Inbound & Agile

Tag: Leadership

  • If Integrity Requires Heroism, the System Is Already Broken

    If Integrity Requires Heroism, the System Is Already Broken

    Robert Coram’s biographies praise people who chose principle over career. They also expose a failure that should scare all of us:

    Too many institutions need exceptional courage to hear ordinary truth.

    In 1967, Lieutenant General Victor “Brute” Krulak went to the White House with an argument President Lyndon Johnson did not want to hear.

    The United States was prosecuting the Vietnam War through search-and-destroy operations and attrition. Krulak believed the decisive contest was for the security and allegiance of the people living in South Vietnam’s villages. The Marines could and should fight large enemy formations, he argued, but destroying units and counting bodies would not by itself defeat an insurgency.

    Robert Coram reconstructs what happened next in Brute, his biography of Krulak. According to that account, Krulak told Johnson that American strategy was producing unnecessary casualties and that responsibility reached the top of the government, “including you, Mr. President.” If the president did not change course, Krulak warned, he would lose the war and the next election.

    Johnson rose, placed a hand on Krulak’s shoulder, and ushered him out without another word.

    Lieutenant General Victor “Brute” Krulak in uniform against a cream, charcoal, and brass editorial background.
    Lt. Gen. Victor H. Krulak.
    Cover of Brute by Robert Coram.
    Brute by Robert Coram.

    Coram argues that the confrontation helped cost Krulak a fourth star and the job he wanted most: Commandant of the Marine Corps. Johnson later selected another Marine. Krulak retired as a three-star in 1968.

    It would be too easy to turn the scene into a polished parable in which a truth-teller confronts a villain, pays the price, and is vindicated by history. Coram’s biographies, however, preserve achievement, ambition, contradiction, collateral damage, and uncertainty in the same frame.

    The easy lesson in Krulak’s story is that leaders should admire moral courage. The harder lesson is that a president responsible for a war needed the information Krulak carried. Why did delivering it have to resemble a career-ending act of heroism?

    The fork in the road tests more than character

    When I interviewed Coram for The Unfolding Thought Podcast, he explained what he had looked for in the military figures he chose to write about. He wanted people who reached a fork in the road, did what they believed was right, and paid a price.

    Robert Coram in a dark suit and round glasses against a cream, charcoal, and brass editorial background.
    Robert Coram.

    That is a powerful test of character.

    A moral fork tells us what a person does when duty, self-interest, loyalty, and fear point in different directions. It reveals something that no résumé, values statement, or performance review can. Coram found versions of that test in Krulak; in fighter pilot and military theorist John Boyd; and, at an almost unimaginable physical extreme, in Medal of Honor recipient and Vietnam prisoner of war George E. “Bud” Day.

    But the fork can also reveal something about the road builder and not just the traveler.

    When people must repeatedly choose between doing good work and preserving a viable career, the institution is not merely discovering their character. It is manufacturing the conflict. It is making personal courage compensate for defects in how information travels, how authority responds, and how careers are governed.

    Some moral choices will remain costly in any organization worth serving. No process can make courage unnecessary. But when routine truth-telling predictably requires career-threatening heroism, the price is no longer just evidence of the employee’s virtue. It is a leadership metric.

    A heroic culture can still be a silent culture

    Organizations often talk about candor as if it were a personality trait:

    • Good employees speak up.
    • Courageous leaders welcome bad news.
    • Weak people stay quiet.

    Decades of organizational research describe a more relational problem. New York University professor Elizabeth Morrison defines upward voice as employees voluntarily communicating suggestions, concerns, or information about problems to people higher in the hierarchy.

    Silence is not the absence of information. It is the withholding of information that may already exist somewhere lower in the system.

    That means a quiet meeting is ambiguous. Everyone may agree. Or the people who disagree may have decided that the cost of speaking is not worth paying.

    In a study of 3,149 employees and 223 managers, James Detert and Ethan Burris found that managerial openness was more consistently related to improvement-oriented voice than transformational leadership was. Employees’ sense of psychological safety helped explain the relationship. The effect of leader behavior was especially strong among high performers, the very people an organization can least afford to train into silence.

    The point is not that every leader must be gentle or that every employee must feel comfortable. Psychological safety, as Amy Edmondson originally defined it, is a shared belief that a team is safe for interpersonal risk-taking. It makes learning behaviors such as asking for help, reporting mistakes, and challenging an assumption more possible. It does not make every assertion correct, remove performance standards, or exempt anyone from the consequences of deliberate misconduct.

    Coram’s account of Krulak contains a small scene that makes the larger problem visible. During a visit to Vietnam, Krulak asked Marines what they thought. They gave him answers he did not expect and did not like. Afterward, he asked his son, Navy chaplain Victor Krulak, what was wrong with the battalion.

    His son suggested that people elsewhere might have been telling the general what he wanted to hear.

    Coram writes that the possibility appeared not to have occurred to Krulak, even though Krulak himself knew that subordinates often manage what senior officers see.

    This does not diminish the courage Krulak later showed with Johnson. It makes the case more valuable. The same person can be unusually willing to carry unwelcome truth upward and still make it difficult for truth to travel upward to him.

    Candor is not secured by placing one brave person in the hierarchy. It has to survive every level of the hierarchy.

    “To be or to do” is both a challenge and an indictment

    John Boyd made the decision point explicit.

    Coram’s Boyd follows the fighter pilot from the cockpit to the Aerial Attack Study, energy-maneuverability theory, the F-15 and F-16 development battles, and the body of work later associated with the OODA loop. Boyd’s accomplishments depended on extraordinary concentration, technical insight, a network of allies, and an appetite for conflict that often made him nearly impossible to ignore and just as difficult to manage.

    John Boyd in flight gear against a cream, charcoal, and brass editorial background.
    Col. John Boyd.
    Cover of Boyd by Robert Coram.
    Boyd by Robert Coram.

    He divided officers into people who wanted “to be” somebody and people who wanted “to do” something.

    Coram shows Boyd applying that test to Air Force officer James Burton. Burton had refused an order to alter a chart comparing aircraft performance because the alteration would create a lie. He was removed from his Pentagon job and passed over for promotion. Boyd told him he had reached a fork: he could pursue the promotion and its trappings, or continue the work he believed was right. He could not, Boyd said, have a normal career and do the good work.

    As a moral challenge, “to be or to do” draws a clear line. As an organizational condition, it should be alarming.

    Why should a normal career and good work be incompatible?

    What information will never reach a decision-maker if the price is obvious before anyone speaks?

    How many capable employees will make the sensible choice, protect their families and futures, and leave the unaltered chart in a drawer?

    This is where hero stories can mislead managers. We admire the outlier who accepts the sacrifice and then quietly build systems that assume the next person will do the same. When the next person does not, we call it a character failure rather than asking what the incentives were designed to produce.

    Boyd also shows why an institution cannot simply reward combativeness. He could be brilliant, abrasive, domineering, and wrong. His crusades imposed costs on colleagues and family members who did not choose them.

    An organization that depends on finding a hero is fragile. A stronger one preserves the function Boyd and men like him serve: challenging assumptions, demanding evidence, and testing doctrine, without requiring every reformer to become him.

    Coram’s exception protects the argument from hero worship

    Coram told me that one of his four military subjects did not fit his moral-fork rule: World War II fighter pilot Robert Lee Scott Jr.

    Brigadier General Robert Lee Scott Jr. in Air Force dress uniform against a cream, charcoal, and brass editorial background.
    Brig. Gen. Robert Lee Scott Jr.
    Cover of Double Ace by Robert Coram.
    Double Ace by Robert Coram.

    Scott was a real combat pilot and a double ace. He flew guest missions with pilots of the American Volunteer Group, the original Flying Tigers, and later commanded the regular Army Air Forces’ 23rd Fighter Group. His 1943 memoir, God Is My Co-Pilot, became a bestseller and then a film. He also told stories so fluently and so often that the border between experience, performance, and memory could become difficult to locate.

    Cover of Robert Lee Scott Jr.'s 1943 memoir God Is My Co-Pilot.
    Scott’s 1943 memoir, God Is My Co-Pilot.

    In one late version of the book’s origin story, Japanese rounds struck Scott’s aircraft from behind and drove rivets from the armor behind his seat into his back. After landing, he was supposedly operated on without anesthesia in a candlelit cave, where the title appeared to him on the cave wall. Coram found that the cave revelation was absent from the original book, that he could find no injury record, and that Scott had not received a Purple Heart. Those absences do not prove that every element was invented, but they leave the later account without the evidence that should support it.

    Scott matters here because moral courage is not a method for determining truth. Neither sincerity, confidence, sacrifice, nor opposition to authority proves a claim. Institutions need people to raise unwelcome information, and they need disciplined ways to test it.

    Those principles reinforce one another.

    If only safe claims can be voiced,
    evidence is filtered before examination.

    If every contrarian claim is celebrated as brave, dissent becomes theater.

    A serious truth-telling system protects the messenger long enough to evaluate the message.

    What ordinary people usually do at the fork

    The most revealing moral fork in Coram’s body of work may not belong to a pilot or general.

    In his memoir Ink, Coram recalls working in 1959 as an untrained attendant at Georgia’s state mental hospital in Milledgeville. By then, it was less a hospital in the ordinary sense than a vast custodial city. The institution averaged more than 11,800 patients in the late 1950s. It sprawled across roughly 200 buildings and 2,000 acres and, by the 1960s, was described as the largest mental hospital in the world.

    That scale did not produce more care. The New Georgia Encyclopedia records overcrowding, “conscious neglect,” and an institution often able to meet only basic daily needs rather than provide appropriate treatment. In a place that large, abuse could disappear into wards, bureaucracy, and sheer numbers.

    That helps explain the importance of Jack Nelson’s investigation for the Atlanta Constitution. His 1959 reporting documented experimental drugs given without patient or family consent, major surgery performed by a nurse without supervision, and staff and doctors drunk on duty. The series would win the 1960 Pulitzer Prize.

    Cover of Ink by Robert Coram.
    Ink by Robert Coram.

    Coram admired Nelson. He understood that this was what a newspaper could do: enter a closed institution and force powerful people to answer for what happened inside.

    Then, Coram writes, a doctor ordered him to dispose of boxes of medicine to hide them from Nelson’s reporting. Coram believed the labels, dates, and drugs might matter to the investigation. He flushed the pills down a patient toilet, carried the packaging to a dump, and said nothing when the reporter returned.

    He believed the material might be evidence. He recognized the choice. He obeyed the institution anyway.

    “I wanted to help him,” Coram writes. “But I did not.”

    It is tempting to read that sentence as a verdict on the young man. I read it as a warning to anyone who designs work for other people.

    Most employees are not Bud Day. They should not have to be.

    They have mortgages, health insurance, reputations, visas, commissions, pensions, children, and an accurate understanding of what authority can do to them. They watch what happens to the first person who raises a problem. They notice whether the concern is investigated, whether the leader becomes curious or offended, and whether the messenger’s next assignment quietly disappears.

    The employee who remains silent may be making a moral mistake. The organization that makes silence rational is making a management mistake.

    The price rarely stops with the person who speaks

    Coram’s biographies also complicate the idea of individual sacrifice by showing who else pays.

    Bud Day endured five years, seven months, and thirteen days as a prisoner of war, including periods of prolonged torture. Dorie carried that captivity into public life, organized with other POW families, preserved information, kept their household functioning, and later lived beside the physical injuries and recurring nightmares that came home with him. Mary Boyd and her children experienced a different version of cost: absence, volatility, financial strain, and the work required to preserve the ideas for which John Boyd received public credit.

    George E. “Bud” Day in uniform against a cream, charcoal, and brass editorial background.
    Col. George E. “Bud” Day.
    Cover of American Patriot by Robert Coram.
    American Patriot by Robert Coram.

    Those family accounts do not erase Day’s courage or Boyd’s achievements. They complete the ledger.

    We recognize, celebrate, and talk about a few heroes, but the cost of their heroism is paid not only by them. It is also paid by the people around them.

    Organizations usually record the visible price: the stalled promotion, the resignation, the lost command. The cost that migrates into a home, a colleague’s workload, a customer’s risk, or a caregiver’s life often disappears from the scorecard. Calling the employee heroic can become a way of praising a sacrifice the institution has no intention of accounting for.

    Build for candor before you need courage

    Values statements are useful only if the operating system makes them credible. An organization that wants truth before a crisis can build for it in at least six ways.

    1. Give unwelcome information a route around the hierarchy

    An open-door policy still requires an employee to walk through the boss’s door.

    NASA’s Aviation Safety Reporting System was designed around a different insight. Aviation workers can voluntarily report safety incidents to an independent program that keeps identities confidential, removes identifying details, and offers limited protection from penalties for qualifying unintentional violations. Deliberate and criminal acts are not sheltered. The design does not confuse learning with impunity; it reduces the personal risk that prevents useful safety information from entering the system.

    Most organizations do not need a miniature NASA. They do need a channel whose independence, confidentiality, response time, and escalation rules are real rather than decorative.

    2. Make dissent part of the work, not a personality contest

    Do not wait for a natural contrarian to challenge the plan. Assign the function.

    Before commitment, ask one group to assume the decision has failed and construct plausible reasons why. Gary Klein’s premortem works because it gives knowledgeable skeptics permission to voice reservations before failure converts them into hindsight. Rotate the dissent role so that disagreement does not become one person’s identity or one person’s career risk.

    Require the decision owner to state what evidence would change the decision. “Convince me” is not a standard; it is an invitation to contest status. A falsifiable threshold is a standard.

    3. Make the most powerful person speak last

    If the senior leader announces a preference first, every answer that follows has been contaminated by information about what the hierarchy wants.

    Collect judgments independently before discussion when possible. Ask for disconfirming evidence, not just concerns. Replace “Does anyone disagree?” with questions that require content: What assumption is carrying the most risk? What would we expect to see if we are wrong? Who has information that does not fit the current story?

    Then wait long enough for an answer.

    4. Keep a decision record that can survive memory

    Record the decision, material assumptions, competing interpretations, predictions, unresolved concerns, owner, and review date. The purpose is not bureaucratic self-protection. It is to keep the organization from rewriting what it once believed after the outcome is known.

    Scott’s stories show how repetition can harden a satisfying account. A contemporaneous record gives later reviewers something other than confidence and status to examine.

    5. Review outcomes without defending rank

    In my conversation with former Royal Australian Air Force fighter pilot and Afterburner CEO Christian “Boo” Boucousis on The Unfolding Thought Podcast, the debrief was the central subject. Boo described an operating rhythm used after every mission, successful or not: compare the objective with the actual result, identify the cause of any gap, and choose a concrete action for the next mission. His organization applies the same discipline to business teams. The purpose is to convert experience into usable intelligence while keeping rank and ego from controlling the account.

    The simplicity is deceptive. A useful debrief has to remain a professional inquiry rather than a trial, a victory lap, or a briefing in which subordinates discover what the leader wants them to say. Preserve competing accounts. Separate facts from inference. Assign changes and return to see whether they occurred.

    6. Audit what happens to the messenger

    Nonretaliation cannot be measured by counting how many people were formally fired for speaking up. Career penalties are often quieter: a missed invitation, a lower-visibility assignment, exclusion from information, an unexplained performance downgrade, or the conclusion that advancement now lies elsewhere.

    Track whether people who raise consequential concerns remain, advance, and continue to contribute. Ask them what happened after they spoke. Review whether concerns were acknowledged, investigated, and closed. Hold leaders accountable for retaliation and for teaching teams through visible behavior that candor is futile.

    The Government Accountability Office’s recent review of federal disclosure systems reaches a similar practical conclusion: accessible confidential channels, credible protection against retaliation, consistent accountability, and visible leadership commitment all influence whether people trust a speak-up system.

    Moral courage is a virtue. It is not an operating system.

    Coram is right to admire Krulak’s willingness to carry an unwelcome judgment to Johnson. The choice revealed the general’s sense of duty and the relative value he placed on career and country. We need people who will act that way when a consequential truth has no safe route to power.

    But admiration can stop the inquiry too early.

    The leader’s question is not only, Will someone be brave enough to tell me? It is also, What expectations have I created about what happens to people who tell me something I do not want to hear?

    A sound institution cannot remove every moral fork. It can keep routine evidence, doubt, error, and disagreement from becoming tests of personal martyrdom. It can make candor ordinary enough that exceptional courage is reserved for exceptional circumstances.

    When doing the work and keeping a viable career repeatedly point down different roads, the organization has confused a character test with a management system.

    Moral courage is a virtue. It should not be an operating system.

    The episode and sources

  • AI Cannot Decide What Your Business Is For

    AI Cannot Decide What Your Business Is For

    Every AI deployment turns vague values into executable instructions. If leaders do not define purpose, knowledge, and reality, the system will borrow its philosophy from metrics, training data, and vendors.

    Imagine a leadership team announcing its AI strategy in one sentence: We will achieve the same growth with 15 percent fewer people.

    That may be a target. It may even be a responsible response to difficult economics. But it is not a compelling account of what the organization hopes to become. It tells employees which cost should fall, not which capability should rise. It describes subtraction as though subtraction were a future.

    Michael Schrage raised a version of this problem in our recent conversation. The trouble with treating AI chiefly as a source of efficiency is not only that the ambition is small. It is that the ambition supplies the system with a purpose. The tools are then asked to optimize labor cost, cycle time, or output without anyone having to say what the work is ultimately for.

    AI does not make philosophy irrelevant. It makes philosophy operational.

    Once a model can recommend, rank, route, generate, negotiate, or act, words such as value, quality, loyalty, productivity, and risk can no longer remain inspiring abstractions. They have to become instructions. Someone, somewhere, must decide what counts as evidence, which categories matter, which tradeoffs are acceptable, and whose outcomes deserve priority.

    If leadership does not make those choices deliberately, the choices do not disappear. They are inherited from the data, the metric, the workflow, or the vendor.

    The danger is not only that AI will fail. It is that AI will faithfully optimize a purpose no one examined.

    Philosophy is already in production

    In his MIT Sloan Management Review essay, “Philosophy Eats AI,” Schrage argues that AI systems inevitably embody positions on three old philosophical questions.

    Teleology asks what the system is for. Is a customer-service agent meant to close tickets, reduce expense, restore confidence, preserve a relationship, or help a customer make a better decision? These purposes overlap until they do not. At the moment of conflict, the system needs a priority.

    Epistemology asks what counts as knowledge. Which evidence may the system trust? Does a recorded click outweigh a customer’s explanation? Is a prediction sufficient grounds for action? How should the system treat uncertainty, disagreement, or information that does not fit its prior patterns?

    Ontology asks what kinds of things exist in the system’s world. Is a person a customer, a user, an account, a risk score, a member of a household, or a participant in a community? Which relationships are visible? Which are erased because the database has no field for them?

    These questions can sound academic until a system denies a loan, selects a job candidate, prioritizes a patient, assigns a sales lead, or decides which employee appears unproductive. Then ontology becomes a data model, epistemology becomes an evidence rule, and teleology becomes an objective function.

    Philosophy has entered production.

    The practical mistake is to imagine that a technically neutral system waits for human values to be added later. There is no neutral ranking, neutral threshold, or neutral definition of success. Every deployment encodes a view of what matters, even when that view arrives disguised as a default setting.

    When metrics become metaphysics

    Organizations need metrics. The problem begins when a proxy stops being treated as a partial representation and becomes the reality the system is permitted to see.

    Suppose a company defines loyalty as repeat purchases. That definition is measurable and useful. It is also incomplete. A customer might repurchase because switching is difficult, because a contract traps them, or because every alternative is worse. The metric registers all three as loyalty. An AI trained to increase the number may improve retention while degrading the relationship that retention is meant to represent.

    The same problem appears when knowledge work is reduced to visible activity. Messages sent, documents produced, tickets closed, or hours online are traces of work. They are not the work itself. Judgment often creates value by preventing unnecessary activity, reframing the question, or recognizing that the requested output should not exist.

    AI makes this confusion more consequential because it can optimize the proxy continuously and at scale. A weak metric in a monthly report can mislead a meeting. A weak metric embedded in an autonomous workflow can reorganize behavior across the company.

    When a metric becomes the target, it can become the organization’s working definition of reality.

    This is why the familiar warning that “the map is not the territory” becomes a governance issue. The model will travel the map it is given. Leaders remain responsible for asking what the map excludes, who drew its borders, and whether arriving at the marked destination would actually count as progress.

    The accumulation of philosophical debt

    Technical debt accumulates when short-term engineering choices make future systems harder to change. Organizations now face a related problem: philosophical debt.

    Philosophical debt accumulates when fundamental questions are left unresolved but systems must still be built. The team does not agree on what a qualified lead is, so it automates the current scoring rule. Leaders have not settled what good service means, so the agent optimizes handle time. No one can define productive collaboration, so the dashboard counts individual output.

    Each choice is defensible as a temporary convenience. Together, they harden into an operating philosophy no one remembers choosing.

    The debt is easy to miss because the system can perform well by its own measures. It can become faster, cheaper, and more consistent. But consistency is not the same as correctness. A system can scale a category error with extraordinary reliability.

    This is one reason vendor selection alone cannot be an AI strategy. Vendors can provide capable models, safeguards, and implementation patterns. They cannot decide what your customers should become, what obligations you have to employees, or which forms of value your business should protect. When leaders leave those questions blank, product defaults fill the space.

    Agency raises the philosophical stakes

    The stakes rise as AI moves from answering questions to taking action.

    A conversational model can produce a flawed recommendation that a person ignores. An agentic system can convert the same flaw into a sequence of actions before anyone notices the premise. It can segment customers, change offers, move money, contact suppliers, or alter a workflow. Greater autonomy shortens the distance between an assumption and its consequences.

    This does not mean every decision needs executive review. That would defeat much of the value of automation. It means the boundary between machine discretion and human judgment must reflect the moral and strategic weight of the decision, not merely the model’s confidence.

    A model may be highly confident that a customer belongs in a low-value segment. Confidence does not answer whether the category is fair, whether the treatment will become self-fulfilling, or whether the company wants to build a relationship in which past value sets the ceiling on future attention.

    “Human in the loop” is therefore too vague to be a governance principle. Which human? At what point? With what information? Empowered to question which premise? Accountable to whom?

    Expert judgment should not be a ceremonial inspection step attached to the end of an automated process. It must help define the categories, evidence, exceptions, and purposes that shape the process from the beginning.

    Critical thinking is not a soft skill

    Schrage asks a bracing question in the episode: How do you contribute meaningful value when you are no longer the smartest creature in the room?

    One answer is that intelligence and judgment are not interchangeable.

    A model may generate more options than a team, retrieve more information than an expert, and expose patterns no individual could detect. It still cannot relieve leaders of the obligation to decide what deserves to be optimized. In fact, greater machine capability makes that obligation harder to avoid.

    Critical thinking in an AI-enabled organization is not simply the ability to spot hallucinations. It is the ability to examine the frame:

    • What problem have we decided this is?
    • What must be true for this recommendation to make sense?
    • Which values have been converted into variables?
    • What does the system make invisible?
    • Which outcome would falsify our theory of value?
    • Who benefits if the model is right, and who absorbs the cost if it is wrong?

    These questions are sometimes dismissed as slowing innovation. Used well, they do the opposite. They prevent a company from scaling a misunderstanding.

    Promptathons as organizational inquiry

    One of Schrage’s most exciting practices is the promptathon, a collaborative setting in which people use generative AI to explore problems, alternatives, and opportunities together. The important word is collaborative.

    Most workplace AI adoption begins as private experimentation. Individuals learn to write faster, summarize more, or automate pieces of their jobs. That can produce real gains, but it also keeps the organization’s assumptions hidden inside personal workflows.

    A well-designed promptathon can surface those assumptions. Different functions can ask the same model to describe a valuable customer, a successful project, or an acceptable tradeoff. The disagreement among the answers is not noise. It is evidence that the organization has been using shared words without a shared meaning.

    That makes the promptathon more than a prompt-engineering exercise. It can become a form of institutional philosophy conducted through prototypes. Teams can test how a definition behaves before embedding it in a system. They can compare what sales, service, finance, operations, and customers each mean by “value.” They can discover which disagreements require leadership decisions and which can remain productively plural.

    The goal is not consensus on every abstraction. It is enough clarity to know which purpose governs a particular system, which evidence it may use, and when competing values require escalation.

    A philosophy brief for every consequential AI system

    AI governance often begins with inventories, risk levels, privacy reviews, and technical controls. Those are necessary. They do not answer the prior question of what the system should be trying to accomplish.

    Before a consequential AI system enters production, its owners should be able to produce a short philosophy brief. It need not quote philosophers. It should answer seven practical questions:

    1. What is this system for? State the human or organizational outcome, not merely the task it performs.
    2. What evidence counts? Identify the data the system may trust, the uncertainty it must expose, and the claims it may not infer.
    3. What world does it recognize? Name the people, relationships, categories, and time horizons represented in the model, along with the important realities that remain outside it.
    4. Which tradeoffs require a person? Specify the conflicts the system may resolve and those that need accountable human judgment.
    5. Who should become more capable? Describe how customers, employees, or partners gain agency rather than merely becoming easier to manage.
    6. What counter-metric could reveal a false victory? Pair the primary objective with evidence that would show the system is winning the measure while losing the purpose.
    7. Who bears the downside? Make distribution visible. An average benefit can conceal concentrated harm.

    The brief will not eliminate disagreement. Its value is that it makes disagreement inspectable before software turns it into routine.

    The strongest AI strategy is not “What can this model do?” It is “What should this system make possible, for whom, and at what cost?”

    From cost effective to cost affective

    Near the end of our conversation, Schrage plays with the difference between effect and affect. Organizations want cost-effective AI, systems that achieve an outcome with fewer resources. He argues that they should also want “cost affective” AI, systems designed with human feeling, motivation, and identity in view.

    The phrase is more than wordplay. Technology changes not only what people produce, but how they experience themselves while producing it.

    An AI system can make an employee more capable, curious, and willing to exercise judgment. It can also make the same employee passive, monitored, and reluctant to think beyond the recommendation. A customer agent can help someone understand a difficult choice, or it can perfect the art of moving them through a funnel. Both systems may reduce cost. Only one enlarges the person.

    This suggests a more ambitious way to measure return on AI. Alongside time saved, errors reduced, and revenue created, ask whether the system improves the human capacity around it. Do people make better judgments after using it? Do teams ask better questions? Can customers act with greater understanding? Does expertise become more transferable, or merely more concentrated in the machine?

    Schrage has long argued that companies should ask not only how to create more valuable innovation, but how innovation can create more valuable people. That shift changes the unit of analysis. The person is no longer a cost adjacent to the technology. Human capability becomes one of the technology’s intended outputs.

    The advantage is clarity

    AI capability will continue to spread. Competitors will gain access to similar models, interfaces, and agents. The durable advantage will not be possession of the tool alone.

    It will be the clarity to give powerful tools a purpose worth pursuing, the discipline to distinguish evidence from convenience, and the imagination to define value as more than whatever happens to be measurable.

    Leaders sometimes hope AI will help them avoid hard choices by revealing the objectively best answer. More often, it reveals how much judgment was concealed inside the question. It forces a company to say what a good customer relationship is, what productive work looks like, which risks are acceptable, and what kind of organization it intends to become.

    Those are philosophical questions, but they are not luxuries. They are design requirements.

    AI cannot decide what your business is for. It can only make your answer more consequential.

    Michael Schrage, MIT researcher, innovation expert, and author.
    Michael Schrage, MIT researcher, innovation expert, and author

    This essay grows out of Eric Pratum’s conversation with Michael Schrage on The Unfolding Thought Podcast. Watch or listen to “Why Philosophy Will Matter More Than AI.”

  • AI Doesn’t Transform Work. Management Systems Do.

    AI Doesn’t Transform Work. Management Systems Do.

    AI can turn knowledge work from craft into process. Whether that produces a golden age of work or an AI slop factory depends on what leaders redesign around it.

    About an hour into my conversation with Neel Doshi, he described a chief technology officer who had a puzzle.

    The company’s engineers were coding faster with AI. The expected business improvement never appeared.

    Neel asked the question that should have come before the rollout: If accelerating engineering did not accelerate the company, what made anyone think engineering was the bottleneck?

    This is question about AI at work separates output from performance.

    AI can help a person write more code, produce more slides, generate more campaign concepts, summarize more meetings, and answer more email. Every one of those activities can increase while the organization stays exactly where it was or gets worse. The code waits for a decision. The slides create another review cycle. The campaign concepts converge on the same familiar ideas. The meeting summary gives ten people ten more things to read.

    The problem is not that the tool failed. The problem is that we accelerated one part of a system and called it transformation.

    Faster work is not necessarily a faster organization

    The early evidence on AI productivity is real, but it is not uniform.

    In a study of 5,179 customer-support agents, researchers found that a generative AI assistant increased issues resolved per hour by 14 percent on average and 34 percent among novice and lower-skilled workers. The tool appears to have helped less-experienced workers apply practices that stronger workers had already learned.

    A separate experiment with 758 consultants found similarly large gains on tasks that sat within AI’s capabilities. People with AI completed 12.2 percent more tasks, worked 25.1 percent faster, and produced higher-quality results. But on a task outside that capability frontier, the people using AI were 19 percent less likely to reach the correct answer.

    Then, there is the uncomfortable counterexample. In a randomized trial involving experienced open-source developers working in codebases they knew well, AI use made them 19 percent slower. Before the work, they expected AI to make them 24 percent faster. Afterward, they still believed it had made them 20 percent faster. The tools have improved since that 2025 study, and METR’s 2026 follow-up says newer systems probably produce more benefit, but selection effects made the size of that benefit impossible to estimate reliably.

    These findings do not cancel one another out. Together, they tell us something more important: “Does AI improve productivity?” is an underspecified question.

    Whose productivity? On what task? At what level of experience? Measured at what point in the workflow? Over what period? At what cost to the next person in the system?

    The practical unit of analysis cannot be the prompt or even the worker. It has to be the system that turns work into an outcome.

    If AI helps a copywriter produce twice as many drafts but the client still approves one campaign a month, draft production was not the constraint. If an engineer produces code faster but product decisions, security reviews, and customer adoption do not move, coding was not the constraint. If managers use AI to generate more feedback but employees need more time to interpret generic advice, the organization has transferred effort rather than eliminated it.

    Activity is easy to accelerate. Throughput is harder.

    From craft to process: why that phrase is dangerous

    Neel’s most provocative claim was this:

    “What AI can do for knowledge work is what mechanized factories did for manufacturing.”

    Manufacturing moved from craft toward process. Neel believes AI can do the same for knowledge work.

    It is an illuminating analogy, but only if we resist the easiest interpretation of it. Process does not mean that knowledge work becomes perfectly repeatable. It means we become more explicit about how work moves, where variation enters, who evaluates it, and how the system learns.

    Modern manufacturing was never as deterministic as it looks from a distance. Materials vary. Machines drift. Conditions change. People discover better methods. Systems such as the Toyota Production System and Six Sigma were built to notice and manage variation, not to pretend it had disappeared.

    Generative AI makes the variation more obvious. The same instruction can produce different answers. A model can perform brilliantly on one task and fail on another that looks almost identical. Its capabilities change. The surrounding data change. The meaning of “good” often depends on context that exists only in the heads of customers, operators, and subject-matter experts.

    That means expert judgment is not a final inspection step tacked onto an AI process. Judgment is part of the production system.

    This is where the factory analogy can mislead us. The worst version of industrialization reduces the person to a monitor of the machine: wait for output, check a box, fix the occasional defect. That would turn a great deal of knowledge work into the cognitive equivalent of watching a conveyor belt.

    The better analogy is a production system in which the people closest to the work can stop the line, diagnose a problem, change the method, and teach the improvement to everyone else.

    AI can make knowledge work more process-like without making people more machine-like. But only if leaders distribute the authority to question and improve the process. Otherwise, we will not get continuous improvement. We will get continuous generation.

    Four modes of AI augmentation, organized by individual versus collective use and unstructured versus structured use.

    Neel described four broad modes of augmentation. Most organizations begin in the lower-left corner and mistake that starting point for the whole field.

    The performance dip is real. It is not a blank check.

    Neel offered a useful definition of transformational change: performance gets worse before it gets better.

    People need to learn new tools, but that is the smaller part of the problem. They also have to learn how to delegate to a system that does not understand responsibility, how to evaluate fluent output that may be subtly wrong, how to break work into parts, how to give useful feedback, and how to decide when a human conversation is the shortest route.

    In the middle of that learning curve, an experienced person can reasonably think, “I could have done this faster myself.” Sometimes they are correct.

    Economists have described a related pattern as the Productivity J-Curve. General-purpose technologies often require investments that ordinary productivity measures do not capture well: redesigned processes, new skills, new organizational structures, and new business models. During the investment period, measured productivity can stagnate or decline. The gains arrive later, if the complementary changes actually work.

    That gives leaders a reason to tolerate a dip. It does not give them permission to romanticize failure.

    Not every valley leads somewhere. A team can get worse because it is learning, or because the use case is poor, the tool is unreliable, the work is badly designed, or the underlying problem was never worth solving. Calling every disappointment “transformation” is how a temporary experiment becomes a permanent drain.

    A responsible performance dip has boundaries:

    • A specific capability the team is trying to develop.
    • A real work outcome, not an adoption target.
    • A baseline against which the change can be judged.
    • A protected period for practice.
    • Clear risks that require human review.
    • A date when the team will decide whether to continue, change course, or stop.

    “Use AI more” satisfies none of these conditions. It is not a strategy. It is not even a complete instruction.

    Individual acceleration can create a collective tax

    One of the people Neel spoke with during an AI transformation was an engineer who had not talked to another person in four days. She was producing work, but the way the company had introduced AI was removing the collaborative part of the job, the part she enjoyed.

    It is tempting to dismiss this as a preference. Some people like quiet, independent work. Not every task needs a meeting, and collaboration can become its own form of bureaucracy.

    But knowledge work depends on more than the sum of individual outputs. Teams combine perspectives, expose assumptions, carry tacit context, notice weak signals, and decide what is worth doing. Those functions can be slow. They are also where much of the organization’s judgment lives.

    Recent research makes the tradeoff clear.

    In one experiment, access to AI ideas helped individuals write stories that evaluators found more creative and enjoyable. Across the group, however, the stories became more similar to one another. Everyone could improve locally while the collective pool of ideas narrowed.

    Another set of experiments found that human-AI collaboration improved immediate task performance but was followed by lower intrinsic motivation and more boredom when people returned to solo work. That does not prove AI will make work demotivating; the studies used bounded online tasks. It does warn us that the experience of performing better and the experience of developing capability are not the same thing.

    The collaboration evidence is not uniformly negative. A field experiment with 791 Procter & Gamble professionals found that individuals working with AI matched the performance of two-person teams without it. AI also helped technical and commercial professionals produce more balanced solutions, crossing some of the functional boundaries that usually shaped their ideas.

    That is a meaningful benefit. It also raises a management question: If AI can reproduce some benefits of a team for one task, what human capabilities still need a team to develop over time?

    The answer will differ by work. The point is to ask before collaboration disappears by accident.

    “AI native and people first” has to be testable

    Neel recommends that leaders describe the goal as “AI native and people first.” It is better language than “AI first” because it refuses to rank the tool above the people, customers, and purpose of the organization.

    But a phrase this appealing can become decoration. To mean anything, it has to change decisions.

    I would look for six signs.

    1. The organization measures the outcome of the system

    Do not lead with prompts written, licenses activated, hours “saved,” drafts produced, or agents built. Measure cycle time from need to usable outcome, downstream rework, error rates, customer response, decision quality, revenue, cost, or whatever the system exists to accomplish.

    The closer the metric is to clicking the AI button, the easier it is to mistake motion for value.

    2. Expertise is inside the loop

    Neel noted that he can detect weak AI writing about his own research but may not recognize errors in an explanation of quantum physics. That should determine where AI is used and who reviews it.

    The person who can judge the output needs enough context, time, and authority to change the process, not merely approve what the system hands them.

    3. The process preserves divergence before it converges

    When everyone begins with the same model, the model can become a hidden agenda setter. It defines the first set of possibilities, and people refine from there.

    For work that depends on novelty, teams should sometimes think independently before using AI, deliberately seek disconfirming views, and compare genuinely different approaches. Faster convergence is valuable only after the search space is rich enough.

    4. Learning is funded as real work

    If people are expected to learn only after their normal work is finished, the organization is choosing nights and weekends as its AI strategy.

    Protected practice time, real tasks, feedback from experts, and permission to discard a weak use case are investments in capability. A one-hour prompt-training session followed by an adoption quota is not.

    5. Leaders redesign their own work

    Leaders cannot remain at the level of saying, “Build an agent for that.” They need direct experience with the tool’s strengths, failure modes, and demands on judgment.

    More importantly, they need to change the calendars, approvals, meeting habits, decision rights, and incentives that keep everyone else working in the old way. Technology added on top of an unchanged management system usually becomes one more layer of work.

    6. The organization protects contribution, not just employment

    Neel’s hopeful scenario is a “golden age of work” in which AI helps more people solve problems and improve the systems around them. That requires more than promising not to eliminate jobs.

    People need consequential choices to make, skills to build, colleagues to learn with, and evidence that their contribution changed the outcome. A job can survive while everything motivating about it is removed.

    The scarce resource is no longer production

    The cost of producing a plausible draft has collapsed. The cost of deciding what deserves attention has not.

    As generation becomes abundant, several things become scarcer: trustworthy judgment, distinctive ideas, shared context, willingness to revise, and the motivation to take ownership of an outcome. Those are not soft concerns around the edge of AI adoption. They are the constraints that determine whether faster generation becomes value or noise.

    Neel is right that AI creates an unusual opening. Organizations can use it to give more people access to context, coaching, and the ability to improve work that once felt fixed. They can also use it to increase output, centralize control, weaken apprenticeship, and fill every channel with artifacts no one needed.

    The technology does not choose between those futures. The management system does.

    Eric Pratum and Neel Doshi, guest on The Unfolding Thought Podcast.
    Eric Pratum with Neel Doshi for The Unfolding Thought Podcast.

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  • The Debrief Is Where Experience Becomes Intelligence

    The Debrief Is Where Experience Becomes Intelligence

    Something Christian “Boo” Boucousis said during our recent conversation on The Unfolding Thought Podcast has been sticking with me: fighter pilots debrief everything.

    They debrief good missions. They debrief bad missions. They debrief missions interrupted by weather or equipment problems. They do not wait for something dramatic to go wrong, and they do not assume that doing the work again means they learned from doing it the first time.

    Most businesses do.

    Think about a project that did not go as planned. Maybe it was a marketing campaign that did not produce enough qualified leads. Maybe a product launch was late. Maybe a good employee left, a client relationship deteriorated, or a strategy everyone liked never produced the expected result.

    What normally happens next?

    People explain. The audience was wrong. Sales did not follow up. The client changed the brief. Someone missed a handoff. The market shifted. We did not have enough time. The technology did something strange.

    Any one of those explanations might be true. Several might be true. But, more often than I think most leaders would like to admit, the explanation is accepted because it is plausible, not because anyone really tested it. The meeting ends, everyone goes back to work, and a few months later the organization encounters a suspiciously similar problem wearing different clothes.

    That is experience. It is not necessarily learning.

    Experience is only evidence. It becomes learning when it changes what you do next.

    Most companies repeat more than they iterate

    Businesses like to say they are iterative. Agile companies work in sprints. Marketing teams run experiments. Product teams release versions. Leaders collect metrics and talk about continuous improvement.

    But repetition and iteration are not the same thing.

    If you run the next sprint with the same assumptions, incentives, decision rights, and behaviors, you did not iterate. You just started over. If you collect customer feedback, put it in a report, and then proceed with the plan everyone already preferred, you did not close a feedback loop. You documented feedback.

    Boucousis makes a useful distinction between speed and velocity. Speed is movement. Velocity includes direction. A business can answer emails faster, create more content, ship more features, and hold more meetings while moving very quickly in the wrong direction.

    This is one reason a good debrief actually begins before the work. You need to know what you are trying to achieve well enough for reality to disagree with you.

    “Launch the campaign” is not a useful objective. That is an activity. “Create 40 qualified sales conversations from this audience without taking acquisition cost above the level we agreed upon” is an objective. It gives you a result to compare with an intention.

    The same is true of meetings, hires, technology implementations, and nearly everything else. “Install the CRM” tells you what people will do. It does not tell you what should be different when they are done. Without that difference being made explicit, the team can complete every task, celebrate the launch, and still have no reliable way to know whether the work was worth doing.

    We explain results before we understand them

    Human beings are very good defense attorneys for our own decisions.

    When a result is poor, circumstances were difficult. When a result is good, our strategy was smart. We might not say it that bluntly, but most of us can find a story that protects what we already believed about ourselves, our teams, and our choices.

    This is not because everyone is dishonest. It is because the story arrives almost immediately. We experience the result through our own expectations, identities, incentives, and incomplete view of what happened. By the time the meeting begins, each person might already have a different version of reality.

    The Plan-Brief-Execute-Debrief model Boucousis uses tries to slow that down with four questions:

    1. What was the objective?
    2. What result actually occurred?
    3. What caused the difference?
    4. What action will change before the next attempt?

    The sequence is important. Start with what you intended. Then describe what happened before explaining it. Otherwise, the explanation has a way of changing the standard by which the result is judged.

    You have probably seen this. A team misses its revenue goal, but the conversation quickly shifts to how much awareness the campaign created. A project is late, but everyone focuses on how much they learned while building it. A new system is barely used, but the implementation is called a success because it went live.

    Awareness, learning, and launching might all be valuable. They are not substitutes for the result you said you wanted before the work began.

    This is also why success needs to be debriefed. Success is much easier to accept and therefore easier to misunderstand. A good result can hide a bad process, an unrepeatable advantage, a near miss, or an assumption that happened not to hurt you this time. Research on after-event reviews found that people who examined successes and failures improved more than those who reviewed failures alone.

    If the campaign worked, you still need to know why. Otherwise, you may take the wrong lesson from it and confidently repeat the part that mattered least.

    This is not an excuse to avoid accountability

    Whenever a conversation turns toward systems, context, and root causes, someone worries that personal responsibility is about to disappear. If everything is a system problem, can anyone ever be held accountable?

    Of course they can.

    A person may have ignored evidence, violated a standard, failed to prepare, or simply not done the job. A leader may also have created incompatible incentives, withheld information, assigned responsibility without authority, or punished someone the last time they raised an uncomfortable issue. Both can be true.

    The point of a debrief is not to make responsibility disappear. It is to make responsibility more accurate.

    Most organizations use accountability too late. They invoke it after the result, when everyone is looking for the person who should own what went wrong. Real accountability starts before execution. It requires a clear objective, an owner, visible constraints, agreed evidence, and the ability to question the plan before everyone commits to it.

    It also requires people to be able to say what actually happened.

    If telling the truth about the work is dangerous, the debrief is theater.

    Boucousis describes debriefing as nameless and rankless. I like the intention, but rank does not disappear because the highest-ranking person says it has. Everyone watches what happens to the first person who contradicts the leader, admits an embarrassing mistake, or says that the favored strategy did not make sense.

    If that person is humiliated, interrupted, ignored, or quietly punished later, the organization still learns a lesson. It is just not the lesson the leader intended. People learn that protecting the story is safer than examining the result.

    Psychological safety sometimes gets described as making everyone comfortable. That is not how I think about it. It is what makes productive discomfort possible. Research on debriefing and psychological safety connects it with speaking up, sharing information, and learning from errors. Safety makes the evidence available. Accountability makes the evidence matter.

    Root cause can become too neat

    A few years ago, I wrote about how businesses tend to self-diagnose and then go straight to a specialist. Revenue is down, so the company decides it has a website problem and hires someone to build a new website. Six months later, the website is new, the underlying problem remains, and everyone is looking for the next solution.

    A debrief should help with this, but only if people resist the desire for an explanation that feels cleaner than reality.

    Organizations like a single root cause because a single cause creates a sense of control. The campaign failed because the offer was weak. The product was late because one team missed a handoff. The client left because the account lead did not communicate.

    Maybe. But complex outcomes usually emerge from several conditions interacting with one another. A weak offer might have worked with a different audience. A missed handoff might not have mattered if the project had enough slack. Poor communication might have been recoverable if months of inconsistent delivery had not already weakened the relationship.

    The goal is not to make the past look inevitable. The goal is to improve what the organization believes before it acts again.

    Sometimes that means ending the debrief with, “We do not know yet.” I would rather hear that than a confident story supported by little evidence. “We do not know whether the audience or the offer was the primary constraint, so our next test will separate them” is a useful conclusion. It turns uncertainty into a better next action.

    This is where red teaming can help. The red team is not there to complain or to make the meeting more dramatic. Its job is to ask what would make the preferred explanation false, which assumptions the next plan depends upon, and what an outsider might notice that the team has learned to ignore.

    AI will make the learning problem more obvious

    AI lowers the cost of doing things. We can produce analysis, copy, code, plans, reports, experiments, and variations much faster than before.

    That sounds like an unqualified advantage until you remember that organizations already have trouble learning from the amount of work they do now.

    When production becomes cheaper, the bottleneck shifts. Judgment, attention, and organizational memory become more valuable because the business now has more actions, results, and explanations to sort through.

    AI can help an organization remember. It can also help the organization repeat a bad explanation much faster.

    AI can compare a stated objective with the reported result. It can find contradictions across accounts, identify causes that recur across projects, retrieve a prior lesson during planning, and red-team a proposal before reality does it for you. Research systems such as Reflexion have even shown that an AI agent can use feedback, create a reflection, retain it as memory, and improve a later attempt.

    But storing a meeting transcript is not the same as creating organizational memory. A report is not intelligence simply because it contains more information. The lesson has to show up when someone is about to make a relevant decision, and it has to change what the person or system does.

    AI also cannot decide which purpose is worth pursuing, which tradeoff is acceptable, or whether a local success damaged the larger system. If the destination and values are vague, AI can help you move faster without helping you move in a better direction.

    What I would actually put in a debrief

    I do not think every project needs a long, ceremonial meeting. In many cases, 15 focused minutes would be an enormous improvement over the hour-long status meetings people are already attending.

    I would make the team answer these questions:

    1. What did we intend to change? Not what did we intend to do. What effect were we trying to create?
    2. What actually happened? Describe the result before anyone explains it.
    3. What evidence supports our explanation? Separate what we know from what we think.
    4. What else could explain the result? Give someone permission to challenge the explanation everyone prefers.
    5. What will we do differently next time? Name the action, the owner, and the condition that should trigger it.
    6. Where will this lesson live? Put it somewhere the next person will encounter it before repeating the decision.
    7. When will we find out if we learned the right lesson? The corrective action needs its own review.

    That final question matters. A corrective action is still a hypothesis. It might be reasonable and still fail. If no one checks, the organization can turn a mistaken lesson into a new standard operating procedure and call that learning.

    What changed because of the conversation?

    The advantage is not simply moving faster. It is learning faster.

    A business with a high learning rate does not need every decision to be right. It needs wrongness to become visible, discussable, memorable, and useful. It needs leaders who can tolerate evidence that complicates their own story. It needs success to be examined instead of merely celebrated.

    When the debrief is over, ask one last question: What changed because of this conversation?

    If the answer is nothing, you had a meeting. You did not debrief.

    This essay develops ideas from my conversation with Christian “Boo” Boucousis, CEO of Afterburner and a former Royal Australian Air Force fighter pilot. Watch or listen to “Why Fighter Pilots Debrief Everything” on The Unfolding Thought Podcast.

    Christian “Boo” Boucousis, former fighter pilot and CEO of Afterburner.
    Christian “Boo” Boucousis, CEO of Afterburner and former Royal Australian Air Force fighter pilot
  • When Success Makes a Soccer Club Weaker

    When Success Makes a Soccer Club Weaker

    What happens when a small youth soccer club develops a team that becomes better than the supposedly elite clubs around it?

    In an open system, the team would earn stronger competition and the club would gain status. In much of American youth soccer, there is no automatic path upward. The club’s best players are more likely to leave for organizations that already possess the right league badge. The original club can point to those departures as evidence that its coaches did good work, but the team that produced the evidence has been dismantled.

    Success can make the institution that created it weaker.

    That paradox reveals an information problem disguised as a competition problem. Parents are buying player development they cannot directly inspect, so they rely on league badges, wins, roster placement, facilities, and social proof. Then, when a smaller club produces convincing evidence of development, the system can move that evidence to organizations whose status has already been established.

    A system that cannot reliably elevate good development or expose poor development cannot tell parents which is which. Eventually, the appearance of development becomes easier to sell than development itself.

    This was the tension I kept hearing in my second interview with Rory O’Neill on The Unfolding Thought Podcast. Rory has spent years coaching and building youth soccer programs. Much of our conversation concerned closed leagues, pay-to-play, promotion and relegation, and the incentives around player movement. Underneath those issues is a harder question: how can a market reward good development if the people buying it cannot confidently recognize it?

    The person paying and the person developing are not the same

    In a simple purchase, one person may play every important role. I buy a sandwich, eat it, decide whether it was good, choose whether to return, and suffer the consequences if it makes me sick.

    Youth sports divide those roles among several people:

    • The parent pays.
    • The child receives the coaching.
    • The coach claims the expertise to evaluate development.
    • The parent usually decides whether the family stays or leaves.
    • The child bears most of the long-term consequences.

    Those forms of power can point in different directions. A child may need harder competition, less playing time, a different position, more unstructured practice, or an honest explanation that progress has stalled. None of those experiences is guaranteed to make the payer happy today.

    Calling the parent “the customer” is commercially accurate and developmentally incomplete. Calling the child “the customer” sounds principled but ignores who controls the revenue. The mistake is assuming that one word can describe a relationship in which payment, benefit, expertise, choice, and risk are divided.

    When satisfaction is visible and development is not, the system learns to sell satisfaction.

    Parents are buying something they cannot easily inspect

    Economists describe some expert services as credence services. Even after buying them, the customer may not be able to determine whether the right service was provided. Most patients cannot independently evaluate a medical diagnosis. Most car owners cannot inspect a transmission repair. Most parents cannot watch a practice and determine whether the coach made the right tradeoffs for a player’s development over several years.

    Youth development is especially difficult to judge because the result is partly counterfactual. The relevant question is not simply, “Is this player better than last year?” A growing child receiving almost any regular practice may improve. The harder question is, “How much better would this child have become in a different environment?” Families never get to observe both futures.

    When buyers cannot inspect the underlying quality, they look for signals. A winning record is a signal. A famous coach is a signal. Travel is a signal. Facilities are a signal. Being selected for an “elite” roster is a particularly powerful signal because it tells the parent that another authority has already evaluated the child.

    Signals are not useless. A strong league may provide better competition. A selective roster may contain better players. Winning may reflect good coaching. The danger comes when a signal becomes easier to produce and sell than the result it is supposed to represent.

    That distinction matters in a closed system. MLS NEXT membership is awarded through an application and evaluation process that considers philosophy, governance, coaching, player development history, affordability, geography, and other factors. Those are reasonable things to examine. But membership is still a selected certification, not a competitive rank that every club can continuously earn on the field. To a parent, the badge can therefore look like objective proof even though the route to obtaining it is more complicated.

    The short feedback loop usually wins

    A player’s development may take ten years. A club’s renewal decision arrives every season. A parent’s frustration can arrive after one lineup.

    The shorter loop produces louder information. The club sees a complaint, a departure, or an unpaid invoice immediately. It may not know for years whether a technically gifted 12-year-old learned to solve problems, whether a late-maturing player received enough patience, or whether a confident 15-year-old learned to accept responsibility.

    Organizations tend to manage what they can observe. Research on performance measurement and incentives has long warned that measurable proxies can distort behavior when they are mistaken for the harder-to-measure goal. In youth soccer, renewals, wins, league status, and roster size are easy to count. Development is not.

    This does not require bad people. A caring coach can know that a player needs a difficult truth. A well-intentioned director can know that a team should emphasize learning rather than a weekend result. But if telling the truth causes the family to leave, while reassurance protects the revenue that pays the coach, the business model has made honesty expensive.

    A coach can be serving the child and risking the customer at the same time.

    A more honest hierarchy would help, but it would not solve the whole problem

    Rory argues that promotion and relegation would make American soccer more meritocratic. A team that performs well could earn stronger competition. A club could not preserve status without repeatedly earning it through open competition simply because it already belonged to the right league.

    That would create useful information. Results would have consequences, and success could open a path that is currently controlled by membership decisions. In that sense, promotion and relegation is not just a sporting preference. It is a mechanism for testing claims.

    But a more objective team hierarchy is not the same as an objective development system. A club can win by recruiting the strongest current players rather than improving the players it has. It can favor early-maturing children, use conservative tactics, narrow positions, and reduce experimentation. A standings table can tell us which team won. It cannot tell us how much each child learned.

    The United States Olympic and Paralympic Committee’s American Development Model emphasizes age-appropriate development, access, quality coaching, fun, and attention to skill and effort. Those goals remind us that competitive results are one signal among several. Replacing a league badge with a winning record would improve one feedback loop while leaving the larger measurement problem intact.

    Success can make the club that produced it weaker

    Consider a small club that develops an unusually strong team. If that team cannot earn access to a higher level of competition, its best players may leave for clubs that already possess the desired badge. The original club can point to those departures as evidence that its coaches did good work, but the team that produced the evidence has been dismantled.

    Success has made the institution that created it weaker.

    That is more than an issue of fairness. It is a loss of information. Parents evaluating the original club no longer see the strong team. They see the remaining roster and the higher-status clubs that absorbed its players. The visible proof migrates away from the people who created it.

    There are attempts to address this. The MLS NEXT Development Grant Program can reward a qualifying elite academy when a player reaches specified professional milestones. That is meaningful progress, but eligibility is narrow and the grant goes to the immediately preceding qualifying academy. Many origin clubs and earlier coaches remain outside the mechanism.

    International soccer offers a broader idea. FIFA’s Clearing House distributes training compensation and solidarity contributions to clubs involved in a player’s development. The details are complex, and no payment system perfectly identifies causal contribution. The important principle is that development should leave some value behind for the institution that helped create it.

    If success causes your best evidence to leave, the system is not rewarding development. It is consuming it.

    Development needs a better scoreboard

    No single measure will solve this. That is the point. When the goal is complex, a responsible system uses several imperfect signals and makes their limitations visible.

    A club that claims to develop players should be able to show more than trophies and placements. It should be able to explain what it is trying to develop, how coaches assess progress, and what families should expect when the child’s needs conflict with the team’s short-term result.

    • Are individual goals documented and revisited over time?
    • Do assessments include technical, tactical, physical, and psychological development rather than one coach’s overall impression?
    • Can another qualified observer review a player’s progress?
    • Do players receive meaningful opportunities to apply what they are learning?
    • Does the club track voluntary retention, player enjoyment, affordability, injuries, and burnout alongside wins?
    • When a player advances elsewhere, does the originating club receive financial or reputational credit?
    • Are families told clearly that development does not guarantee a roster label, starting role, scholarship, or professional future?

    FIFA’s talent-identification guidance calls for a clear philosophy, defined player profiles, comprehensive scouting environments, player observation, data analysis, structured selection, and a process that can operate over the long term. A child cannot be reduced to one tryout, one coach, or one team result. Neither can the quality of the environment developing that child.

    Make the truth less expensive

    Transparency will not remove conflict. It can make conflict more honest.

    Before a season begins, clubs can separate the development promise from the status promise. They can explain how playing time is decided, what “elite” means, what evidence coaches use, and how a family can appeal or seek an independent view. They can make it easier for a child to move when another environment would be better. They can reward coaches for long-term progress and candor, not only wins and renewals.

    Parents also have a role. If we say we want development but punish every decision that makes our child uncomfortable, the club learns what we actually value. If we treat a badge as proof, we help turn the badge into the product. If we demand guarantees that no honest coach can make, reassurance will eventually displace judgment.

    The goal is not to make parents passive. It is to give them better information and clearer expectations so that dissatisfaction can reveal a real problem rather than merely the distance between a development process and an imagined outcome.

    This is not only a youth sports problem

    The same information problem appears in schools, healthcare, consulting, nonprofits, elder care, and business services. One person may pay, another may use the service, a third may evaluate quality, and the consequences may arrive after the contract ends.

    Customer satisfaction matters in all of those settings. It can tell us whether the payer received the experience they expected. It cannot, by itself, tell us whether the institution fulfilled its purpose. Leaders need to ask who pays, who benefits, who can judge quality, who decides whether the relationship continues, and who bears the cost if it fails. If the answers name different people, the most visible feedback is not necessarily the most important.

    American youth soccer does not lack people who care about children. It lacks a reliable way to keep care, truth, revenue, and development pointing in the same direction. Until development becomes more visible and more valuable to the institution producing it, status will remain easier to market than progress.

    The question is not whether American youth soccer has coaches capable of developing players. It is whether the system can recognize their work before success dismantles the proof.

    Listen and read further

    Rory O’Neill and Eric Pratum on The Unfolding Thought Podcast.
    Listen to Rory O’Neill on The Unfolding Thought Podcast.
  • When the Process Outlives the Problem It Was Built to Solve

    When the Process Outlives the Problem It Was Built to Solve

    An institution can be orderly, disciplined, and increasingly ineffective at the same time.

    That kind of failure is hard to see because it still looks like competence.

    The weekly report arrives on time. Every project clears the required stage gate. The dashboard is green. The audit finds no missing fields. Yet customers are leaving, quality is slipping, decisions take longer, and the people closest to the work have learned that raising an anomaly creates more trouble than ignoring it.

    Nothing is obviously broken because the organization has become excellent at demonstrating fidelity to its process. The difficulty is that fidelity to process and fidelity to purpose are not the same thing.

    That distinction is what I kept returning to after my conversation with Eliot Frick on The Unfolding Thought Podcast. Frick makes a much larger philosophical argument about whether modernity has entered the final stage of its life. His description of late-stage systems is immediately recognizable in organizations.

    A system begins with generative power. It solves problems that could not previously be solved. Its methods become credible because they work. Over time, however, the methods become inseparable from the system’s identity. When results deteriorate, the system rarely concludes that its operating assumptions may be exhausted. It concludes that people are no longer following them faithfully enough.

    So it adds oversight, tightens compliance, and treats deviation as the cause of decline. The process that once produced the outcome becomes the ritual through which the organization proves it still deserves to exist.

    A process is a memory of a problem

    Most recurring processes began for a reason. Someone shipped defective work, exposed the company to risk, made an expensive decision without enough evidence, or forced other people to reconstruct information that should have been recorded. A checklist, review, approval, or report was created to keep that failure from recurring.

    When the process works, the original problem becomes less visible. New employees encounter the solution without experiencing the conditions that made it necessary. They know the form must be completed but not what judgment the form was meant to improve. They know who must approve a decision but not what uncertainty that approval was supposed to reduce.

    The process is therefore a kind of organizational memory. It preserves an answer after the question has faded.

    That can be useful. We do not want every generation of employees to rediscover fire safety or financial controls from first principles. But a memory becomes dangerous when the organization cannot distinguish the enduring purpose from the historical method. Conditions change while the ritual remains. Eventually, people measure whether the process occurred because they have lost the ability to measure whether it still helped.

    Success teaches a system what to stop noticing

    Every successful institution develops an operating grammar. It creates categories, incentives, reporting systems, professional language, and approved ways of reasoning. This grammar lets large groups coordinate. It also makes the organization increasingly good at recognizing what it already knows how to see.

    James March described a related tension as the difference between exploiting old certainties and exploring new possibilities. Exploitation improves what the organization already knows how to do. Exploration searches for something better but produces uncertain returns. March’s warning was that adaptive systems often refine exploitation more quickly than exploration. That can make them effective in the short run and self-destructive over time.

    The problem is not that managers foolishly choose the old over the new. The old process comes with evidence, owners, budgets, benchmarks, and political support. The alternative begins as a question. One side can produce a forecast. The other can only promise learning.

    The imbalance compounds. The more an organization invests in a process, the more careers, systems, and explanations depend on that process remaining legitimate. Evidence that fits the current model is easy to absorb. Evidence that challenges the model arrives looking incomplete, undisciplined, or irrelevant.

    A failing institution often becomes more faithful to its process as it becomes less capable of producing its purpose.

    Threat makes the rulebook harder

    Declining results ought to create curiosity. In practice, they often create rigidity.

    Barry Staw, Lance Sandelands, and Jane Dutton’s work on threat rigidity describes two common responses to adversity: information processing narrows and control constricts. Organizations rely more heavily on familiar knowledge, reduce the number of voices involved, centralize authority, and formalize procedure.

    Those responses are understandable. A threat creates urgency, and coordination can matter more during an immediate crisis. The problem comes when a short-term crisis response becomes the operating model for a system whose assumptions are failing. The organization reduces variation at precisely the moment it most needs alternatives.

    This creates a cruel feedback loop. The system produces weaker results. Leadership tightens the system to protect performance. Tighter control suppresses dissent and experimentation. The organization receives less information about why the system is failing. Its leaders become even more convinced that inconsistent execution is the problem.

    People who question the process are then easy to cast as undisciplined. Yet they may be carrying the information the process was designed to exclude.

    Collapse stories can be another form of loyalty

    Frick makes a surprising argument about stories of collapse. We assume that someone predicting the end of a system has escaped its influence. Often the opposite is true.

    The defender says the institution must be saved. The critic says it must be destroyed. Both keep the institution at the center of the imagination. The defender uses its categories to explain what must continue. The critic uses the same categories to explain what must end. Neither has necessarily described what could make the old conflict less important.

    This pattern appears in organizations whenever two factions fight over control of a process whose usefulness neither side is examining. One department wants stricter enforcement. Another wants the process abolished. Both assume the available choices are compliance or rebellion.

    The opposite of defending a failing system is not attacking it. Both can keep the system at the center.

    A more useful question is what problem the process was built to solve and whether that problem still exists in the same form. If it does, perhaps the method needs repair. If it does not, the fight over the method may be consuming attention that belongs somewhere else.

    A new system may not win the old argument

    Thomas Kuhn’s account of scientific revolutions is helpful here. Transformative ideas do not always emerge by accumulating better answers inside the accepted model. Anomalies build until a different framework can organize them.

    A new framework may not defeat the old one by its own measures. It can change which observations matter, which problems deserve attention, and what counts as an explanation. Questions that once felt decisive can become smaller or disappear.

    Organizations routinely make this transition harder than it needs to be by requiring every experiment to justify itself with the current system’s metrics. But those metrics contain assumptions about value, time, quality, and risk. An idea that tests the assumptions cannot always prove itself using measures designed to preserve them.

    This does not mean experiments should escape accountability. It means their first obligation may be to produce information rather than scale, efficiency, or immediate financial return. Leaders need to know what the experiment is trying to learn and what evidence would cause it to stop. That is different from demanding that it look like a small version of the established business.

    Leadership is the preservation of options

    Frick uses the word “aperture” for a protected opening through which unfamiliar possibilities can develop. The metaphor matters because most new ideas do not begin with enough power to survive the full force of an established institution.

    A leader does not need to believe every unconventional proposal. Most will be incomplete, and many will fail. Leadership requires preventing the current operating system from eliminating all unfamiliar ideas before any can produce evidence.

    Amy Edmondson’s research on psychological safety helps explain one condition for such an opening. Teams learn when people believe they can take interpersonal risks. That does not mean conflict disappears. It means uncertainty, error, and disagreement can enter the conversation without immediately becoming evidence that someone does not belong.

    Authenticity matters too. Research by Charlan Nemeth, Keith Brown, and John Rogers found that a genuinely held minority position produced better quantity and quality of solutions than several forms of assigned devil’s advocacy. An organization does not receive the full benefit of dissent by appointing someone to perform disagreement inside a meeting whose real boundaries remain untouched.

    Leadership does not require knowing what comes next. It requires refusing to let the present eliminate every alternative.

    What protecting an aperture looks like

    Protected exploration does not have to mean an innovation lab separated from the real work. It can begin with a few operating choices:

    • Separate delivery work from exploratory work so the two are not judged by identical expectations.
    • Give small experiments explicit sponsors, modest budgets, and expiration dates.
    • Ask which measures reflect the purpose and which merely prove compliance.
    • Record anomalies before explaining them away.
    • Invite authentic dissent from people who actually hold a different view.
    • Require experiments to produce learning before requiring them to produce scale.
    • Periodically ask what problem each recurring process still solves.
    • Retire rituals whose connection to outcomes can no longer be demonstrated.

    These practices will not reveal the next paradigm on command. That is not the promise. Their value is that they preserve variation long enough for the organization to learn from it.

    Jim Dator’s four generic futures include continuation, collapse, discipline, and transformation. Frick’s argument rearranges that sequence, but the categories remain useful. Organizations are usually comfortable planning for continuation. They can imagine collapse because fear supplies the story. They understand discipline because tightening control feels actionable.

    Transformation is harder because it cannot be fully described in the language of the system it may replace.

    The practical question is therefore not whether modernity, an industry, or a company is truly at the end of its life. The better question is diagnostic: are our institutions still producing results, or are they spending more of their energy demonstrating loyalty to the way those results used to be produced?

    A process deserves protection when it remains connected to purpose. When that connection is gone, enforcing the process more intensely will not restore it. Leadership begins by noticing the difference and preserving enough room for another possibility to become visible.

    Listen and read further

    Eric Pratum and Eliot Frick on The Unfolding Thought Podcast.
    Listen to Eliot Frick on The Unfolding Thought Podcast.