Many leaders adopt AI in customer support backwards: they buy the tool and then hunt for problems to fix. Leaders need to start with the feeling they want customers to have, the moments that break that feeling, and the constraints they must operate within to deliver (e.g., privacy, policy, brand risk). Only then do you use AI to make the experience more consistent and easier, not just simply cheaper.
The real choice is not "automation vs. humans." Let AI fully resolve requests with clear rules and low risk, such as status checks, returns eligibility, password resets, and simple changes. For cases requiring discretion, like disputes, safety issues, regulated topics, churn risk, or unclear intent, use AI to gather context, clarify the problem, and propose next steps, while a person remains accountable.
Moreover, outcomes depend more on your knowledge and process quality than on model choice. If policies are inconsistent or your internal content is stale, AI will scale the confusion. Treat knowledge as a product: one source of truth, named owners, tight review cycles, and instrumentation that shows where answers fail.
Design for completion. “Resolved” should mean the work is done, like a refund issued or shipment changed, with auditable system actions and clear permissions. Make escalation frictionless, be honest when the AI is uncertain, and log what it relied on so you can audit and fix failures. Measure success using customer outcomes, not just deflection or handle time. So track metrics around repeat contacts, time-to-resolution, customer effort, complaint themes, and completed transactions.
Adopt AI as a living system that includes governance, monitoring, rapid policy and content updates, and a steady focus on eliminating the root causes that originally created the tickets. The goal is simpler customer support and more reliable business decisions, reserving humans for moments that truly require judgment.
