Understanding the difference between Conversational AI & chatbots

Businesses today want to improve customer experiences while lowering their service costs, and they're quickly discovering that chatbots and conversational AI can help them achieve these goals. Experts predict that the global chatbot market will reach $9.4 billion by 2024.

Chatbots and conversational AI are frequently used interchangeably—they sound similar but are not. So, what’s the noise about, and why do we need to know? After all, the ultimate goal of both is to provide customer satisfaction and ensure engagement. Despite their interconnectedness, both concepts should not be used interchangeably.

Relationship between Chatbots and Conversational AI

Author: Gaurav Singh, CEO & Founder, Verloop.io

Basics first, Chatbots are computer programmes that simulate human conversation so as to provide better customer experiences. Some bots follow predefined conversation flows, while others use artificial intelligence and NLP to understand user questions and send automated responses in real-time.

Conversational AI on the other hand is a broader term for AI-powered communication technology such as chatbots and virtual assistants. Data machine learning (ML), and NLP are used by conversational AI platforms to recognize vocal and text inputs, mimic human emotions and facilitate conversational flow.

Chatbots typically fall into two categories rule-based chatbots or AI chatbots. The most basic type of chatbots is rule-based chatbots, also known as decision-tree, menu-based, script-based, button-based, or basic chatbots. These chatbots communicate using predefined rules. Conversations are sometimes designed in the form of a decision-tree workflow, with users selecting answers based on their use case. These bots are similar to automated phone menus in that the customer must make a series of choices to find the answers they seek. The technology is ideal for answering frequently asked questions and addressing basic customer concerns.

AI customer service chatbots, also known as contextual chatbots or virtual agents, understand user intent and form responses using machine learning, natural language processing, or both. These bots can continuously learn from customer interactions, allowing them to provide more helpful responses over time. Both types of chatbots add a friendly layer of self-service between a company and its customers.

Conversational AI enables machines to comprehend, plan, and apply past data. It also uses NLP to respond to human queries. With the help of conversational AI, customers can communicate with apps in a simple and straightforward manner. It aids deep learning algorithms in determining user intent and better understanding human language by using a large set of training data. Conversational AI platforms enable interaction between voice, text, and actions. Customers benefit from superior performance and sophistication, which enhances their overall experience. Conversational AI solutions, such as chatbots, virtual agents, and voice assistants, have grown in popularity in recent years, with accelerated adoption due to COVID-19.

Future of chatbots and Conversational AI

Chatbots are expected to take over the customer support space, generating more than $8 billion in revenue per year. In the banking industry, 90 per cent of customers are using chatbots. As conversational AI advances, chatbots with less human dependency will benefit both businesses and consumers. However, conversational AI continues to face specific challenges, such as understanding user intent and recommending/resolving queries based on their specific needs.

According to Deloitte, the global conversational AI market, which includes chatbots and intelligent virtual assistants, is expected to grow at a 22 per cent CAGR from 2020 to 2025, reaching nearly US$14 billion by 2025. The report also indicated, set up challenges, such as training data and maintenance, were among the top reasons for enterprises not implementing chatbots.

Conversational agents have limitations, but many have already demonstrated their worth. With technological advancements on the horizon, it's critical to remember that success with conversational AI is dependent on more than just technology; good experience design informed by behavioural science is critical.

Author: Gaurav Singh, CEO & Founder, Verloop.io