It seems likely Artificial Intelligence (AI) and chatbots will play a key role in helping users, in the future. Amazon, Facebook, Google, IBM and Microsoft, as well as smaller technology companies, are all developing platforms for simulating an intelligent conversation with human users.
This raises a question:
Will chatbots mean we’ll write a how-to task in the chatbot app, again in the Help, and again in the tutorials?
It’s not very productive to write the same content three times, in three different places. It makes even less sense if you need to update the content on a regular basis, or translate that repeated content into multiple languages.
One solution is to store different types of data in its native format until it is needed, and then serve that information to the AI or chatbot system. You write the content once, and “serve” it to the chatbot, the online Help, the tutorial, and so on.
This requires that content to map accurately to the chatbot’s information structure – the use cases; the user’s intent, role and sentiment; and the entity (i.e. the problem and product) that relates to the user’s question.
As a technical communicator, this means you can start by making sure your content is in a structured format. For example, it has metadata (and uses a taxonomy) that will help the AI system or chatbot know which piece of information to serve the user. This includes common metadata such as product, symptom, problem, version, user role and operating system. It may also include new metadata relating to responses based on the user’s current mood (“sentiment”), and the context in which the question is made to the chatbot.
This approach makes it more likely that your documentation will AI and chatbot ready, at the time when it’s needed.
Tryo Labs has published a useful summary of the different approaches and technologies you can use for creating chatbots. See: Building a Chatbot: analysis & limitations of modern platforms.