Conversational AI (2023):
A Comprehensive Guide

What is conversational AI?

Artificial Intelligence, or AI, refers to the technology that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision making. AI systems are designed to mimic human cognitive processes and to adapt to new situations, enabling them to perform complex tasks with speed and accuracy.

Recent advancements in AI have led to the creation of a wide range of applications, including virtual assistants, fraud detection, medical diagnosis and self-driving cars. According to IBM Institute for Business Value (IBV), AI is ranked in top three technologies based on revenue impact and offers a huge opportunity for businesses. The COVID-19 pandemic accelerated industry adoption of AI globally. AI adopters are already seeing at over 5% revenue boost with new AI led initiatives. And those who are in more mature stages of AI adoption are observing up to 10-12% gains.

AI enables a broad range of use cases across the key business functions. However, organizations are reaping the most rewards from AI in customer-facing functions such as marketing, sales and customer support. Using AI-powered virtual assistants, businesses are generating higher revenue by reducing customer acquisition costs, engaging users proactively and improving customer satisfaction. Virtual assistants, or chatbots as they are popularly known as, belong to a subset of artificial intelligence called conversational AI.

Conversational AI refers to the use of artificial intelligence technologies that allows a computer or a program to carry out natural, human-like conversational experiences with humans. It can be used in a wide range of applications, from customer service chatbots to personal assistants like Google Assistant, Siri, and Alexa. Conversations can be transactional or informational. You can ask about the weather, book a flight ticket, order food, check your account balance, track your order, or get support. Chatbots, virtual agents, and voice assistants are some popular examples that leverage conversational AI today.

Conversational AI enhances the customer experience by enabling smarter chatbots with the ability to understand, process, and respond to human language. Unlike traditional rule-based chatbots, AI chatbots are trained by engaging in millions of conversations and continue to learn from each interaction.

Why does conversational AI matter now?

Businesses’ and customers’ communication across the industry is evolving rapidly. Customers now expect businesses to be always available and deliver personalized, interactive customer experiences.

1. Business messaging is becoming the new normal

People want to communicate with businesses in the same way they communicate with friends and family – on messaging apps.

– Messaging is instant and asynchronous – People don’t have to wait a long time to receive a response. They can also multitask and go about their routine while they are conversing.

– People are already on messaging apps – People are familiar with messaging apps that they use daily. They prefer to use these apps to communicate with businesses as well instead of downloading a new app.

– The experience of messaging apps is evolving every day – Messaging apps are now more than just a tool to connect with friends and family, they are the future of commerce, payments, and business in general. Hence, all top chat apps are competing to build the most intuitive messenger app.

2. Rising customer expectations

Change in consumer behavior has led to a rise in customer expectations. Consumers now expect businesses to have omnichannel experiences, offer self-serve customer care, and deliver faster responses. For businesses, it’s a challenge to keep up with these expectations without the help of better conversational technology. Conversational AI is playing an important role in helping businesses scale customer conversations.

3. The conversational AI market is growing fast

Conversational technologies are trending with the launch of tools like OpenAI and ChatGPT. people are getting more aware of the power of conversational AI. Today, conversational AI is not just limited to big enterprises anymore. It has become more accessible and affordable, which expands possibilities and offers new business opportunities. The global conversational AI market size is projected to reach $32.62 billion by 2030 at the forecasted compound annual growth rate (CAGR) of 20.0% for this decade. The market was valued at $5.78 billion in 2020.

Types of Conversational AI technology 

There are several types of conversational AI, each with its own strengths, capabilities, and use cases. Some of the most common types of conversational AI include:

AI Chatbots: Chatbots are computer applications designed to simulate natural conversation with human users, through text-based messaging channels and platforms. Messaging apps such as WhatsApp, Facebook Messenger, Telegram, and WeChat are increasingly being used for conversational AI-powered chatbots that provide natural human-like conversational experiences for a wide range of use cases across marketing, sales, and customer support.

Voice assistants: Voice assistants, such as Google Assistant,  Amazon’s Alexa, or Apple’s Siri, are designed to respond to voice commands and questions from users. Voice assistants are typically used for simple tasks such as setting reminders, playing music, listening to news, and controlling smart home devices.

Interactive voice assistants or Voicebots: Voicebots are similar to chatbots but are designed to work with voice-based interfaces, such as phone systems or voice-enabled devices. They interpret spoken instructions and queries using voice recognition and use natural language processing to provide relevant responses. Voice bots can be used for tasks such as automated customer service, order tracking, and banking services.

Each type of conversational AI application has its unique strengths and limitations, and businesses should choose the type that best fits their needs and requirements.

Conversational AI vs Rule-based chatbots

Conversational AI and traditional chatbots both involve the use of computer programs to simulate conversation with users, but there are some key differences between the two approaches.

Traditional chatbots are typically based on rule-based algorithms and they follow a set of pre-programmed rules to determine how to respond to user queries. For example, a chatbot to provide customer support might be programmed to respond with a specific set of replies depending on the user’s query. Traditional chatbots are restricted to specific conversational flows but can be effective for simple tasks, such as answering basic queries, FAQs or sharing product information, and more.

Conversational AI, on the other hand, uses machine learning and natural language processing to enable more sophisticated and complex interactions with users. Chatbots need Conversational AI that can understand and interpret user queries more accurately, enabling better-personalized responses and a more natural conversation flow. Conversational AI can continuously learn from user interactions, improving over time and becoming more effective at handling complex tasks.

Components of Conversational AI 

Conversational AI systems typically consist of several components working together to enable natural language understanding and response. Some of the key components of conversational AI include:

Natural Language Processing (NLP): NLP is a subset of AI that focuses on comprehending human language. NLP enables the AI system to understand and interpret the meaning of human language, including syntax, semantics, and context.

Intent Recognition: Intent recognition is the process of identifying the user’s intent behind a particular query. This is typically done using NLP algorithms that analyze the user’s input and identify the most likely intent.

Dialog Management: Dialog management is the process of managing the 2-way conversation between the user and the AI system. This involves deciding which response to provide based on the user’s input, maintaining context across multiple interactions, and guiding the conversation toward the desired outcome.

Natural Language Generation (NLG): NLG is the process of generating natural language responses based on the intent and context of the user’s query. NLG algorithms can be used to generate text-based or voice-based responses.

Machine Learning: Machine learning algorithms are used to enable the AI system to learn from user interactions and improve its output with time. This can include processes such as supervised learning, where the system is trained on a dataset of labeled examples, and reinforcement learning, where the system is rewarded for providing an accurate response.

Active Learning: Active learning allows conversational AI models to learn from their mistakes and improve their performance over time. It improves the accuracy of a chatbot’s responses and makes it more effective at understanding and engaging with human users. AI identifies the low-confidence areas and tries to improve its model by getting information and feedback from users.

Document Cognition: Ability to automatically understand and interpret information in text-based documents such as business documents, guides, and knowledgebases. This uses various techniques to extract relevant information from the documents and convert it into a structured format that can be used by other processes.

Overall, multiple components of a conversational AI system work together to enable natural and seamless interactions between users and the AI system, improving the user experience and driving business outcomes.

How does Conversational AI work?

Conversational AI works by using a combination of components we talked about such as natural language processing (NLP), dialogue management, machine learning, and others to understand user queries and provide natural language responses.

Here is an overview of how conversational AI works to generate human-like responses:

The user initiates a conversation: A user interacts with a conversational AI system by typing a message or via voice query.

Input is analyzed: The system uses NLP algorithms to analyze the user’s input and extract the meaning behind it. This involves breaking down the sentence into its constituent parts, such as nouns, verbs, and adjectives, and identifying the relevant entities and concepts. If the query is voice-based, speech recognition algorithms are used to convert the speech into text.

Intent identification: The system tries to identify the user’s intent or purpose after the analysis. For example, if a user asks “What’s the weather like today?”, the intent might be to get the current weather conditions for their location.

Dialogue management and context tracking: Based on the intent, the system uses dialogue management to formulate an appropriate response to the user’s query based on the context of the conversation, the user’s previous interactions with the system, and any relevant knowledge or data.

Response generation: Once the appropriate response has been determined, the conversational AI system generates a response that is appropriate for the user’s intent and context. This response may be in the form of text, speech (using Text-to-speech), or a combination of both.

Continuous learning and improvement: The system is usually designed to learn from each user interaction and use machine learning algorithms to improve its performance and accuracy over time. Users can also provide feedback on the conversational AI system’s response, which can be used to further improve the system’s performance.

Conversational AI use cases and industry applications

There are a wide range of business applications for Conversational AI. Businesses are leveraging conversational AI to enhance their customer experience across marketing, commerce, and support. Here are a few examples of how businesses across different industries are using conversational AI to communicate with customers by enhancing workflows.

Conversational AI in Retail and E-commerce

Conversational AI is helping retail and ecommerce brands acquire new customers, provide customer service and build long-term customer relationships.

Few conversational AI use cases for retail and e-commerce:

1. Product discovery
2. Product recommendations
3. Order Placement
4. Inventory and pricing-related questions
5. Enable cross-sell and upsell opportunities
6. Provide after-sales support
7. Facilitate returns, exchanges, and refunds

Conversational AI in BFSI and Fintech

Conversational AI can help financial institutions improve customer engagement, increase efficiency, and reduce costs. By providing personalized and proactive support, they can build long-term customer relationships and stay competitive in a rapidly changing industry.

Few conversational AI use cases for BFSI and fintech:

1. Loan or credit card applications
2. Customer eKYC and onboarding
3. Account balances and statements
4. Bill payments and reminders
5. Nearby ATM or branch locator
6. Manage claims and renewals
7. Gather customer feedback and reviews

Conversational AI in Healthcare

Conversational AI can help patients become more engaged in their care and help healthcare providers make more informed decisions. It can help improve patient outcomes, increase efficiency in healthcare delivery, and reduce costs.

Few conversational AI use cases for healthcare:

1. Check general symptoms
2. Answer to common health-related queries
3. Prescription and report delivery
4. Regular checkups on patients
5. Medication reminders
6. Book appointments
7. Medicine delivery

Conversational AI in Travel and Hospitality

Conversational AI enables travel and hospitality businesses to acquire, engage, interact with, and support travelers throughout the journey.

Few conversational AI use cases for travel and hospitality:

1. Ticket booking and reservations
2. Packages and destinations recommendations
3. Deals, discounts, and offers on travel bookings
4. Manage bookings – Rescheduling, modifications, cancellations, check-ins
5. Timely notifications about itinerary changes
6. Price change alerts
7. Feedback and reviews

Conversational AI in Education

Conversational AI can be used in education to acquire new learners, provide personalized learning experiences, and adapt to their individual learning styles and preferences.

Few conversational AI use cases for education:

1. Admission assistance
2. Course and exam-related updates
3. Fee payments
4. Personalized tutoring and consultation
5. Book demo classes
6. Doubt solving

Conversational AI in Media and OTT

Conversational AI has been increasingly used in the media and OTT industry to enhance user engagement while delivering personalized and engaging experiences to their viewers.

Few conversational AI use cases for media and OTT:

1. Subscription renewals and reminders
2. Personalized content recommendations
3. Viewer engagement via contests
4. Personalized notifications and content alerts
5. Referral programs
6. Customer service and support

Conversational AI in Gaming

Using Conversational AI, gaming businesses can provide an uninterrupted and immersive gaming experience by offering alternate channels for users’ redundant needs and support.

Few conversational AI use cases for gaming:

1. In-game purchase promotions
2. Premium subscription nudges
3. Invite/referral friends
4. Rewards and redemptions
5. Achievements/milestone updates and nudges
6. Tournaments and live game alerts
7. Self-serve customer support and FAQs

Conversational AI in Food and Beverage

Conversational AI enables brands to create a streamlined interaction channel across customer touchpoints while maintaining the standard of dining and delivery service.

Few conversational AI use cases for food and beverage:

1. Online food ordering and delivery tracking
2. Personalized meal recommendations
3. Contactless dining menu and orders
4. Reservations and personalized assistance
5. Feedback and review
6. Customer service and support

Conversational AI in Advertising

Conversational AI can help advertising agencies reach more audiences, reduce the cost of acquisition and generate higher conversions using conversational engagement.

Few conversational AI use cases for advertising:

1. Automate lead capture to the messaging channel
2. Convert high-intent search users
3. Deliver innovative campaigns coupled with tailored conversational flows
4. Convert offline prospects to digitally engaged audience
5. Re-engage high-value leads
6. Collect user information

Conversational AI in Real Estate

Conversational AI is useful in the real estate industry as it allows real estate agents to automate many of their communication tasks, provide 24/7 customer service, and improve their overall efficiency.

Few conversational AI use cases for real estate:

1. Automate lead capture
2. Property Search
3. Book site visit
4. Re-engage with personalized campaigns
5. Promotion for new project launches
6. Tailored recommendations

How to get started with Conversational AI

How do you move from your traditional model of communication to conversational AI, whether through chatbots or voice assistants? Here are some general guidelines to follow:

Define your goals: Before implementing conversational AI, it’s important to define your goals and determine what you hope to achieve. Are you looking to acquire new users, improve customer engagement, reduce costs, or provide better customer service? By defining your goals, you can identify the use cases that will be most valuable for your business.

Select communication channels: Identify the preferred communication channels(such as WhatsApp, Instagram, GBM, voice, and others) that your customers are likely to use.

Choose a platform: There are several conversational AI platforms available such as Gupshup which can help you get started quickly with pre-built industry-ready chatbots and customizable solutions that can be tailored to your business’s needs.

Design your chatbot: Determine what types of conversational flows the chatbot will be able to provide, what tone and personality it will have, and how it will interact with customers. It’s also important to consider the user experience and ensure your chatbot is easy to use and understand.

Train your chatbot: After designing your chatbot, you’ll need to train it to understand natural language and respond to customer inquiries. This will typically involve providing sample conversations and using machine learning algorithms to improve the accuracy of responses over time.

Test and iterate: Once your chatbot is up and running, it’s important to test it and gather feedback from users. This will help to identify areas for improvement and refine the chatbot over time.

Monitor and analyze: As chatbot interacts with customers, it’s important to monitor its performance and analyze data on customer interactions. This will help to identify trends, measure the effectiveness of your chatbot, and make data-driven decisions to improve customer experiences.

By following these steps, you can get started with conversational AI and start realizing the benefits of this powerful technology. It’s important to remember that conversational AI is an ongoing process that requires continuous improvement and iteration, so be prepared to adapt and refine your chatbot over time to suit customer preferences and needs.

How a Conversational AI platform can help

A conversational AI platform can provide a competitive advantage by helping businesses speed up the deployment of conversational AI applications that suit business needs and outcomes. By leveraging the power of machine learning and natural language processing, businesses can deliver better customer support, build stronger customer relationships, and drive business growth.

The conversational AI platform offers omnichannel no-code bot solutions along with pre-built conversational journeys for top business use cases. This enables businesses to quickly build and deploy AI-powered chatbots and voice bots for various use cases across messaging channels. Businesses can reduce costs and improve efficiency by automating repetitive queries and allowing customer support teams to focus on more complex issues.

By analyzing customer interactions and data, a conversational AI platform can provide valuable insights into customer behavior, preferences, and needs, helping organizations to make data-driven decisions.

A conversational AI platform can help organizations scale their customer support efforts, providing consistent and high-quality support even as the volume of customer inquiries increases.

Get started with your conversational AI journey with Gupshup

Gupshup is the #1 Conversational Engagement Platform for Marketing, Commerce, and Support automation powered by conversational AI chatbots, for businesses to deliver human-like conversations, that boost customer satisfaction and revenue at scale. By unlocking the power of conversational engagement, Gupshup makes it easy for businesses to engage, interact and transact with their customers over 30+ channels – anytime and anywhere. With an industry-trained Conversational AI platform and real-time unified profiles, Gupshup enables over 45,000 businesses in more than 60 countries to create one-on-one, frictionless experiences across the customer lifecycle.

Request for a demo today and empower your customer conversations.