Did you miss a session at the Data Summit? From Siri to Alexa to Google, we are surrounded by AI systems that have been designed with a single goal: to understand us. We've seen incredible progress already. By performing hundreds of billions of calculations in the blink of an eye, the latest AI techniques can understand certain types of text with human-level accuracy. The challenge becomes significantly more daunting, however, when text is part of a larger conversation, where it requires considering context to interpret what the user means and decide how to respond. Still, chatbots like Facebook's BlenderBot 2.0 seem to foreshadow far less frustrating interactions with AI.

Customer behavior has experienced a whirlwind of changes due to the ongoing pandemic. And obstacles have never been lower while customers virtually connect with agents via phone, email, and texts. However, when it comes to interacting with business, points of friction are more numerous. Customers are often forced to deal with endless waits for an email response and companies often struggle to accommodate the limited availability of service desk agents during business hours. Moreover, service desk agents have soaring pressure to do more with less.

As more companies use conversational AI bots as a front line of customer service for consumers, the experience can typically be either helpful or maddening. Ganesh Gopalan, the CEO and co-founder of conversational AI vendor Gnani.ai, Early conversational AI systems were built around keywords, which when mentioned by a consumer caller, would then invoke a related response, said Gopalan. The problem was that this process often lacked context and did not adjust for regional dialects or casual wordings. For users, that could be frustrating because when the bot did not understand them, they were transferred to a live agent and had to repeat all they had already shared with the bot.

Can we add some Emotions to Bots? According to the collins dictionary, "An emotion is a feeling such as happiness, love, fear, anger, or hatred, which can be caused by the situation that you are in or the people you are with." So, when I speak with my Robot, I expect good responses with emotions based on the conversation that happened between me and the Bot. To achieve this, either we should create an Artificial Brain or identify and respond with appropriate words. Artificial brain research is going on in most of the world-famous AI labs.

Conversational Artificial Intelligence (AI) empowers enterprises to employ chatbots, messaging applications or virtual assistants to build highly engaging and valuable relationships with customers. This cutting-edge technology is spreading rapidly across every industry and more excitingly, providing enterprises with a huge potential to accelerate their growth and innovation. From whatever little it has seen of AI assistants so far, the enterprise world has built great expectations. They are visualizing a future full of bots, so smart and powerful, that they help humans with almost any kind of support required very substantially. No wonder conversational AI has become the much-awaited technology in today's enterprise world and attracting the attention of business leaders across the globe. But how much of it is hype, and how much is really closer to reality?

Chatbots and AI assistants need further development to reach the point where they can fully understand the nuances of language and engage in real conversations with people. But good design can help organizations overcome the limitations of existing conversational AI bots, according to developers at companies on the leading edge of the trend. "I think it's a myth that we'll have to wait until the best AI materializes to improve assistants," said Cathy Pearl, head of conversation design outreach at Google. "Actually, we can do a lot now with good design principles and a little technology that we have now." Conversational AI was one of the big discussion topics at the 2019 Re•Work Deep Learning Summit in San Francisco.