The adoption of chatbots in the enterprise market

We have seen the rise of chatbots in the past couple of years, more and more customer facing websites do implement a chatbot as part of the customer experience. Even though most people have had a negative experiences with chatbots the way they work is improving rapidly. Where chatbots used to be clumsy and not really good this is rapidly changing. The AI models behind chatbots is improving rapidly and they become more and more "human". As the maturity of chatbots is growing we see a growing adoption with chatbots by enterprises for both customer facing as well as internal facing communication.

As part of a Forbes article on the digital transformation trends in 2019 Chatbots have been placed on second place in the list.

1. Chatbots Good to Great: Hear me out on this one. I know we’ve all had extremely frustrating chatbot experiences as we round out 2018. But the good news is that huge steps continue to be made in the way of natural language processing and sentiment analytics—so many, in fact, that some believe NLP will shake up the entire service industry in ways we’ve never imagined. Think about all the services that could be provided without humans—fast food lines, loan processors, job recruiters! What’s more, NLP allows companies to gather insights and improve their service based on them. Some 40% of large businesses have or will adopt it by the end of 2019—which makes it one of our top 2019 digital transformation trends. Now, I know many are alarmed by where AI and Chatbots may impact the workforce, but I’m also bullish that companies are going to be upskilling their work forces rather than displacing them as machines may be good at delivering on clearcut requests but leave a lot to be desired when it comes to dealing with empathy and human emotion required to deliver great customer experiences."

Introducing a chatbot in the organisation
Enterprises in general are implementing chatbots for two main reasons; improving the efficiency to communicate with customers and improving internal processes. A commonly seen model is that enterprises take a two phase approach to introducing chatbots to the business.

Phase 1 - Internal use
In phase 1 chatbots are implemented and used to optimize internal processes. for example standard internal HR processes, supporting internal requisitions and internal IT support are commonly seen as first adopters of a internal enterprise chatbot.

Phase 2 - External use
In phase 2 chatbots are used externally facing as part of the enterprise website, shopping site or as part of enterprise mobile applications.

In general phase 1 and phase 2 overlap, while the go-live of phase 1 is in effect phase 2 is already being prepared for external use. By creating the correct overlap the momentum of the chatbot team is maintained and the lessons learned from phase 1 are included in phase 2. It is important from both a team velocity as well as an adoption point of view to ensure you keep the momentum and ensure an overlap or a minimal gap between phase 1 and phase 2.

It is not done in a day
Contradicting the popular believe that building and implementing a chatbot is an easy task one will have to prepare for a "real project". Even though the use of a cloud platform and chatbot framework can speed the technical implementation up extremely a healthy part of the work is in ensuring your chatbot has the correct vocabulary and ensuring your conversation design is properly done.

Two aspects are important when developing your chatbot project planning. The first is to ensure you enough space for conversation design and ensuring the right vocabulary. Conversation design will go into design of how a flow of a conversation between your bot and a human will go. Even though this might sound straightforward initially it might be a very good practice to ensure you have an experienced conversation design expert on your team.

The other important part is to include a marity model for your chatbot in your project planning and strategy. The moment you want to launch internal and the moment you want to launch externally might be on a different point in the maturity model. An example of a chatbot maturity model, developed by Leon Smiers at Capgemini, can be seen below.

Use a chatbot framework
Building a chatbot from the ground up, building all the AI and all the other parts needed to make a good chatbot is an amazing project. However, such a project is only interesting from a technical understanding and research point of view and not so much from a business point of view. As a developer who just wants to build and include a chatbot interaction it is a better solution to leverage an existing platform. As an Example, Oracle provides a intelligent chatbot platform.

The below developer conference video showcases how to build a chatbot.

You can find more information and developer code examples via this link to get started quickly with your first intelligent chatbot to include in your enterprise landscape.