Why knowledge bases and chatbots are the future of tech support?

When it comes to chatbots, the market is full of noises. In a battle to win more businesses in their clientele list, every chatbot company is claiming that their solution has AI.

What do we mean by AI in chatbots? Is it some kind of proprietary program or an open-source API from GitHub to make your chatbot intelligent?

I am afraid, but it’s none.

AI isn’t an API or software solution that you can integrate into your tech support to acquire intelligence and tech support automation overnight. AI is a concept that works on a mix of two main technologies: Natural Language Processing (NLP) and Machine Learning (ML).

When it comes to AI in chatbots, the same two technologies work collaboratively to automate a plethora of tasks that otherwise require human intervention in one or another form.

For example, Understanding a query written by a customer in the live chat window and then replying to it based on the information referred from the knowledge base. Even if a human chat executive is handling the chat support, this particular sequence of steps is eminent. With AI in your chatbot, you automate this exact sequence and a lot of others as well.

The NLP helps the chatbot to understand even those queries that we don’t program. Whereas, the ML helps the chatbot to find a precise response for the query, based on the knowledge base that we feed to a chatbot.

The knowledgebase is fuel for AI chatbots

The knowledgebase is the backbone of not just chatbots but any AI application. It is where an AI chatbot finds the resources it needs to make an autonomous decision. Without a proper chatbot knowledge base management, chatbots can’t have artificial intelligence at all.

Think of the situation when you sit on live chat support of your eCommerce site and a user comes on it to ask the following question:

“Hi, could you please tell me my order status? My order number is #XYZ”

Would you be able to give a proper reply to this query if you can’t access your customer database and see the order status from there?

Of course not.

That’s chatbot knowledge base to the AI chatbots. The best part is, with deep machine learning, chatbots can access not just legacy customer data but also analyze every bit of complex information you feed them and come up with even complex applications, besides fetching simple order status information from the customer database.

A chatbot knowledge base is not a mere repository of information but a complex architecture, which comprises of every bit of useful information you can feed on all possible resources, such as CRM system, email marketing tool, FAQs, company policies, solutions given to the legacy issues, Inventory management system, manufacturing standards, and many more.

Chatbots and Knowledgebase -a symbiotic existence

AI Chatbots can understand queries, retrieve vital information from a knowledge base, understand contexts in a question, and offer precise answers. However, it is not as simple as it sounds. The role of AI in chatbots is to enable them to process and transform natural interactions into meaningful insights. Further, after understanding the context of an instruction, the chatbot uses ML to create value from the information that you feed it from different sources in a corporate information management system. We call this entire process as “Knowledge Engineering”, without which, a Chatbot is just an event-driven program, and not an AI bot.

In short, you can’t achieve AI (Artificial Intelligence) in your chatbot without IA (Information Architecture).

Call it Information Architecture, Knowledge Management, or simply Knowledge Engineering, chatbots need a knowledge base to organize data and drive AI-led customer service or tech support system. Without a Knowledgebase, your chat support is just a traditional search and information retrieval system that doesn’t work for the events you don’t program. With AI, you make chatbot conversations more natural via natural language inquiries, context identification, and apt responses to both known and unknown queries.

On the other hand, the same knowledge base, which is fuel to your chatbot, helps your business in vice-versa situations as well. When chatbots have access to your knowledge base and every bit of corporate information, they can also help you to organize your data more efficiently:

· More organized: The traditional systems to store information and databases in directory or metadata structure are complex and less organized. If you need to access any information from your traditional knowledge base, it takes some time and effort to traverse through different directories or search using limited keywords. Chatbots can organize your information more neatly and provide direct responses to your data-driven queries in microseconds. You won’t have to parse through a deep directory but ask the chatbot and it will fetch what you want.

· Direct response: Not every user of your traditional knowledge is skilled in the back-end processes and file hierarchy system. It takes some skills and pre-possessed information to acquire the knowledge of steps you need to follow too, first retrieve a piece of information, and then process it to gain meaningful insights. Instead of driving you through a complex back-end process, a chatbot can give direct responses and show just the information you need without any clutter.

Chatbots are future of tech support, but only with Knowledge Management

AI-powered chatbots are indeed the future of customer support and business processing systems. However, there is no existence without proper knowledge management. Without information, chatbots lack the primary fuel that enables them to take decisions. That’s why having a smart chatbot from a big chatbot provider is not enough. You need a resourceful chatbot knowledge base in your organizational architecture to let Machine Learning do what it’s good at; that would be making your chatbot Intelligent.

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