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Conversational AI: How it Works, Benefits and Use Cases

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Conversational AI: How it Works, Benefits and Use Cases

In this conversational AI blog, we learn about what it is, how the AI behind it works and see some examples of how conversational AI helps organisations engage customers to provide delightful experiences. 


  1. What is conversational AI?
  2. Trends/Predictions for conversational AI
  3. How does conversational AI work? 
  4. Challenges faced in the implementation of conversational AI
  5. Benefits of using conversational AI
  6. Conversational AI use cases by Industry
  7. Frequently asked questions on conversational AI

Conversational AI is changing the way companies interact and engage with their customers. It helps organisations meet customer demands – be available 24×7, respond quickly, personalise the interaction and most importantly, provide options for customers to choose their channel and language of communication as per their convenience.  

Industry leaders are using conversational AI platforms to scale and streamline their operations. That is, they can provide customers with the option of self-service and leverage their resources on more important tasks. This in turn helps them save costs and increase their CSAT

It’s no wonder that conversational AI bots are on the rise. You must have seen a conversational AI chatbot on your favourite company’s website or one of their other official pages on social media. 

Today, conversational AI is the key differentiator for customer and agent experiences. In this conversational AI blog, we will break down the technology to help you understand how it works and how you can use it for your success. 

What is Conversational AI?

Definition: Conversational AI is the use of AI to communicate with users in natural, human-like conversations. This is done by understanding their text or speech inputs, identifying the intent behind them and responding with relevant information. 

The main components of conversational AI are: 

  • Machine Learning
  • Natural Language Understanding
  • Automatic Speech Recognition 
  • Text-to-Speech and Speech-to-Text

Conversational AI uses these components to interact with users through chatbots and voicebots to enhance their experience. These conversational support solutions help users to find relevant information, resolve queries, transact, track orders and much more through contextual, natural conversations. 

conversational support

Through conversational AI, companies are able to engage with their customers in a contextual, personalised fashion, bringing them closer to their users. With these conversations, brands also get insights on customer behaviour, which further helps them enhance the experience at each touchpoint. This not only improves CSAT but also improves sales and hence, revenues. 

The conversational AI market is growing at a rapid pace. Currently, the global market size of conversational AI is USD 6.8 billion (2021) and is expected to grow to USD 18.4 billion by 2026.  

This conversational AI 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. 

According to a Deloitte survey on the future of conversational AI, setup challenges such as training data and maintenance were the top reasons enterprises were not implementing chatbots. However, with new innovations and low code solutions, the future is very bright for conversational AI. 

Some statistics related to conversational AI are:

  • Gartner predicts that by 2022, 70% of white-collar workers will interact with conversational platforms on a daily basis. 
  • 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. 

Suggested Reading: 100 Best Chatbot Statistics for 2021

These numbers clearly show an accelerated adoption of the technology. Now let’s look at top trends brands will adopt to improve customer engagement. 

1. Self-service with NLP enabled chatbots

Customers want a quick resolution of their tickets. One of the best ways to give them this is with NLP powered self-service chatbots. These conversational chatbots understand the intent behind customer questions and respond to them with accurate answers. What’s more, these chatbots are self-learning and improve themselves after each interaction.  

2. Chatbots with a human touch

While customers want self-service for simpler questions, they want to interact with human agents for sophisticated queries. That’s where conversational AI comes into the picture to seamlessly hand over chats to human agents along with context and detailed customer profiles. Chatbots with a human touch will be indispensable for a superior customer experience. 

3. Voicebots and conversational AI

Probably one of the fastest developing technologies today is voice AI. With customers preferring voice-based interactions, companies are adopting voice AI to automate support queries, reduce call wait times and decrease the load on support representatives to give a truly seamless experience to customers. 

4. Payments on chatbots

Conversational commerce is on the rise. Customers are shopping with brands on WhatsApp, Instagram, etc. while conversing with them on chatbots. To enhance customer experience, many companies are integrating payment gateways on chatbots — a one-stop shopping experience. This has helped them increase sales and provide faster ROI on AI-powered solutions. 

5. Chatbots with multilingual capabilities

With increasing globalisation, it’s important companies provide localised content while interacting with their customers. By using a chatbot with multilingual capabilities, businesses are not only amplifying their customer base but also increasing trust in a new market by speaking their language, leaving no room for misunderstanding. 

6. Social media interactions powered by chatbots

Customers spend a lot of their time on social media. Companies that leverage social media to interact with their customers see higher engagement which leads to higher sales. Social media customer care will help companies automate and scale interactions on these platforms while also carrying forward content from one channel to another. 

conversational AI support

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How does Conversational AI Work?

Earlier, we briefly touched upon the main components of conversational AI. But how does it work? It looks simple when a user speaks/types in their query and gets an instant response in natural conversational language. But there are many technologies and components that work together to make this process smooth and quick. 

Broadly, we can divide the process into four steps and we’ll cover them briefly. 

1: Input Generation

2: Input Analysis

3: Output Generation

4: Reinforcement Learning

how does conversational AI work?

1: Input Generation

In the first step, the user inputs their query. In the case of chatbots, it will be via text messages. Whereas in voicebots, it’s through voice notes. 

2: Input Analysis

Voicebots have one extra sub-step here when compared to chatbots. If the user is interacting with the brand using a chatbot, the machine learning layer of the platform uses NLU and NLP to break down the text into parts and pull meaning out of the words. 

However, if the user is using voice AI to speak with customer support, automatic speech recognition (ASR) is first applied to the voice note to parse the sound into a language the machine can understand. The ASR system should be good to understand the user’s language and accent and cancel unwanted noise from the surroundings. Once that’s done, NLU and NLP understand the intent behind the query, similar to the chatbot. 

Unlike a rule-based chatbot that only responds to exact matches of their training data, AI-powered chatbots understand the context even if there isn’t an exact match. 

The NLU and NLP machine understands the query for spelling, identifies synonyms, interprets grammar and recognises users’ sentiment. Not every user uses perfect spelling and grammar, that’s why NLU is trained on different spellings, similar words, slang, homophones etc. to understand the user and the sentiment behind their query. 

This is where the offerings of most conversational AI companies differ. Conversational AI is better only if it is trained on huge datasets that cover multiple intents and utterances. If not, it will not be able to analyse the user’s input and hence, not give an accurate answer. Advanced NLU engines can also identify multiple intents in a sentence and respond to users accordingly. 

3: Output Generation

Now that the AI has understood the user’s question, it will match the query with a relevant answer and with the help of natural language generation (NLG), it will respond to the user. The response is communicated in a conversational manner, just like how humans interact with each other.  

When the output is generated, the AI interacts with the integrated systems to go through the user’s profile and previous conversations to add a layer of personalisation in the response. 

As in the Input Generation step, voicebots have an extra step in the Output Generation. The AI’s response is converted back from text to speech. The user hears the voice response from the Voice AI, all in real-time. 

4: Reinforcement Learning

This is where the self-learning part of a conversational AI chatbot comes into play. The user’s inputs are analysed and the AI is trained to refine its response. With each interaction, businesses get variations of intent and utterances which are used to train the AI. Over time, the user gets quicker and more accurate responses. 

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Challenges faced in the implementation of Conversational AI

While conversational AI adoption is on the rise, it comes with its fair share of challenges in implementation. 

To become “conversational”, a platform needs to be trained on huge AI datasets which have a variety of intents and utterances. To add to this, the platform should be compatible with other tools and tech stacks for smooth integrations and sharing of data. Another challenge comes in the form of security and adoption.  

challenges in implementation of conversational AI

We look at some of the challenges in the implementation of conversational AI below.

1. Training data  

It’s not easy for companies to build a conversational AI platform in-house if they do not have enough data to cover variations of different use cases. Once a business gets data, it would need a dedicated team of Data Scientists to work on building the ML frameworks, train the AI and then retrain it regularly.

2. Conversations in user’s language 

Conversational AI platforms are usually trained in the English language but only 20% of the world population speaks it. Training the AI in multiple languages is not common. Many companies converse in multiple languages, but they work as rule-based chatbots because their AI is not trained in those languages. 

3. Data security 

User’s data security and privacy is a big concern when implementing conversational AI platforms. The conversational AI platform should comply with the region’s data regulation guidelines and be secure enough to overcome any attacks from hackers. 

Suggested Reading: Conversational AI Chatbot Security – What You Need To Know

4. Ease of use

A conversational AI platform should be designed such that it’s easy to use by the agents. If the user experience is not good, the agents will not use the platform. This includes creating conversational flows, responding to end-users, analysing data, changing settings, etc. 

5. Self-learning capabilities

The AI should be able to learn from the conversations it has with users. If it doesn’t have the reinforcement learning capabilities, it becomes obsolete in a few years. Then, the companies will not see a return on investment after it is implemented. 

6. Costs

When implementing conversational AI for the first time, businesses find the costs expensive. However, in the long run, it shows a quick ROI. The challenge lies in trusting the AI and making the first investment. If the AI is not good, companies will not benefit from it. 

7. Discovery and adoption

Soon after implementation, businesses using conversational AI suffer from a lack of customers using chatbots to interact with them. Companies need to put in some effort to inform their users about the different channels of communication now available to them and the benefits they can see from it. 

8. Scale

Like any other technology, the conversational AI platform should be able to handle multiple conversations simultaneously. Enterprises see 500K plus chats on average. The conversational AI architecture should be strong to handle the traffic load it sees on the chatbot with crashing or delay in response. 

9. Integrations  

A conversational AI platform can personalise customer conversations if it integrates with other tools and the tech stack of a company. During the implementation stage, this becomes one of the biggest challenges – the platform is not compatible with other software. Integrations are important for seamless syncing and personalising the customer experience. 

10. Connecting to agents

Customers want human interactions. And when it comes to complex queries, the conversational AI platform needs to hand over the chat to a human agent. While implementing the conversational AI platform, adding agents/departments to the platform and ensuring the handover is smooth and to the right person can be a challenge for some. 

However, once you overcome these challenges, there are many benefits to gain from this technology. 

Suggested Reading: 12 Things to Consider Before Selecting a Conversational AI

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Benefits of using Conversational AI

Conversational AI has many benefits — both for customers and companies. It enables quick and accurate responses, reduces the load on agents, scales engagement and reduces the time taken to close a conversation. 

All this enables brands to have more meaningful one-on-one conversations with their customers, leading to more insights on customers and hence more sales. 

Even though different industries use conversational AI for different purposes, the major benefits are the same across all. We can broadly categorise them under benefits for customers and benefits for companies. Let’s look at them in detail below. 

Conversational AI benefits for customers

benefits of using conversational AI for customers

1. Quick, accurate response

One of the biggest benefits of using conversational AI is the quick and accurate responses users get. As soon as customers input their queries, they get a response from the chatbot or voicebot. A well-trained AI replies with accurate information, allowing the customer to resolve their questions with self-service. 

2. Personalised interactions

Customers get personalised responses while interacting with conversational AI. By integrating with CRMs, conversational AI creates a customer profile with all the relevant information on the customer. This is then used to personalise interactions and add context to the conversation. This increases the customer satisfaction score. 

3. Reduce wait/hold time

Customers are most frustrated when they are kept on hold by the call centres. Conversational AI reduces the hold and wait time when a customer starts a conversation. It replies instantly and reduces the average resolution time. And if the conversation is handed over to an agent, the conversational AI instantly connects to an online agent in the right department. 

4. Convenience 

Conversational AI takes customer preferences into account while interacting with them. Customers can interact with brands on their preferred channel such as website, mobile app, Facebook, Instagram, WhatsApp, Viber, etc., converse in a language they are familiar with through multilingual bots and resolve queries round the clock. 

5. Contactless customer experience

With the onset of pandemic, customers do not want to step out of their homes and interact with humans in person. Conversational AI enables them to resolve their queries and complete tasks from the comfort of their homes. Be it finding information on a product/service, shopping, seeking support, sharing documents for KYC, they can do this without compromising on personalisation. 

Conversational AI benefits for companies

benefits of using conversational AI for companies

1. Scale

A growing business or an enterprise company sees thousands of queries every day. This can increase the burden on agents who then cannot respond to customers on a timely basis. Conversational AI can help these companies scale their support function by responding to all customers and resolving up to 80% of queries. Conversational AI also helps a company reach a wider audience by being available 24×7 and on multiple channels. 

2. Upsell opportunities

Conversational AI takes into account customer preferences. Based on their behaviour it can offer the best upsell at the right time. Conversational AI can also reduce cart abandonment by answering customer queries instantly and encouraging them to complete their purchases. It also ensures a smooth form filling process which in turn makes it easier for the sales team to act on the leads faster. 

3. Reduced costs

Customer support division can be expensive, particularly if you respond to customer queries 24×7 and in multiple languages. Conversational AI can help companies save on operational costs by resolving queries on their own and reducing the load on agents. With conversational AI, companies do not have to add extra agents to handle scale, it reduces human errors and is available 24×7 at no extra cost. 

4. Customer insights

A good conversational AI platform captures customer details and uses them to get insights into customer behaviour. With this data, businesses can understand their customers better and take relevant actions to improve the customer experience. This in turn leads to happier customers which leads to return customers and increased loyalty and sales. 

5. Agent efficiency

Conversational AI not only reduces the load of repetitive tasks on agents but also helps them become more efficient and productive. It provides them with tools to respond to customers quickly and personalise each interaction. This lets them handle more queries in less time. Agents can then take up challenging work that increases a company’s revenue. 

Suggested Reading: 5 Ways Conversational AI Makes Your Agents Life Easier

conversational AI

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Conversational AI Uses Cases by Industry

When people think of conversational AI, they usually only think of chatbots to get quick responses to customer queries. But a conversational AI platform has a variety of applications and use cases. 

Irrespective of the industry, conversational AI allows brands to interact with customers at various touchpoints and streamline processes. It enhances customer and agent experiences multifold. And with newer innovations in AI, it will only get stronger and more useful. 

Let’s look at some use cases by industries. 

Conversational AI for customer service and sales

Customers are seeking online support through chatbots and voicebots to get their queries resolved quickly. A conversational AI enables self-service, answering frequently asked questions and proactively communicating with customers on new updates or general disruptions in service. Conversational AI is changing the customer-brand interactions as we know them. Now, brands engage with customers 24×7, across channels, languages and modes (chat and voice). 

To this end, conversational AI has a positive impact on customer service and your net promoter score. Some of the common use cases of conversational AI in the customer service industry are:

  • Resolve customer queries on a timely basis
  • Share updates and notifications with customers
  • Connect customers to the right agent at the right time
  • Help customers get relevant information during the purchase process
  • Allow customers to share and upload documents
  • Follow up with customers on service and get their feedback

Suggested Reading: Faster, Smarter, Better — AI is Improving Customer Service

Conversational AI in retail and eCommerce

Conversational Chatbots allow e-commerce and retail companies to reach out to their customers in real-time, around the clock, in a two-way conversation. Companies can answer queries, track order statuses, send payment confirmations, process returns and collect product reviews all in a single interface. 

Some of the common use cases are:

  • Generate leads for the sales funnel
  • Automate FAQs and support customers pre-and post-purchase
  • Browse the catalogue and place orders on multiple channels
  • Share notifications on shipment, refund and return orders
  • Collect feedback and CSAT from customers
  • Retarget abandoned carts and increase sales
  • Update customer’s details in real-time and personalise interactions

Suggested Reading: Conversational Commerce: Redefining Ecommerce

Conversational AI in real estate

Using a conversational AI platform, a real estate company can automatically generate and qualify leads round the clock. It can collect customer details such as names, email IDs, phone numbers, budget, locality, and get answers to other qualifying questions. Conversational AI can also hand these leads seamlessly to your agents and close more leads every day. 

Common use cases include:

  • Generate and qualify leads for the sales funnel
  • Share appropriate and relevant options with customers based on their preferences
  • Schedule site visits as per their convenience and send confirmation reminders
  • Let customers submit documents online to complete processes
  • Set payment/EMI reminders at regular intervals
  • Answer questions about payments, maintenance, etc. with ease

Suggested Reading: AI in Real Estate: Build Meaningful Bond with Customers

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Conversational AI for banking and fintech

As consumers move away from traditional brick and mortar financial institutions, conversational AI can help these organisations provide a smooth online banking experience. Using a conversational chatbot, customers can submit KYC documents, get debit and credit notifications, receive portfolio performance updates, apply for loans and file fraud complaints. 

The prevalent use cases are:

  • Generate leads for the sales funnel and qualify them with relevant information
  • Answer FAQs and transfer to an agent for high-level queries
  • Share documents, verify them and complete the KYC process online
  • Send alerts & notifications on bank updates, downtime, payment dues, etc.
  • Allow customers to check bank and credit balances 
  • Authenticate transactions and allow customers to report fraudulent activities
  • Collect feedback and reviews on customer service, products, etc. 
  • Send offers and discounts on new and upcoming products/ services

Suggested Reading: Top 10 Conversational AI Use Cases in Banking

Conversational AI for insurance

Customer challenges for insurance companies include customers dropping off mid application or renewing expiring contracts. Companies can use a conversational AI platform to tackle these hurdles. Conversational chatbot’s media-friendly interface allows customers to submit ID proof, health declaration forms, income statements, and signed proposal forms. 

The conventional use cases in this industry are:

  • Generate leads for the sales funnel
  • Qualify leads with relevant customer information
  • Assist users in policy selection
  • Share important information on available quotes
  • Handle customer queries
  • Allow customers to submit documents
  • Intimate customers on policy renewal, updates and alerts
  • Automate re-engagement
  • Discuss the first notice of loss
  • Let customers cancel/renew a policy

Suggested Reading: Conversational AI in the Insurance Industry

Voicebots Are Disrupting Communication In The Insurance Space

Conversational AI in logistics

Low-level queries inundate logistics companies. Questions about order statuses, refund policies, cancellations, and returns clog support channels. Instead of having service reps manning phones and email all the time, move to a conversational AI platform. Simply enter a tracking number and immediately talk to the bot about where your package is and how long it might take to get to you. 

Popular use case includes:

  • Generate leads for the sales funnel
  • Qualify leads with relevant customer information
  • Provide payment options to customers
  • Track orders from the time of placing order till the customer receives it
  • Send reminders and notifications
  • Start two-way messaging to answer customer queries
  • Show nearest pick-up/drop-off service centre detector
  • Collect feedback, reviews and CSAT

Suggested Reading: How To Use Conversational AI in Logistics Management

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Conversational AI in travel and tourism

This industry goes beyond time and geographical boundaries. Customers come from all over the world and from different time zones. A conversational AI solution can help your customers find relevant information at their convenience. You can provide customers with boarding passes, gate notifications, web check-ins, and add-on purchases. 

Some of the common use cases include:

  • Generate leads for the sales funnel
  • Qualify leads with relevant customer information
  • Build personalised itineraries
  • Book air tickets, rooms and events
  • Reschedule and cancel bookings
  • Automate customer’s FAQs
  • Transfer high-level queries to live agents
  • Notify customers with updates, alerts, travel guidelines, etc.
  • Collect feedback, reviews and CSAT

Suggested Reading: Boost Customer Engagement in Travel using Conversational AI

Conversational AI in food services

The food services industry is one of the fastest-growing industries in the world. On a day-to-day basis, Foodtech companies process hundreds of thousands of orders. Unequal distribution of demand is more than a macro-level problem – and exists on a day-to-day or week-to-week basis. A conversational AI platform can solve many support related issues and make the customer experience better. 

Some of the use cases are:

  • Inform customers where an order is
  • Troubleshoot any problems related to order placing or delivery
  • Provide tracking information in real-time
  • Automate refund process 
  • Inform customers about the latest offers and deals
  • Change account details
  • Collect feedback, reviews and CSAT

Suggested Reading: How Conversational AI Plays a Key Role in Foodservice Industry

Conversational AI in edtech

The Edtech industry is one of the fastest-growing industries in the world. Conversational AI for education can solve many support related issues and make the student, parent and teacher/admin experience better. 

Potential use cases of conversational AI in education include:

  • Generate and qualify leads
  • Re-engage students
  • Process applications
  • Register & enrol students
  • Support students and parents and answer FAQs
  • Announce timetable updates, course communication
  • Update on extra-curricular & club activities
  • Collect feedback from parents 
  • Notify course communications
  • Share learning materials
  • Build community

Suggested Reading: WhatsApp Chatbot for Edtech: A Complete Guide

Conversational AI in telecom

AI in telecom can help companies reduce call volumes. Conversational AI automates FAQs and streamlines processes to respond to customers quickly and decreases the load on agents. With instant messaging and voice solutions, conversational AI encourages self-service to resolve queries, find relevant information and book meetings with technicians. 

Below we list some of the common use cases for conversational AI in the telecom industry. 

  • Automate customer support queries for level 1, 2 and 3 requests. 
  • Answer questions on billing details, process payments and data usage
  • Reduce SLAs on tickets by collecting customer information and sharing it with an online agent
  • Reduce error rates in the data provided to customers
  • Improve IVR interaction with Voice AI
  • Schedule visits and update customers on the field technicians locations for home service
  • Alert and notify customers on updates and downtime 
  • Promote new plans and festive offers
  • Increase conversion rates by reducing cart abandonment rates
  • Allow customers to update user information like email address, phone number, address, etc. 
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Conversational AI for healthcare

Medical chatbots are revolutionising the healthcare industry. A report suggests that the healthcare chatbots market will be worth $703.2 million by 2025.  Clearly, medical chatbots are serving this industry well. 

Below we discuss some of the use cases of chatbots in the healthcare industry:

  • Check symptoms and identify if the patient needs immediate care 
  • Find healthcare services near customers
  • Schedule doctor’s appointments to avoid long queues
  • Raise awareness about a healthcare issue and reduce misinformation
  • Provide 24/7 support to patients and connect to a caregiver
  • Share medication and routine check-up reminders with patients
  • Simplify billing and registration process at hospitals and clinics
  • Integrate with EHR/EMR systems to maintain a patient’s medical data
  • Collect feedback and ask survey questions in an easy to answer format 

Suggested Reading: Happy Health Is Just A Text Away on WhatsApp

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Frequently asked questions on conversational AI

1. What is the difference between conversational AI vs chatbot? 

Chatbots can be of two types: rule-based chatbot and NLP-powered chatbot. Rule-based chatbots are simple chatbots that follow a flow chart system to respond to customer queries. It’s not smart to answer any deviation from the set rules. On the other hand, a conversational AI chatbot is an NLP-powered chatbot that understands customers’ intent and sentiment and responds accordingly.

Read more about the difference between chatbot vs conversational AI here

2. What is the difference between Conversational AI vs NLP

Conversational AI uses multiple technologies to converse with customers in natural, human-like language. One of the technologies it uses is NLP. Natural language processing (NLP) is an AI technology that breaks down human language such that the machine can understand and take the next steps. 

3. What is the difference between Conversational AI vs NLU

Similar to NLP, NLU is a subset technology of AI that is used to understand text/voice inputs

4. What is conversational AI voice?

Conversational AI voice, or voice AI, is a conversational AI solution that uses voice commands to receive and interpret directives. With this technology, devices can interact and respond to human questions in natural language. 

Know more here: Voice AI: What is it and How Does it Work?

See conversational AI in action

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

According to a Statista report, 

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

Despite these numbers, implementing a conversational AI solution can be tricky and time-consuming. At Verloop.io we support businesses like Nykaa, ADIB, AbhiBus, Kanmo Group, BLF Group, TravelStart, GlobeMed, Watania and more to delight their customers with a seamless customer support experience. 

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

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