Since their evolution, chatbots have grown from delivering linear, scripted user experiences to providing unsupervised and contextually-aware engagement.
One of the earliest chatbots created at the MIT Artificial Intelligence Laboratory, Eliza interacted using scripts and leveraged pattern matching and substitution technology. It had no built-in provision for contextualizing events. Like Eliza, many first-generation, rule-based chatbots were used for answering simple FAQs. Such chatbots did not leverage automated, machine-learning technology and required 6-9 months to train manually. Moreover, training them was an ongoing process, and the entire investment did not deliver the requisite ROI.
Over time, as customers and employees started demanding interactive, real-time, and personalized omnichannel engagement, organizations needed sophisticated AI-enabled chatbots to meet their expectations. Consequently, chatbots evolved to conversational AI with powerful capabilities, including machine learning, natural language processing (NLP), intent extraction, and sentiment analysis.
