The story of chatbots could have been written—and, indeed, was being written—without the help of a global pandemic.
Prior to the COVID-19 pandemic, some estimates had pegged the chatbot market would grow from $2.6 billion to $9.4 billion in the next five years. This projected growth was mostly attributed to increased adoption rates and improvements in AI and natural language processing technology.
But in the span of a few days in March, bank branches closed, call centers cleared out, and most other customer service-adjacent workers were sent home, forcing businesses to suddenly confront a virtual-only reality.
Where Chatbots Stood Pre-Pandemic
Most early examples of chatbots as we know them can be traced to technology companies like Apple Inc (NASDAQ: AAPL) (Siri) and IBM (NYSE: IBM) (Watson), as well as business-to-consumer websites like Expedia Group Inc (NASDAQ: EXPE) and Alaska Air Group Inc (NYSE: ALK). Each of those companies launched various chatbots between 2006-2011, with Expedia and Alaska Air’s chatbots being more question-and-answer based and Apple and IBM’s versions utilizing artificial intelligence to enable more conversations.
Fast forward to 2016 and Google (NASDAQ: GOOG) (NASDAQ: GOOGL) (Google Now), Amazon.com (NASDAQ: AMZN) (Alexa), and Microsoft Corporation (NASDAQ: MSFT) (Cortana) had all entered the fray with AI-enabled virtual assistants of their own, while Facebook Inc (NASDAQ: FB) (Bots For Messenger) had launched a platform for developers to build chatbots of their own.
It wasn’t long before the technology had spread to other industries—namely financial services. Banks like Wells Fargo & Co (NYSE: WFC), Capital One Financial Corp. (NYSE: COF), and Bank of America (NYSE: BAC) have used chatbots for years, as have insurance companies like Allstate Corp (NYSE: ALL) and Geico to process claims faster. TD Ameritrade even launched a chatbot to facilitate trading in 2017.
Chatbots During COVID-19
But the pandemic sent the need for chatbots into overdrive, and everybody from government agencies to hospitals to small and medium-sized businesses have rushed to implement chatbots in recent months.
IBM reported a 40% increase in traffic to its Watson Assistant from February to April. Google launched the Rapid Response Virtual Agent to answer questions related to the pandemic, and a survey from Indiana University found that respondents actually viewed experiences with chatbots more favorably than interactions with real people.
This has created an unprecedented demand for chatbots and the artificial intelligence they’re based on. But is this technology ready for its moment in the spotlight?
How Chatbots Will Get Better
The short answer to that question is yes. But it’s going to take time.
The technology has already come a long way from its basic question-answer days. Today’s conversational bots are going to get better simply because the machine learning and natural language processing they run on will enable them to learn as they collect more data.
“Large data sets will better train chatbots to understand and adapt to a wider range of linguistic prompts, leading to more natural and effective conversations,” said Phillip Rosen, founder and CEO of Even Financial.
As Bob Legters from FIS wrote last year, conversational chatbots have “the capacity to deliver highly personalized service for customers, akin the emotional and contextual understanding that only a human could previously provide.”
These chatbots will be able to have human conversations, understand intent, and automate tasks. It’s not a question of if—it’s a question of when.
The use case for AI-powered chatbots is already here. They will be able to provide 24/7/365 support, and in terms of costs alone, Juniper Research estimates that chatbots could save firms $7.3 billion by 2023. And when it comes to financial services specifically—an industry dealing with near-constant fee compression—the growing consensus is that chatbots are going to become table stakes.
“Financial services firms will see chatbots developed to better meet the needs of their users and clients in both tone and helpfulness,” said Rosen, whose platform uses machine learning to generate personalized financial services offers. “We’re going to get to the point where a good or bad experience with a chatbot could be the difference between keeping and losing a customer.”
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