Everyone has an AI chatbot — but most still fail basic expectations of helpfulness and context.
Hot take: despite chatbot ubiquity, user experience is stagnating — companies ship bots that can generate text but can’t actually solve customer problems gracefully[5]. Business Today looks at why widespread deployment hasn’t translated into customer satisfaction, from poor handoffs to humans to brittle context handling and bad prompt engineering[5].
What happened: enterprises fast-tracked chatbot rollouts to cut costs and scale support, but many of these bots produce irrelevant answers, escalate too early, or frustrate users with canned responses — creating annoyance rather than delight[5]. This pattern shows that front-line AI UX still needs serious product work, not just bigger models or fancier demos[5].
Why it matters to you as a developer: shipping an LLM-backed chatbot isn’t a checkbox — it’s a product challenge. You need instrumentation, clear escalation paths, context windows tuned to your domain, data pipelines for continual improvement, and guards for hallucinations. Practically, invest in human-in-the-loop flows, session-level context storage, metadata-aware retrieval, and SLA-driven fallback strategies rather than deploying a raw model behind a chat UI and hoping for the best[5].
My take: the tech is tempting, but UX and ops win here. If you’re building chat experiences, focus on observability and recovery — not just accuracy metrics. What small UX change could you ship this week to make your bot actually useful to customers?
Source: Business Today Malaysia