Conversational AI explodes to fulfill CX gap

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COVID-19 has led to a dramatic acceleration in the adoption and implementation of digital transformation initiatives. Nowhere was this more obvious than in customer experience (CX). Organizations have been quick to adopt new technologies such as chatbots powered by artificial intelligence (AI) to fulfill customer expectations of timely response to queries and problem resolution. 

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Chatbots are an example of how AI can be used to augment human capabilities, providing a convenient way for customers to interact with organizations 24/7. In the customer service context, they can provide an efficient and cost-effective way to handle large volumes of customer inquiries. This frees up human agents to focus on more complicated queries. With the increase in customer demand for digital channels during COVID-19, organizations that have invested in chatbots have been able to scale customer support and address prospect queries.

Conversational AI

In the customer service world, “automation” has been a dirty word. The customer wants to talk to a human being, not a machine. Employees bristle at the thought of being replaced by an automated system. However, as voice search, smart speakers and voice assistants gain adoption and acceptance, automation is becoming indispensable for providing an excellent CX. 

Conversational AI allows customers and employees to get the answers they need quickly and easily, without having to wait on hold or jump through hoops. On the back end, total-experience automation brings all of your customer, product and employee data together in one place. This makes it easy to track customer journeys, spot areas for improvement and deliver consistently accurate responses to queries. In today’s customer-centric world, total-experience automation is essential for providing an outstanding CX and employee experience (EX).

Conversational AI allows human-like conversations between users with websites, applications and devices via texts, voice and commands. The five most important elements that come together to make conversational AI a reality are the following:

  • Conversational customer-facing user interface that receives and delivers inputs and outputs
  • Natural language processing (NLP) engine
  • Dialogue manager
  • Search engine that traverses data repositories through enterprise integrations
  • Machine learning (ML) capability

When all components are in place, the conversational AI experience can tap into many of the aspects that make the human language such a versatile and rich communication medium.

Total experience automation

Total-experience automation is an approach to CX and EX that involves automating conversations across platforms on the front end and integrating with enterprise systems on the back end. The goal of total-experience automation is to provide a more seamless, efficient and personalized CX and EX by using conversational AI to automate interactions. The challenge is to improve the quality of the experience while managing costs in an era of exploding data, channels, customer expectations and employee turnover.

To do this, businesses need to have a robust back-end infrastructure in place that can connect customer data from various sources and enable real-time communication between front- and back-end systems. By doing so, they can provide a more cohesive CX and empower employees to focus on higher-value tasks. In addition, total-experience automation can help businesses reduce costs by automating low-value, repetitive tasks such as forecasting demand and mitigating supply chain disruptions. is leading the way in conversational AI by providing a highly efficient interface with stakeholders. They currently have more than 1,200 customers across financial services, retail, energy, education and gaming including Unilever, P&G, Schlumberger, Roche and Amazon. 

The core of the front-end is multilingual. It supports more than 100 languages across more than 35 text and voice channels including WhatsApp, Google Business Messages, Apple Chat, Instagram, Messenger, Viber, WeChat, Alexa, Telephony and others

The platform is built on a proprietary NLP engine. The Natural Language Understanding (NLU) and NLP engine compound the self-learning of the Dynamic AI Agents through multifactorial intent recognition, effective engagement and on-point resolution – all in real-time and with 98% accuracy. 

The predictive/AI layer predicts the future conversation or allows third-party tools to predict conversation and manage workflow. This enables end-users to place orders which in turn allows partners to use data feeds to build models for future predictions with little data or feature engineering.

Use cases

Diaggio has a bartender assistant that helps bartenders make cocktails and concurrently helps with demand management using predictive analysis.

Asian Paints customer interactions create real-time predictions of inventory needs.

American Shipping customers place orders and get status updates throughout the shipping, transportation and delivery process which is used to predict shipping demand.

AI + human emotion

AI and automation have the capability to ingest and analyze huge amounts of information and data in milliseconds.

Yellow-ai provides an interface between the customer and backend systems. It skips over the front office while also supplying an NLP interface. Dynamic AI agents uniquely learn from all human-answered queries to rapidly decrease future AI-to-human hand-offs, achieving 60% automation in the first 30 days of go-live.

Humans have emotional and empathetic abilities. While doesn’t try to synthesize that, it does have the ability to bring in humans at the right point of the interaction. This creates training points to improve the models. 

For example, when it comes to complex conversations that need empathy, the end-user is looking for human intervention. As for a customer looking for details on COVID-19 protocols, they want to connect with a human. This is also true for conversations that might involve customers at risk of churning or high-value transactions. The key to overcoming these challenges and creating an effective conversational AI strategy is to enable seamless handoff to human beings, thereby, leveraging the collaborative intelligence of humans and AI.

The more seamless and successful these transitions become, the faster we’ll see the adoption and acceptance of conversational AI chatbots in all industries.

Originally appeared on: TheSpuzz