Conversational AI is growing more prevalent every day. Not just in business, but for entertainment purposes as well. Whenever computers have conversations with humans, there’s a lot of work engineers need to do to make the interactions as human-like as possible. Whether you’re messaging with a chatbot, responding to an automated email or speaking with a virtual assistant, computers are working hard behind the scenes to interpret what is being said (sometimes called “intent classification”), determine the appropriate response, and respond in a way that’s natural and easily understandable to humans. This article will highlight the key elements of conversational AI, including its history, popular use cases, how it works, and more.
The concept of Conversational AI has been around for decades, but it wasn’t always something that was wildly talked about. According to data from Google Trends, interest in “conversational AI” was practically non-existent from 2005 through 2017. However, over the last 3 years, interest in Conversational AI has grown exponentially.
In the US, search volume for conversational AI has never been higher.
What is Conversational AI?
At a high level, conversational AI is a form of artificial intelligence that facilitates the real-time human-like conversation between a human and a computer.
It’s important to note that conversational AI isn’t a single thing; it’s a combination of different technologies, including natural language processing (NLP), machine learning, and contextual awareness.
Conversational AI vs Chatbots
There is a lot of ambiguity surrounding the differences between conversational AI and chatbots. The discrepancies are so few that Wikipedia has declared – at least for the moment – that a separate Conversational AI Wikipedia page is not necessary because it is so similar to the Chatbot Wikipedia page.
Why the confusion?
One reason why the two terms are used so interchangeably is because the word “chatbot” is simply easier to say. A chatbot also feels tangible to our imagination – I visualize a tiny robot that has conversations behind a computer screen with people. Whereas a conversational AI is more conceptual than physical in nature.
One common application for conversational AI is to be incorporated into chatbots. Chatbots provide convenient, immediate and effortless experiences for customers by getting customers the answers they need quickly. Instead of scrolling through pages of FAQs or waiting on hold to speak to an agent, customers can receive a reply in seconds. However, not all chatbots use conversational AI.
It might be more accurate to think of conversational AI as the brainpower within an application, or in this case, the brainpower within a chatbot.
Perhaps you’ve been frustrated before when a website’s chatbot continually asks you for the same information or failed to understand what you were saying. In this scenario, you likely engaged with a scripted, rules-based chatbot, with little to no conversational AI.
There are several notable differences between conversational AI chatbots and scripted chatbots. Traditional scripting chatbots require companies to write out all the responses to anticipated customer questions beforehand. Next, these responses are matched to keywords. Whenever a customer’s reply or question contains one of these keywords, the chatbot automatically responds with the scripted response.
Scripted chatbots have multiple disadvantages compared to conversational AI. First and foremost, these bots cannot provide the correct response if a customer uses a phrase or synonym that differs even slightly from what has been pre-programmed. Companies that implement scripted chatbots need to do the tedious work of thinking up every possible variation of a customer’s question and match the scripted response to it. Think about the Comcast example above. When you consider the idea of having to anticipate the 1,700 ways a person might ask one straightforward question, it’s clear why rules-based bots often provide frustrating and limited user experiences. Compare this to conversational AI enabled chatbots that can detect synonyms and look at the entire context of what a person is saying in order to decipher a customer’s true intent.
Scripted chatbots are also unable to remember information across long conversations. Because it’s impossible to write out every possible variation of a back-and-forth conversation, scripted chatbots need to repeatedly ask for information to match a response to a pre-set conversational flow. This rigid experience does not provide any leeway for a customer to go off script, or ask a question in the middle of a flow, without confusing the bot. Meanwhile, conversational AI chatbots can use contextual awareness and episodic memory to recall what has been said previously, provide a relevant reply and pick up a flow where it left off. All in all, conversational AI chatbots provide a much more natural, human-like interaction.
Why Conversational AI is becoming so critical today
Businesses use conversational AI for marketing, sales and support to engage along the entire customer journey. One of the most popular and successful implementations is conversational AI for customer service and customer experience, a $600B industry with a lot of repetitive knowledge work.
Because conversational AI doesn’t rely on manually written scripts, it enables companies to automate highly personalized resolutions at scale. This makes every interaction feel unique and relevant, while also reducing effort and resolution time. As a result, customers tend to report higher levels of satisfaction.
The more advanced conversational AI can enable companies to analyze and identify when customer questions and issues to identify common pain points to preemptively intervene before a customer ever reaches out.
Conversational AI for CX is incredibly versatile and can be implemented into a variety of customer service channels, including email, voice, chat, social and messaging. This helps businesses scale support to new and emerging channels to meet customers where they are.
Top Conversational AI Use Cases
- Automate resolutions to common FAQs
- Increase customer engagement
- Improve product accessibility
Many businesses have 5-7 different kinds of questions that make up over 50% of the total customer service questions by volume. A powerful AI can interpret the various different ways people might ask the same question. For example, an airline might deploy a travel chatbot to resolve highly repetitive questions, like “can I change my flight?”, without human agent intervention.
Conversational AI can proactively reach out to customers at key points along the customer journey or based on behavior signals to provide information at the exact moment of relevance. This can help to drive revenue, decrease churn and eliminate frustration.
Businesses are relying on artificial intelligence to provide more inclusive services to all of their customers. A powerful AI can leverage NLP and NLU to automatically translate text. By doing so, businesses can help those with disabilities use their products better.
How Does it Work?
Earlier we mentioned the different technologies that power conversational AI, one of which is natural language processing (NLP). NLP isn’t different from conversational AI; rather it’s one of the components that enables it.
NLP is frequently interchanged with terms like natural language understanding (NLU) and natural language generation (NLG), but at a high level, NLP is the umbrella term that includes these two other technologies.
Because human speech is highly unstandardized, natural language understanding is what helps a computer decipher what a customer’s intent is. It looks at the context of what a person has said – not simply performing keyword matching and looking up the dictionary meaning of a word – to accurately understand what a person needs. This is important because people can ask for the same thing in hundreds of different ways. In fact, Comcast found that there are 1,700 different ways to say “I’d like to pay my bill.” Leveraging NLU can help conversational AI understand all of these different ways without being explicitly trained on each variance. Sophisticated NLU can also understand grammatical mistakes, slang, misspellings, short-form and industry-specific terms – just like a human would.
Once a customer’s intent (what the customer wants) is identified, machine learning is used to determine the appropriate response. Over time, as it processes more responses, the conversational AI learns which response performs the best and improves its accuracy.
Finally, natural language generation creates the response to the customer. This technology leverages its understanding of human speech to create an easy-to-understand reply that’s as human-like as possible.
More advanced conversational AI can also use contextual awareness to remember bits of information over a longer conversation to facilitate a more natural back and forth dialogue between a computer and a customer.
How To Build Conversational AI
We’ve gone over the advantages of conversational AI and why it’s important for businesses. Now, we’ll discuss how your organization can build and implement a conversational AI for your business.
While some companies try to build their own conversational AI platforms in-house, the fastest and most efficient way to bring conversational AI to your business is by partnering with a company like Netomi. These technology companies have been perfecting their AI engines and algorithms, investing heavily in R+D and learning from real-world implementations. With customer expectations rising for the interactions that they have with chatbots, companies can no longer afford to have anything interacting with customers that’s not highly accurate.
There are a few simple steps that go into creating a strategy for conversational AI:
- Define your goals – Are you trying to increase customer satisfaction or decrease resolution time? Do you want to alleviate mundane work from your human agents? Can you introduce proactive customer service to solve issues before you even know they exist?
- Train your AI – Train your AI based on your historical tickets. That way, you can leverage your existing data to understand how your customers have asked a specific question in the past, increasing the accuracy of your conversational AI.
- Design journeys and workflows – Design conversations and user journeys, create a personality for your conversational AI and ensure your covering all of your top use cases.
- Integrate – Depending on your use cases, you might want to also integrate with your other back-end systems like your CRM or accounting software. This way, the conversational AI can actually pull in data from these sources to resolve customer service issues on an individual basis without human intervention.
- Measure – You’ll want to measure the impact your Conversational AI is having on your customer service KPIs, including first response rate, average handle time, CSAT, AI and human agent collaboration, and more.
- Optimize – Over time, as the AI has more customer service interactions, you can uncover further opportunities to train the AI and empower it to solve even more tickets. You can also help retrain the AI if it did not provide the correct response in a specific scenario, enhancing the experience over time.
Conversational AI is growing in popularity and for good reason. More and more businesses are beginning to leverage this artificial intelligence to improve their customer support, marketing, and overall customer experience.
Customers care more today about every interaction they have with a company. There is an inherent demand for immediate, effortless resolutions across an increasing number of channels. Even one bad experience can turn someone off from ever doing business with a company again. Conversational AI can help companies scale the experiences that people expect by providing resolutions to everyday questions and issues in seconds. That way, human agents are only brought in when there is a complex, unique or sensitive request.