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Conversational AI 101: Explained with Use Cases and Examples

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Conversational AI 101: Explained with Use Cases and Examples

Conversations are very integral to human interactions. People talk to each other every day, and businesses want to talk to their customers in a natural and personalized way. But replicating human conversations on a larger scale is challenging. 

That’s where conversational AI steps in.

Conversational AI is an NLP (natural language processing) powered technology that allows businesses to duplicate human-to-human interaction for human-to-machine conversations. 

How does it do that? Conversational AI swiftly delivers accurate answers to customer questions. Although it responds instantly, it follows a multi-step process to reach the outcome. 

But before we do that, let’s get  into a step-by-step understanding of conversational AI, considering its key components:

  1. Machine Learning
  2. Natural Language Understanding (NLU)
  3. Automatic Speech Recognition
  4. Text-to-Speech and Speech-to-Text

We will now explore how these components come together to make conversational AI work seamlessly.

How Does Conversational AI Work?

Conversational AI swiftly answers customer queries with precision. The process broadly unfolds in the following four steps:

Step 1: Input Generation

When you want to ask something, the process begins. You can type your question (using chatbots on websites, WhatsApp, Facebook, Instagram, etc.) or speak it out (using voicebots or voice assistants). And AI takes your question as an “intent.”

Step 2: Understanding Your Input (Input Analysis)

After you ask your question, the machine learning part starts to kicks in. It uses NLU and NLP to break down your words into smaller pieces and break down your intent. If you’re talking to customer support using voice, automatic speech recognition (ASR) first turns your voice into a language the machine can understand.

Once there’s text, the AI in the decision engine (using deep learning and neural networks) looks at it to understand what you’re asking.

This is where conversational AI becomes a big deal for companies. Depending on how well the AI is trained (which also depends on the data it learned from), it can answer questions about different things and in different ways. 

Step 3: Output Generation – Giving You An Answer

Now that the AI knows your intent or your question, it starts to find a fitting answer. Using natural language generation (NLG), it responds to you. Before it gives the answer, the AI checks with the company’s customer databases, looking at your profile and past conversations.

This helps narrow down the answer based on your data and adds a personal touch.

Sometimes, the AI might not connect your intent with the database. 

In those cases, it hands the conversation to a human agent. If it involves voice, there’s an extra step for voicebots – the AI’s response changes from text to speech, and you hear the voice response in real-time.

Step 4: Reinforcement Learning to Get Smarter 

This is where the conversational AI chatbot starts learning from itself. Depending on how happy you were with the answer, the AI gets better for the next chat. With each talk, businesses collect lots of data with different questions and ways of asking. They use this data to teach the AI more. Over time, you get faster and more accurate answers, making your experience better when talking to a machine, or an AI tool.

What is the Future of Conversational AI?

The conversational AI market is growing at a rapid pace. The global market size of conversational AI is expected to grow to USD 18.4 billion by 2026.  

This growth is in part due to the digitisation of customer interactions, innovation in technology and the changing customer demands. 

More and more companies are adopting AI-powered customer service solutions to meet customer needs and reduce operational costs. Of these AI-powered solutions, chatbots and intelligent virtual assistants top the list and their adoption is expected to double in the next 2-5 years. 

Some future predictions for conversational AI are:

  • By 2030, chatbots and conversational agents will raise and resolve a billion service tickets. This chat-first strategy will increase self-service and deliver fast ROI according to Gartner
  • IDC predicts that AI will define 50% of customer interactions. 
  • By 2025 nearly 95% of customer interaction (both online and telephone) will be taken over by AI according to a conversational AI report. 
  • Companies that use AI to automate their customer engagement will see a 25% increase in their operational efficiency. 

These numbers clearly show an accelerated adoption of the technology. 

See Conversational AI in Action

The future of automated conversations is bright. More people are ready to use a conversational AI solution and hence more companies are adopting it to interact with their customers. 

According to a Statista report, 

  • 78% of service organisations use CAI for simple self-service tasks. 
  • 77% of companies leverage conversational chatbots to assess the type and difficulty of a question and accordingly hand it over to an agent. 
  • 70% of companies use a conversational solution to assist agents in retrieving information, canned responses, etc to resolve queries faster. 

Despite these numbers, implementing a CAI solution can be tricky and time-consuming. At Verloop.io we have helped businesses like Nykaa, ADIB, AbhiBus, Kanmo Group, BLF Group, TravelStart, GlobeMed, and Watania get started with their conversational AI journey and delight their customers with seamless support experiences. 

If you’re curious if conversational AI is right for you and what use cases you can use in your business, schedule a demo with us today! We’ll take you through the product, and different use cases customised for your business and answer any questions you may have.

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