Back Industry News

Conversational AI Is Becoming More Mainstream As Demand Increases Posted on Sep 11 - 2018

Share This :

Artificial Intelligence (AI) is evolving and showing strong growth for enterprises who are deploying conversation AI for customer service solutions. In the last eighteen months, the demand for conversational AI platforms and predictions from major analyst firms shows the trend is set to continue strongly in 2018. But throughout this, there is an underlying message; enterprises need to deploy conversational platforms that are capable of truly understanding the customer.

Fueled by interacting with the likes of Siri and Alexa, it’s no surprise that Gartner predicts that by 2020, customers will manage 85% of their relationship with an enterprise without interacting with a human.

Drive Customer Interaction

For enterprises using advanced AI-driven conversational platforms, the rewards are great. Not just the increase in customer satisfaction, but in the actionable data that conversational interfaces generate. In order to achieve this, enterprises need to ensure that conversational chatbots can understand the context and the sentiment behind the conversation, and that the conversational AI solution can seamlessly integrate with back-end data and third-party databases to enable deeper personalization. It also needs to be capable of creating detailed analysis of the chat logs in real-time to provide feedback into the conversation, improve and maintain the system and deliver actionable insights to the business.

Understanding the conversational data generated by intelligent digital assistants will reap huge rewards for enterprises. This is because when people communicate in a natural, conversational way, they reveal more than just the words they’re saying. Their individual preferences, views, opinions, feelings, inclinations and more are all part of the conversation. But conversational data must be interpreted within its proper context before it can be turned into actionable information.

Integration with external systems is key for improving business agility, increasing personalization and customer satisfaction. As the use of AI in businesses develops it will be essential for information and data assets to be shared across the enterprise. An example of this is Shell Lube.

Shell has implemented the LubeChat “bot”’ and deployed it in multiple languages across multiple geographies. The bot needs to know about millions of different combinations – ranging from knowing what lubricant goes into over a million or more engine types to understanding the various physical properties and attributes of tens of thousands of Shell and competitor lubricants. View More

x

Get the Global Big Data Conference
Newsletter.

Weekly insight from industry insiders.
Plus exclusive content and offers.