Customer Service AI

The peak-season pattern was predictable and painful. Support volume swung three to five times higher than baseline for roughly eight weeks. Scaling headcount for the peaks was slow, expensive and the quality slipped anyway because new agents could not learn the product fast enough. The team was burning out by week three and the leadership team was looking at the same problem coming round again.

We started with the volume forecast rather than the tooling. The peak was not uniform: roughly half the ticket increase was order status, a quarter was returns and exchanges, and the remaining quarter was genuinely novel questions that needed a human. That breakdown framed the work. Anything in the first half was a routing and automation problem. Anything in the second half was a triage and tooling problem.

Tool selection ran against explicit ROI criteria. For each candidate (response automation platform, AI triage layer, storefront chatbot) we wrote the cost per ticket avoided, the implementation effort and the failure mode if it answered wrong. Two of the early candidates failed the failure-mode test outright. The shortlist that survived was smaller than the original brief, but every tool on it had a defensible business case.

Response automation went first because it had the cleanest payback. Order status questions got handled end to end without human touch: the customer asked, the bot pulled live tracking from the carrier, the response went out within seconds. Returns and exchanges got templated responses with the policy applied automatically, leaving the agent to handle the edge cases.

AI triage was the next layer. Incoming tickets got categorised, prioritised and routed before an agent saw them. The agents stopped triaging and started resolving. The storefront chatbot caught a meaningful share of pre-sales questions that would otherwise have hit the inbox, and the ones it could not answer arrived at the agent already pre-qualified.

Peak season arrived. The team absorbed the spike without adding headcount. Response times improved against baseline rather than degrading. The lessons were less about the AI and more about the discipline that came with it: route before you automate, automate the boring before the interesting, keep AI inside safe categories until the audit log says otherwise.