Mapping Where AI Adds Value Across the Customer Support Lifecycle

Where can AI be put to work in customer support? Walk through the broad uses you would expect a support organization to consider. ```hint Follow one support issue Trace a single customer problem from before the customer reaches out to after the case is closed, and note who is involved at each step. Each step and each person points to a different kind of AI help. ``` ### Clarifying Questions - Should I answer from the view of a company running its own support team, or of a vendor selling AI to many support teams? - Which channels are in scope: chat, email, messaging apps, phone, or all of them? - Should I cover only automation that talks to customers, or also tools for human agents and support managers? ### What a Strong Answer Covers - A structure for the landscape, such as the stages of a support case or the people served (customer, human agent, support leader), instead of a flat list - The difference between answering questions and resolving issues by taking actions in business systems, and why the second matters - Uses that help human agents and support leaders, including quality review and turning conversations into product insight - The main risk of each use (wrong answers, wrong actions, policy breaches, keeping customers away from a person) and the guardrails it needs - How value would be measured, and which uses to start with given volume, risk and ease of measurement ### Follow-up Questions - If you could ship only one of these first, which would it be and why? - How would you tell whether a conversation the AI marked as resolved was truly resolved, rather than abandoned by a frustrated customer? - A customer demands a refund that policy does not allow and grows angry with the AI agent. How should the agent behave, and how would you make sure it does?