Why AI and Data Matter When It Comes to Chatbots
Both the benefits and the limitations of chatbots reside within the AI and the data that drive them.
AI considerations: AI is very good at automating mundane and repetitive processes. When AI is incorporated into a chatbot for these types of tasks, the chatbot usually functions well. However, if a demand is made on a chatbot that extends beyond its capabilities or makes its task more complicated, the chatbot might struggle—and that has negative consequences for businesses and customers. There are questions and issues that chatbots simply may not be able to answer or resolve—for example, complex service issues that have a large number of variables.
Developers can work around these limitations by adding a contingency to their chatbot application that routes the user to another resource (such as a live agent) or prompts a customer for a different question or issue. Some chatbots can move seamlessly through transitions between chatbot, live agent, and back again. As AI technology and implementation continue to evolve, chatbots and digital assistants will become more seamlessly integrated into our everyday experience.
Data considerations: All chatbots use data, which is accessed from a variety of sources. As long as the data is high quality and the chatbot is developed correctly, the data will be a chatbot enabler. However, if the data quality is poor, it will limit the chatbot’s functionality. And even if the data quality is good, if the chatbot’s ML training wasn’t modeled properly or is unsupervised, the chatbot can perform poorly—or unexpectedly, at the very least.
In other words, your chatbot is only as good as the AI and data you build into it.