Practical guide
Judgment and synthesis: make a conditional recommendation
Compare competing explanations and options in a fictional support-backlog decision without hiding uncertainty.
Judgment and synthesis means combining evidence, constraints and tradeoffs into a recommendation whose reasoning another person can inspect. This original support-backlog exercise asks the reader to distinguish facts from forecasts, compare options consistently and state what would change the recommendation. The aim is not to manufacture certainty or pretend that an ambiguous workplace decision has one universally correct answer.
Distinguish the situation from its explanation
A measured problem and a proposed cause are different kinds of claim.
The fictional support team has a growing backlog, two absent specialists and a recent product incident. Those facts do not yet establish which factor explains most of the delay. The ticket mix may have changed as well. Start by listing observations separately from causal explanations and proposed remedies.
Ask AI to organize competing explanations, but require each to point to supplied evidence or an explicit gap. Do not let a coherent narrative turn a plausible cause into a finding. A recommendation built on the wrong explanation may solve a visible symptom while leaving the underlying constraint untouched.
Compare options using the same criteria
Use a small, explicit comparison that keeps tradeoffs visible rather than hiding them in an unexplained score.
The exercise offers overtime, temporary help and a narrower service commitment. Compare each against time to take effect, specialist capacity, customer impact, reversibility and evidence needed. These are suggested criteria for this case, not a universal decision formula. The decision owner may need different criteria in a different situation.
Keep unknown entries unknown. If temporary help cannot handle specialist tickets without training, say so. Do not fill a blank with an attractive estimate simply to complete the table. Where the options address different parts of the backlog, explain that difference before ranking them.
Challenge the preferred option
Look for evidence that would make the leading recommendation weaker, not only evidence that makes it sound convincing.
Suppose the initial draft recommends overtime because it can start quickly. Ask whether the delayed tickets require unavailable specialists or whether the product incident is still generating new demand. Either condition could limit the usefulness of extra generalist hours. The challenge is specific to the proposed mechanism, not a generic request to list disadvantages.
Invite AI to draft a counterargument using only the supplied pack, then inspect its factual premises. A persuasive objection with invented ticket data is not useful scrutiny. Keep the valid objection, remove unsupported detail and identify the missing evidence needed to resolve the disagreement.
Show sensitivity to a consequential assumption
Explain which uncertainty could change the choice and how the recommendation depends on it.
In this case, incident duration may matter more than a small difference in hourly cost. Compare a short-lived surge with a continuing specialist bottleneck. These are hypothetical scenarios, not forecasts. Describe how the preferred response changes between them and which observation would tell the owner which situation is developing.
Do not attach precise probabilities unless the exercise supplies a defensible basis. A conditional statement can be more useful: proceed with a reversible short-term measure while confirming specialist demand, then revisit the plan at a named checkpoint. The condition should be observable enough to trigger a real review.
Deliver reasoning the owner can inspect
A recommendation should connect the chosen action to evidence, alternatives and a clear revision condition.
Finish with the proposed action, why it fits the current evidence, the strongest alternative and the facts that would change your view. Identify the next decision and its owner. Keep the source map accessible so the reader can challenge a material premise without reconstructing the entire analysis.
A reviewer can examine whether the worker acknowledged uncertainty, compared options consistently and updated the recommendation after new ticket information. Different defensible recommendations may satisfy those requirements. This exercise supports a task-specific discussion of judgment; it does not justify a fixed label about the person's general decision-making ability.
Sources and scope
NIST addresses AI risk management. Skills England describes workplace AI foundations.
These sources provide background, not endorsement of this exercise. The worked example and suggested review method are original illustrative guidance. They are not customer results, validated benchmarks or evidence of a particular product capability. Adapt the exercise to the task and use qualified review where consequences require it.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England