AI chatbots have moved well past the scripted, keyword-matching bots that used to frustrate more customers than they helped. Modern versions can read a full customer message, check it against a business knowledge base, and hold a coherent back and forth conversation instead of just spitting out a canned answer to the nearest matching keyword.
That said, a chatbot is not a replacement for a support team, it is a filter. The realistic use case is handling the repetitive first-layer questions, order status, business hours, return policy, pricing tiers, so human agents spend their time on issues that actually need judgment. When a bot hits something it cannot resolve, a clean handoff to a live agent matters more than how clever the bot sounds in a demo.
Before adopting one, it is worth testing with real support tickets rather than sample data. A chatbot for business should connect to your existing help content instead of requiring you to rebuild a knowledge base from scratch, and it should give you visibility into what it actually told customers so mistakes get caught early rather than discovered from a complaint.
Setup time is another practical filter. If a platform needs a developer team for weeks just to get running, that is a hard sell for a small support department that wants results this quarter, not next year.