Chatbots fail to deliver the desired user experience for a number of reasons. Chief among them is dated technology. For example, rule-based bots might have thousands of question-and-answer pairs stored in a repository, but without AI-powered features like natural language processing, the bot may not provide satisfactory information—if the inquiry isn’t worded perfectly.
Another example of how outdated tech can force a chatbot to fail is with complex or unique user interactions. While machine learning is allowing new chatbot solutions to avoid this dilemma, older chatbots have a harder time knowing when to redirect the conversation to a support agent. Even when they do, it’s often after several frustrating failed exchanges with the user.
Lastly, chatbots that cannot integrate into disparate software programs can create information bottlenecks. Because they aren’t connected to an organization’s other systems, retrieving, storing, and modifying knowledge siloed within those programs is impossible.
