Understanding the importance of Conversational AI in current Market

AI has been helping brands and businesses improve their relationship with customers for a while now. Thanks to it, companies can mine insights by sifting vast data sets, identify the bottlenecks, observe and forecast customer behavior trends and much more. However, while AI is helping make this process automated and data driven, customers also expect its tools to be more proactive, humanistic and natural. Hence, businesses are being forced to re-think the way that they reach their audience, engage them, and stay with them throughout the entire purchase or service journey. Enter conversational AI. It is a form of AI which is specifically designed to deliver more natural and engaging touchpoints between brands and their customers by simulating real conversations.

Today conversational AI is proving itself to be of immense value in the market with vast potential as a new powerful form of two-way communication. It is empowering companies to surmount the challenges and limitations faced during digital communication with their clientele, audience and patrons. Moreover, its accessibility and consistency across multi-platforms have also helped authorities in pro-active distribution of real-time information. This helps to disseminate request data, receive feedback, while evaluating needs, and anticipating trends. These attributes of conversational AI helped it rise to the status of indispensable tool amid the COVID outbreak.

The recent developments in natural language processing (NLP) has been critical in success of conversational AI. Apart from NLP, it uses various technologies such as Automatic Speech Recognition (ASR), Advanced Dialog management, and Machine Learning (ML) to understand, react and learn from every interaction. ASR enables to detect voice of the customer, capture and analyze it for brands, which allows them to achieve more profound insights into what their consumers think, feel, and need. The quality of ASR technology will greatly impact the end-user experience, so finding the right software is crucial. Natural Language Understanding (NLU) which is a part of NLP, helps process incoming user messages and using information extraction ML techniques, interpret their intents. Then dialog management generates an appropriate response based on the information provided and deliver an appropriate response to the user via natural language generation. In case the model does not understand what has been requested by the user, a generative and "neural conversation model" bot is employed to go back to ask the user a variation of their query. Apart from these, the Conversational AI model leverages machine or reinforced learning, that allows it to accept corrections and learn from the experience to deliver a better response in future interactions.

In the wake of COVID pandemic, conversational AI helped in providing information across various touchpoint on real-time basis. IDC states that conversational AI solutions will continue being a natural choice for disseminating health information and help businesses remain agile by increasing service availability during the coronavirus pandemic. Further, it will play an instrumental role in transforming customer experience, empowering employees, and enriching business insights.

According to Ananth Nagaraj, CO-Founder, and CTO Gnani.ai, conversational AI is at a very nascent stage now. "In the next 7-10 years, we will see developments in the field which will empower bots with emotional intelligence which in turn will lead to a new phase in customer service automation. Humans and Machines will start seeing each other as a partner," he says. Ananth adds, that perhaps, there will be newer emerging technologies making their contribution to the field of Conversational AI to make it more human-like.

Conversational AI-powered virtual agents can also resolve customer issues quickly, with more consistency. In marketing, conversational AI tools can capture potential leads and automate follow-ups and lead scoring efforts. It can help brands by offering personalized recommendations across social media channels for its customers. Other primary use cases include, customer engagement, streamlining IT service desk functions, Sales support, E-Commerce. Owing to its multitude of benefits and features conversational AI is preferred over rule-based, decision tree following chatbots. Though chatbots also are present 24/7, it is not always scalable. Also, customers who interacted with chatbots expected to have natural conversations, but the experience was often unhelpful, resulting in frustration and loss of time. So, we can count on conversation AI powered bots and virtual assistant for a hassle free customer experience.