How Do You Decide When to Trust AI’s Answer?

An employee sends you an analysis before a meeting. The explanation is clear. The recommendation seems reasonable. The work arrived much faster than you expected.

They tell you they used AI to help prepare it.

What do you ask next?

Perhaps you want to know which tool they used or how much time it saved. Both can be useful questions. Before the recommendation shapes a decision, though, you also need to understand what information it relied on and what someone checked.

That responsibility shows up across a credit union. Accounting may use AI to help explain an expense variance. HR may use it to organize a training plan. Operations may ask it to improve a procedure. A lending manager may use it to prepare questions about portfolio performance.

These are different tasks with different consequences. A useful review process recognizes those differences and gives employees a clear way to bring their work forward.

Before reviewing the output, clarify the decision or action it is meant to support.

A brainstorming list can contain ideas that will be explored and discarded. A recommendation that changes how employees handle member requests needs a firmer foundation. An explanation going into a management report needs to match the underlying records.

Consider a hypothetical accounting example. AI suggests that an increase in processing expenses resulted from higher transaction volume. The explanation sounds plausible, but the information supplied to the tool included only monthly expense totals.

Those totals establish that the expense changed. They do not establish why.

The reviewer would need additional evidence, such as transaction counts, invoices, pricing changes, or an unusual charge. Until that check happens, the proposed explanation is a possibility to investigate.

A helpful management question is: “Which parts of this are supported by the information we have, and which parts are assumptions?”

An answer can be well written and still rest on an outdated policy, an incomplete spreadsheet, or a misunderstood instruction.

Ask what the tool was given. Was the procedure current? Did the spreadsheet cover the right period? Were important exceptions included? Was the tool working from an approved internal source or general information?

For example, an HR communication could accurately summarize an older version of a benefit policy. Editing the tone would do nothing to resolve that problem. The reviewer needs to compare the draft with the current approved policy and the terms that actually apply.

Use approved tools and information permitted for that use. A useful exercise can often begin with a hypothetical example or an approved document, without introducing member or employee records.

When AI supplies a source, open it. Check whether it says what the answer claims, whether it applies to the situation, and whether it is current. A citation is a starting point for verification.

Managers need a review standard people can use in the course of an ordinary workday.

For a training agenda, someone familiar with the topic might check that the content is accurate, the sequence makes sense, and no essential subject is missing.

For a procedure change, the review needs to go further. Who owns the process? What is the purpose of each step? Does the proposed revision affect a control, another department, or the information a member receives?

Imagine that AI recommends eliminating a second review to reduce turnaround time. Before testing that change, the team needs to understand what the review catches and who has authority to change it. The process owner may also need input from compliance, risk, or another specialist.

That same principle applies when outputs inform lending, employment, or other consequential decisions: follow the institution’s required decision and approval processes, and involve the appropriate people.

The question is how much evidence and review the intended use deserves. A clear answer helps employees move work forward with fewer guesses about what their manager expects.

“Someone will review it” leaves a lot unresolved.

Who is that person? What are they checking? What would make them stop the work or send it back?

Useful checks are concrete. Recalculate the total. Compare the explanation with the source records. Confirm that the policy is current. Test a proposed workflow against a normal case and an exception. Ask the affected department whether the recommendation creates additional work or removes information it needs.

Asking the same AI to double-check its answer may improve a draft, but it should not be the only check supporting a consequential decision. A confident second answer can still depend on the same incomplete information.

The reviewer needs access to evidence and enough knowledge to recognize when something does not fit. When those are missing, the next step is to seek help or obtain the information.

A manager does not have to personally repeat every calculation or rewrite every document. Employees can take responsibility for preparing work that is ready for review.

Give them a simple way to explain it:

“I used AI to help with this task. These were the inputs. Here is what I verified, here is what I changed, and this is the question I still need help resolving.”

That explanation makes the review more useful. You can spend time on the unresolved judgment instead of trying to reconstruct the entire process.

It also creates room for learning. If an employee catches an unsupported assumption, ask how they noticed it. If the same issue appears repeatedly, improve the instructions, source material, or review process. Encourage people to share corrections without making them feel that admitting a problem defeats the purpose of using the tool.

Choose a recurring task and agree on a few things before the next output arrives: its intended use, the approved information it can rely on, the checks required, the reviewer, and the circumstances that call for escalation.

Try that approach on a small, authorized example. Afterwards, discuss what the review caught, what remained uncertain, and how much effort it took. Adjust the process based on what you learn.

This is an opportunity for managers throughout the organization. You can help your team get useful work from AI while developing its ability to question an explanation, recognize an assumption, and support a recommendation.

Before approving the next AI-assisted piece of work, ask: “What gives us confidence this is ready to use?”

Then make time for the answer.

Keep Ascending.