Let's face it: don't you have a chatbot proof-of-concept in your drawer somewhere in the office? It's lying there, gathering dust, patiently waiting for the moment when it will finally be used. But that moment probably won't come... Why do so many companies struggle with bringing chatbots into live production or making them run successfully in the long run? There are actually many companies working on various conversational AI initiatives. However, only a few are actually implemented and go into live production. And then they are often taken offline again after a short time.

There are three main problems that are probably responsible why chatbots and virtual assistants remain in the POC phase:

  • The operation and maintenance of chatbots and virtual assistants
  • The interaction between departments and IT
  • The wrong choice of technology

The first problem: The chatbot operation

After the chatbot has gone live, the operation of the chatbot begins. Most companies spend a lot of time developing their chatbots or voice assistants, but rarely consider the resources and processes to actually run them successfully afterwards. There are four aspects that should be carefully considered before starting Conversational AI projects.

  • Build a successful Conversational AI team
  • Train your chatbot and make it better fast
  • Improve your virtual assistants in the long term with a dedicated roadmap
  • Set KPIs and metrics to measure the performance of your virtual assistant

The second problem: The cooperation between departments and IT

Another critical factor why chatbots and voice assistants do not make it into production is the sometimes difficult relationship between the business units and the company's IT department.

What usually happens is that IT selects technologies and software with which to build the virtual assistant, but departments such as customer service or human resources cannot work properly with it. This is because they have fundamentally different needs in terms of technologies. It is of great importance for IT to flexibly choose the right technology stack for the virtual assistant and to be able to switch between NLP services without technology lock-in. In addition, an open system with APIs to integrate enterprise systems, analytics systems or other functions is needed. Whereas business units such as HR, customer service or marketing departments that will operate the chatbot to engage with customers have different needs. They need to take care of intent management, analyse data and need a handover tool to real employees. Software that meets the needs of an IT department does not necessarily meet the needs of content/intent managers or the departments. If the different needs of the stakeholders are not taken into account equally, problems will inevitably arise in the long run.

In summary, there must be a solution that meets both IT and departmental requirements, preferably combined in one product.

The third problem: the choice of technology

Choosing the right technology for Conversational AI projects is quite difficult. The market is full of different technology providers and NLP services. It is hard to keep track and judge which provider to choose or who will prevail in the future. Another challenge is that many providers only offer a partial solution, such as natural language understanding and processing, a channel connector or an analytics system. In addition, it is often realised during the course of the project that the selected technology does not meet the needs of the company. For example, after some development time, it is realised that enterprise systems cannot be connected or certain APIs are missing. As we have learned, there are many complex requirements and needs for companies to successfully scale and operate virtual assistants.

Of course, there are many other reasons why chatbots are not operated successfully. Often, important aspects such as a good user experience, which should be ensured from the very beginning of the conceptual design, fail. This can only be achieved with project participants with the appropriate experience in conversational design or copywriting. This and much more is shown and discussed in more detail in our white paper "3 reasons why chatbots fail".