Earlier this year, a product team at a midsized SaaS company shared an unexpected issue. They had integrated AI into their customer support workflow to speed up responses. It worked well at first: Tickets were resolved faster, and customer satisfaction improved. But after a few weeks, edge cases started to appear: responses that were technically correct but contextually wrong, subtle inconsistencies in tone and decisions that no one on the team could fully trace back to a clear rule or logic.
Nothing was “broken” in the traditional sense. The system was working. But it was working in a way that the team could no longer fully explain or control.
That moment captures why the conversation around AI is shifting so quickly from excitement to control.
