What is Conversational AI?

Conversational AI or conversational artificial intelligence refers to the machine's capabilities to interact in a human-like natural language, which takes human conversations as input, processes it, and responds back in a human language.

Conversational AI includes technologies such as machine learning, natural language processing & understanding, text-to-speech (TTS), and automatic speech recognition.

Overall conversational AI is a combination of Natural language processing(NLP) and machine learning(ML).

The most common examples of conversational AI applications are Chatbots & Voicebot. And the key differentiator of a conversational AI application is the mode of communication, for instance, the mode of communication for a chatbot can be chat and for a voice bot it will be a voice.

Natural language processing (NLP)

Natural language processing is an AI method that detects and analyzes the input language and generates output based on the user intent. Input is deciphered by NLU (natural language understanding) that a machine can process and return the response back to NLU. Once the response is detected by the AI engine, Natural language generation(NLG) - a component of NLP formulates the user response. If the input is speech-based input, then the ASR (Automatic speech recognition) comes into play which recognizes the speech and transcripts the speech into text that a machine can understand.

Machine learning (ML)

Machine learning is a part of artificial intelligence that learns from every user input and user behavior, as the data increases with the increased number of user inputs, it trains itself to respond more accurately to the user queries.

With the help of conversational AI applications such as Chatbots and Voicebots are implemented to automate multiple use-cases for businesses that help businesses to get the most out of Artificial Intelligence.

How conversational AI works?

Think of conversational AI as a brain of the machine, that takes the input, transforms the query in a way a machine can process, processes the response, and formulates the answer imitating a human language.

Once the user query comes from the application, NLP takes the user input to decipher into machine language and transfers it to AI, AI matches the user intent and the response is sent back to the NLP, NLG generates the output in a human language, and send it to the application. The whole process of user query generation and response takes a fraction of a second.

Benefits of conversational AI

Conversational AI Applications help in automation for multiple business use cases. The technology revolution has helped businesses to develop and deploy top-notch applications to various customer-facing channels.

PERSONALIZED EXPERIENCE

AI Chatbots and Voicebots have the ability to offer personalized and custom experiences to a particular user based on their previous interactions.

QUICK RESPONSES

Gone are the days when users would wait in long queues and keep on punching numbers. With Conversational AI, users can get real-time responses that are accurate and to the point.

OMNICHANNEL AND MULTILINGUAL SUPPORT

Most Conversational AI has the ability to interact with the user on the channel they prefer and in their native language.
Call, Text/SMS, WhatsApp, Facebook Messenger, and Mobile Apps are all supported. What's more, is that one can seamlessly switch between text and voice if need be.

ASSIST AND HELP HUMAN AGENTS

Conversational AI can assist human agents in serving customers more efficiently by suggesting appropriate answers, fetching information, and scheduling appointments.

INCREASED SALES

By effectively communicating promotional sales, offers, and deals and offering a delightful customer experience, sales are bound to increase and help in the growth and expansion of business.

Challenges of conversational AI and how to overcome those challenges

With the technology transformation, there is always the possibility of loopholes and challenges. Challenges differ in terms of use cases and their implementation. Here are a few of the common challenges faced while implementing conversational AI.

Complex user queries

For an AI application to give accurate responses it is important to train it over time. But for the complex queries that are out of context or that are long for an application to understand than those queries cannot be addressed properly by the system.

SPEECH VARIATION

Tone, sarcasm, slang, jargon, and noise are some of the factors that affect the performance of Conversational AI. Different regions have variations and accents too, these take time to be processed and stored for future responses.

PRIVACY & SECURITY

As with any other digital medium, storage of user information for processing and improvements in ML and NLP may raise concerns about user privacy. Not everyone is comfortable with their voice samples being stored online. This needs to be addressed with proper regulations and data protection laws.

How you can incorporate conversational AI into your business

If you belong to any business, then you know how important it is to address customer experience and customer satisfaction as your first priority. Conversational AI can help your business with both. Implementation of conversational AI is not restricted to any use case or scenario. It can work in any kind of scenario for your business, whether it is customer-facing or internal. It has the capability to automate any kind of task or process that needs constant human intervention. Especially for customer-facing channels, customers love to have conversations with brands nowadays. The most common conversational AI applications are chatbots & voice bots.

Here are a few of the steps you can follow before considering conversational AI

1. Finalize the use case or scenario for which you want an AI.
2. Jot down all the possible queries and user flow for the scenario that you want to address.
3. Research and finalize the best AI tool available that can help you with the process. (If possible go for a no-code platform or DIY platform).
4. Create your own conversation AI application and test it rigorously.
5. Train it over time to make the responses accurate.

Conversational AI use-cases

There can be any number of use cases when it comes to conversational AI and automation. Here are the most common use cases for a business.

Customer Support & Service

Customer support is the most common and most implemented use case for a business. Conversational AI is driven the most for the customer-facing channels and it is worth it. An AI application is capable enough to serve customers 24x7. It can respond to customer queries in no time.

Sales & Marketing

When it comes to customer-facing channels, marketers are no behind in their marketing game with an AI application, AI helps a brand to connect with customers and market their products & services well. It also helps understand their customer's needs & wants based on their behavior with the application.

User Engagement

User engagement is very important for qualifying leads; an AI application can help a business drive more user engagement by providing them with the required information. Helping all the prospects 24x7 creates a positive impact on a business to be available 24x7 for their queries and this builds trust.

Surveys

An AI application can also be useful to replace traditional boring forms with a conversational approach that is more interactive. This increases the chances of more user participation in the surveys than the number of participants for a traditional form filling.

IoT Devices

IoT devices are all over the place, and people like to use them on an everyday basis. An AI application that is available for people on their favorite devices makes it easy for a business to connect with customers.

Industries where conversational AI will make the impact

CONTACT CENTERS

The main use of conversational AI is to automate customer support. You can launch AI-Powered Voicebots and Chatbots on customer-facing channels to assist them 24x7.
Also, Conversational AI can assist customer agents to provide a delightful customer experience. They can also be helpful in screening calls and lead generation.

BFSI

By providing 24*7 assistance, enabling faster transactions, generating leads, streamlining core banking services, automating claim processes, and aiding in the prevention of fraudulent claims, conversation AI helps immensely in the BFSI sector.

IOT

Smart TV, Google Now, Alexa, and Siri are some of the practical implementations and examples of Conversational AI in the IoT domain.

Future of conversational AI

People love to connect with brands and that is the reason why conversational AI is widely accepted. By 2030, the global conversational AI market size is projected to reach $32.62 billion. With the increased market size there is a lot of scope for technological advancement and improvements that will help improve overall customer experience with AI and will also be able to solve a lot of real-time problems that still need human intervention. Hence future of conversational AI is bright for sure.

Final Thoughts

Conversational AI is truly next-gen tech, that opens a whole new world of opportunities. It has applications in a diverse set of areas. Yes, there are certain challenges with regard to privacy and security apart from speech variation, but those can be improved upon by more exposure and stronger data protection laws. With Conversational AI, businesses can focus on complex problems and take on more important issues. It proves as a booster for innovation also and helps drive sales.
If you are thinking to adopt a conversational approach for your business, then explore Floatbot’s products. Floatbot is a perfect AI platform that helps you build your own conversational AI application without coding. In case you are not able to decide, then connect with us now.