Let's be honest: Don't you have a chatbot proof-of-concept in your drawer somewhere in the office? It's sitting there, gathering dust, patiently waiting for the moment when it finally gets deployed. But that moment is unlikely to come... Why are so many companies struggling to get chatbots into live production or to keep them running successfully in the long run? There are actually many companies working on various Conversational AI initiatives. However, only a few actually get 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:inside sales or HR 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 human resources, 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, evaluate data, and need a handover tool, also called a handoff 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 parties involved are not taken into account equally, problems will inevitably arise in the long term.

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 difficult to keep track of them and to judge which vendor to choose or who will prevail in the future. Another challenge is that many vendors only offer a partial solution, such as natural language understanding and processing, a channel connector, or an analytics system. In addition, it is often realized during the course of the project that the selected technology does not meet the needs of the business. For example, after some development time, it is realized that enterprise systems cannot be connected or certain APIs are missing. As you have learned, there are many complex requirements and needs for enterprises 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 during the conceptual design, fail. This can only be achieved with project participants with appropriate experience in conversational design or copywriting. This and much more is shown and discussed in more detail in our whitepaper "3 reasons why chatbots fail".