Conversational AI has come a long way since the early days of chatbots with limited capabilities. Over time, more and more industries are realizing the capabilities and benefits that these advancements bring.
Digital assistants like Alexa and Siri have consumers wondering why the same abilities can’t be used at work. Although there are enterprise versions of Alexa and Cortana, conversational AI is still not at a point where a user can ask any question and receive a consistent answer. Like most other types of AI, the best use cases are narrow rather than broad.
The level of risk associated with conversational AI also comes into play. For example, if Alexa or Siri make a mistake in a consumer context, that mistake will likely be mildly annoying, if not laughable. In a commercial environment, however, the stakes rise as the accuracy of AI impacts the perceived quality of a brand.
“Most chatbots are designed to connect to one particular service, such as news, food, hotel reservations, weather information, or flight reservations,” said Adnan Masood, chief architect of the AI at digital technology service provider UST Global. “Multi-purpose robots are being developed to perform multiple tasks with the same interface.”
However, current challenges include supporting multimodality in dialog systems, the ability to process and understand visual dialogs, learning data-efficient dialog models (learning from sets of data), using knowledge graphs, and collaborating with IoT devices to maintain context.
“The next generation of conversational AI systems will address multiple ethical and technical challenges associated with general conversational AI systems, including bias, security, multi-turn context, consistency, knowledge management and synthesis” , Masood said.
Examples of Conversational AI in Verticals
Within financial services, Wings Financial Credit Union uses conversational AI to authenticate members. The company uses Nuance Gatekeeper, an AI-enabled biometrics platform that can verify members in as little as 0.5 seconds with a 99% authentication success rate. The platform takes into account more than a thousand physical and behavioral factors specific to each person. As a result, member accounts are more secure.
“Conversational AI has had huge successes in financial services, but has also encountered challenges. Success has always come in two places: FAQs that require generic answers or when an answer is completely filled out and personalized” said Wayne Butterfield, Partner at ISG Automation, a unit of global technology research and consulting firm ISG. “The industry has struggled with anything outside of those two types of queries.”
Health insurance companies, like Humana, also need better ways to answer customer questions. Working with IBM, Humana has developed an IBM Watson-based voice agent that can provide faster, friendlier, and more consistent support to healthcare provider administrative staff. The solution relies on conversational AI to understand the intent of a provider’s call, verify that they are authorized to access the system and member information, and determine the best way to deliver the information requested.
Similarly, just as the healthcare industry as a whole has benefited from AI during the pandemic, CVS has worked with IBM Consulting to use Watson Assistant to manage a tenfold increase in call volume as the United States were rolling out their COVID-19 vaccination program.
“One of the biggest trends to emerge was the active role patients played in their own healthcare, but pharmaceutical companies didn’t want their customers – healthcare providers and patients – seeking information about products on Twitter or TikTok,” said Alisa Hummings, senior director and global head of medical information services at IQVIA, a healthcare intelligence company.
Examples of Conversational AI Software Vendors Are Building for Customers
Messaging platform provider Satisfi Labs has built a ticket sales assistant so that its customers’ customers can search and buy tickets directly in a chat. It also created a ticketing service assistant that handles post-purchase questions on how to access mobile tickets, transfer tickets, or receive ticket account assistance. The platform can also capture information about customer buying preferences throughout the funnel.
“We recently conducted an analysis of our sports customers who use our ticket trading functionality and found that on game day, 81% of fans request the cheapest tickets,” said Randall Newman, chief product officer. at Satisfi Labs. “However, three or more days before a match, only 47% of fans are asking for the cheapest tickets and are willing to buy the nicer and more expensive seats. This kind of information can help our customers manage inventory and predicting demand, as well as creating a better ticket promotion strategy.”
Marsh McLennan, a professional services firm specializing in risk strategy, has used Five9’s call center software to launch a multilingual, global HR chat solution that provides 24/7 support. Messages can be written in a local language and translated into English so that an English-speaking HR representative can respond. When they do, their response is translated back into the original language so both parties can communicate without speaking the other’s language.
“Conversational AI doesn’t work well when there’s a lot of back and forth required or many steps,” said Jonathan Rosenberg, CTO and Head of AI at Five9. “Building them is a mix of art and science and is best done by those with experience. Similarly, you probably wouldn’t build a user interface for a complex mobile application using a team that doesn’t has never built a mobile UI before, you wouldn’t be building complex conversational AI with a team that has never built it.”
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