Conversational AI has been widely accepted by many consumers for several years. Requesting an update from Siri about tomorrow’s weather forecast or telling Alexa to “turn on the lights” has become a part of everyday life.
Many organizations have successfully deployed conversational AI technologies in their customer service programs to improve operational efficiencies and transform the customer experience.
Despite the popularity of consumer-facing virtual assistants, many still need to be convinced about enterprise use cases due to misconceptions about the technology and previous poor experiences with immature systems.
Here, we’ll dispel some common myths and provide a clearer understanding of this exciting technology and its benefits.
Myth 1: “Conversational AI is not intelligent or capable of understanding context.”
Callers have spent years enduring frustrating experiences with automated systems that often respond with, “I’m sorry, I didn’t quite get that”. For many, this has created the misconception that all conversational assistants are incapable of understanding context.
PolyAI has developed several Natural Language Understanding (NLU) models trained on billions of real-world conversations. This means our conversational assistants can understand callers whatever they say and however they say it, enabling even the most complex questions to be successfully contained. With machine learning, our assistants can improve understanding over time.
Moreover, using Spoken Language Understanding allows PolyAI conversational assistants to understand every caller equally well regardless of accent, background noise, or complexities of everyday speech.
Myth 2: “Conversational AI is expensive.”
With high agent attrition rates and the increasing cost of hiring and training new agents, contact centers are facing a labor shortage. To overcome this challenge, many companies are looking to conversational AI. However, there is a perception that investing in this technology is prohibitively expensive.
Deploying legacy voice technologies has proven expensive because of the manual work necessary to gather the training data needed for the technology to handle customer calls effectively.
Recent advancements in conversational AI have been substantial. Large Language Models (LLMs) are trained on vast data sets and only require a small amount of training to understand callers in new contexts. Companies no longer need to provide thousands of transcripts of customer calls; existing FAQs and contact center training materials will do. This means deployment is achievable in weeks and considerably more cost-effective than hiring and training agents.
Companies can also adopt usage-based pricing models to deploy conversational assistants to control ongoing costs, allowing them to only pay for the services they use.
Myth 3: “Conversational AI is not accessible or user-friendly.”
Legacy conversational technologies such as IVRs and chatbots have created robotic experiences and haven’t given callers the freedom to explain their problems in their own words.
Instead, customers have been forced into keyword-driven conversations that result in a self-service loop and callers crying out, “I want to speak to an agent!”
By allowing callers to engage in customer-led conversations, conversational AI gives callers the freedom to explain their queries in their own words. Callers can use non-technical terminology, regional slang or tell long stories to get a solution to their problem.
Conversational AI creates better voice experiences by allowing callers to speak naturally, where callers trust they are getting the best possible service with no input required by an agent.
Myth 4: “Conversational AI is a replacement for human interaction.”
Arguably the most persistent myth about conversational AI in the contact center is that it will replace live agents. But, conversational AI should supplement the contact center workforce, not replace it.
Many organizations use conversational assistants to reduce the volume of calls that reach the contact center by handling repetitive call types such as FAQs, order tracking and troubleshooting. A reduction in call volume gives more time and resources back to the contact center and allows customer service representatives to focus on more complex and sensitive customer queries.
With a percentage of the workload managed by conversational AI, customers no longer have to deal with long wait times, and agents can focus on more meaningful interactions.
Alleviating the pressure on agents means job satisfaction improves, and the contact center has a better chance of reducing agent attrition.
Conversational AI can drastically improve the customer experience and efficiencies by automating processes and reducing cost-to-serve. By dispelling these common myths, companies can make more informed decisions about incorporating conversational AI into their operations.