Building AI Customer Support Is Easy. Trusting It Is Hard.
AI customer support sounds simple. Give an AI your documentation, let users ask questions, and let the AI answer. That was roughly what I thought when we started building AI support into FeedLog . Then we actually started testing it. And I realized the hardest part isn't getting AI to answer. It's deciding when it should answer at all . The happy path is easy When a user asks: How do I export my…
Creating AI customer support appears straightforward initially. One introduces an AI with relevant documentation, allows users to pose inquiries, and expects the AI to provide answers. This was my initial understanding when we began integrating AI support into FeedLog. However, upon testing, it became apparent that the most challenging aspect isn't the AI's ability to respond, but rather determining when it should.
The ideal scenario unfolds seamlessly when users ask questions whose answers are clearly documented. For instance, a user might inquire about exporting their data, and the AI, equipped with the necessary information, promptly provides the answer. The user poses the question, the AI locates the pertinent details, and the user receives the response. It's as simple as that.
Yet, real-world interactions with AI often deviate from these straightforward queries. Users may ask complex questions like: Why was I billed twice? My account is malfunctioning. Despite following the documentation, the issue persists. In such cases, the AI faces a critical decision: should it respond, attempt to clarify further, acknowledge its limitations, or involve a human? This is where AI support becomes intriguing.
An incorrect AI response is far more detrimental than no response at all. Unlike a standard chatbot where a misinformation is merely irritating, in customer support, a confident yet erroneous answer can lead to significant problems. If the AI provides incorrect billing details or invents non-existent features, you have saved precious support time and potentially added another support ticket.
This prompts us to reconsider AI support not merely as: How many questions can AI answer? but rather: How many questions can AI safely resolve? These are fundamentally different metrics.
The objective isn't to eliminate human intervention. I don't believe that the emergence of good AI support necessitates the complete disappearance of humans. Perhaps a more effective approach is to allow AI to handle repetitive, well-documented questions. The AI should recognize when the required information is absent. It should discern when a conversation is becoming overly complex. Only when truly necessary should humans intervene.
The critical element lies in the transition between AI and human involvement. While we continue to develop FeedLog, one thing has become abundantly clear: a dependable AI support agent should not only know how to answer but also when not to. If you are already utilizing AI for customer support, I would be interested in learning about your level of trust in its autonomous operation.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
