If an organization expects people to remain accountable for AI-supported decisions, eventually someone has to answer a difficult question: how do we know whether they are actually capable of exercising good judgment?
A knowledge test will tell you whether an employee understands that AI can hallucinate, reflect bias or produce incorrect information. A policy quiz can confirm whether they know which tools are approved and when sensitive data should not be entered. Those are useful things to know, but neither tells you what the person will do when the AI produces a plausible recommendation under time pressure and there is no obvious right answer.
That is where judgment becomes visible.
At Cybermaniacs, we use AI judgment to describe the human ability to determine when, whether and how AI-generated information, recommendations or actions should influence a real-world decision. Measuring it means getting closer to the decisions people actually have to make, rather than assuming knowledge about AI is evidence that they can supervise it well.
For CISOs and AI leaders, this distinction is becoming increasingly important. Organizations are placing more human responsibility around AI at exactly the same time that AI is taking over more of the work people once used to build experience.
Why isn't AI literacy enough to measure good AI judgment?
AI literacy gives people important foundations. Employees should understand the capabilities and limitations of the systems they use, the organization's policies, basic risks such as hallucination and over-reliance, and the circumstances in which additional verification may be needed.
Judgment starts when those general principles collide with a specific situation.
An employee may know that AI-generated information should sometimes be checked, but still has to decide whether this answer deserves independent verification. A manager may know that automation bias exists while deciding whether to accept a recommendation from a system that has performed reliably for the last six months. A supervisor may understand an agent's limitations but still need to decide whether unusual activity is serious enough to stop the workflow.
Those decisions require context, domain expertise, an understanding of consequence and often a degree of comfort with uncertainty.
IBM's 2026 workforce study illustrates why the difference is becoming material. Seventy-one percent of CHROs surveyed identified the ability to supervise, validate and override AI outputs as the workforce's most essential skill, yet only 29% of employees ranked judgment as important. The same study found critical thinking and human judgment rising in importance as organizations redesign work around AI. IBM's 2026 CHRO study suggests that organizations may already expect more judgment from employees than employees themselves recognize as part of the job.
Our Guide to AI Literacy vs AI Judgment explores the distinction in more depth. From a measurement perspective, the implication is straightforward: a literacy assessment and a judgment assessment should not be expected to tell you the same thing.
What does good AI judgment look like in practice?
Good judgment is contextual, which is one reason it is harder to measure than knowledge.
Consider two employees who receive the same AI-generated recommendation. Both know the system can make mistakes. One notices that a key assumption no longer fits the current situation and investigates further; the other accepts the recommendation because the supporting explanation appears detailed and credible.
The difference is not necessarily AI knowledge. It may be domain expertise, skepticism, experience with similar cases or the ability to recognize that the consequences justify more scrutiny.
Good judgment can also mean deciding not to intervene.
A person who questions every AI output regardless of consequence may appear cautious, but indiscriminate skepticism is not necessarily better judgment than indiscriminate trust. If an approved system is performing a routine, low-risk task well, repeatedly recreating the work manually is a poor use of human attention.
This is why Cybermaniacs focuses on calibration. Judgment shows up in whether the amount of scrutiny, verification, escalation or intervention changes appropriately with the situation.
The organization is not looking for people who always trust AI or people who never trust it. It needs people who can tell the difference.
Can you measure AI judgment with a multiple-choice assessment?
To a point, but the design of the assessment matters enormously.
A traditional knowledge question often has a clear factual answer. A judgment question is more useful when several responses look reasonable and the employee has to interpret the conditions around the decision.
For example, imagine an AI system recommends approving a customer transaction. The recommendation fits normal patterns and is supported by credible evidence, but one unusual detail suggests that the case falls outside the employee's ordinary authority.
The interesting question is not “Can AI make mistakes?”
It is what the employee does next and why.
They might approve because the evidence is strong, reject because any uncertainty feels unsafe, investigate the unusual detail or escalate because the issue has moved outside their decision rights. The quality of judgment becomes visible through how the person weighs evidence, consequence and authority together.
Scenario-based assessment is therefore much closer to the construct organizations actually care about than a recall test.
Cybermaniacs uses scenario and decision-based approaches within our broader Human Risk Assessment & Assurance work, but the detailed scoring logic, weighting and diagnostic relationships remain part of our proprietary methodology. The public principle is that judgment should be measured through choices that resemble the decisions employees will actually face.
Should AI judgment be measured differently for different roles?
Yes, because good judgment is inseparable from what someone is expected to decide.
A general employee using an AI productivity tool may need to recognize when information requires verification, when data should not be shared and when the work has moved beyond an approved use case.
A lawyer reviewing AI-generated contract analysis needs different judgment. So does an engineer supervising a coding agent, a clinician reviewing AI-supported information or a finance leader deciding whether an AI-generated forecast should influence a material business decision.
The AI principles may overlap, but the domain context does not.
That makes a universal “AI judgment score” difficult to interpret unless it is connected to the work people actually perform.
This is one of the places where broad enterprise measurement and role-specific assessment need to coexist. Organizations may want a general view of how well the workforce recognizes AI risks, while higher-consequence populations require scenarios that reflect their own decision environments.
The level of measurement should follow the consequence of the role.
How do you test whether employees know when to verify AI output?
Verification provides one useful window into judgment because the person has to decide when independent evidence is necessary.
Our Guide to How Should Employees Verify AI-Generated Work? makes the case that verification should be proportionate. Checking every AI output is inefficient, while accepting everything at face value is obviously weak.
Assessment can therefore focus on the boundary between the two.
Give employees examples where AI output is routine and low consequence alongside situations where the evidence is weak, the conclusion is surprising or the outcome carries meaningful risk. Then examine whether their verification decisions change.
The reasoning behind the decision matters as much as the choice itself. Someone may reach the correct answer for the wrong reason and struggle when the surface details change.
Microsoft Research has been exploring similar questions around human critical thinking and appropriate reliance. Its 2026 work found that advanced AI users were more likely to treat AI output as a starting point, preserve independent thinking and deliberately decide which parts of work should remain human-led. Microsoft's 2026 Work Trend Index reinforces the idea that good AI use is increasingly about directing and evaluating AI rather than simply operating it.
Those are behaviors worth measuring.
How can you measure whether employees are over-relying on AI?
Over-reliance is tricky because asking employees whether they trust AI too much is unlikely to produce very useful data.
People generally experience their own level of reliance as reasonable.
A stronger approach examines how behavior changes across situations. Does an employee become less likely to verify because the system has performed well before? Do they challenge an AI recommendation when evidence conflicts with their own expertise? Does confidence remain high even when the AI is wrong?
Microsoft Research's work on appropriate reliance has found that people can become more likely to accept incorrect AI outputs when the system provides persuasive explanations. Research into cognitive forcing functions has also shown that prompting users to examine assumptions can reduce over-reliance without necessarily increasing cognitive burden. Microsoft Research's 2026 study is particularly useful because it treats critical reflection as something that can be influenced by how work is structured.
That suggests measurement should not stop with individual capability.
If a team shows weak judgment, the reason may sit partly in interface design, workload or workflow rather than in the people themselves.
Our Guide to How Do You Know When Employees Are Over-Relying on AI? looks at those conditions in more depth.
How do confidence and self-efficacy affect AI judgment?
Confidence is useful until the organization mistakes it for capability.
An employee who feels comfortable using AI may work more effectively because they are no longer intimidated by the technology. But high confidence can also coexist with weak verification or poor understanding of when the system should be challenged.
Low confidence creates a different problem. Someone may possess good domain judgment and still defer excessively to AI because they assume the technology knows more than they do.
The relationship is not linear.
This is one reason Cybermaniacs does not treat competency, psychology and behavior as interchangeable measures. A person can know the right thing, believe something different and behave differently again when pressure enters the situation.
Measuring AI judgment therefore benefits from looking at decisions alongside confidence, perceived control and risk perception.
If someone repeatedly makes strong decisions but reports little confidence, the intervention may be different from the one needed for an employee who is highly confident and repeatedly makes weak decisions.
The difference matters if measurement is supposed to lead to action.
Can good AI judgment be measured without observing employees at work?
Partly.
Scenario-based assessment gives organizations a controlled way to examine how people reason across different conditions. Surveys can provide evidence about confidence, attitudes and perceptions. Knowledge measures help establish whether employees understand relevant concepts.
But work behavior adds another layer.
Patterns of escalation, verification, override and exception handling can show whether people are exercising judgment in the operating environment. Near misses and incidents can reveal conditions that assessment scenarios never anticipated.
None of those operational signals should be interpreted naively. A low override rate does not automatically mean people are passive, just as a high override rate does not automatically mean they possess strong judgment. The system might simply be producing more errors.
The value comes from interpreting different forms of evidence together.
At Cybermaniacs, that is part of what we mean by Human-AI Risk Intelligence: combining evidence about capability, behavior, psychology, culture, authority and working conditions so the organization can understand why human risk is emerging.
A single measure rarely supplies that answer.
How does organizational culture affect AI judgment?
Good judgment is not exercised in a vacuum.
An employee can recognize that an AI recommendation deserves challenge and still decide not to raise the issue. Perhaps the deadline is tight, the senior sponsor strongly supports the technology or previous escalations have been treated as unnecessary friction.
IBM's 2026 study found that 41% of CHROs believe employees may not feel safe challenging or overriding AI outputs. In organizations where HR shared responsibility for deciding which decisions remained human-led, employees reported substantially greater confidence in questioning or overriding AI recommendations.
That changes how judgment should be interpreted.
If employees consistently identify the right risk in assessment but rarely intervene in practice, another course on critical thinking may not be the answer. The gap may sit in psychological safety, leadership behavior or unclear decision authority.
Cybermaniacs' Human Risk Management approach deliberately includes those conditions because they affect whether human capability can be used when it matters.
The employee may have good judgment.
The organization may simply have made exercising it expensive.
How should CISOs measure AI judgment in people supervising agents?
Agentic AI changes the problem because the human may no longer evaluate each individual output.
Instead, the person may set objectives, monitor activity, respond to exceptions and decide when autonomy should be constrained.
Judgment therefore shifts toward recognizing conditions that warrant intervention.
Our Guide to How Much Human Oversight Does AI Actually Need? explains why supervision should scale with autonomy, consequence and reversibility. For the person doing the supervising, that translates into a different set of judgment questions.
Can they recognize when the agent has moved outside its expected operating context? Do they know when unusual behavior requires escalation rather than observation? Can they distinguish a harmless exception from a change that alters the risk of continued autonomous action?
This is also where Human-Agent Accountability matters. If someone remains accountable for an agent's actions, their judgment needs to be appropriate to the authority that has been delegated.
Assessment should follow that shift in responsibility.
Testing whether an employee can identify an AI hallucination is useful. It is not sufficient evidence that they can supervise an agent operating across multiple systems.
What should an AI judgment assessment actually tell the organization?
A useful assessment should tell you more than who scored highest.
It should help explain where judgment becomes weak and what conditions may be contributing to it.
A population might struggle because employees lack AI understanding. Another group may understand AI extremely well but have difficulty applying that knowledge when consequence is ambiguous. A third may make sensible decisions in assessment while showing weak escalation behavior in practice.
Those populations should not receive identical interventions.
This is where measurement becomes part of a broader Human Risk Management system rather than an isolated testing exercise.
Cybermaniacs' Human Risk Assessment & Assurance work examines competency alongside adaptive behavior, psychology, culture and organizational conditions. Our AI Enablement & Change (AIECM) work uses that broader picture to support workforce readiness, learning, adoption and change, while Agentic Readiness & Change (ARC) extends the same concern into agentic workflows, supervision and accountability.
The proprietary value sits in how those dimensions are assessed, interpreted and connected to interventions. Publishing the entire scoring engine would not make the public discussion better, and it would make our lawyers needlessly interested in the content calendar.
The principle buyers should care about is whether the measurement can distinguish what people know, how they decide, why they behave that way and what the organization should change because of it.
What should CISOs take away?
As AI takes on more execution, the human contribution is becoming more concentrated around judgment.
That makes judgment a workforce capability, a governance dependency and, in some environments, a security control.
Organizations should therefore be cautious about treating AI training completion or literacy scores as proof that employees are ready to supervise consequential AI-enabled work. Those measures can establish important foundations, but they do not show whether someone can recognize when the normal pattern no longer applies.
Good AI judgment becomes visible in decisions: when people verify, when they challenge, when they escalate, when they allow the AI to proceed and when they decide the situation deserves human control.
Measurement needs to get close enough to those decisions to tell the organization something useful.
Because the question is no longer merely whether employees understand AI.
It is whether the enterprise can rely on their judgment when the AI needs them to use it.
