What You Think About Chatbots That Aren’t True
These days organisations seem to have adopted conversational artificial intelligence (AI) chatbots to help with communication and the problem of keeping customers glued to their products and services. Conversational artificial intelligence (AI) chatbots are now heavily used by websites, social media platforms, phone text messages, and even in the tourism sector and anywhere a company can talk about its products and services. A Gartner report predicts that by 2027, chatbots will become the primary customer service channel for many organizations. This is already shaping up.
The face of new customer service is changing, and it is changing fast. But, some people have expectations of chatbots, and it is almost as if they do not know that the chatbots would only give good answers from the data it has access to. To blame the chatbots because they couldn’t answer a question means, perhaps, we do not fully understand how chatbots work. No doubt, conversational chatbots help build customer relationships but will still follow the data input in the design.
If you have ever wondered why when you engage with a chatbot, it is replying with answers that are far off from the mark, the reason is simple, the data it has to help with your questions is limited, and so it tries to look for the closest answer to what you asked.
Most customers think chatbots should have all the answers, but the truth remains that chatbots cannot give what they don’t have. Although they use Natural Language Processing (NLP), as I explained the differences between Machine Learning (ML) here, they are as good as their data. People think having an AI-supported chatbot means providing everything you need to know about the organization’s operations, products, and services. This is not true and the earlier you realise, the better, so you don’t have undue expectations.
A lot goes into designing a chatbot, equivalent to when a product is being developed. You must also realize that human input plays an insane amount of role in how chatbots function. Humans would have to input data about the organization, which is what the chatbots run with. Chatbots are not magical. They do not just automatically know all the answers to your questions. There is time and effort put into designing a great chatbot. The idea that organizations’ salespeople propagate that the AI chatbots have the answers to all the questions has surprised customers when they find out that chatbots do not have answers to their questions. This is where the problem starts. It is important to note that the AI chatbots feed on human input and are as good as what they take in as data. So no, AI chatbots are not magical, and just as human customer service personnel do not have all the answers if they do not know, chatbots also do not know till they have sufficient data to cover the likely questions they may face.
Great chatbots require great design. When a company is building a product, there is a lot of thought put into the design and how effectively it will serve the purpose for which it was made; the same applies to chatbots. The design is probably faulty whenever you witness a “bad” chatbot. The UX with the chatbot must be top-notch, and this can be done when conversational stylistics are considered in building the chatbots.
Also, chatbots cannot always understand the questions you pose to them. There is always the problem of context. It is the same with humans. However, AI chatbots can solve the context problem with more data. A chatbot can only answer questions based on the level of data provided. The context of conversations is as important as the conversation itself to keep the relationship between the chatbots representing the organization and the customers.
