On our blog, we talk a lot about the benefits of Conversational AI and Intelligent Virtual Assistants. From better customer experience, to reduced operational costs, to improved agent experience, these technologies can transform your business in so many ways. But in this blog we are taking it back to basics to discuss the technology itself—specifically Conversational AI: how it works on a technical level as well as what you should look for to make sure the technology works in your business.
To many, AI is an intangible and mysterious concept. I was recently watching an episode of Mad Men where the company purchased a computer back in the 1970’s when a computer was a machine that took up an entire room. The employees were skeptical and baffled that a technology could hold so much promise.
It immediately made me think of how many view AI today. Because many don’t really understand how it works, it’s impossible for them to conceptualize the potential benefits. When in fact, users of IVAs powered by Conversational AI observe a 64% greater year-over-year increase in annual company revenue while increasing customer profit margins by 64% as well, according to an Aberdeen report. We’ve put together the basics of Conversational AI to help you conceptualize how it works so you can better understand how it can work for your business.
What is Conversational AI and how does it work?
Conversational AI is the set of technologies behind automated messaging and speech-enabled applications that offer human-like interactions between computers and humans. Simply put, it’s how humans can talk with computers as if they were, well, humans. Conversational AI can enable communication like a human by recognizing speech and text, understanding intent, deciphering different languages, and responding.
Conversational AI uses various technologies such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog management, and Machine Learning (ML) to understand, react, and learn from every interaction.
Read on to learn 5 key considerations about Conversational AI. Then, if you’re hungry for more, check out our Conversational AI whitepaper to take a deeper dive.
Conversational AI is not one technology, it’s many.
As mentioned before, Conversational AI is made up of several different technologies, including ASR, NLP, Dialog Management, and Machine Learning. Choosing the right kind of technology for each of these tasks and ensuring that these technologies integrate and work in harmony can be daunting without the relevant know-how. If your company is not an expert in AI, it’s best to find a partner who is.
Intent is important.
Conversational AI applications aren’t just designed to know what words are being said, but to actually understand the intent behind the words. This is essential to understanding why some applications succeed while others fail.
The human language is extremely complex. Depending on the speaker, situation, and cultural bias, words can mean different things in different contexts. NLP enables machines and software applications to make sense of a human language, recognize intent despite the order of words or the way they are used, and produce an appropriate response.
If the Conversational AI technology behind an application is poorly designed or not advanced enough, you will find yourself having to change the structure of your phrase to dictate the appropriate response.
AI is not self-sufficient. Humans are needed to tune and train.
Conversational AI needs to be trained on clean and relevant data to remain relevant, useful, and efficient. Continuous improvements to the conversation flows are needed as well to deliver the highest quality results. This is because as the consumer learns they can speak naturally, they will begin to speak in more free flowing terms, and the AI solution needs to be able to respond appropriately. For your business, this means that there should be resources dedicated to the upkeep of the technology so that it operates at the highest level.
For successful Conversational AI, there must be design expertise.
There is a reason why some conversations with virtual assistants feel natural and effortless while others feel clunky and robotic. Building Conversational AI systems is as much of an art as it is a science. Successful AI projects need unique expertise that is beyond the scope of API and application development. Conversational AI projects need to be led with design in mind. Including computational linguists and NLP scientists on the team can help with this.
You don’t need a PhD in computer science to understand the basics of Conversational AI, but you should turn to the experts in order to build an application for your business. It’s best to partner with a vendor that understands the technology and design aspects of a successful application in order to deliver the best customer experience for your business. To learn more about Conversational AI, check out our complete guide to Conversational AI for virtual assistants.