Pop culture is full of examples of humans and computers talking to each other, from HAL 9000 to KITT to Jarvis. This ability to communicate with computers has fascinated humans long before the invention of virtual assistants, chatbots, and conversational AI. In the 1940s, computer scientists began working on Natural Language Processing (NLP) to help computers understand human language. However, it is only in recent years that advances such as BERT, Google's open-source NLP engine, have led to more natural human-computer interaction and improved experiences for employees, customers, and businesses.

The conversational AI market is currently valued at $6.8 billion USD and is projected to grow at a rate of nearly 29% annually between now and 2025. This growth is driven in part by advancements that have decreased the development and training costs formerly associated with conversational AI, making it a more attractive investment for many companies. Newer, more powerful use cases for conversational AI are emerging as well, offering additional incentives for businesses to get started with this technology.

Chatbots lead to increased customer satisfaction

HSBC employs Liveperson's Conversational Cloud and Conversational Builder to automate its contact center operations. By utilizing Conversational Builder, HSBC's contact agents are able to code-free create new automated chatbot conversations, as well as build and manage chatbots. In addition, agents are also able to join chatbot conversations and offer supplemental information and support to guarantee customer satisfaction. Since implementing this system, HSBC has seen a week-over-week increase of 90% in customer satisfaction.

Virtual assistants help automate business processes

As conversational AI becomes more prevalent, companies are beginning to explore different ways to integrate it into their business. Resorts World Las Vegas has deployed an intelligent virtual agent platform, called RED, which acts as a digital concierge for guests and employees. RED can handle tasks such as ordering tickets or room service, and also automates the IT help desk. The resort plans to eventually expand its use of conversational AI technology to other departments such as HR.

Conversational AI transforms call centers

Bradesco, Brazil’s largest bank, wanted to find a faster way to respond to employee’s questions so customers wouldn’t have to wait. The company worked with IBM to deploy a conversational AI call center that would respond to employee’s questions in seconds, instead of minutes. After just 5 months of training, IBM Watson was able to understand and answer 100% of written questions and 83% of those that were spoken. At the 10th month mark, Watson had mastered each question with 96% accuracy. Currently in production, Watson answers 283,000 questions monthly with 95% accuracy.

These are just a few examples of how conversational AI can provide better experiences for employees, customers, and businesses in their call centers—and throughout their organizations.

A fundamental component to the success of any AI solution is the quality of the data used to train its machine learning algorithms and the frequency of retraining those algorithms for continuous learning. According to Deloitte, 20% of the conversational AI-related patents that have been filed in the U.S. over the last two years are focused on improving the training process.

When you are just starting to develop your Conversational AI strategy, you will want to make sure that you have a thorough training data strategy that supports your goals and can scale as you deploy the technology to an increasing number of users. LXT has extensive experience improving Conversational AI solutions, and it can develop a custom data program to meet your needs.

To speak with one of our AI training data experts, contact us today at [email protected].