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Start with the decision, not the tool
An AI tool recommends which customer complaints to handle first. Its ranking looks precise, and the team is under pressure to clear the queue. But the recommendation may reflect incomplete records, an outdated rule, or a pattern that rewards speed while missing the cases with the greatest consequences. Before acting, a manager needs to ask what the recommendation is based on, what it leaves out, and what could happen if it is wrong.
AI recommendations can help people notice patterns or sort information, but a confident answer is not proof that it is suitable for a particular decision. The practical question is whether this recommendation is reliable enough for this task, given the evidence, the people affected, and the cost of an error.
Define what a good result means
Before using a recommendation, define what decision needs to be made and what a good result would look like. In the customer complaints example, does “handle first” mean the oldest complaint, the most urgent safety concern, the customer most at risk of leaving, or the issue that could affect many customers?
If the team has not agreed on the goal, the AI may optimize for a convenient measure that does not match the real need. A ranking based on speed, for instance, may not be appropriate when fairness, safety, or long term trust matters more. Clear decision criteria give the manager something concrete to compare against the tool’s output.
Check the information behind the recommendation
Ask what information the system used, whether that information is current, and whether important cases are missing. Complaints recorded by phone, for example, may not appear in the same dataset as online complaints. A category may have changed, or a recent issue may not yet be reflected in historical patterns.
Look for evidence that can be checked independently. If the tool gives a reason for its recommendation, verify the underlying records and definitions. If it cites a source, confirm that the source exists and says what the tool claims. If the reasoning cannot be inspected, treat that as a limitation when deciding how much weight to give the answer.
Consider who may be affected
A recommendation can appear consistent while producing uneven effects for different people or groups. Ask whether the data represents the customers, employees, or communities affected by the decision. Consider whether the system may be relying on a proxy that seems neutral but stands in for a sensitive characteristic or an old business practice.
The higher the consequence, the more care the review requires. A suggestion used to organize a low impact task may need only a quick check. A recommendation that affects access to a service, a person’s work, or a significant business decision deserves stronger evidence and a clear person responsible for the final choice.
Use an accept, question or reject check
Accept a recommendation for the task when it matches the goal, uses relevant information and agrees with evidence the team can verify. Question it when the goal is unclear, the data may be incomplete, the reasoning is hard to inspect, or the result differs from what experienced staff would expect.
Reject or pause the recommendation if it relies on information that is wrong or out of date, conflicts with a required rule, creates an unacceptable risk, or affects people in a way the team cannot justify. A manager may test a recommendation on a limited scale, collect missing information, or pause its use until a concern is resolved. Record what was checked and who made the decision when the consequences are significant.
Keep a person accountable
Using AI does not remove the need for clear ownership. Someone should understand the decision, review relevant evidence, and be able to explain why the recommendation was accepted, changed, or set aside. Teams should also monitor what happens after acting on it. If the results differ from expectations, revisit the process rather than assuming the tool was correct.
This reflects a broader management skill: using information thoughtfully while remaining accountable for decisions. Business professionals need to interpret data, question assumptions, and weigh operational goals alongside their effects on people and organizations. Those skills are useful whether the information comes from a report, an experienced colleague, or an AI system.
Connect new tools to sound management practice
Studying business and management can help professionals build a structured approach to decisions: define the problem, examine evidence, compare options, consider risks, and review outcomes. At UBIS, learners are encouraged to connect management ideas with practical professional questions. AI can be one input in that process, but the decision still needs a clear purpose, reliable evidence, and accountable judgment.
Guidance from the National Institute of Standards and Technology emphasizes verifying sources and citations in generative AI outputs and defining human oversight roles. The specific review a workplace needs depends on its decision, data, and risks, but the core habit is straightforward: check the recommendation before relying on it.
Frequently asked questions
Should managers accept AI recommendations at work?
Only when the recommendation fits the decision, uses relevant information, and can be checked against evidence. Managers remain responsible for the decision and should consider the impact of errors.
What should a manager check before using an AI recommendation?
Clarify the goal, check the source and quality of the information, consider who may be affected, look for risks, and identify a person accountable for the final decision.