Handling incorrect AI output in your process, before and after

4 days ago
3 min read

Updated: 4 days ago

Introduction

Errors will occur. That is not a criticism of any particular product; it is a property of tools that produce plausible output from incomplete information, and it applies to human work as well. The question that determines the consequences is not whether an error happens but what the business does in the hour after one is noticed.

Most organisations have no answer to that, because the question was never asked. The error is discovered by a customer, somebody apologises, the immediate instance is corrected, and nothing else changes. The same error recurs, nobody knows how often it has already happened, and confidence in the whole approach erodes for reasons that are never examined.

1. Handling incorrect AI output in your process starts before the error

Decide the response in advance.

Who is told, who corrects it, who contacts the customer, and who checks whether it happened elsewhere. Ten minutes of planning, and it prevents the improvised response that makes things worse.

2. Correct the instance and then look for the others

The step that is skipped.

If one output was wrong, others produced the same way may be wrong too. Checking the surrounding period is what distinguishes handling an error from handling a complaint.

3. Tell the customer plainly if it affected them

Better than any alternative.

An error explained and corrected quickly is survivable. One discovered by the customer later, having been known internally, is a different kind of problem and it damages the relationship far more than the mistake did.

4. Do not blame the tool in front of the customer

It is not a defence.

The output was sent by your business and it is your responsibility. "The system generated it" reads as an absence of control, which is a worse impression than an ordinary mistake.

5. Record it, with enough detail to be useful

The learning record.

What was wrong, what produced it, how it was detected, what it cost, and how long it had been happening. This is the data that tells you where the tool is unreliable in your context.

6. Ask why the check did not catch it

The process question.

Every error that reaches a customer passed a review, or there was no review at that point. Both answers are actionable and both are more useful than fixing the individual output.

7. Decide whether to keep, adjust or stop

An honest assessment.

Sometimes the answer is a tighter check, sometimes a narrower scope, and sometimes withdrawing the tool from that process. Having all three available prevents the choice between defending it and abandoning everything.

8. Watch for the errors nobody reports

The larger category.

Staff who fear blame will correct errors quietly, which means the pattern never surfaces. Making error reporting explicitly safe is what makes the record complete enough to be worth keeping.

9. Review the log quarterly

Where the value accumulates.

Thirty entries show which categories fail, at what rate, and whether it is improving. This is the only reliable basis for deciding where these tools should and should not be used in your business.

Where an error causes loss to a customer, involves personal data, or occurs in regulated advice or safety-related work, there may be notification obligations and time limits that apply. These vary by jurisdiction and sector and are worth knowing before the situation arises.

Conclusion

Decide the response before the error, because the improvised version is always worse.

Correct the instance and then check whether others were affected, tell any affected customer plainly and promptly, never present the tool as the explanation to a customer, record enough detail to be useful, ask why the review did not catch it, choose deliberately between tightening the check, narrowing the scope and stopping, make error reporting safe so the quiet ones surface, and review the whole log quarterly.