As AI assistants become part of everyday work, a new challenge is emerging. The answers they produce are increasingly trusted because they sound confident, structured and well-reasoned.
Then, somewhere in the process, something does not add up. The numbers do not match the source. The model agreed with a flawed assumption instead of challenging it. It told the user they were right when they were not.
Organizations have moved quickly from skepticism to reliance, often without building the controls that reliance requires. The gap between how confident an AI system sounds and how correct it actually is has become one of the more consequential blind spots in enterprise AI adoption.