The human in the loop is not a governance model
An AI system spends the night reviewing production plans, supplier constraints or security events. By morning, it has assembled a long queue of recommendations. Nothing has happened because every decision still requires a person to approve it.
The manufacturer can claim that humans remain in control. Operationally, however, it has created a bottleneck. The system works at machine speed while the organization responds at the pace of inboxes, shifts and approval meetings.
This is the weakness hidden inside one of industrial AI’s most reassuring phrases. “Human in the loop” sounds like a governance principle, but it rarely explains how authority should be divided between people and intelligent systems. It does not say which decisions require approval, how quickly a person must respond, what evidence they should receive or who remains accountable when a recommendation is accepted, altered or ignored.
As AI moves beyond analysis and begins initiating workflows, updating systems and influencing physical operations, manufacturers need something more precise. They need a decision model that defines where people intervene, where automation can proceed and how authority changes as confidence grows.
Approval is not the same as oversight
The instinct to require human approval is understandable. Manufacturing decisions can affect safety, quality, delivery performance and expensive physical assets. A recommendation that is wrong only once may still cause more damage than thousands of correct decisions can justify. Yet inserting a person into every workflow does not automatically make the system safer. It can simply transfer the risk from an automated decision to an overloaded employee.
Will Dutton, Director of Supply Chain Solutions at UiPath, describes the problem in terms of cognitive capacity. As organizations deploy more agents, people may stop entering data manually only to find themselves supervising an expanding volume of machine-generated work. “You can manage by exception, you can put thresholds in,” he says. “There’s no single rule. You must fit what works to the business. In high-volume, low-margin operations, companies may be comfortable automating a greater proportion of routine decisions. Higher-value or commercially sensitive activities may still warrant approval.”
This distinction is more useful than a universal insistence on human involvement. A routine inventory adjustment within agreed tolerances does not carry the same consequence as changing a production recipe, shutting down a critical asset or blocking traffic across an industrial network. The decision should not be whether a human is present somewhere in the process. It should be whether human judgment is being reserved for the moments when it genuinely improves the outcome.
Poorly designed oversight can also create automation bias. Once employees have approved hundreds of accurate recommendations, they may stop examining them closely. The person technically remains in the loop, but their role has become ceremonial. The organization retains the appearance of control without receiving meaningful scrutiny.
Effective governance therefore begins by categorizing decisions according to consequence, reversibility and uncertainty. Low-risk actions can proceed within defined parameters. Unusual cases, conflicting evidence and decisions with significant operational consequences should be escalated with enough context for a person to make an informed judgment.
Guardrails need to describe real authority
Manufacturers increasingly talk about “autonomy with guardrails.” The phrase is helpful only when those guardrails define what an intelligent system can see, decide and change. At Microsoft, Corporate Vice President for Manufacturing and Mobility Dayan Rodriguez sees manufacturers progressing from prediction toward action. Systems are no longer limited to warning that a machine may fail. They can recommend a response, prepare the intervention and, in some circumstances, act within an agreed operational boundary.
“The manufacturers doing this well are introducing autonomy in stages,” he says, beginning with targeted scenarios where decisions can be tested before they influence wider operations. He describes the model as “autonomy with guardrails,” with people retained for critical decisions. The important word is not autonomy. It is boundaries.
A useful governance model must specify which actions an AI system can take independently, the conditions under which that authority applies and the point at which responsibility returns to a person. Those boundaries may depend on production state, asset criticality, financial exposure, safety implications or confidence in the underlying data.
They should also define what happens when the system encounters something unfamiliar. An agent trained to resolve common supply shortages should not improvise when the available options conflict with regulatory requirements or customer commitments. Its authority should narrow as uncertainty rises.
Rodriguez argues that governance should be cross-functional rather than confined to IT. Operational teams, engineering, business leaders and technology functions all have a role because each understands a different part of the risk. IT may control access and system behavior, but production leaders understand the consequences of a delayed decision, while engineers know when a technically plausible action would be unsafe in practice.
This is why accountability cannot be reduced to a software setting. A named business owner must remain responsible for the decision domain, even when an intelligent system performs most of the routine work within it.
Context determines whether the same action is safe
The difficulty becomes sharper on the factory floor because the risk of an action changes with operational conditions. Stopping a machine may be harmless during a planned changeover and disastrous during a time-sensitive process. Adjusting a parameter may improve one product while pushing another outside specification. A recommendation can be analytically sound and still be wrong for the production state in which it arrives.
Leonor Marques, Architecture and Advocacy Director at Critical Manufacturing, argues that intelligent decisions require the runtime context supplied by manufacturing execution systems. Sensor values alone cannot explain which product is being made, how equipment is configured or what sequence of events produced the current condition. “If you take the wrong insights, it’s worse than having no insights,” she says. “You will take wrong decisions based on wrong data.”
This creates a governance requirement that is often overlooked. Manufacturers do not merely need confidence in the model. They need confidence that the recommendation has been evaluated against the current physical reality of production. A maintenance agent may identify a familiar fault pattern, but the correct response depends on asset criticality, production demand and whether an alternative line is available. A quality system may detect deterioration, yet the decision to stop production depends on the product, tolerance and cost of allowing another cycle to complete.
Marques expects MES platforms to act autonomously in more familiar situations while people continue to decide critical issues, particularly in regulated manufacturing. Her distinction suggests a practical operating principle: autonomy should expand where the process is understood, the context is complete and the response has already been validated. Human authority should increase as the situation becomes less familiar or the consequence of error rises.
This is more sophisticated than asking a person to approve every recommendation. It allows the system to handle recurring operational patterns while preserving judgment for exceptions that extend beyond its evidence.
Trust should change the operating envelope
Manufacturers will not establish the final division of authority on the day an AI system is deployed. Trust develops through experience, and governance should allow responsibility to evolve with it.
Cybersecurity provides a useful example because automated defenses already operate at a scale and speed no team of analysts could match. AI can examine billions of events, identify suspicious behavior and recommend policy changes, yet an incorrect action may block legitimate traffic or disrupt production. Rich Campagna, Senior Vice President of Product Management for Network Security at Palo Alto Networks, says most customers initially allow agents to recommend a policy without placing it into production until a person has validated it. Repeated exposure to sound recommendations then changes the relationship.
“It’s all about developing trust that the decisions the agent is recommending, and later making, are sound decisions similar to what the human expert would take,” he says. “Organizations may then remove human approval for a defined class of decisions while retaining it elsewhere.”
Trust, however, should not be treated as a general feeling about the system. Confidence earned in one activity does not justify autonomy in another. An agent may prove highly reliable when prioritizing security alerts without earning the right to impose a network policy that could isolate production equipment.
Authority should therefore expand by decision class. Manufacturers can compare recommendations with the judgments of experienced personnel, measure false positives and examine how the system behaves during unusual conditions. Once performance is established, the operating envelope can widen for those specific actions.
The reverse must also be possible. Changes in data quality, production conditions or model behavior should reduce authority automatically. A system that normally acts without approval may need to return to recommendation mode when its confidence falls or when it encounters a condition outside its validated experience.
Governance becomes dynamic rather than fixed. The organization is not choosing permanently between manual control and autonomy. It is continually adjusting the degree of authority appropriate to the evidence and risk.
Designing the decision, not decorating the technology
“Human in the loop” persists because it appears to resolve the tension between AI capability and human accountability; in reality it postpones the difficult design work.
Manufacturers need to decide which activities can be automated, which require supervision and which must remain under direct human control. They need thresholds for escalation, clear ownership of each decision domain and records showing what the system knew when it acted. Employees need enough context to challenge a recommendation rather than merely approving it.
The human role must also be designed around realistic capacity. An employee who returns each morning to hundreds of pending actions is not governing an intelligent system. They are serving as its administrative delay. The more credible future is selective autonomy. Routine decisions proceed within narrow, observable limits. Exceptions move to people with expertise and authority to resolve them. Every outcome contributes to a stronger understanding of where the boundary should sit next time.
People remain accountable, but they do not need to touch every transaction to exercise that accountability. Their most valuable contribution lies in defining the rules, validating unfamiliar situations and deciding when the system has earned more freedom.
Manufacturers will gain little from AI that can recommend and act at machine speed if every decision waits for a human signature. Equally, they cannot delegate authority merely because the technology can accept it. Governance begins when an organization stops asking whether a human is in the loop and starts deciding exactly where human judgment belongs.
