a question is framed determines what kinds of answers become possible.

6. Architect Scenarios

The objective gradually shifts from observation to exploration. This stage requires imagination.

The work now involves actively examining different ways the tension might be understood, interpreted, or addressed, not searching for a single answer, but exploring multiple possibilities simultaneously.

Scenarios become particularly useful because they create space for alternative and speculative futures, structures, interventions, and organizational forms to coexist without requiring immediate commitment to any of them. This helps resist the temptation to converge too quickly on a preferred solution.

During the literacy investigation, I became increasingly interested in why so many people struggled to understand, retain, and articulate complex ideas. As the inquiry progressed, I encountered the Science of Reading and became particularly curious about the relationship between reading, comprehension, memory, expression, and learning.

What if the challenge was primarily educational? Behavioral? Social? Physical? What if screens were part of the problem rather than the solution? What if reading itself could be supported differently?

One scenario explored what a reading companion might look like if it were designed around physical books rather than screens. This eventually led to Leggio, an exploration of what a reading companion might become when freed from the assumptions of digital-first learning.

The most valuable insight was not the concept itself, but the realization that scenario work can expose assumptions so deeply embedded that they become invisible. In this case, the assumption that any literacy challenge should be addressed through more screens and more technology.

Scenario work often reveals assumptions that would otherwise remain hidden: not which future is most likely, but what each possible future would require — and what it reveals about the dynamics already in motion.

The purpose of this stage is not prediction. It is to expand the field of possibilities before deciding where to focus attention next.

7. Use AI as a Critical Companion

Most of the time, we are using AI to move faster toward answers. My experience was different.

AI became a space where observations, hypotheses, tensions, and emerging frameworks could be continuously examined before they became fixed — a sustained interlocutor that helped keep inquiry honest, open, and sufficiently uncomfortable. In practice, it served four distinct roles.

Research companion. Navigating large volumes of material across disciplines, identifying references, comparing perspectives, discovering adjacent concepts. The value was not the information itself. It was the ability to move fluidly across fields and build context around an emerging observation without getting trapped inside a single disciplinary lens.

Critical interlocutor. The most important role. AI was repeatedly used to challenge assumptions, identify inconsistencies, expose missing actors, surface risks, and stress-test emerging frameworks. Many of the most significant shifts in these investigations emerged through critique. A conversation that begins with one question and gradually reveals that the real question is something else entirely, that is where the most valuable AI work happened.

Synthesis engine. As investigations expanded, the volume of accumulated thinking became difficult to manage. AI helped organize, compare, and restructure large amounts of material into increasingly coherent frameworks. This role requires caution: AI can oversimplify, distort context, or confidently synthesize incomplete information. Effective synthesis requires deep familiarity with the subject. The process must be a dialogue, not a transfer.

Documentation partner. Transforming fragmented thinking into artifacts that could be shared, revisited, and refined, research reports, governance frameworks, pilot proposals, strategic narratives. This creates a traceable record of the inquiry, allowing ideas to be challenged and evolved rather than disappearing into notebooks.

The honest observation: AI did not primarily function as a productivity tool. Its greatest contribution was not speed. It was extending the inquiry, creating conditions where questions could remain open longer, assumptions could be challenged more thoroughly, and alternative interpretations could be explored before conclusions were reached.

It helped me think longer, not simply faster.

8. Conceive New Inquiry Methods

Some questions require new ways of learning.

As understanding deepens, it sometimes becomes clear that existing methods were designed for different questions. Rather than forcing an inquiry into a predefined framework, it can become necessary to construct a lens that feels appropriate to the subject itself — drawing from what has been learned throughout the investigation, together with lived experience, in combinations that serve the inquiry.

NEEM emerged this way during the aging investigation. I became increasingly interested in how needs and emotions influence the decisions people make as they navigate major life transitions. Existing approaches helped illuminate parts of the picture, but none seemed sufficient on their own.

NEEM brought together human needs theory, nonviolent communication, ethnographic practice, and social science research on aging into a single inquiry method. Human needs theory provided a lens for understanding motivation. Nonviolent communication introduced a way of observing emotions while suspending judgment. Ethnographic practice offered a means of understanding lived experience over time. Aging research provided the broader demographic and social context.

The method did not precede the inquiry. It emerged from it and was subsequently refined through fieldwork.

Looking back, NEEM became evidence that inquiry can sometimes produce not only new insights, but entirely new ways of learning.

Methods, in this sense, are living artifacts that occasionally emerge when an inquiry requires a new way of understanding reality.

9. Design a Foundational Pilot

Understanding alone becomes insufficient.

The inquiry needs to encounter reality directly, not through observation or synthesis, but through action. A pilot creates the conditions for this encounter. It allows ideas, assumptions, and emerging frameworks to interact with people, organizations, constraints, and consequences.

The purpose of a pilot is to create an environment where new understanding can emerge through participation and experience, to reduce uncertainty, generate evidence, build relationships, and produce enough momentum to enable the next stage.

The pilot is often the first meaningful expression of the solution, not a reduced version of it.

This is also where the inquiry becomes most vulnerable. A pilot requires other people to believe the work is worth pursuing before the outcomes are fully known. Ideas are rejected. Proposals go unanswered. Potential supporters lose interest. These moments are often as informative as success: they reveal weaknesses in the inquiry, gaps in communication, and assumptions that have not yet been fully examined.

But the pilot tests something beyond the idea. It tests the person behind it. It reveals how much conviction exists beneath the inquiry, and challenges patience, communication, and the ability to continue when enthusiasm is not immediately shared.

The pilot tests the concept. It also tests the investigator.

10. Return to Reality

The cycle continues beyond the pilot.

Eventually, there is a moment to step back and observe what actually happened. What worked. What failed. What was misunderstood. What became visible. What new questions emerged.

Some inquiries move forward. Others enter a period of hibernation, and these are not necessarily failures. Many factors influence whether an inquiry continues: timing, resources, institutional readiness, collaborators, the ordinary realities of life. The most meaningful inquiries rarely disappear completely because the structural issues are still unresolved. They remain present in the background, waiting for conditions to become favorable.

The inquiries that survive the pilot and continue their journey often do so in imperfect condition. Funding remains uncertain. Questions remain unanswered. New challenges appear. But something important has changed: the inquiry is no longer personal. What began as a single observation has become a shared endeavor involving other people, organizations, and collaborators who bring their own questions and perspectives.

Looking back, the understanding gained along the way often proves more valuable than the pilot itself, or the initiative that may emerge from it.

The Role of AI

What proved more valuable was something less obvious. AI changed the conditions under which thinking happened.

Most professional inquiry operates under invisible constraints: the need to appear decisive, the cost of changing your mind publicly, the pressure to converge, and the discomfort of sustained uncertainty. These forces shape what gets explored, what gets abandoned, and how quickly conclusions are reached.

In my experience, AI created a different environment. Observations, hypotheses, frameworks, and half-formed ideas could be examined repeatedly without the social pressures that often accompany intellectual work. Questions could remain open longer. Assumptions could be challenged more freely. Entire lines of thinking could be abandoned and rebuilt without consequence.

Most thinking partners I have worked with professionally have had some stake in the conclusion. AI does not.

This document is itself an example. What changed it was not better information. It was the freedom to be wrong repeatedly, in private, until something more honest emerged.

AI became a space for sustained inquiry. Its greatest contribution was not helping me arrive at answers faster, but helping me remain with questions longer.

What Changed

A reasonable question is whether this process actually produced anything useful.

The answer depends on how usefulness is defined.

Some of the inquiries evolved into tangible initiatives. The Aging Observatory is preparing its first pilot program focused on housing and demographic transition. AIUTO is collaborating with one of Italy's largest NGOs to explore new approaches to economic instability and civic participation. Leggio continues to investigate voice interaction, on-device intelligence, and screenless approaches to learning. LIFT remains in hibernation, although the underlying research continues.

None of these investigations arrived fully formed. None became successful ventures overnight. In many ways, they remain works in progress.

What they produced was a different understanding of the questions that gave rise to them, and a clearer sense of where meaningful work might exist. Some are moving toward pilots. Some toward partnerships. Some toward methods. Some may eventually become organizations. Others may never become anything visible at all.

For me, that has repeatedly proven worthwhile.

Open Questions

This method is still evolving. I am still learning, experimenting. There are questions that remain unresolved — and they are part of the method too.

How much understanding is enough before action becomes necessary? 

How do you recognize the difference between an idea that needs more time and one that needs to be abandoned?

When does inquiry become avoidance?

When does a pilot become too small to matter?

How can AI support inquiry without flattening the ambiguity that makes inquiry productive?

How does the method bring rigor but leave enough air for experimentation?

How do you keep going when you know a question deserves years of attention?

These questions do not have answers here. They are offered as companions for anyone who recognizes them.

Closing Thought

This document is a record of a way of working that emerged through practice — through thousands of hours of reading, observing, questioning, synthesizing, testing, and returning to reality.

Looking back, aging, literacy, civic participation, foresight, and organizational change turned out to be different expressions of a single curiosity: how people, organizations, and institutions navigate transition and make sense of uncertainty in a world changing faster than their structures were designed to accommodate.

Its deepest subject is how to remain with a question long enough for a more truthful version of it to emerge.

Not every inquiry becomes a venture, a pilot, a client engagement, or an organization. Some remain inquiries. Their contribution is not what they become, but what they reveal.

C'est le temps que tu as perdu pour ta rose qui fait ta rose si importante.

It is the time you have spent on your rose that makes your rose so important.

Antoine de Saint-Exupéry