Whether your current experience with chatbots allows you to believe me or not, better customer experiences are possible with human-intelligence-powered AI chatbots. Research by Gartner indicates that within a few years from now, 89% of businesses will compete mostly on customer experience versus 36% four years ago. That means that businesses need to be deploying all the help they can get in CX.

A well-trained AI-powered knowledge management system can help to provide more accessible information to your employees, helping them to be more efficient. It can also bring consistency to your CX experience, granted that you provide the AI with the intelligence it needs from your human agents. Conversing with your business’ AI chatbot doesn’t have to feel like nails on a chalkboard – and here is how.

Customers get notoriously frustrated with chatbots. Why?

The biggest frustration is that one has to speak chatbot and every bot has its own quirks. When a customer starts to chat with a business’ online web page- it is very clear that they are not talking to a human. Then they go back and forth with the chatbot, trying to figure out how to get the bot to do what they want.

Unless you say exactly the right thing- the chatbot often doesn’t understand what you want. It’s like learning a new language, and picking the wrong category can get you kicked out because the preprogrammed structure of the conversation isn’t open for any kind of margin.

This is caused by the two most common problems with chatbots, which are as follows.

The first problem is that bot scripts are written to cover the bare minimum necessary. A typical analyst writes the 20 most common questions and leaves it at that. The knowledge is then extremely limited for the chatbot, not accounting for every iteration that a question could be framed as.

Oftentimes you are also starting from scratch each time you reach out to an AI chatbot as well, even if you don’t have to. AI chatbots should have permissioned access to all of the information about a customer’s interaction with the company, which will better inform the bot on how to help. That way, the chatbot experience is carefully crafted, so the user doesn’t need to repeatedly tell the bot who they are or what they want. Then, the chatbot hands off the conversation to a human when need be. With the combination of human agents and AI, systems can be practically lossless and do everything they can to avoid making the customer say, “here we go again.”

Problem number two, AI chatbot replies are often not the ones you are looking for.

For example, if you go onto any online chatbot engine, it will first have you state your question. If your question doesn’t exactly fit into the if-that/then-this equation that the chatbot has been programmed to understand, then you will continue to get wrong answers or maybe the classic “I don’t know that, try another question.” This is bound to be frustrating as you go around in circles, constantly having to restart at square one. This can continue on until either you or the chatbot decides to give up and just leave the interaction.

Combining the power of AI and humans

Augmented Intelligence is the symbiotic human and machine system. There is a mutually beneficial relationship to be had between your human representatives and AI chatbots to improve CX experiences.

First, the benefits of the machine. A notable advantage of AI is that a machine never forgets and can recall details at the speed of thought. Augmented machines learn by observing and pick up knowledge as they go along – constantly enhancing their capabilities. 

Machines can also easily take over mundane tasks, freeing up humans to tackle more intellectually stimulating problems that require more attention. AI relieves employees of tedious tasks such as writing down every question and answer that they got during the course of a week. AI extracts organizes, and archives information for future re-use. Employees can then focus on exceptions and deal with ambiguous situations that haven’t been dealt with before, while the AI observes and learns from these new interactions.

Secondly, the benefits of AI with human intelligence applied. Combine the capacity of AI with your employees’ strong suits, remembering that the key to this relationship is how the machine inconspicuously learns and what is required from the human. 

If the machine needs to be explicitly taught everything, the training effort will not be worth it. Manually entering all of their experience for the machine to understand is something no human is going to be motivated to do. And with that, the machine will never be able to perform the task effectively with one employee’s interpretation of knowledge. This is the gap where most attempts at making machines smarter and reducing the human workload fails.

We shouldn’t expect human beings to fill in forms, shuffle knowledge cards, and annotate every piece of information – it is a misuse of both their time and their skills. The organization and retrieval of data can be left to AI with its access to the collective knowledge database, thereby absorbing wisdom from your employees. Let your agents do what they do best and have real conversations with customers. Then allow the AI to discover, organize, and recommend knowledge from these conversations – unobtrusively, of course.

Powering chatbots with a knowledge graph of human intelligence

It is difficult, if not near impossible, to represent knowledge from conversations in a standard database. “Knowledge Graphs” are proving very useful in organizing as well as linking insights and intelligence extracted from unstructured conversations. These knowledge graphs are machine-readable, and therefore can be used to power next-generation chatbots and better customer experiences.

The knowledge graph can model relationships between your documented information, such as Q/A boards, documents, and archived conversations. Human to human conversations in CX are an excellent and often overlooked source of knowledge as well. When your chatbot has access to this data, it can better understand and facilitate a more meaningful conversation with customers. It is important to get this virtuous cycle going – as the whole customer experience improves because responses are derived from a wider body of knowledge, not an inflexible script. This human-powered-machine-intelligence can then help the bot do more, learning from conversations and gracefully handing the discussion over to human agents when need be.

Natural Language Processing assisting with emotion analysis

Natural Language Processing (NLP) enables AI to determine how a customer is feeling. These emotion analytics can be brought into every conversation, allowing for a better view of the customer’s needs and better success in the interaction. Well-trained AI can be unbiased, cannot lie, and will not take offense or be emotionally invested in interactions with customers – and this gives bots the ability to approach problem-solving reliably.

With AI, there is metadata created for every interaction. This allows your system to retrieve data faster and more consistently, so overall analytics are better, and you have more meaningful data to train your chatbot with. This manifests a virtuous circle of better data leading to better interactions, better interactions leading to better data, and a better cycle leading to better experiences. AI can consistently tag, classify, and organize interaction data making it a more dependable source for analytics data as well. This solidifies your company’s view of what issues customers are facing all the way to what product features they love. What is more is that your support agents don’t need to spend time collecting this data from their notes and conversations, leaving them with more time to resolve bigger issues and create a better quality of reports.

Better together

The human brain and AI-powered-chatbots are stronger together, despite some of the kinks that chatbots have had to work through. With an increasingly remote work world and a progressively competitive CX market, it is important to get your AI chatbot right. By highlighting both the problems and solutions in this article, we hope to have opened a conversation about conversational AI that can help you find the right fit with your chatbot.

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