The Promises, the Challenges, and the Pitfalls
By Ray Naeini
Adoption of intelligent virtual agent (IVA) or chatbots is a popular topic in today’s industry, as it can offer a broad range of benefits to both enterprises and their customers. A survey published by DMG Consulting in January 2018 showed that “increasing use of self-service” is one of the top three “enterprise servicing goals for 2018.” IVA uses artificial intelligence to automate customer service for chat or audio interactions with customers. It has the potential to operate as, or improve the performance of, live agents. Today’s customers mostly prefer self-service, especially through digital channels. IVA is a promising solution for improving customer satisfaction.
Automation Is Inevitable and Evolutionary
Automation has been a progressive, irreversible, and unstoppable trend. Automation has made drastic changes to our way of living and doing business. A few decades ago, customer service started with live switchboard agents manually connecting customer calls to the right customer service agents.
In the 1980s live switchboard agents were replaced by interactive voice response (IVR) that could automatically prompt questions and route calls. IVA is the next evolutionary step in automation, going beyond the IVR functionality. It penetrates deeper into the enterprise organization and further automates various functions of customer service.
Benefits of IVA
IVA offers a broad range of compelling benefits. It can assist live agents with real-time access to knowledge management systems, improving the quality and the speed of service. In certain cases, it processes customer service requests directly without the need for a live agent. In general, IVA can significantly improve the quality and the speed of the service while reducing live agents’ workload or payroll costs. It also reduces enterprise challenges related to live agent staffing, training, and retention.
IVA is available 24/7 from anywhere and can offer consistent customer service with an unlimited, real-time access to information during customer engagements. It automates repetitive tasks and can assist with or take over sophisticated customer service transactions. It supports multichannel via text-based chat or audio-based interactions. The use of IVA can go beyond customer service to benefit other departments such as sales and marketing for customer surveys or lead generation and qualifications.
Artificial Intelligence Is the Brain Behind IVA
What makes IVA smart enough to intelligently automate customer service is its use of artificial intelligence (AI) technologies. AI is a broad concept that started in the mid-1950s. It promised delivering intelligence similar to the human brain through progressive technological milestones. Advancements in mathematical modeling and natural language understanding, combined with faster and more cost-effective computers, make each technological milestone more capable of offering solutions to real-world problems.
The first two AI technological milestones that provide real solutions are called machine learning (ML) and deep machine learning (DML). The concept is to create mathematical models capable of continuously receiving, parsing, and categorizing a vast amount of relevant or training data to progressively increase the capabilities of the computers in natural language understanding, image processing or recognition, medical diagnostics, and so forth. This is similar to the basic functions of the human brain, as we were born with an inherited ability to continuously receive enormous amounts of data through our senses and then parse and categorize the information.
The use of ML and DML in IVA mainly focuses on natural language understanding (NLU) to converse with customers. In an IVA driven by ML, the data is analyzed and categorized by trying to understand the intent of the data (conversation) and extract the information associated with the intent (called entities) to prepare a response. The more intents and entities are analyzed and categorized, the more intelligent the IVA becomes. The ML approach, however, has certain limitations due to its single-layered analysis. In a DML-driven IVA, the data is analyzed by multiple layers or stages (using technologies such as neural network), and then at the end a collective scoring of the results from all layers is used for categorization.
IVA Challenges and Pitfalls
While IVA can deliver many benefits, it also creates challenges. Experiences related to the deployment of disruptive technologies tell us to avoid the hype of IVA and focus on applying it to each specific application systematically and progressively. IVA requires continuous training using a significant amount of valid and relevant data to improve its accuracy and performance.
We should also remember the pitfalls of early deployment of IVR that created significant customer dissatisfaction and try to avoid those mistakes. IVA deployed in a contact center environment should be capable of seamlessly integrating with the contact center’s overall workforce optimization (WFO).
Ray Naeini is the chairman and CEO of OnviSource.