If you had to ” hire ” a chatbot in your company to take over from the customer service department, what would you choose – a “classic”, rule-based one, or an “advanced” one that uses artificial intelligence? Of course, the temptation is great and most would lean towards “smart” chatbots, able to understand various requests, to dynamically adapt their answers, to learn on their own, etc. But the answer is more about the usage scenario than the technology. Because, beyond the indisputable advantages of “AI-powered” chatbots, the choice is dictated by the specific needs of each company, the resources it has and the existing good practices in that field of activity.
There are already hundreds of validated and industry-specific usage scenarios - take a look at our Chatbot Gallery to get an idea of the diversity of the offering.
In order to make an informed choice, you need to understand the essential differences between the two types of chatbots. So:
• “Rule-based chatbots” – are classic chatbots, which operate on the basis of a clear system of predefined rules and which largely respect the principle of a decision tree. Rules, defined using specific languages, such as AIML (Artificial Intelligence Markup Language), can be simple or complex to cover different types of scenarios. However, no matter how elaborate these rules may be, it is virtually impossible for them to cover every possible scenario. Rule-based chatbots also quickly show their limitations when their requests do not follow the patterns they were “trained” to follow. Classic chatbots are not able to understand the context or to identify and extract the interlocutor’s intention, the conversation model being a logical one, such as ” If this / Then that“. Therefore, in order to overcome imitations and not waste the time of interlocutors, chatbots of this type are “trained” to automatically signal situations when they do not understand the requests and / or to integrate options that allow a quick takeover by a human operator.
• “AI-powered chatbots”- are next-generation chatbots with a high level of complexity, using advanced technologies such as Natural Language Processing (NLP) and Machine Learning (ML) algorithms. Unlike rule-based solutions, AI-powered chatbots are constantly evolving, being trained by human specialists to decipher free conversations. Some of these solutions allow you to monitor conversations – using the so-called Human-on-the-Loop (HITL) method – and intervene when chatbots fail to understand requests or have a confidence level below a predefined threshold. A chatbot who wants to be really smart needs a database to store the information on the basis of which the solution learns to identify relevant data and associated responses and a “central nervous system ” (such as Microsoft LUIS, IBM Watson, etc.), with which to extract, correlate, and process data to provide answers and learn from mistakes.
Each type of chatbot has its own advantages and limitations. For example, rule-based ones are more limited, but they are cheaper, easier to develop and put into use, and provide predictable levels of efficiency and customer satisfaction. These are also the main reasons why Amazon, for example, at one point gave up a good part of the ” AI-powered ” chatbots in favor of the “dumb” ones. Advanced chatbots have a larger coverage area, but also higher operating costs. In addition, when they fail in their mission to deliver the required answers, it is difficult to detect where the logic of the neural network has failed and a complex “reverse engineering” process is required.
So there are a number of criteria that you need to consider when considering a chatbot solution. The above is for guidance only – if you want more details, the RolaxIT team is at your disposal to help you make the best choice tailored to your needs and resources.