An organizational chart tells us who reports to whom. It rarely shows how the work actually gets done.

Understanding the current state requires observation. Leaders need to watch the work, talk to the people doing it, map the flow and understand what actually happens rather than relying solely on documented processes or reporting relationships.

That discipline becomes even more important when AI makes structural change appear deceptively easy.

Before deciding that a management layer is redundant, understand how information actually moves through that layer.

• Who interprets the information? 
• Who makes the real decisions? 
• Who gets called when something unusual happens? 
• Where does work wait? 
• Where are people already overloaded? 
• What controls failures from reaching the customer? 
• What workarounds have developed because the formal system does not work particularly well?

Those questions often reveal a very different organization from the collection of boxes and connecting lines on an org chart.

Think about finding a nail in your tire. The nail clearly does not belong there, and the instinctive response may be to pull it out. If the tire is still holding air and you are not prepared to repair the puncture, removing the nail can turn a slow leak into an immediate flat. The nail is undesirable, yet under the current conditions it may also be partially containing the failure.

Organizations have their own versions of that nail. A cumbersome approval process may compensate for weak accountability. A management layer may compensate for unclear decision rights. A recurring meeting may compensate for poor information flow.

The mechanism may absolutely need to disappear. The important step is understanding what the mechanism is holding together and preparing the system to absorb or replace that function before making the change.

The purpose of studying the current state is to understand it well enough to design something better.

The Work Doesn't Disappear. It Moves.

As information-processing work disappears, organizations need to understand what remains in a role and where that work will go if the role disappears with it.

Human organizations are remarkably adaptive. When a position disappears, other employees divide the responsibilities among themselves. When a formal information channel fails, people create an informal one. When technical expertise leaves, an experienced employee often becomes the person everyone else calls.

That adaptability is one of the great strengths of human organizations. The same adaptability can hide poor organizational design because good people can make bad systems work for a surprisingly long time.

Suppose an organization eliminates a layer of management. The salary reduction is easy to calculate. The movement of the work is harder to see. Supervisors may absorb more escalations. Directors may attend more operating meetings. Engineers may assume additional coordination responsibilities. Frontline employees may receive less coaching.

The organization has removed capacity without necessarily removing demand.

A cost removed from one area can reappear elsewhere as workload, delay, variation, risk, lost capability or reduced resilience. A redesigned organization can look leaner on a spreadsheet while becoming more brittle in practice.

AI makes this capacity question even more important because the composition of human work is changing. As technology assumes more routine analysis, reporting and administrative coordination, the work remaining for people may become increasingly concentrated around ambiguity, judgment, interpersonal interaction, exceptions and complex decisions.

Those activities create a different kind of load. A system may be technically capable of presenting a manager with dozens of recommendations or exceptions in an hour, while the manager has nowhere near the cognitive capacity to make dozens of sound decisions in that same period. Faster information processing can eliminate one bottleneck and expose another.

AI may therefore reduce the human effort required to process information without reducing the organization's need for people who can interpret ambiguity, make tradeoffs, verify outputs and own decisions. A reduction in transactional workload should trigger a capacity-allocation question before it triggers a headcount question.

Leaders need to understand what work the remaining people will carry, whether they have the capacity to carry it well and whether newly released capacity can be redirected toward more valuable work.

Human factors become more important as information becomes cheaper because judgment and attention become more concentrated points of constraint.

Don't Pass the Defect Downstream

Cheaper information also creates a quality problem.

A well-designed manufacturing process identifies abnormal conditions as close to the source as possible rather than knowingly passing a defect to the next operation. AI output deserves the same discipline.

A fast, polished answer can still be a poor answer.

AI can synthesize large amounts of information and generate analysis that appears complete and authoritative. That polish can create a halo effect. A downstream user may assume that because the output looks finished, someone has already verified the underlying sources, assumptions and logic.

The failure mode is straightforward. AI generates an analysis, a person accepts the result without sufficient scrutiny, the output moves to the next step and everyone downstream assumes appropriate verification occurred earlier. Information moved faster while the quality-control mechanism weakened.

Verification should be proportional to consequence and uncertainty. A meeting agenda requires a different level of review than a recommendation involving a major capital investment, workforce decision, safety issue or customer commitment.

The human role therefore changes as information-processing work shifts to technology. People may spend less time gathering and formatting information and more time understanding inputs, challenging assumptions, recognizing missing context, assessing plausibility and deciding whether an output is appropriate to pass downstream.

Trust, but verify.

AI can generate a work product. Accountability remains with the leader, engineer, manager or process owner who decides how that output will be used.

Well-designed AI-enabled processes therefore need intentional checkpoints, escalation paths and circuit breakers. Organizations should decide where automated flow can continue, where human verification is required and which conditions should stop the process before a questionable output becomes someone else's input.

The goal is to accelerate information flow while preserving judgment.

Measure Twice. Cut Once.

There is an old construction and woodworking adage that applies surprisingly well to organizational design: measure twice, cut once.

Organizations sometimes reverse that sequence. They reduce headcount or remove a management layer, observe what breaks, redistribute the work and patch the resulting problems. Human adaptability often allows the organization to stabilize, and the initial cost reduction may even appear successful.

That approach relies on brute force rather than thoughtful system design.

A more deliberate approach begins by studying the current state. Understand how the work actually happens, how information and decisions flow, where load and capacity reside, which controls manage risk and which workarounds compensate for weaknesses elsewhere in the system. Separate work that creates value from work that remains necessary and from work that can genuinely disappear.

Only then should leaders begin cutting, automating or reallocating work.

Current State, Future State, Roadmap

AI gives leaders an unusual opportunity to reconsider organizational assumptions that have accumulated over decades. Some structures should disappear. Some work should be automated. Some management layers may exist primarily to move or synthesize information that technology can now make directly available.

The future-state organization should be designed around the work the organization needs to accomplish and the capabilities now available to perform it.

First, understand the current state. Go see the work. Study how information moves, how decisions are made, where capacity resides, what controls manage risk and how employees compensate for weaknesses in the formal system.

Second, design the future state. Begin with the work rather than the boxes on the current org chart. Eliminate true waste. Automate transactional and information-intensive work where technology is well suited to perform it. Reduce necessary non-value-added work where the underlying need can be redesigned. Redirect human capacity toward judgment, problem-solving, leadership, relationships, decision-making and verification.

Judicious automation matters here. Manufacturing has long shown that full automation is not automatically the best design. Effective systems allocate work deliberately, allowing machines to handle repetitive processing while people remain engaged where judgment, quality awareness and abnormal conditions require human capability.

Third, build the roadmap between the current and future states. Sequence changes so that necessary functions, controls, capacity and knowledge are replaced or redesigned before the mechanisms currently providing them disappear. Evaluate the risks of the transition alongside the economics of the destination.

A quality inspection should not disappear before the underlying process can consistently produce quality. An approval should not disappear before an appropriate replacement control exists. A management layer should not disappear before decision rights, workload and exception handling have been redesigned.

This is ultimately a work-system design challenge. Leaders need to consider people, processes, technology, information, decision rights, controls, capacity and organizational structure together rather than optimizing each independently.

The economics of information are changing quickly. Organizations should use that opportunity to challenge structures built around constraints that may no longer exist, remove transactional work that no longer warrants expensive human effort and give people better tools for finding and analyzing information.

As information becomes cheaper, the scarce resource shifts. Experience, contextual understanding, attention, verification, judgment and decision capacity become more valuable because the organization can spend less human effort producing information and more human effort deciding what to do with it.

That is the opportunity AI creates.

Redesign should begin with observation rather than subtraction. Study the current state. Understand why the system behaves as it does. Design the future state intentionally. Determine where automation creates value, where human judgment belongs, where verification is required and where risk needs a circuit breaker. Build the roadmap and execute the change deliberately.

The objective is not the flattest organization, the organization with the fewest people or the organization with the most AI. The objective is the work system we would intentionally design if we were starting today.