Give AI a Place in the Org Chart

At 35,000 feet, the pilots are still there.

Commercial aviation is among the most automated working environments most of us will ever trust with our lives. Autopilot can hold altitude, follow a route, manage speed and assist with landing. Yet on the commercial aircraft built for two pilots, two qualified pilots remain required in the cockpit. Automation did not make their expertise obsolete. It changed where that expertise matters.

The pilot no longer has to make every small correction by hand. The machine handles sustained execution. The pilot holds the intent, understands the environment, monitors the system and intervenes when reality departs from the plan.

That is the pattern I believe is coming to knowledge work.

The current conversation about artificial intelligence is still largely about making an individual faster. Give every employee a copilot. Sell access by the seat. Charge by the token. Add an agent to a workflow. This can produce real gains, but it leaves the structure of work almost untouched. The AI has capabilities, but no durable place in the organization. It has no defined responsibility, no clear human owner and no unit of output against which its cost can be judged.

Tenor is built on a different conviction. AI should enter the organization as accountable workers with defined responsibilities, sitting under the employees who direct them. Every employee should become a manager of AI workers. Their existing manager remains their people manager. These are different jobs.

A people manager develops human beings. They coach, motivate, resolve conflict, build trust, evaluate performance and create the conditions in which a team can grow. An AI worker manager directs digital execution. They define the task, provide context and permissions, set the standard, monitor the work, review exceptions and remain accountable for the result.

One manages people. The other manages work performed by machines. The first remains necessary. The second should become universal.

The chart was an invention

The modern organization can feel permanent because nearly every company inherited the same basic shape. But the org chart was invented to solve a specific technological problem.

In the 1850s, the New York and Erie Railroad stretched for hundreds of miles and employed thousands of people. The informal methods that had governed smaller railroads could no longer coordinate trains, stations, maintenance and telegraph messages at that scale. Daniel McCallum, the railroad's general superintendent, developed a system of delegated authority and reporting, then represented it in what is widely recognized as the first modern organizational chart. The chart was not corporate decoration. It was an operating technology for routing information and responsibility through a network too large for one person to see.

Frederick Winslow Taylor later applied the same spirit to the work inside the hierarchy. Scientific management divided labor into measurable tasks, separated planning from execution and searched for repeatable methods. It raised industrial productivity, but it also reduced the worker's discretion. Taylor's system treated judgment as something management should extract, formalize and move upward.

The corporation we inherited combined these two ideas. McCallum gave it reporting lines. Taylor gave it decomposed work. Managers coordinated information and judgment; employees executed the tasks assigned to them.

Technology kept altering that settlement. The spreadsheet did not simply make arithmetic faster. It moved accounting work away from maintaining rows of figures by hand and toward interpreting them, part of a broader shift from clerical production toward professional expertise. ATMs did not initially erase the bank teller. Economist James Bessen found that the number of ATMs in the United States rose from roughly 100,000 to 400,000 between 1995 and 2010, while teller employment rose from about 500,000 in 1980 to 550,000 in 2010. ATMs reduced the number of tellers required per branch, but they also made branches cheaper to operate, encouraged more branches to open and shifted tellers toward relationship-based work.

This history does not offer a comforting law that automation always creates more jobs. It does not. Research by Daron Acemoglu and Pascual Restrepo found that industrial robot adoption reduced employment and wages in exposed American labor markets. Technology creates leverage, but institutions decide where that leverage goes. It can be used to remove the worker, to control the worker or to elevate the worker.

I believe AI gives us a chance to choose the third path deliberately.

Two views of what comes next

In their March 2026 essay, “From Hierarchy to Intelligence,” Jack Dorsey and Roelof Botha offer one of the most serious accounts of how AI could reshape the company itself.

Their argument begins with the Roman army, the Prussian General Staff and the railroads. Hierarchy, in their account, is an information-routing system built around the limited number of people any human can manage. More people require more layers. More layers slow information. AI creates a way out: a company-wide world model can maintain context, coordinate work and distribute intelligence without requiring human managers to relay information up and down the chain.

At Block, they envision intelligence living in the system and people operating at the edge, close to customers, craft and reality. Individual contributors build capabilities. Directly responsible individuals own cross-cutting outcomes. Player-coaches develop people while continuing to build. The permanent layer of middle management largely disappears because AI performs much of the coordination work that justified it.

There is a great deal in this diagnosis that I share. Copilots alone do not change a company. Hierarchy often slows information. People closest to the work should have more authority. AI should reshape the operating system of the firm, not merely decorate its existing software.

I differ on the route.

Block can plausibly imagine a world model coordinating the company because Block is remote-first, its work creates machine-readable artifacts, and its transaction network produces an unusually rich customer signal. Most organizations do not begin there. Their knowledge is scattered across systems, conversations, habits and people. Their authority is embedded in budgets, professional obligations, regulation, trust and relationships that cannot be reconstructed from a stream of artifacts alone.

More importantly, organizations resist structural change with astonishing force. In a McKinsey survey of organizational redesign, fewer than 30 percent of respondents rated any phase of a redesign as very successful. Asking a company to adopt an unfamiliar technology while simultaneously dissolving the hierarchy through which it allocates authority compounds the risk. Even firms in decline often defend their structure longer than they defend their future.

Tenor's approach is to change the work without first demanding that the company redraw who reports to whom.

AI workers embed into the layers that already exist. A finance analyst can direct workers responsible for reconciliation, variance investigation and reporting while continuing to report to a finance manager. A marketer can direct workers responsible for research, campaign operations and performance monitoring while their people manager remains responsible for development, priorities and team health. The employee moves upward from repeated execution to direction, evaluation and judgment. The human hierarchy remains intact while productive capacity grows beneath every person in it.

We agree on the destination: people closer to judgment, creativity, customers and consequential decisions; machines handling more coordination and execution. But I do not believe companies must first abolish hierarchy to get there. Embedding AI into the organization can deliver the shift at a fraction of the switching cost.

What cannot be written down

This is not only an argument about adoption. It rests on a view of human judgment.

In 1960, J.C.R. Licklider described a future of human-computer symbiosis in which people would “set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations,” while computers handled routinizable work. Two years later, Douglas Engelbart framed computing as a means of augmenting human intellect, increasing our ability to understand complex situations and solve important problems.

Their ambition was not to build a faster typewriter. It was to redesign the division of cognitive labor.

The philosopher Michael Polanyi supplied the reason humans remain central: “We can know more than we can tell.” Expertise is not merely a collection of explicit rules. A skilled operator recognizes what matters before they can fully explain why. Hubert Dreyfus developed this argument against the early confidence of artificial intelligence, showing how expert action depends on context, involvement and distinctions that formal rules struggle to capture. Herbert Simon approached the problem from another direction. Human beings make decisions under bounded rationality. We never possess all the information, time or computational capacity that perfect optimization would require. We judge, prioritize and act within limits.

Modern AI changes those limits. It can search more material, track more variables, generate more options and execute more steps than a person could unaided. But more information does not decide what deserves attention. A model can produce an answer without bearing the consequences of choosing the wrong question.

Anil Seth makes a useful distinction between intelligence as doing and consciousness as being. Whatever claims people make about future machine minds, today's systems are extraordinarily capable at doing. That does not turn them into human beings, and Tenor does not require us to pretend otherwise. Calling something an AI worker is not a claim about its soul. It is an organizational designation. It gives a capable system bounded work, a named owner, an observable record and a place from which a human can direct and correct it.

That correction loop matters. Autopilot is not a perfect analogy for Tenor. It is one system operated by a small cockpit crew, while an employee may eventually direct many autonomous workers. Pilot staffing also did not collapse, so aviation cannot prove the economics of software leverage. A cockpit is bounded and certified in ways most knowledge work is not.

What transfers is the supervision model. Automation handles execution within defined limits. The human retains intent, judgment and authority over exceptions.

Air France Flight 447 is the warning against complacency. When inconsistent airspeed readings caused the autopilot to disconnect, the crew failed to recover from the resulting stall. The lesson is not that automation should be rejected. It is that the human cannot become ceremonial. Skills must remain sharp, control transfer must be explicit and the system must be designed for intervention. Tenor's employee stays in the correction loop because accountability without the power to correct is only blame assigned after the fact.

The exception corpus

Most companies remember outcomes and forget how they reached them. A person notices something unusual, applies judgment, fixes the problem and moves on. The result remains, but the exception and the reasoning disappear into a conversation, an inbox or memory.

AI workers change this. Because work is assigned and executed within a system, each task can leave a structured trace: what the worker attempted, where the normal procedure failed, when the human intervened, what they decided and what followed. Routine work becomes observable. More importantly, the exception becomes reusable.

I think of this accumulated record as the exception corpus. It is not merely a log of machine mistakes. It is a growing account of how an organization exercises judgment when its rules run out. Each resolved exception can refine an instruction, create an evaluation, inform another authorized AI worker or become a precedent for the next unfamiliar case.

This is how tacit knowledge begins to compound without pretending it can all be formalized. The employee remains the teacher. Their interventions turn experience into organizational memory. Over time, reports no longer have to be reconstructed after the fact. They emerge from the work itself: what was completed, what failed, what it cost and what the organization learned.

From consumption to accountability

Companies are already spending heavily on AI, but much of what they can see is consumption. Seats. Tokens. API calls. Model costs. Usage rates. These numbers tell a company what it bought, not what the purchase accomplished.

That gap is visible in the research. In McKinsey's March 2025 survey, only 1 percent of executives described their generative AI rollouts as mature. Tracking well-defined performance indicators was the scaling practice most strongly associated with bottom-line impact. By later that year, nearly nine in ten respondents said their organizations used AI regularly, yet almost two-thirds had not begun scaling it across the enterprise and only 39 percent reported enterprise-level EBIT impact.

The FinOps Foundation's 2026 survey found that 98 percent of respondents were managing AI spend, up from 31 percent just two years earlier. Their leading problems included seeing AI costs clearly, assigning them to business units and determining value or return on investment.

An accountable worker changes the unit of measurement. Each worker owns discrete responsibilities. Its actions are logged. Its output can be reviewed. Its cost can be connected to the work produced. A company can ask not only how many tokens it consumed, but what was completed, to what standard, under whose direction and at what cost.

That is how AI becomes part of the company rather than another software bill.

The leverage gap

Companies do not hire people as undifferentiated capacity. A role comes with a salary, a responsibility and a set of expectations. We ask what that person produced, whether it met the standard and how it contributed to the objectives of the business. AI workers should be no different.

Each should have its own responsibilities, performance indicators and operating budget. Tokens, compute and tool calls are inputs, not achievements. The relevant measure is what the worker produced with them: whether the work was accepted, what it cost, how often it required intervention and how it contributed, directly or indirectly, to a business outcome.

Once AI workers have objectives and budgets, another measure becomes visible: how effectively the employee above them manages that capacity.

I expect this ability to vary widely. Two employees given access to the same models and the same budget will not produce the same result. One may spend heavily on work that never matters. Another may identify the decisive problem, divide it intelligently, assign it to the right workers, recognize weak output and redirect the system before resources are wasted. The difference is not access to AI. It is judgment, prioritization, delegation and control.

Over time, leaders will allocate AI capacity as they allocate capital and headcount today. Employees and teams that repeatedly turn bounded resources into valuable outcomes will earn responsibility for larger AI workforces and budgets. Those decisions should account for complexity, risk and strategic importance, not collapse into a crude ranking by tokens consumed or tasks completed. But the distribution of managerial leverage will become increasingly difficult to ignore.

This changes competition inside the company. The person who outperforms you may not work longer or produce more by hand. They may be better at directing AI workers toward the company’s goals. Access to intelligence will become common. Judgment about where to apply it will not.

The task for companies is not to use that difference as a threat. It is to develop the capability across the workforce. Every employee should have the opportunity to move from performing repeated work to commanding productive capacity responsibly. In that world, contribution is no longer measured only by what a person can do. It is also measured by what they can direct, improve and make accountable.

Put people above it

Workers are right to be uneasy. In a 2025 Pew survey, 52 percent of American workers said they were worried about how AI would be used in the workplace. Only 6 percent expected it to create more opportunities for them, while 32 percent expected fewer.

No slogan can resolve that fear. AI can be used to eliminate roles, and some companies will use it that way. Augmentation is not an automatic property of the technology. It is an institutional choice.

The choice I want us to make is to give employees authority over this new capacity. Let the repetitive execution move downward to AI workers. Let judgment remain with the person who understands the domain, the customer, the history and the cost of being wrong. Let that person grow from completing every task to directing a workforce that completes them. Let their manager remain a manager of people, responsible for human performance, development and trust.

The real question is not whether AI will enter the org chart. It is where the human will stand when it does: beneath the system, outside it or above it.

I believe the human belongs above it.

The org chart does not need to disappear. It needs a place for AI. Put AI beneath the people who know what good work is, give it responsibilities they can supervise, and make its output accountable. Then every employee gains leverage without surrendering judgment, and every company can grow its capacity without losing sight of who remains responsible.

At 35,000 feet, the pilots are still there. The machine carries more of the load. The human carries the meaning of the mission and the authority to change course.

Knowledge work is ready for the same promotion.