Conversational AI—A New Wave Of Chat-Enabled Customer Service
Conversational AI is estimated to grow into a $15.7 billion market by 2024. However, with this incredible growth comes challenges including the ability to effectively navigate a crowded vendor landscape.
Much of the confusion for companies looking to adopt a conversational AI solution stems from a misunderstanding of what technology is out there, and how it can be used to improve customer experience.
Presently, the market is flooded with rule-based chatbots. These are the kind of interactive chat experiences that became popular on platforms like Facebook, but which are ultimately limited in their scope. Rule-based chatbots offer no genuine artificial intelligence, instead relying on buttons to drive a conversation forward rather than natural-language technologies that more accurately reflect the way we, as humans, communicate.
Chatbots gained prominence in the market due to how simple they are to build, but while they can be helpful in use cases—such as booking a restaurant reservation or ordering a bouquet of flowers—they quickly break down as business needs begin to scale.
Indeed, Gartner predicts that this year, 40 percent of the chatbot projects that were started in 2018 will be abandoned. Many of these abandoned projects are likely to center on rule-based chatbots, which didn’t deliver on the promise of conversational customer experience due to their inability to scale effectively.
Prior experience with an unsuccessful chatbot project has led many businesses to search for what comes next. They see the potential in the technology, but also realize that genuine artificial intelligence is crucial to delivering a level of customer service that is not only on par with existing support channels, but to moving the needle far beyond them.
This is where conversational AI-powered virtual agents have begun to take over from the inefficient chatbots of the previous decade, standing up to current customer service requirements as we move into 2020 and beyond.
In the enterprise space, online customer interactions can range from simple informational queries to complex transactions that require multiple API calls to third-party and back-end systems. Conversational AI is built from the ground up as an enterprise solution to effectively handle these extreme variations in customer intent at scale.
Some of the advanced enterprise functionality that sets conversational AI apart from rule-based chatbots includes superior language understanding via NLU and deep learning algorithms, as well as simultaneous multilingual support. In addition, contextual-awareness to carry on a conversation even if it has veered off-track, is essential for success.
Advanced spelling correction and support for slang and dialects is crucial, as is the ability to set conversation goals and clearly track customer engagement. Consequently, enterprise implementations rely on integration with backend systems and third-party platforms such as Genesys and Salesforce.
User authentication to allow transactions to be completed on behalf of customers, from a security and privacy perspective, is a key feature. Strict data security and privacy features, including compliance with GDPR, are growing in importance as the regulatory landscape expands.
Conversational AI is also designed to help augment existing support staff and to not only assist customers. For example, if a virtual agent is ‘stumped’ by a request that is outside of its designated scope, it is able to seamlessly handover that customer to a human chat operator within the same chat window.
A dedicated set of features and functionality set conversational AI apart from simple rule-based chatbots in the enterprise. However, a crucial piece of the puzzle in delivering the highest-level customer experience possible actually lies in the body of data that a vendor uses to build a virtual agent from the ground up.
