Conversational AI refers to technology (like chatbots, voice assistants, or conversational applications) that simulates a human conversation. Let’s take a look at some use cases, examples, and companies that are succeeding with conversational AI.
What is conversational AI?
Types of conversational AI technology
Natural language processing (NLP)
Chatbots vs. conversational AI
The next generation of chatbots: conversational apps
Why does conversational AI matter now?
Business messaging is the new normal
Rising expectations are sparking a digital industrial revolution
The conversational AI market is booming
Which industries are using conversational AI?
Conversational AI in ecommerce
Conversational AI in banking
Conversational AI in insurance
Conversational AI in healthcare
Companies using conversational AI
Conversational AI platforms
Conversational AI is the simulation of an intelligent conversation by machines. It refers to the different technologies that help machines understand, process, and respond to human language.
The concept of “understanding” is an important one. It’s what separates artificial intelligence from basic automation. In other words, it’s the difference between something like a rule-based chatbot and an NLP chatbot. (We will compare these in more detail later).
With conversational AI, the degree to which the computer “understands” the conversation depends on which type of technology it uses.
There are two main types of conversational AI technology.
- Natural language processing (NLP)
- Machine learning (ML)
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written.
Natural language processing works in three main steps. First, the computer reads the language. Then it works to understand it. Finally, it formulates a response.
Step one: Reading inputs
First, conversational AI uses Natural Language Processing (NLP) to break down requests into words and sentences that the computer can read.
Step two: Understanding inputs
Then, Natural Language Understanding (NLU) helps the computer analyze the input text, and uses that to determine the meaning behind the user's request. It does this by matching what's said to training data that corresponds to an 'intent'.
Step three: Generating responses
Then, the computer uses Natural Language Generation (NLG) to formulate a response. In this step, the computer uses structured data to create a narrative that answers the user’s intent. It combines the user intent with a structured hierarchy of conversational flows to present the information clearly.
Machine learning programs make predictions based on patterns learned from experience. It is considered more “intelligent” than NLP. The more data it collects, the more it learns, and the more accurate its predictions become.
Amazon’s Alexa is an example of conversational machine learning technology. Each time Alexa makes a mistake in interpreting a request, it automatically ‘learns’ how to do better the next time around.
Most of us are familiar with chatbots, so naturally, the question becomes: how is conversational AI different from a chatbot?
Chatbots fall into the category of conversational AI if they use machine learning or NLP. However, not all of them do.
Some chatbots use basic logic to automate responses. These are called rule-based chatbots.
The real difference between chatbots and conversational AI can be seen when we compare rule-based chatbots to conversational AI.
Rule-based chatbots (or decision-tree bots) use a series of defined rules to guide conversations. They do this in anticipation of what a customer might ask, and how the chatbot should respond.
Rule-based chatbots can use very simple or complicated rules. They can't, however, answer any questions outside of the defined rules. These chatbots do not learn through interactions. Also, they only perform and work with the scenarios you train them for.
While rule-based bots have a less flexible conversational flow, these guard rails are also an advantage. You can better guarantee the experience they will deliver, whereas chatbots that use conversational AI can be a bit less predictable.
Some other advantages of rule-based chatbots are that they:
- are generally faster to train (less expensive)
- have clear guardrails that guide the user flow
- carry out routine or predictable tasks with a high degree of certainty
As we already know, conversational AI uses natural language processing and/or machine learning to understand the context and intent of a question before formulating a response.
Conversational AI generates its own answers to more complicated questions using natural-language responses. The more you use and train conversational AI, the more it learns.
Some other advantages of conversational AI are that it:
- can learn from information gathered
- can continuously improves as more data comes in
- can understand patterns of behavior
- has a broader range of decision-making skills
Let’s compare conversational AI and rule-based chatbots side by side.
Conversational applications are the next generation of chatbots. They combine the best conversational technology (like conversational AI and rule-based automation) with the best graphic user interfaces for an optimal user experience.
Conversational apps tend to operate within messaging channels like WhatsApp, Messenger, and Telegram. That means that companies can build branded experiences inside of the messaging apps that their customers use every day.
Again, “conversational apps” is a more appropriate term for modern-day chatbots. It takes into consideration how communication is evolving. Conversations today are increasingly visual and touch-based. We don’t just “chat” -- we swipe, tap, touch, press buttons, share pictures, locations, and more.
Conversational apps are built for a messaging-first future. Soon, they will rival websites as the main interface between businesses and customers.
The way businesses and customers communicate is changing. We’re in the middle of a paradigm shift and conversational AI is at the center of the conversation.
Conversational AI is helping businesses adapt in a world where messaging is the new normal. People want to communicate with businesses in the same way they communicate with friends and family -- on messaging apps.
- Messaging is asynchronous. People don’t have to wait on hold for an answer or wait more than 24 hours to get a response via email. Messaging allows them to multi-task and go about their routines at the same time.
- People are already on messaging apps. In a world where there’s an app for everything, people are sick of being forced to download yet another app. They’d much prefer to use the channels they’re already on.
- The UI/UX of messaging apps is getting better every day. Facebook, Apple, Google are all in a race to build the most intuitive messenger app. They know that messaging apps are more than just a communication tool, they are the future of commerce, payments, and business in general.
Consumer behavior is changing customer expectations are rising. People now expect self-serve customer care, omnichannel experiences, and faster responses. And it’s impossible to meet these expectations without the help of conversational technology.
Conversational AI is playing an important role in helping businesses scale conversations. Businesses can deliver the service customers expect without going on a hiring spree.
Businesses recognize the importance of conversational technology. And conversational AI is not just for massive enterprises anymore. It’s more accessible and affordable, which expands possibilities and fuels competition.
So it’s no surprise the conversational AI market is booming. The global conversational AI market size was valued at $5.78 billion in 2020 and is projected to reach $32.62 billion by 2030. The forecasted compound annual growth rate (CAGR) is 20.0% from 2021 to 2030.
Conversational AI is making a significant impact on the ecommerce industry. It helps brands form customer relationships that last, hold conversations that have context, and ultimately sell more products.
Below are a few conversational AI use cases for ecommerce:
- cross-sell and upsell products
- find specific products
- make suggestions about the right sizing
- place orders
- help with returns
- answer FAQs
In order to maintain a competitive edge, traditional banks must learn from fintechs, which owe their success to providing a simplified and intuitive customer experience. Conversational AI can be used in banking to facilitate transactions, help with account services, and more.
Below are a few conversational AI use cases for banking:
- Help customers check their bank balances
- Send billing reminders and notifications
- Help find a nearby ATM
- Assist with mobile deposits
- Help customers apply for loans
Like in banking, the insurance industry is also in the middle of a digitally-driven shake-up. Conversational AI represents a new means of distributing products and resolving claims. These shifts have ushered in an era of new products built on data and analytics.
Below are a few conversational AI use cases for insurance:
- Manage claims and renewals
- Gather customer feedback and reviews
- Customer awareness and education
Healthcare is an industry that is ripe for conversational AI. Conversational AI has the potential to make life easier for patients, doctors, nurses, and other hospital staff in a number of ways.
Below are a few conversational AI use cases for the healthcare industry:
- Check symptoms
- Answer common health questions
- Book appointments
- Check up on patients
- Send medication reminders
- Escalate emergency cases
Online floral dealer 1-800-Flowers uses IBM’s Watson AI system to deliver its “digital concierge”—an AI customer service bot that takes customer orders through their website and mobile app.
Titled “GWYN” (“Gifts When You Need”), this chatbot uses natural language understanding and natural language generation to take customer orders in a more intuitive way than a traditional online order form.
Instead of a structured process of filling out a form on a website, people can type into GWYN, and the conversational AI will guide the customer through the process of selecting and buying a gift.
To make healthcare more affordable, Babylon uses AI and technology to help its doctors and nurses complete administrative tasks more efficiently, and gain insights to make more informed decisions.
With its symptom checker, Babylon is helping people avoid the confusion and anxiety that comes with researching health symptoms online. Through conversational AI, it can analyze your symptoms, potential causes, and possible next steps. It can identify most issues that primary care doctors tend to see.
By now, you've heard of Lemonade, an insurtech selling home, renters, and now pet insurance. The company found a way to connect with young customers and make buying insurance quicker and simpler -- mostly through conversational AI.
In 2017, Lemonade showed us how many steps in the insurance process were ripe for conversational AI with its insurance chatbot, Jim. One claim that Jim processed took only a few minutes, and the claim was actually paid within three seconds of submitting it.
What makes Lemonade and other insurtechs disruptive is the commitment to adding value and increasing efficiency. “You see, A.I. Jim works at the speed of light, 24/7, but costs only a few pennies in electricity bills. It’s one of those rare cases where the best service comes with the best price tag,” says Daniel Schreiber, Co-founder of Lemonade.
The conversational technology you’ll need will depend on your industry and potential use cases. You’ll need a conversational strategy that can grow with you as the demands of customers change and the needs of your different business units evolve.
At Hubtype, we work with our clients to recommend the right level of automation for their business goals and objectives. While we integrate with conversational AI platforms like Dialogueflow and IBM Watson, we find that most of our clients succeed with rule-based automation and visual user flows.
Our conversational applications go beyond simple carousels and buttons, they use media-rich components like floating elements, web views, and more. Using these graphical elements enriches the experience for the user while improving the capacity for automation.
Our conversational applications also integrate with your tech stack, aggregate messaging channels, and deliver critical insights to help you continuously improve.