AI can change a task in an afternoon.

A team starts using a new assistant. A manager begins relying on generated summaries. A service group lets AI draft responses. An analyst uses a model to surface patterns that would have taken much longer to find manually.

The work has changed.

But something else may still be sitting exactly where it was yesterday: who is allowed to decide, who must review, who is accountable when the output is wrong, and where human judgment is still required.

That lag matters.

This week’s OCM Intelligence points toward an increasingly connected set of organizational questions around work redesign, skills, decision rights, capacity, governance, mobility, and human judgment. It does not prove that every organization has the same problem, or that one intervention will solve it. It does make one question worth examining more closely:

What happens when AI changes the work faster than the organization changes the decision structure around it?

Section 01

AI can change the task before it changes the authority

A useful distinction is emerging in current AI research: assigning work to AI is not the same thing as redesigning who has authority over the result.

MIT CISR’s 2026 research on AI decision rights describes this as a core organizational design problem. Based on 30 interviews with 27 executives across nine global companies, the researchers argue that organizations need to make explicit how humans and AI share decisions, particularly as ambiguity and risk change. They distinguish among who frames the decision, who acts, and who learns from the outcome.

That is a different question from whether the technology can perform the task.

A model may be able to recommend which customer issue should be escalated. That does not determine who is allowed to override the recommendation.

An AI assistant may be able to generate a performance summary. That does not determine whether a manager may rely on it, what must be verified, or who is responsible if the summary is materially wrong.

An agent may be able to execute a workflow across several systems. That does not determine which decisions can be delegated and which still require explicit human approval.

Technical capability can move before organizational authority does.

Put it to work

The lag is not theoretical

The OECD’s 2025 employer survey on algorithmic management gives this problem a useful empirical edge.

The survey covered more than 6,000 mid-level managers across six countries. Managers using algorithmic management tools often reported benefits such as more information and faster decisions. But the most commonly reported trustworthiness concern was unclear accountability when an algorithmic decision was wrong. Difficulty following the logic of algorithmic decisions was nearly as common.

That combination is important.

Organizations can experience greater speed and decision support while still being uncertain about who owns the consequence.

The practical risk is not simply “AI made a bad decision.” The risk is that the organization has not made the surrounding decision system explicit enough to know:

Questions to consider

  • who can accept the recommendation;
  • who must challenge it;
  • who can override it;
  • who owns an exception;
  • who is accountable for the final outcome;
  • who updates the process when the result exposes a weakness.

Section 03

Governance can fall behind the work

The same tension appears in governance research.

MIT CISR’s 2026 work on “minimum viable governance” argues that generative AI is changing faster than conventional centralized review mechanisms can comfortably absorb. The point is not that organizations should remove governance. It is that governance has to operate at a pace and level of specificity that fits the work being changed.

NIST’s AI Risk Management Framework makes the accountability requirement explicit from another direction. It calls for organizations to define roles and responsibilities for human-AI configurations and oversight, and for executive leadership to take responsibility for AI-related risk decisions. Its playbook also recommends documenting human oversight, overrides, errors, escalations, and organizational accountability.

Those recommendations are not a recipe for one universal governance model.

They do show that “human in the loop” is too vague to be useful on its own.

Which human? At which point? With what authority? Using what evidence? Accountable for what?

If those questions are unclear, adding a nominal approval step does not necessarily create meaningful oversight.

Put it to work

New work can sit inside old decision structures

This is where AI-enabled change can become deceptively difficult.

The task changes first.

A person who used to create an answer now reviews an answer.

A manager who used to evaluate a completed piece of work now evaluates both the work and the quality of the AI-supported reasoning behind it.

A team that used to make a decision directly now receives an AI recommendation before deciding.

A service process that used to route exceptions manually now has an automated first pass.

Those are changes in work.

But if decision rights do not move with them, the organization may end up with a mismatch:

Questions to consider

  • AI drafts the output: who is responsible for its accuracy?
  • AI recommends an action: who may accept, reject, or override it?
  • AI routes an exception: who owns the exception when the route fails?
  • AI changes what a manager reviews: what is the manager expected to verify?
  • AI accelerates a decision: which decisions still require human deliberation?
  • AI performs more of the routine work: where do people continue building judgment?

Section 05

Managers are often where the ambiguity lands

When decision rights are incomplete at the enterprise level, managers frequently become the translation layer.

They are expected to answer questions such as:

Can my team use AI for this?

Do I need to review every output?

What happens if the AI recommendation conflicts with my judgment?

Am I accountable if I approve something the system generated?

Can an employee challenge the system’s recommendation?

Which policy takes precedence when the technology changes faster than the guidance?

A manager can improvise answers locally, but local improvisation creates another problem: different teams begin operating under different rules.

One group treats AI output as a draft. Another treats it as a recommendation. Another treats it as an answer. Another avoids it entirely because nobody wants to own the risk.

The technology may be standardized while the operating interpretation fragments.

That is a change-management issue because people are being asked to perform differently under conditions that may still be organizationally ambiguous.

Section 06

Human judgment needs a defined job

There is a tendency to resolve this tension with a reassuring phrase:

Keep a human in the loop.

That is directionally useful and operationally incomplete.

Human judgment has to have a defined role.

Is the human expected to verify facts? Evaluate risk? Interpret context? Challenge a recommendation? Make the final decision? Handle exceptions? Notice when the system is being used outside its intended purpose?

The answer may differ by task.

MIT CISR’s decision-rights research is useful here because it does not treat all AI-supported decisions as equivalent. It argues that ambiguity and risk should influence how humans and AI share the work of framing, acting, and learning.

NIST takes a similarly contextual view. Human roles and responsibilities should be defined according to the specific human-AI configuration rather than assumed from the presence of a person somewhere in the process.

That is a much stronger standard than simply requiring human oversight.

Put it to work

What leaders should settle as AI changes the work

Organizations do not need every AI decision centralized.

They do need enough clarity that the new work is governable.

Questions to consider

  • What decision changed? Name the task, recommendation, approval, escalation, or judgment that is now different.
  • Who has authority to act? If AI produces a recommendation, who can accept it? If it performs the action automatically, who authorized that delegation?
  • Where is review actually required? The organization should know where review is mandatory, discretionary, or unnecessary.
  • What can the human override? Oversight without authority to intervene is not meaningful oversight.
  • Who owns the outcome? Accountability should not disappear because part of the work moved into a system.
  • What happens when experience changes the answer? Someone needs the authority to update the workflow rather than simply work around it.

Section 08

The organizational lag is the point

AI does not have to replace an entire job to create an organizational change.

It can change one decision. One review step. One escalation. One manager expectation. One handoff. One source of evidence.

That may be enough to alter who needs information, who carries risk, and who is accountable for the result.

The technical change can happen quickly.

The organizational change often requires deliberate decisions.

The important question is therefore not only: What can AI do now?

It is also: What authority, review, accountability, and human judgment need to change because the work changed?

When those decisions lag, organizations can end up asking people to perform new work inside an old decision system.

That is not simply an adoption problem.

It is an organizational design problem that has already reached the work.

Section 09

Transparency

This article was developed with AI assistance and substantively reviewed, revised, and approved by Change Collaboration. The final content, source use, professional judgment, and conclusions remain Change Collaboration's responsibility.