With the number still increasing, users of messaging apps worldwide reached the 2 billion mark in 2018. With the rise of messaging as the preferred type of communication, chatbots simulating interactive human conversation become increasingly important for businesses as well. According to a study, it is expected that by 2025, the global conversational AI market will grow to $13.9 billion. Integrating Artificial Intelligence is necessary for companies to handle the increasing amount of incoming requests.
The advantages of Artificial Intelligence (AI) for companies are clear, AI can scale processes, reduce complexities and improve productivity to name a few. Even though we are only at the beginning of using AI in companies, a variety of use cases in different industries and departments have emerged in the past years. While the application of AI is flexible and scalable once integrated, qualitative and structured data need to be fed in first because Data is “like oil for cars”, a necessary part of any AI model. In comparison to traditional code, AI algorithms depend on the data used for training.
Training an AI model
Training an AI algorithm is similar to teaching a young child. Normally, a toddler’s first words will be the ones that have been encountered the most such as ‘mom’ or ‘dad’. With time, parents help to identify more and more from the child’s environment. Similarly, AI works by feeding in data into a model, so that when a certain situation comes up the model knows what to do by looking at similar patterns in the past. Just like a child, an AI model learns faster the more (structured) data is fed into the model.
Often, data to train an AI consists of frequently asked questions that have been encountered in customer service before. In most companies, the majority of these FAQs are a set of questions that mean the same but are differently phrased. Likewise, an algorithm of an AI model should be able to recognise intents and entities to understand variations of word patterns in customer service. But what if an AI model encounters a question that has not been asked before and where the system has not enough data to provide a good answer?
Agent + AI hybrid model
AI is not human, so it might not always be certain about a question and might fail if asked a complicated question. A hybrid Agent+AI model makes sure that no wrong answers are sent out since the support agents train the AI during the operational process.
If the AI encounters a question for the first time, the agent is included in the chat and the system provides the most viable suggestions according to existing data. This model ensures that no wrong answers are sent out to customers which is essential for every company. Moreover, the support agents don’t have to spend excessive time on formulating answers. With the suggestions provided by the system, the agents can simply select or edit the right answers from the suggestions.
The bot is trained in a dynamic process, without any extra effort or personnel. With every incoming request and answer that is sent out, the AI increases its knowledge and therefore the number of answers that can be sent out automatically.
To sum up, a hybrid AI model enables automated responses, pre-filled suggestions, no wrong answers and a constantly improving database.
The future of business will be with AI, why not work already with a smart Agent + AI hybrid solution today?