Start with the question, not the technology
It's tempting to look for places to "add AI" to a support workflow. A more useful starting point is identifying which parts of support are genuinely repetitive and well-defined enough for a model to handle reliably.
Where generative AI tends to help
Answering frequently asked, well-documented questions, drafting first-pass responses for an agent to review, and summarizing long conversation threads are all areas where generative AI tends to perform reliably and save real time.
Where it tends to struggle
Ambiguous, emotionally sensitive or account-specific issues are harder to hand fully to an automated system. These cases benefit more from a clean handoff to a human than from an AI attempting to resolve them end to end.
Designing a reliable handoff
The systems that work best in practice have a clearly defined boundary: the AI handles what it's confident about, and hands off cleanly — with context preserved — when a query falls outside that boundary.
Measuring what actually matters
Resolution rate alone can be misleading. Tracking customer satisfaction on AI-handled interactions specifically, alongside how often conversations are escalated, gives a clearer picture of whether the system is genuinely helping.
