Operating by John Brewton: Collaborations & Guest Authors

The Value of We Don’t Know Part Two

John Brewton — Thu, 20 Aug 2026 11:18:30 GMT

The argument I keep making in Operating is that the companies that survive and become greater than their current location this decade will not be the firms that adopted AI. They will be the organizations that rebuild their operating philosophies and structure from AI-native principles. They will redesign every role, every process, every means of production through this new lens, with these new tools.

My good friend Alex Pawlowski has written a warning to leaders and executives that personifies this expectation, and I am thrilled to have published this work in two parts. Part One can be found here and today we are releasing Part Two below.

Alex is a friend and one of the sharpest people I know on agentic systems, orchestration, and the architecture of AI-native work. He spends his time building and investing in the next internet. We have spent much of the past year working on similar problems from varying viewpoints. I come at it through strategy, organizational capability, and judgment. He studies and speaks to the machinery that makes the transformation possible.

His claim in this article is as follows: Organizations have never managed knowledge. They manage distributed uncertainty. The internal engineer sees, develops and manages the architecture. The marketer speaks to the customer. Legal worries about exposure. Finance manages risk. Intelligence gets assembled out of incomplete views colliding before a decision hardens. In that system, “I don’t know” is a routing signal to send the query to another department.

Generative AI collapses the interval between question and answer to seconds, eliminating cross-functional departmental debates and meetings. The explanation that arrives is fluent, internally consistent, and often correct.

Encoding your operation is still the right move. Encode the answers, not the questions, and you have built a company that reaches conclusions faster than it ever could.

I’m sincerely excited about these two pieces and am privileged to be publishing them via Operating by John Brewton.

Hope this helps, friends.

- j -

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Subscribe to Alex’s Substack, The Strategy Stack, here.

IV. Explanation Is Becoming Cheap. Judgment Is Not.

Every major technological shift changes the economics of something. The steam engine reduced the cost of physical labour. Industrial manufacturing reduced the cost of production. Computing reduced the cost of calculation. The internet reduced the cost of accessing information.

Generative AI appears to be changing something altogether different. It dramatically lowers the cost of producing explanations.

That distinction deserves careful attention because explanations occupy an unusual position within organizations. They satisfy a deeply human need to make events appear coherent. A strategy failed because the market shifted. A customer left because pricing was too high. A competitor succeeded because its product was superior. Most organizational life is surrounded by narratives that transform complex, multidimensional events into stories that feel understandable enough to support action.

The problem is not that explanations are inherently misleading. Organizations cannot function without them. Every roadmap, investment proposal, product strategy and board presentation depends on a shared interpretation of reality. The difficulty lies elsewhere. Coherence and accuracy are related, but they are not identical. A persuasive explanation may still overlook critical variables, underestimate uncertainty or mistake correlation for causation. History is filled with decisions that appeared perfectly reasonable because the underlying narrative was internally consistent, only for reality to reveal later that the organization had misunderstood the problem from the very beginning.

Generative AI introduces a fascinating dynamic into this relationship because it produces coherence with remarkable ease. Faced with an unfamiliar question, the system does not experience hesitation, uncertainty or intellectual discomfort. It generates a plausible interpretation almost immediately, drawing together patterns from enormous volumes of text into a response that feels structured, complete and often highly persuasive. Even when the underlying reasoning is imperfect, the explanation itself carries many of the characteristics that humans naturally associate with understanding: fluency, confidence, logical progression and linguistic precision.

From the perspective of organizational psychology, this matters because human beings rarely experience uncertainty as an abstract philosophical concept. We experience it emotionally. Uncertainty creates tension. It interrupts momentum. It delays decisions. It exposes the possibility that our current understanding may prove inadequate. Throughout history, that discomfort encouraged investigation. The effort required to reduce uncertainty often became the mechanism through which better knowledge emerged.

AI changes that emotional sequence.

The question is asked.

An explanation appears.

The feeling of uncertainty recedes.

Whether uncertainty itself has actually diminished remains an entirely separate question.

This difference between objective uncertainty and subjective certainty may become one of the defining management challenges of the coming decade. Organizations have always operated in environments characterised by incomplete information, delayed feedback and complex causal relationships. None of those conditions disappear because explanations become easier to generate. Markets do not become more predictable because summaries become more elegant. Customers do not become more consistent because presentations become more convincing. Competitive advantage does not emerge because every employee can now produce a beautifully written strategic analysis in under five minutes.

Reality has not become simpler.

Only our interface with it has.

Perhaps the most significant consequence is not technological but social. Every organization depends on countless small signals that reveal where understanding remains incomplete. Someone asks another department for help. An engineer escalates an issue to an architect. A consultant admits they need to investigate further before making a recommendation. A physician requests another opinion. These interactions do more than exchange information. They reveal where uncertainty currently resides inside the organization, allowing expertise to flow toward it.

If explanations become permanently available, many of those signals begin to disappear. Employees no longer need to expose uncertainty before receiving a plausible answer. Teams become increasingly capable of operating independently, but independence and understanding are not synonymous. An individual equipped with an excellent language model may appear highly informed while remaining disconnected from colleagues whose experience would have fundamentally altered the interpretation of the problem. Knowledge becomes more accessible at precisely the moment collective sensemaking risks becoming less frequent.

This is an irony worth reflecting upon. Organizations adopted digital collaboration platforms to connect people more effectively across functions and geographies. Generative AI may inadvertently reverse part of that dynamic by allowing individuals to resolve questions privately that previously required conversation. The immediate productivity gains are obvious and, in many cases, entirely justified. The longer-term organizational effects are more ambiguous. Every question answered without conversation is also a conversation that never takes place, an assumption that is never challenged and a perspective that never enters the decision-making process.

The implications extend beyond collaboration into the architecture of organizational learning itself. Much of what organizations know is not stored in documents, databases or process manuals. It exists in the interactions through which people compare interpretations, negotiate meaning and gradually refine their understanding of ambiguous situations. Organizational theorists have long distinguished between explicit knowledge—the things that can be documented—and tacit knowledge—the intuitions, experiences and contextual judgments that resist formalization. AI dramatically improves access to the former. Its influence on the latter remains far less certain.

Perhaps the greatest risk, then, is not that AI produces incorrect answers. Incorrect answers have always existed. Organizations have developed countless mechanisms for discovering and correcting them. The more subtle possibility is that explanations become so immediate, so abundant and so persuasive that they quietly reduce the number of occasions on which people realise they have reached the boundary of their own understanding. If that boundary becomes less visible, organizations may discover that they have become exceptionally efficient at producing explanations while simultaneously becoming less effective at recognising when explanation should give way to further inquiry.

V. Designing Organizations That Preserve Uncertainty

The discussion surrounding agentic AI often begins with capability. How many tasks can an agent perform? How many systems can it connect? How much human intervention can be removed from a workflow? These are sensible engineering questions, but they say surprisingly little about organizations themselves. Enterprises have never struggled because they lacked the ability to execute instructions. They struggle because they repeatedly execute assumptions that later turn out to be incomplete.

Viewed through that lens, the next generation of organizational design may depend less on what AI can accomplish autonomously and more on the role it plays within the organization’s broader process of judgment. Every technology eventually becomes embedded in an institution, and institutions shape technology at least as much as technology reshapes institutions. The real design challenge, therefore, is not simply building more capable agents. It is deciding which organizational functions those agents should strengthen and which human capabilities they should deliberately leave intact.

Current development trends reveal a strong preference for completion. Agents draft reports, prepare presentations, write code, summarize meetings, coordinate workflows and increasingly orchestrate other agents. The underlying objective is remarkably consistent: reduce the amount of work that remains unfinished. Yet completion has never been the defining characteristic of high-performing organizations. A completed analysis that frames the wrong problem creates more risk than an unfinished one that still invites challenge.

That distinction suggests a different design philosophy.

Instead of asking how an agent can replace the next human task, organizations might begin by asking which forms of uncertainty disappear too early in their existing decision processes. The objective shifts from maximizing automation toward improving the quality of organizational sensemaking.

An agent designed around that principle behaves very differently.

Rather than presenting a single recommendation, it identifies competing interpretations.

Rather than optimizing for confidence, it estimates the quality and completeness of the available evidence.

Rather than summarizing agreement, it highlights disagreement.

Rather than producing the shortest path to execution, it identifies where additional observation would meaningfully change the decision.

The result is not a slower organization.

It is an organization that allocates its attention more intelligently.

That distinction becomes easier to appreciate when viewed through familiar organizational practices.

Conventional Agent

Epistemic Agent

Produces answers

Maps uncertainty

Completes tasks

Identifies missing evidence

Optimizes efficiency

Optimizes decision quality

Reinforces existing assumptions

Challenges existing assumptions

Maximizes confidence

Calibrates confidence

Ends the workflow

Decides whether the workflow should continue

This is not merely a different interface.

It represents a different philosophy of organizational intelligence.

Much of today’s AI landscape assumes that knowledge work consists primarily of producing outputs. Reports, presentations, analyses and recommendations become the measurable products through which productivity is evaluated. Yet organizations rarely derive competitive advantage from documents alone. They compete through the quality of the decisions those documents support. A beautifully written recommendation based on an incomplete understanding of the environment remains strategically inferior to a less polished analysis that exposes uncertainty before resources are committed.

Seen from this perspective, the role of agentic systems begins to resemble something closer to institutional architecture than digital labour. Good institutions have never existed to eliminate disagreement. Their purpose has been to ensure that disagreement appears early enough to improve judgment. Constitutional courts, scientific peer review, investment committees and aviation safety investigations all slow the journey from observation to action in carefully chosen places because experience has demonstrated that unchecked certainty becomes extraordinarily expensive.

Perhaps AI should be designed with the same principle in mind.

Instead of asking whether an agent can make a decision independently, organizations might ask a different set of questions.

Which assumptions is this recommendation built upon?
What evidence would most likely change this conclusion?
Which perspectives are currently absent?
Where does confidence exceed available evidence?
Which part of this decision still depends on human judgment rather than computational capability?

Notice that none of these questions reduce the value of automation. They simply recognize that automation and judgment solve different organizational problems. One increases operational capacity. The other determines whether that capacity is directed toward the right objective.

This distinction becomes increasingly important as organizations evolve from isolated AI tools toward networks of collaborating agents. A single language model answering questions for an employee introduces one set of challenges. Hundreds of specialized agents coordinating product development, legal review, procurement, finance and customer operations introduce another. Once agents begin exchanging conclusions with one another, organizations face the possibility of creating systems that reinforce certainty at machine speed. If every agent accepts the outputs of every other agent without introducing mechanisms for challenge, contradiction or independent verification, the organization risks scaling coherence faster than understanding.

Perhaps the most valuable agent inside an enterprise will therefore not be the one that writes the report, negotiates the contract or schedules the meeting.

Perhaps it will be the one that interrupts.

The agent that notices conflicting assumptions between departments before anyone else does.

The agent that detects unusual confidence unsupported by evidence.

The agent that identifies the missing customer perspective in an otherwise compelling strategy.

The agent that quietly asks whether the organization has begun answering a question it never properly understood.

Those responsibilities sound remarkably familiar because organizations have always depended on people who performed exactly that role. The difference is that they rarely occupied positions of formal authority. They were the experienced architect who challenged an elegant design, the analyst who questioned the assumptions hidden inside a financial model, the physician who requested one additional test despite overwhelming consensus, or the strategist who noticed that everyone had become unusually comfortable with the current narrative.

The future of agentic AI may ultimately depend on whether we choose to replicate only the visible work those people performed or whether we also preserve the invisible function they served within the organization. The first path produces faster execution. The second strengthens institutional judgment itself.

VI. Judgment as Competitive Advantage

Every generation tends to believe that the defining technologies of its time fundamentally alter the conditions under which organizations compete. Sometimes they do. Railways changed geography. Electrification transformed manufacturing. The internet redefined distribution. Artificial intelligence will undoubtedly reshape many aspects of organizational life in ways we can only partially anticipate today.

What history also suggests, however, is that enduring competitive advantage rarely emerges from technology alone. Technologies diffuse. Best practices spread. Capabilities that once distinguished a handful of organizations gradually become available to everyone else. What remains difficult to replicate are the institutional habits through which organizations interpret reality, revise their assumptions and make decisions under conditions that remain fundamentally uncertain.

This observation deserves greater attention because it changes the way we think about intelligence itself. The coming decade will almost certainly produce organizations capable of generating extraordinary volumes of analysis, recommendations, simulations and strategic options. Access to explanation will continue to expand, while the cost of producing sophisticated reports, market assessments and technical evaluations will continue to fall. In many industries, these capabilities will become expected rather than exceptional. They will shape the baseline from which organizations operate, not the advantage that separates one organization from another.

The differentiator will increasingly shift elsewhere.

Not toward who has the most explanations.

Nor toward who automates the largest number of workflows.

But toward those organizations that remain capable of recognizing when their own explanations have become inadequate.

That capability has always occupied an unusual position within strategy. Markets rarely reward organizations for answering yesterday’s questions more efficiently than their competitors. They reward organizations that notice emerging questions while others continue refining outdated answers. Every major strategic inflection point contains this asymmetry. One organization continues optimizing an existing model because the evidence still appears internally coherent. Another begins questioning assumptions that have not yet visibly failed. Looking backward, these moments are often described as innovation or visionary leadership. Looking more closely, they frequently begin with something less dramatic: an institution preserving enough epistemic humility to recognise that reality may already have moved beyond its current understanding.

Seen in this light, judgment begins to look less like an individual attribute and more like an organizational capability. It is embedded in governance structures, decision processes, incentive systems and cultural norms that determine whether uncertainty is explored or suppressed. Organizations do not become adaptive simply because they employ intelligent people. They become adaptive because they create environments in which intelligent people can revise their thinking without treating revision as failure. Learning is not the accumulation of correct answers; it is the continuous recalibration of mental models as reality changes around them.

Generative AI enters this landscape as both an extraordinary opportunity and a subtle design challenge. It dramatically expands humanity’s capacity to produce explanations, synthesize information and coordinate increasingly complex work. Used well, these capabilities can elevate the quality of organizational decision-making in ways that were previously unimaginable. Used without careful attention to organizational design, they may also encourage institutions to mistake fluency for understanding and completion for judgment. The technology itself remains neutral. Its consequences depend on the architecture into which it is introduced.

Perhaps that is the deeper lesson emerging from this moment.

Organizations have spent decades investing in systems that improve access to information. We are now entering an era in which access to explanation is becoming equally abundant. The question quietly moving toward the centre of management is no longer whether people can obtain answers. Increasingly, everyone can. The more consequential question is whether organizations continue to preserve the conditions under which answers are challenged, refined and occasionally abandoned altogether.

That challenge reaches beyond management practice. It touches the way societies organize expertise, distribute authority and cultivate trust. Universities, scientific communities, engineering disciplines and democratic institutions all evolved around the recognition that certainty benefits from structured disagreement. Their objective was never to eliminate uncertainty but to prevent confidence from outrunning evidence. As organizations become increasingly AI-native, they inherit the same responsibility. The technologies may be unprecedented, yet the institutional challenge remains remarkably familiar: preserving the quality of judgment while expanding the scale of intelligence.

John and I approached this question from different directions. One of us spends much of his time thinking about how organizations build strategic capability, how decisions shape competitive advantage and how institutional learning becomes an enduring source of resilience. The other explores the architecture of agentic systems, orchestration and the operational models that will increasingly define AI-native enterprises. Those perspectives converge on a common conclusion. The future of organizational intelligence will not be determined solely by the sophistication of the systems we build, but by the quality of the environments into which we introduce them. The design of an agent and the design of an institution are becoming inseparable questions.

Perhaps that is where this conversation ultimately leads.

The defining organizations of the AI era will almost certainly produce better analyses, faster recommendations, and more efficient workflows than any generation before them. Those achievements will matter, but they are unlikely to be sufficient. Long after explanation becomes inexpensive and ubiquitous, organizations will continue competing through something that cannot be downloaded, prompted, or automated in isolation: the collective discipline of recognizing where their understanding remains incomplete, resisting the temptation of premature certainty, and creating institutions in which better questions continue to emerge before better answers are accepted.

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The Value of “We Don’t Know” - Part One

John Brewton — Thu, 30 Jul 2026 11:17:12 GMT

My good friend Alex Pawlowski has written a warning to leaders and executives that personifies this expectation, and I am thrilled to be publishing it in two parts, this week and next.

Encoding your operation is still the right move. Encode the answers without encoding the questions, and you have built a company that reaches conclusions faster than it ever could.

I’m sincerely excited about these two pieces and am privileged to be publishing them via Operating by John Brewton.

Hope this helps, friends.

- j -

Subscribe now

Subscribe to Alex’s Substack, The Strategy Stack, here.

I. The Disappearing Space Between Question and Answer

Every organization, regardless of its industry or ambition, exists because no individual possesses a complete understanding of the environment in which it operates. Markets shift without warning, technologies evolve faster than planning cycles, customers behave inconsistently, competitors conceal their intentions, and the internal dynamics of even the best-run companies rarely resemble the tidy diagrams found in annual reports. Organizations were never designed to eliminate uncertainty. They emerged because uncertainty exceeded the capacity of any single person to manage it alone.

Seen from that perspective, knowledge has always been something of a collective achievement. The engineer understands the architecture but not the market. The marketer recognizes customer behavior but not the underlying code. Legal counsel anticipates regulatory implications that product teams overlook, while finance evaluates risks invisible to both. What eventually appears as organizational intelligence is assembled from dozens, sometimes thousands, of incomplete perspectives that gradually converge into a decision. The organization becomes intelligent not because everyone knows the same thing, but because different people know different things.

This also explains why the phrase “I don’t know” occupies a far more important place in healthy organizations than most management literature acknowledges. Contrary to popular intuition, uncertainty is not the opposite of expertise. It is often one of its most reliable indicators. Experience rarely removes ambiguity. Instead, it sharpens the ability to recognize where established knowledge ends and where additional evidence becomes necessary. Senior physicians request further tests not because they lack competence, but because they understand how easily complex systems produce convincing yet incomplete explanations. Seasoned investors hesitate before declaring certainty because markets have repeatedly demonstrated how fragile apparently robust assumptions can become. The same pattern appears in engineering, intelligence analysis, scientific research and strategy. Expertise does not eliminate doubt; it changes the reasons for doubting.

That observation has quietly shaped the institutions through which modern societies produce reliable knowledge. Universities, peer review, investment committees, architecture reviews, clinical conferences and scientific replication all emerged from the same underlying recognition: individual judgment improves when it encounters competing perspectives before hardening into conviction. None of these institutions were created because people lacked intelligence. They were created because intelligence, left entirely on its own, has a remarkable talent for mistaking coherence for accuracy.

For a surprisingly long time, the practical mechanics of acquiring knowledge reinforced that discipline almost automatically. A difficult question rarely produced an immediate answer. It initiated a process. People searched libraries, consulted colleagues, revisited old notes, designed experiments, waited for data, challenged assumptions and occasionally abandoned conclusions that had seemed persuasive only days earlier. The interval between curiosity and explanation was often frustrating, yet it performed valuable intellectual work. It slowed judgment just enough for alternative interpretations to remain alive, making it less likely that the first plausible explanation would become the final one.

Generative AI compresses that interval with astonishing effectiveness. Questions that previously initiated investigation now frequently produce polished explanations within seconds. The technological achievement is extraordinary, but its organizational consequences deserve equal attention. Every coherent response shortens the period during which uncertainty remains visible. The question no longer lingers long enough to invite additional perspectives before an explanation arrives, and because that explanation is fluent, internally consistent and delivered with confidence, it often satisfies the immediate psychological need that originally motivated the question. Whether it fully deserves that confidence becomes a separate matter, one that many users never reach because the cognitive tension has already dissolved.

An intriguing study published recently offers an early glimpse of what this shift may mean. Researchers observed that participants who relied on generative AI became less willing to acknowledge when they did not know something. Confidence increased even when underlying understanding did not improve accordingly. Read in isolation, the finding resembles another entry in the growing literature on overconfidence and hallucinations. Read more carefully, however, it points toward a different possibility. The study is less about the reliability of AI than about the changing role of uncertainty in human reasoning. It suggests that one of the oldest transitions in the production of knowledge—the movement from not knowing toward understanding—may itself be acquiring a fundamentally different shape.

That possibility reaches far beyond individual cognition. Organizations learn because uncertainty travels. Questions move between departments, assumptions encounter disagreement, partial explanations are revised through conversation, and decisions gradually improve as competing interpretations collide with one another. Remove uncertainty too early from that process and something subtle begins to change. Conversations become shorter. Alternative hypotheses receive less attention. Confidence appears earlier than evidence. The organization continues to generate answers, but it may become progressively less effective at recognizing which questions still deserve to remain open.

The challenge, therefore, is not to decide whether artificial intelligence produces better explanations than humans. In many domains it already does. The more consequential question concerns the role that uncertainty has quietly played inside organizations all along. If explanation becomes abundant, inexpensive and permanently available, what happens to the intellectual habits that were originally built around its scarcity? Before exploring how AI may transform work, management or strategy, it is worth examining something more fundamental: the organizational function that uncertainty has performed for far longer than most of us have ever noticed.

II. Organizations Do Not Manage Knowledge. They Manage Distributed Uncertainty.

Traditional management literature often describes organizations as repositories of knowledge. The language is understandable. We speak about knowledge workers, knowledge management, knowledge transfer and organizational learning as though the defining characteristic of an enterprise were the amount of information it possesses. Yet this perspective places the emphasis on the outcome rather than on the process that produces it.

The more closely one observes how organizations actually function, the less convincing this description becomes. Companies rarely fail because the relevant information exists nowhere inside the business. More often, the necessary pieces are already present, scattered across departments, embedded in individual experience or hidden within operational routines that have never been connected into a coherent picture. Product teams understand technical constraints that commercial teams rarely encounter. Legal departments anticipate regulatory implications long before they become strategic concerns. Customer support notices weak signals months before they appear in market research, while finance often recognizes structural patterns that remain invisible to both. Every function operates with a legitimate yet necessarily incomplete representation of reality.

The organization exists because none of those representations is sufficient on its own.

That observation changes the way we think about expertise. Expertise is frequently imagined as the progressive elimination of uncertainty through accumulated experience. Organizational life suggests something rather different. As people become more experienced, they certainly develop deeper knowledge within their own domains, but they also become increasingly aware of the boundaries surrounding that knowledge. The experienced engineer rarely assumes that technical elegance guarantees commercial success. The experienced strategist understands that market data cannot predict technological discontinuities with confidence. The experienced executive eventually learns that every important decision contains variables no dashboard is capable of measuring directly.

The consequence is that organizations spend remarkably little time managing certainty. Their daily work consists largely of coordinating uncertainty between specialists whose perspectives overlap only partially. Meetings, design reviews, investment committees, cross-functional workshops and executive discussions all serve the same underlying purpose. They are mechanisms through which incomplete models of reality encounter one another before irreversible decisions are made. The value of these conversations lies less in exchanging facts than in exposing assumptions that remained invisible within individual disciplines.

This also explains why disagreement often becomes a productive rather than destructive force inside high-performing organizations. Two departments rarely disagree because one possesses knowledge and the other lacks it. They disagree because each observes reality through a different set of constraints. Engineering optimizes feasibility. Sales optimizes customer opportunity. Finance optimizes capital allocation. Legal optimizes compliance. Strategy attempts to reconcile those competing perspectives into a coherent direction. What appears as conflict on the surface frequently represents something far more valuable beneath it: the organization discovering where its own understanding remains incomplete.

Much of organizational learning therefore occurs before anyone learns anything new. It begins when existing explanations encounter friction. A marketing assumption collides with operational data. A customer interview contradicts internal expectations. A prototype behaves differently than predicted. A financial model no longer reflects observed market behavior. Progress starts at precisely the moment when confidence becomes difficult to maintain. The objective is not to eliminate disagreement as quickly as possible but to understand what reality is attempting to reveal through it.

Viewed from this perspective, uncertainty begins to resemble an organizational resource rather than an organizational defect. It identifies where current models no longer describe the environment with sufficient accuracy. It highlights where additional observation, experimentation or conversation is likely to generate the greatest returns. Organizations capable of recognizing those moments early often appear unusually adaptive, not because they possess superior forecasting abilities, but because they remain willing to revise their understanding before circumstances force them to do so.

Generative AI enters this landscape in a fascinating and somewhat paradoxical way. On the one hand, it increases access to information on a scale that would have seemed extraordinary only a few years ago. Individuals can summarize research, compare legal frameworks, draft technical documentation or explore unfamiliar domains within minutes. In many respects, AI lowers the cost of acquiring explicit knowledge more dramatically than any previous technology.

At the same time, lowering the cost of explanation also changes the signals flowing through the organization. If every uncertainty immediately produces a coherent narrative, the distinction between possessing an explanation and understanding the problem becomes progressively more difficult to detect. Questions that previously travelled through multiple perspectives may now terminate after the first plausible answer. Cross-functional conversations become shorter because apparent certainty arrives earlier. Teams may still collaborate, yet they increasingly collaborate around explanations that have not undergone the same process of collective scrutiny that organizations historically relied upon to improve decision quality.

The strategic challenge therefore extends well beyond accuracy. Even if future models become dramatically more reliable than those available today, organizations will still face the question of how uncertainty should move through the enterprise before action is taken. That movement has always been the hidden architecture of organizational learning. Decisions improve because uncertainty circulates between different perspectives, gathering evidence, exposing contradictions and forcing assumptions to justify themselves against reality. If that circulation gradually slows because explanation becomes abundant, organizations may discover that they have optimized access to knowledge while quietly weakening one of the mechanisms through which knowledge becomes trustworthy in the first place.

The discussion surrounding artificial intelligence often assumes that better answers naturally produce better organizations. History offers a more nuanced picture. Enduring institutions have rarely distinguished themselves through the speed with which they arrived at conclusions. Their advantage came from designing environments in which conclusions had to survive repeated encounters with alternative perspectives before they were allowed to shape consequential decisions. What deserves protection, therefore, is not uncertainty for its own sake, but the organizational processes that transform uncertainty into judgment rather than replacing it prematurely with confidence.

III. “I Don’t Know” as Organizational Infrastructure

When people speak about organizational capabilities, they usually think of engineering excellence, operational efficiency, strong leadership or an innovative culture. Rarely does anyone mention uncertainty. Yet many of the practices that distinguish mature organizations exist for one surprisingly simple reason: they create environments where uncertainty can be expressed before decisions become irreversible.

Consider how many institutional mechanisms depend on someone being willing to acknowledge that the current explanation may not be complete.

These practices were not invented because organizations distrusted intelligence. They were invented because intelligent people repeatedly arrive at convincing conclusions that later prove incomplete. The stronger the expertise, the more sophisticated those conclusions often become, which makes them even harder to challenge.

The phrase “I don’t know” therefore performs a function that extends far beyond individual humility. It becomes an organizational signal. It tells everyone else that the current model of reality has reached its limits and that additional perspectives are now valuable.

Interestingly, the most experienced professionals often become increasingly comfortable with this signal. Not because they know less than their junior colleagues, but because experience changes their relationship with confidence. After enough product launches, acquisitions, clinical cases or market cycles, certainty begins to feel less like expertise and more like a hypothesis waiting to encounter reality.

This observation creates an intriguing inversion of how expertise is commonly perceived.

None of these characteristics imply indecision. Organizations cannot function if every question remains permanently open. Decisions eventually have to be made, resources committed and strategies executed. The distinction lies elsewhere. High-performing organizations separate the exploration of uncertainty from the execution of decisions. They encourage broad disagreement while understanding the problem, and disciplined alignment once a direction has been chosen.

That separation has quietly become one of the defining characteristics of modern organizational life. Aviation investigates every incident regardless of how minor it appears because small anomalies often reveal systemic weaknesses. Hospitals conduct morbidity and mortality conferences not to assign blame but to examine where existing understanding failed. Intelligence agencies invest heavily in structured analytic techniques precisely because history demonstrates how easily coherent narratives become accepted without sufficient evidence. Across these very different domains, one pattern repeats itself: uncertainty is treated as something to be examined before it becomes something to regret.

Perhaps this explains why organizations so often struggle after periods of exceptional success. Success has a remarkable ability to reduce the perceived need for uncertainty. Strategies that repeatedly work begin to feel universally applicable. Processes that solved yesterday’s problems gradually become assumptions about tomorrow. Confidence accumulates not because reality has become simpler, but because past experience begins to substitute for fresh observation. By the time external conditions finally challenge those assumptions, organizations frequently discover that the habit of questioning them has quietly disappeared.

This is where the conversation returns to generative AI, although from a different direction than most current discussions. The central concern is not whether a model occasionally hallucinates or whether a summary omits important context. Organizations have always possessed imperfect information. Their resilience depended less on avoiding mistakes than on maintaining mechanisms capable of discovering them before they spread. The more interesting question is whether systems designed to produce immediate explanations unintentionally weaken one of those mechanisms by making uncertainty less visible inside everyday work.

John and I have spent much of the past year exploring organizations from different vantage points. One of us has focused primarily on strategy, organizational capability and judgment; the other on agentic systems, orchestration and the emerging architecture of AI-native work. Those conversations repeatedly converged on the same observation: the future of enterprise AI may depend less on how well systems answer questions than on how well they preserve the conditions under which good questions continue to emerge.

That possibility leads naturally to a broader question. If uncertainty has always functioned as an invisible form of organizational infrastructure, what happens when explanation becomes effectively abundant? What changes inside the enterprise when every employee, every team and eventually every autonomous agent can generate convincing narratives on demand? And perhaps more importantly, what kinds of systems should organizations build if their objective is not merely to generate explanations, but to preserve the quality of organizational judgment itself?

Tune in next week for the concluding sections of the essay…

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Creator Economics Are Platform Economics

John Brewton — Thu, 19 Mar 2026 11:10:59 GMT

Start Here

Every digital platform you use is a marketplace.

LinkedIn, YouTube, Substack, Amazon, Google, TikTok, eBay, Instagram, etc.

Each platform exists to solve a coordination problem.

Economists call these n-sided markets.

You probably call them your “content strategy.”

The moment you understand that on each of these platforms you are participating in a…

Creator Economics and the N-Sided Marketplace

John Brewton — Thu, 19 Mar 2026 10:50:56 GMT

Available for iOS and Android

Operating Guest Authors: Complaints, Complaints, Complaints w/ Jens Stark

John Brewton — Thu, 26 Feb 2026 14:46:21 GMT

Every week, I bring in one amazing friend and expert I trust to share hard-won lessons from the trenches of their work worlds. This week, my friend Jens Stark is in the chair. He brings 30,000+ hours of experience in post-sales environments to a topic most founders treat as an afterthought until there’s a five-alarm fire: customer care.
Jens runs Scalin…

Operating Leaders: How JP Morgan Chase Showed the Rest of Us What AI Transformations Can Achieve (Part One)

John Brewton — Mon, 19 Jan 2026 15:04:23 GMT

Real Talk with Brewton and Berry

John Brewton — Tue, 13 Jan 2026 15:53:35 GMT

Thank you , , and many others for tuning into my live video with ! Join me for my next live video in the app.

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Build or Be Left Behind

John Brewton — Mon, 29 Sep 2025 12:37:53 GMT

In 2025, AI‑assisted tooling will let a single operator prototype most department‑level data pipelines at low cost in 8–12 weeks, provided they enforce basic governance and testing. This does not replace enterprise systems. It unlocks the unapproved projects in your backlog and converts domain experts into credible builders.