Those conditions helped shape the organizations we have today. Hierarchy has always served more than an authority function. It has also acted as an information-processing system.

Frontline information moved upward through several levels, becoming more condensed and interpreted along the way. Decisions and direction then moved back down through many of the same channels. Staff groups gathered and analyzed information that operating leaders could not easily access themselves, while meetings brought fragmented information, expertise and decision-makers together.

Technology has steadily reduced those costs. Data warehouses and data lakes gave organizations increasingly powerful places to store enormous amounts of information, yet someone still had to fish in those ponds. People still had to formulate the question, locate and analyze the relevant information and turn the result into something actionable.

Artificial intelligence dramatically compresses that cycle. AI can retrieve information, compare sources, identify patterns, synthesize large amounts of material, generate analyses and develop an initial recommendation in a fraction of the time those activities once required.

The cost of turning data into a usable answer is falling rapidly in both elapsed time and human effort. That changes more than productivity. It changes some of the economic assumptions underneath organizational design.

If structures were built partly to manage scarce information, expensive analysis and difficult coordination, leaders should reconsider them as those constraints change. The challenge is determining what should change, what should remain and how to redesign the system without removing capabilities the organization still depends upon.

AI should trigger a work-system design exercise, not simply an org-chart exercise.

Two Traps in AI-Enabled Redesign

Organizations face two predictable traps as the economics of information change.

The first is automating too little. Highly capable people remain consumed by transactional information work: collecting data, reconciling spreadsheets, preparing recurring reports, searching for information and moving information between systems and functions. AI can increasingly perform portions of that work faster and with far less human effort.

As those alternatives improve, organizations have an opportunity to redirect human capacity toward work that makes better use of judgment, experience and context. Problem-solving, interpretation, decision-making, customer interaction, coaching and verification all become more important when people spend less time processing information manually. The work should be worthy of the worker.

The second trap is removing too much. Leaders see the productivity opportunity, eliminate roles or layers and assume that the responsibilities performed by those people will disappear with them. The organization may later discover that necessary expertise has disappeared as well: decision capacity, technical judgment, informal coordination, risk controls, coaching, exception handling.

Freeing human capacity and removing human capacity are different decisions.

The larger shift is that the scarce resource may be changing. Information and analysis are becoming cheaper, while experience, judgment, contextual understanding, attention and decision capacity become relatively more valuable. Effective redesign should free people from transactional work that does not require those capabilities and redirect that capacity toward the work that does.

When AI releases part of a person's workload, the first question should not automatically be how much headcount can be removed. Leaders should ask what higher-value work that person with newly available capacity can perform.

Not Everything Non-Value-Added Is Waste

As AI removes some of the information-processing work that helped justify existing structures, leaders will naturally begin questioning those structures’ cost. That scrutiny is healthy, although cost alone does not tell us whether an activity should disappear.

Consider inspection in manufacturing. Inspecting a part generally does not change its fit, form or function. Customers would prefer the part to be produced correctly in the first place rather than pay for someone to inspect it afterward. In a narrow sense, inspection does not add value to the product.

The inspection may still be necessary. If the underlying process cannot consistently produce good product, inspection protects the customer. Improving the process can eventually reduce the need for inspection; removing the inspection first simply removes the protection.

Organizational structures deserve the same discipline. A management layer may coordinate competing priorities. An approval may control meaningful risk. A staff role may contain expertise that has never been embedded into the underlying process. A recurring meeting may compensate for fragmented information systems.

Some apparent duplication may also be deliberate. Quality checks, independent reviews, backup systems and secondary approvals impose costs while creating resilience. The relevant question is whether the redundancy represents accumulated bureaucracy or an intentional second line of defense.

Before eliminating an activity, leaders need to understand the function it performs, why that function is necessary today and whether the underlying need can be eliminated or addressed differently.

Cost is a reason to investigate. It is not proof that the mechanism serves no purpose.