What is Conversational AI?
Conversational AI is the set of technologies behind automated messaging and speech-enabled applications that offer human-like interactions between computers and humans.
Conversational AI can communicate like a human by recognizing speech and text, understanding intent, deciphering different languages, and responding in a way that mimics human conversation.
Applied Conversational AI requires both science and art to create successful applications that incorporate context, personalization and relevance within human to computer interaction. Conversational design, a discipline dedicated to designing flows that sound natural, is a key part of developing Conversational AI applications.
Though chatbots have gained popularity (and a fare share of bad name), conversational AI solutions can be offered over both text and voice modalities and hence various channels and devices that offer support these modalities – from SMS and web chat for text modality to phone call and smart speakers for voice modality.
The best Conversational AI offers an end result that is indistinguishable from could have been delivered by a human. Think about the last time that you communicated with a business and you could have completed the same tasks, with the same if not less effort, than you could have if it was with a human. That’s Conversational AI at its highest quality.
How does Conversational AI work?
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.
Components of Conversational AI
First, the application receives the information input from the human, which can be either written text or spoken phrases. If the input is spoken, ASR, also known as voice recognition, is the technology that makes sense of the spoken words and translates then into a machine readable format, text.
Second, the application must decipher what the text means. It uses Natural Language Understanding (NLU), which is one part of Natural Language Processing (NLP), to understand the intent behind the text.
Next, the application forms the response based on its understanding of the text’s intent using Dialog Management. Dialog management orchestrates the responses, and converts then into human understandable format using Natural Language Generation (NLG), which is the other part of NLP.
The application then either delivers the response in text, or uses speech synthesis, the artificial production of human speech, or text to speech to deliver the response over a voice modality.
Last, but not least, is the component responsible for learning and improving the application over time. This is called machine or reinforced learning, where the application accepts corrections and learns from the experience to deliver a better response in future interactions.
What is the difference between Conversational AI and a chatbot? What can Conversational AI be used for?
Conversational AI applications can be programmed with varying levels of complexity resulting in dramatically different end products, which can be used as personal assistants, to facilitate conversations between customers and businesses, and within businesses to automate operations.
The simplest example of a Conversational AI application is a FAQ bot, or bot, which you may have interacted with before. These are basic answer and response machines, also known as chatbots, where you must type the exact keyword required to receive the appropriate response. In fact, these chatbots are so basic that they may not even be considered Conversational AI at all, as they do not use NLP or dialog management or machine learning to improve over time.
The next maturity level of Conversational AI applications is Virtual Personal Assistants. Examples of these are Amazon Alexa, Apple’s Siri, and Google Home. They serve a general purpose and are linear, and do not carry context from one conversation to the next. These assistants use ASR and NLP, but have simple dialog management.
Next we have Virtual “Customer” Assistants, which are more advanced Conversational AI systems that serve a specific purpose and therefore are more specialized in dialog management. You have probably interacted with a Virtual customer assistant before, as they are becoming increasingly popular as a way to provide customer service conversations at scale. These applications are able to carry context from one interaction to the next which enhances the user experience.
On the same level of maturity as Virtual Customer Assistants, are Virtual Employee Assistants. These applications are purpose-built, specialized, and automate processes, also called Robotic Process Automation. They are used in businesses to streamline enterprise operations. Both Virtual Customer Assistants and Virtual Employee Assistants often use the most advanced Conversational AI technologies, and are well integrated into the companies back office systems to provide contextual and personal experience to customers and employees.
What are the main challenges in Conversational AI?
Conversational AI faces challenges which require more advanced technology to overcome. You’ve most likely experienced some of these challenges if you’ve used a less-advanced Conversational AI application like a chatbot.
1. Constantly changing communication
From languages, dialects, and accents to sarcasm, emojis, and slang, there are a lot of factors that can influence the communication between a human and a machine. Conversational AI systems need to keep up with what’s normal and what’s the ‘new normal’ with human communication.
2. Security and Privacy
Especially when dealing with sensitive personal information that can be stolen, Conversational AI applications must be designed with security in mind to ensure that privacy is respected and all personal details are kept confidential or redacted based on the channel being used.
3. Discovery and Adoption
Although Conversational AI applications are becoming increasingly easy to use and normalized for the general population, there are still challenges that can be overcome to increase the number of people who are comfortable using technology for a wider variety of use cases. Educating your customer base on opportunities can help the technology be more well-received and create better experiences for those who are not familiar with it.
Why is ASR important?
Automatic Speech Recognition (ASR) is essential for a Conversational AI application that receives input by voice. ASR enables spoken language to be identified by the application, laying the foundation for a positive customer experience. If the application cannot correctly recognize what the customer has said, then the application will be unable to provide an appropriate response.
The quality of ASR technology will greatly impact the end-user experience. Therefore, it’s important when evaluating Conversational AI applications to inquire about the accuracy of its ASR models.
How does ASR work?
ASR is built upon acoustic models that analyze phonemes in order to decipher the words that will be used by Natural Language Processing (NLP) to uncover intent. It can be broken down into 3 parts:
- The customer speaks into a device, creating a sound wave.
- The application, depending on its level of advancement, reduces background noise and normalizes the volume.
- The sound wave is broken down into phonemes, and those phonemes are connected using analytical models to interpret the intended words spoken.
The more advanced the models, the more accurate that the ASR will be able to correctly identify the intended input. The models will improve over time with more data and experience, but they also must be properly tuned and trained by language scientists.
Challenges with ASR
Although seemingly straightforward, ASR has its fair share of challenges. Imagine a customer calling from a noisy airport who also has a thick accent and bad reception. This may even be difficult for a human to understand!
Alphanumerical characters are also difficult for ASR systems to accurately detect because the characters often sound very similar. Therefore, giving phone numbers and spelling out email addresses, two common utterances in the customer service space, both have a high chance of failure.
So what happens when an ASR engine fails to recognize the input? Many times the customer has to repeat themselves over and over to clarify what they are trying to say. This creates a bad customer experience and can lead to lost sales.
On the bright side, there are many technological advancements that are finding solutions to this problem as our world becomes more reliant on voice devices. In fact, Interactions Conversational AI applications are uniquely positioned with 100% accuracy.
What’s next for Conversational AI in the Contact Center?
As technology continues to advance, the way that Conversational AI is used in the contact center will continue to shift to make room for new capabilities and functions.
Currently, the contact center model is still led by humans, and supported by Conversational AI technology. While this model was the most beneficial when automation technology was subpar, it is not indicative as the most beneficial model in the future.
This current model of the contact center does not use technology to its full potential, and instead results in robotic, disjointed experiences for customers. Why? Although the technology may be advanced enough to have a conversational experience with a customer, it is only used to direct customers to a human agent. Therefore, even if the Conversational AI automation can handle enough traffic, the scalability is limited to the amount of human agents.
We’re at a crossroads where technology has advanced to need a new model of the contact center to see its benefits. In other words, the most advanced technology cannot thrive in a human-led contact center model.
Conversational AI is advancing to a place where it needs to lead customer interactions, with humans supporting the conversation. This doesn’t mean that humans will never talk with customers, but rather that technology will be the main driver of the conversation flow. This change will result in greater scalability and efficiency, as well as lower operating costs.