Spot over-reliance on AI before it affects customers
AI can make an answer look complete before the underlying question has been resolved. Over-reliance becomes visible when people stop checking evidence, cannot explain important decisions or assume a fluent output is ready for a customer.
Editorial responsibility: Nardus Oelofse, Chief Consultant.
By Chase & Marshal
Watch how people reach a conclusion
Ask a team member to explain the sources, assumptions and judgement behind an output. Can they identify what the tool contributed and what they checked? Can they recognise when the answer is outside their knowledge or authority?
The concern is a gap in the decision process, rather than simply frequent tool use. Someone using AI throughout the day may retain strong judgement. Someone using it occasionally may still accept a consequential error because the output sounds convincing.
Look for practical warning signs
Repeated unsupported facts, copied citations that nobody opened, unexplained changes in recommendations and identical answers to different customer situations deserve attention. So does a review process that approves material without access to the original evidence.
In an illustrative account team, an assistant produces a confident explanation for a decline in a customer's orders. If the representative accepts it without checking the order history or speaking to the customer, the team may act on a plausible story instead of a verified cause.
Strengthen the review at the right point
Set the review standard according to the consequence of an error. Identify which outputs need source checks, which require a qualified decision-maker and which can be used as low-consequence working drafts. Make responsibility visible before an output leaves the team.
Use examples from the actual work to practise these distinctions. Ask people to identify an unsupported claim, a missing assumption and a situation requiring escalation. This builds judgement more directly than a general reminder to be careful.
Make challenge part of normal work
Managers can model the questions they expect: What supports this? What might be missing? What would change our decision? Give people enough time and information to answer. A demand for immediate output can quietly undermine the review standard.
Keep a small record of recurring errors and useful corrections. Use it to improve training, approved information and workflow design. The objective is to retain human understanding where it matters, so AI assistance adds capacity while the team remains accountable for the work.
Questions answered
Should we reduce all AI use if we find mistakes?
Respond to the task and consequence. Some uses may need to pause, while others need clearer sources, review or training. A blanket response can miss the specific weakness that produced the mistake.
Related
Sources
Sources supporting the factual statements in this article. Findings should be read within their stated dates, geography and scope.
- Top 5 risks of AI overdependence in the workplace | TechRadarTech Radar Pro. www.techradar.com. 2026-05-20T08:42:29Z
Strengthen judgement in everyday AI use
We can help your team practise using AI with judgement, including how to check outputs and recognise when a task needs a different approach.
