Chatbots vs Conversational AI: Comparing Key Differences and Impact on Digital Experiences

AI technology can effectively speed up and streamline answering and routing customer inquiries. Although chatbots and conversational AI are sometimes used interchangeably, they aren’t the same thing. Today we’ll review the difference between chatbots and conversational AI and which option is better for your business. Without deep integrations with company-specific data and the systems and apps within your organization, conversational AI use cases will be lackluster at best and downright useless at worst.

Newer chatbots may try to look for certain important keywords rather than reading entire sentences to understand the user’s intent, but even then, may not always be able to respond accurately. If you’ve ever had a chatbot respond along the lines of “Sorry, I didn’t understand” or “Please try again”, it’s because your message didn’t contain any words or phrases it could recognize. Chatbots that leverage conversational AI are effective tools for solving a number of the biggest problems in customer service. Companies from fields as diverse as ecommerce and healthcare are using them to assist agents, boost customer satisfaction, and streamline their help desk. It’s important to know that the conversational AI that it’s built on is what enables those human-like user interactions we’re all familiar with.

What is a Customer Satisfaction (CSAT) Score? And Why Does it Matter?

Automated bots serve as a modern-day equivalent to automated phone menus, providing customers with the answers they seek by navigating through an array of options. By utilizing this cutting-edge technology, companies and customer service reps can save time and energy while efficiently addressing chatbot vs conversational ai basic queries from their consumers. Now that your AI virtual agent is up and running, it’s time to monitor its performance. Check the bot analytics regularly to see how many conversations it handled, what kinds of requests it couldn’t answer, and what were the customer satisfaction ratings.

As a result, AI chatbots can mimic conversations much more convincingly than their rule-based counterparts. Chatbots for customer service, as mentioned, sit on the front of a website and allow customers to speak with an artificial agent to solve simple inquiries. Repetitive questions that companies see everyday are handled well with a chatbot since support teams can manage incoming customer questions better and answer them efficiently. We’ve seen big advancements in conversational AI over the past decade, starting with the release of Siri, Google Assistant, and Alexa. These services use natural language processing (NLP) to understand human language and respond with unique responses beyond predefined ones. To form the chatbot’s answers, GPT-4 was fed data from several internet sources, including Wikipedia, news articles, and scientific journals.

Chatbot use cases in customer service:

It can increase your team’s efficiency and allow more customers to receive the help they need faster. Major companies like Google, Microsoft, and Meta are heavily investing in the technology and building their own offerings. With the advent of advanced technologies like LLMs and ChatGPT, the enterprise is set to be transformed in ways we can hardly imagine. Not all chatbots use conversational AI, and conversational AI can power more than just chatbots. Krista orchestrates software release management processes across the DevOps toolchain and stakeholders using an easy-to-follow conversational AI format. A bot is a software application that is designed to automate certain tasks.

• Rule-based chatbots—also known as decision-tree, menu-based, script-based, button-based, or basic chatbots—are the most rudimentary type of chatbots.
• To say that chatbots and conversational AI are two different concepts would be wrong because they’re very interrelated and serve similar purposes.
• If you’re ready to get started building your own conversational AI, you can try IBM’s watsonx Assistant Lite Version for free.
• The customer journey, from acquisition to retention, is filled with potential incremental drop-offs at every touchpoint.

They have to follow guidelines through a logical workflow to arrive at a response. This is like an automated phone menu you may come across when trying to pay your monthly electricity bills. It works, but it can be frustrating if you have a different inquiry outside the options available. So, in the case of conversational machine learning, it allows the machine to use its interactions to inform and create better conversational experiences in the future. Engage with shoppers on their preferred channels and turn customer conversations into sales with Heyday, our dedicated conversational AI tools for retailers. The result is that no customer service interaction is held back by language barriers.

However, they lack adaptability to handle complex user inputs, cannot learn from interactions, and have limited knowledge beyond their programmed rules. This allows conversational AI to better understand and process human language and power more intelligent automated conversations with humans. Recognizing these key differences allows businesses to assess the appropriate solution for their needs.