AI and humans working together? Weird? Maybe not… The fact is that the future of the industry is the hybrid chatbot. The success of these chatbots lies in the collaboration of humans and AI. Chatbots have been developed left right and center since its explosion into the realms of AI in 2016. They have multiple advantages over humans, including their efficiency, cost savings and ability to collect data, but they are far from perfect. The thought now is that the combination of human effort and chatbot ingenuity can transform the industry into something much more valuable. This alliance promises to provide a greater level of service at a much lower cost.
When chatbots first entered the market, it was thought by many that they would take over almost all human interaction, but as chatbots over-promise and under-deliver, the real value added lies in the power of a hybrid chatbot. In simple terms, this is a chatbot that takes care of the simple and routine tasks and allows humans to use their time with complex conversations, to create solutions for customers but also to keep chatbots in conversations where they add value. A chatbot can take care of around 60% to 80% of customer requests, which opens up more time for companies to respond to the other important and complex requests at hand. Hybrid chatbots can also be part conversational and part a guided conversation made up of menus and buttons. By building such a chatbot, users will have a set of expectations and the chatbot is less likely to default to the dreaded “Sorry, I don’t understand what you said”.
Chatbots will no longer be limited in what they are capable of because of the potential to cooperate with a human. They can perform automated tasks that are clearly scoped in the planning stage of development. It is important to not try and make them answer all the possible questions, but instead set the expectations of what they are capable of. Humans can then interact with the user outside the set scope, which has proven to be very effective.
A solution that is fully reliant on a chatbot may work well if all your interactions with customers are simple transactions, like ordering a pizza or getting some news. However, when the conversation becomes more complex, a human will likely be the only one to find a solution in the expected amount of time. Although Natural Language Processing (NLP) engines are quite advanced at understanding voice and text-based human input, the kind of AI required to create solutions to complex business problems require knowledge and skills that only a human within the business would typically have (for more information on NLP check out this blog). Thus, a hybrid chatbot solution would create points within the logic of the chatbot that notifies a human to intervene.
When developing a chatbot, it is important to keep in mind that humans are very good at handling non-standard situations with their common sense, and bots are very good at holding thousands of interactions and digging through required data. So, there needs to be a clear distinction between the types of interactions that will be handled by each party in the system. The chatbot should be capable of pushing the conversation to the relevant human in a systematic and user-friendly way. The human whom deals with concerns outside the scope should be able to improve the chatbots logic in return, when it makes sense of course. Users have different expectations when they are speaking to a chatbot and when they are speaking to a human. So it is important to make it clear to them who they are talking to.
An intelligent and well-trained chatbot logic is not enough. Passing off the conversation to a human has the potential to become a disastrous user experience if it takes to long or involves moving the user to another channel. A good solution will have a platform that allows for intelligent routing to multiple different staff whilst ensuring the whole interaction can take place within the same chat which will provide a naturally smooth experience for the user. A great chatbot will also notify the human of the context of the conversation so that the user does not have to say anything obvious or repeat things again, nor must one go through the conversation to continue it. Chatbots that can gather data on the user and allow the human to use it following the switch will be the norm in the future. As machine learning gets better and better, we expect that chatbots will be capable of learning from the human interaction and how the human handled the situation. This way, the cooperation between the human and chatbot will continue to improve.
We now look at two effective use cases for a hybrid chatbot:
Support Hybrid Chatbot
A hybrid chatbot is useful post-sale as it means companies can ensure that they keep their customer service at a high level, without using a lot of resources. As mentioned before it is important to have a good platform behind the chatbot to support the hybrid process. A great example of how this might work is when a company needs to update a production order. What happens if you are well into a production process and need to change your order? A chatbot can probably only tell you whether this is possible or not, or that you are too far in the production line to change your order. But this is far from useful and fails to provide a solution to the customer, which is what they are looking for.
Unless the chatbot is updated in real time, it will not have information on what solution is possible and will have to default to a useless response. Thus a human transfer will be important at this point of the interaction. It is important to have the correct platform in place to allow you to collect analytics and control the chatbots activation. Collecting analytics also allows you to learn what use cases are important for improvement of your service and chatbot. The hybrid chatbot solution provides the great customer service to your customer and allows you to have more flexibility.
On the one hand, recruitment is costly, takes time and needs to be done in an effective way to find the right people for the right job. A chatbot can solve the first two problems quite convincingly. Generation Z spends most of their time on messaging apps. This means that if recruiters target these apps, they are likely to reach out to a wider variety of potential candidates. Starting an application in a messaging platform is user-friendly and relatively easy for the applicant. Furthermore, with applicants searching for more than one job at the same time, the instant and quick process reduces wait times, speeding up the recruitment process, meaning firms do not lose out on great applicants selecting other jobs first.
A hybrid chatbot has the capability to pass on the applicant to a recruiter at any stage of the application where human contact is necessary. For example, the chatbot could sort and filter applicants based on the analytics it collects and pass on the applicants to recruiters that meet certain base criteria (such as relevant experience or skills) for an interview.
With the data collected from the chatbot available for the recruiter to use in further stages of the process, it means that recruiters can spend less time asking simple questions and have more time to dig deeper into the person’s skills and attributes. This will result in a higher quality recruitment process.
There are clear advantages of using a chatbot in your operations. However, without a hybrid chatbot strategy, the value a chatbot has is seriously limited. Chatbots that fail to recognize this important next step in their development will fail to gain traction die out. It is important to start automating low-end tasks and allow humans to take over the complex tasks to move your way up the value chain. Hybrid chatbots will get a great return on investment. The partnership between humans and chatbots is exciting, we can’t wait to see where it will take us and so should you.