Many leaders deploy AI to close tickets faster, but speed alone does not lead to real success. A ticket can be marked as solved while the customer’s underlying issue, confusion, or friction remains untouched. When that happens, the problem simply resurfaces later as repeat contact, churn, or quiet dissatisfaction.Effective AI adoption starts by reframing the goal. The moment leaders optimize for “ticket solved” over “problem resolved,” AI is pushed to hide friction instead of exposing it. Before asking AI to automate responses, it should be used to understand why customers are reaching out in the first place. Customer support is not about answering emails, chats, or calls; it is about identifying root causes like product or policy gaps, unclear UX/UI, broken handoffs, and systemic failures across teams.It is tempting to focus on how much can be automated, or how AI can slow the growth of customer support teams, which are often among the largest teams in a company. But if AI responds to users without feeding insight back into the system, it creates a bad loop: the user contacts support, receives a polished response, nothing changes in the product, and the same issue returns, either from the same customer or the next one, or the customer leaves altogether.When AI is optimized for ticket closure, it reduces cost. When it is optimized for problem resolution, it improves the business itself. Leaders who get this right stop asking how many tickets AI solved and start asking how many problems no longer exist because of it.
