Enterprise Chatbots are using most of the technology regular chatbots use to create use cases tailored to a given organization and with the targeted employees in mind. Examples would include:
A major obstacle for creating an enterprise chatbot with a high adoption rate is data binding. Admins will find it easy to set up text responses and teach the chatbot the necessary Natural Language understanding to safely match a question to an answer.
But often times the answer will be 'canned' and merely contain a hyperlink to a knowledge base article somewhere on the Intranet. Or it gives the user a lengthier, non-specific response they need to read through to pick what is applicable to them. That places nearly the same cognitive load on the user as just going to the Intranet search or Enterprise search portal directly. Thus often chatbots end up not leaving the 'experimentation' stage, as they're not sufficiently better than what was before – thus failing to 'retrain' user's habits.
In order to give enterprise chatbots the capacity to give the user a personalized, direct answer to their question, the chatbots needs to be integrated with all existing business applications from both the cloud and on-premise sources. This requires the chatbot platform to offer API integrations and a 'designer' or 'configurator' that lets the admins pick out which part of an API response would be used in the answer given to the user.
The goal would be for the chatbot not to say "You need to click here to log into the portal to find out how much leave allowance you have", but instead just respond "You have 6 more days this year. Want to request more?"