Chatbot

Classic chatbots work with decision trees: fixed questions, fixed answers, fixed click paths. They're predictable, but rigid — any phrasing the system didn't anticipate leads nowhere. Modern, AI-powered chatbots use language models and understand freely worded requests, even with typos, colloquialisms or foreign languages.

Data quality determines performance. A good business chatbot answers from real company data (FAQs, product info, opening hours, terms and conditions) rather than from general model knowledge — otherwise it risks giving wrong promises.

For most businesses, the chatbot is the visible part of a larger system. Behind the scenes sits an AI agent that doesn't just respond but also looks up data, creates tickets, schedules appointments or routes requests directly to the right person. The line between chatbot and agent is blurry — the difference is whether the system is permitted to act.

Three factors determine success or frustration. First, the knowledge base — the bot must answer only what's in the stored company data and honestly defer or hand off everything else. Second, handoff to humans: visible, fast, and with all information already gathered so customers don't have to repeat themselves. Third, disclosure: users must recognise they're talking to AI — the EU AI Act requires this.

After launch, maintenance matters more than perfection at go-live. Real user questions in the first weeks show exactly which content gaps exist in the knowledge base. Close these gaps regularly and within a short time you'll have a bot that reliably handles the vast majority of standard requests.

Whether a chatbot actually helps or just annoys customers can't be judged by gut feeling — it comes down to a few meaningful metrics. The most important is the resolution rate: what share of conversations end satisfactorily without human takeover? Equally relevant is the handoff rate — how often and at which points does the bot transfer to staff. If it keeps transferring on the same topics, that's not a flaw — it's precise feedback on what's missing from the knowledge base.

Analysing unanswered or abandoned conversations is also valuable. That's exactly where improvements lie: questions the bot couldn't handle are candidates for the next knowledge base expansion. A well-run chatbot gets measurably better over time because these gaps close systematically — not because it knew everything from day one.

Don't underestimate qualitative feedback. A simple 'Was this helpful?' prompt at the end of a conversation delivers direct input and surfaces frustration points before they show up in bad reviews. Critical: never make the bot a dead end — the path to a human must always be visible and quickly accessible.