What is conversational AI?

Conversational artificial intelligence (AI) is a rapidly growing application of AI technology that is transforming the way customers interact with businesses.

Conversational AI builds on a combination of natural language processing (NLP), machine learning and backend system integration to automate text-based or voice-driven conversations with customers. The technology is what enables conversational bots to engage with humans in a more realistic manner and gives brands a powerful option for extending the customer experience beyond agent-dependent interactions.

Conversational AI vs. chatbots

In their early iterations, chatbots were a fairly limited digital tool that could handle yes/no questions and little else. They’ve come a very long way in a short amount of time.

Today, most chatbots can be divided into two categories.

Rule-based chatbots are designed to respond to a narrow range of pre-programmed queries, which make them great at answering basic questions or acting as interactive FAQs. These types of chatbots are often used to help navigate users through a company website or serve as an automated social media responder.

Meanwhile, AI-driven chatbots utilize conversational AI to support more complex interactions using NLP and machine learning. These branches of AI help chatbots better interpret and respond to humans. Virtual assistants such as Apple’s Siri and Amazon’s Alexa are examples of sophisticated conversational AI applications. However, this technology can also be used for the written word; for instance, a company may use an AI-driven chatbot to help triage customers seeking support, and can even solve some of the lower-level queries without any human intervention at all.

Chatbots utilizing conversational AI give customers more options on how they receive support. For instance, if a customer doesn’t like speaking on the phone or if they are hard of hearing, they might prefer to interact with a chatbot rather than wait to speak to an agent. Or, a person with impaired vision might prefer to speak to their virtual assistant to get help with a product. When more customers use these digital tools, they reduce support volume and free up agents to support more complex inquiries.

But conversational AI doesn’t only operate in its own siloed channel. Rather, it’s a key component of omnichannel customer service because of its cross-channel abilities. It can help customers through complex, multi-step tasks across voice, chat, email and social media by leaning on the customers’ own preferred technologies — whether those are virtual assistants, smartphones, desktop computers or smart devices.

Examples of conversational AI

Perhaps the most widely recognized application of conversational AI is the smart speaker. We can now ask a device to let us hear the latest news, provide answers to trivia questions or place an order for groceries.

At least a quarter of American adults now own at least one smart speaker, and the market for virtual assistants is expected to hit $6.27 billion by 2026. Growing adoption of the smart speaker/virtual assistant consumer market is making people more comfortable with the idea of using them for increasingly complex queries. For instance, people are using these devices, and other chatbots, to help them make and modify travel reservations, inquire about the status of insurance claims and request customer support on purchases.

That’s because advanced conversational AI systems have the ability to update backend business systems, allowing them to do things like complete transactions or check on open customer service tickets. This can all be done without additional human interaction.

Training conversational AI

Training conversational AI involves collecting, annotating and validating diverse sets of data.

A wide variety of recorded customer service interactions and chat transcripts must first be gathered, keeping in mind the need for different accents, dialects, tones, cultural variations, languages and more. The source material must then be annotated with the correct labels to identify key entities in the conversation.

While some annotation can be done with automated techniques, there are limits. Many chatbot applications benefit from a human-in-the-loop annotation services because humans can pick up on subtleties, slang and intonations in ways that computers can’t. Data and training models may also require additional analysis to detect bias.

The underlying rule for any AI project is garbage in, garbage out and it applies to conversational AI: The quality of the data is critical for the success of the project.

The future of conversational AI

All signs point to businesses continuing to adopt conversational AI in the future.

Deloitte projected in mid-2021 that the global conversational AI market will reach nearly $14 billion by 2025. The market intelligence firm also pointed out that the volume of interactions handled by conversational AI leapt by 250% since the pandemic began and that 90% of companies reported faster complaint resolution because of conversational AI.

Additionally, the more advanced features of conversational AI, such as interacting with backend systems on a customer’s behalf, will increasingly become the norm rather than simply responding to common information inquiries. This will drive two closely coupled trends. The first is that consumers will continue to use and expect conversational AI when interacting with a business. Second, conversational AI interactions will become a more personalized experience for customers.

With its current benefits and limitless potential, it’s no wonder why companies across all industries are adopting conversational AI at record pace. But brands who have yet to implement the technology are not too late! Reach out to our team of AI experts to learn how we can help.