An AI chatbot uses machine learning and natural language processing to understand customer intent from naturally phrased language and generate contextually appropriate responses, learning and improving from interaction data over time. A traditional, rule-based chatbot instead relies on predefined decision trees and keyword matching, requiring customers to phrase requests in specific ways the system recognizes and offering limited flexibility outside those predefined paths. AI chatbots can handle variation in how customers express the same request, understand context across multiple conversation turns, and often execute backend transactions rather than simply providing scripted responses. Traditional chatbots are generally less expensive to build and more predictable in narrow use cases but fail more frequently when customer language deviates from the anticipated patterns, often leading to frustrating dead-end interactions that require human escalation.
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