{"id":9099,"date":"2019-03-04T17:42:06","date_gmt":"2019-03-04T22:42:06","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/hi-my-name-is-c-3po-how-can-i-assist-you-today\/"},"modified":"2019-03-04T20:28:30","modified_gmt":"2019-03-05T01:28:30","slug":"hi-my-name-is-c-3po-how-can-i-assist-you-today","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/hi-my-name-is-c-3po-how-can-i-assist-you-today\/","title":{"rendered":"Hi, my name is C-3PO. How can I assist you today?"},"content":{"rendered":"

Automation reaches the cubicle <\/strong><\/h3>\n

Just as automated machines revolutionized factory labor models over the last century, artificial intelligence (AI) has the potential to dramatically alter the labor models of services over the next hundred years<\/strong>. [1] Increasingly over the late 1900s, robots could more reliably execute repetitive (and\/or dangerous) manufacturing tasks at lower cost than their human counterparts. With the development of greater compute power \u2013 thanks to Moore\u2019s Law \u2013 alongside increasing consumer comfort with digital interactions and self-service, AI has emerged promising to automate human tasks far from the shop floor. The call center, with high volumes of similar inbound queries and rote requests, is one of the most promising near-term applications of this digital transformation.\u00a0[2][3][4][5]<\/p>\n

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The typical call center today is a sea of cubes filled with agents on headsets sitting in front of screens, talking and typing at all hours of the day, most days a year. While online self-service has never been more accessible, call center volumes remain high, as rising customer expectations can often more than offset the volume decrease from self-service. [6] Companies like LiveOps, which leverages an online platform to flexibly staff a large network of at-home agents, have already begun to chip away at typical call center format, but the real threat to agents comes from AI. [7][8] Typical customer inquiries often concern account status (e.g., checking balances, noting address changes, requesting cancellations), technical help (e.g., \u201cmy wifi isn\u2019t working<\/em>\u201d), purchase advice (e.g., \u201cwhich of these phone plans best fits my needs<\/em>\u201d), and complaints and returns (e.g., \u201cmy delivery never arrived and I want a refund<\/em>\u201d or \u201cyour employee was rude to me<\/em>\u201d). [9] The vast majority of these inquiries take the form of predictable (if not always simple) questions with predictable answers, suggesting virtual assistants powered by AI could perform most of these tasks at a comparable or even superior level to human agents.<\/p>\n

<\/h3>\n

Virtual assistants in, human agents out<\/strong><\/h3>\n

Given their superior value creation at lower cost, virtual assistants are likely to increasingly replace human agents in the call center<\/strong>. Using text and voice to communicate with humans, virtual assistants are powered by algorithms, often machine learning algorithms (including natural language processing), which can classify incoming queries and determine appropriate responses. By analyzing large amounts of data on past interactions as well as some pre-programmed associations and customer records, the virtual assistant can determine which past interactions the incoming request is most like, and respond with the associated response most likely to be relevant and successful for the customer and query at hand. For example, after examining a database of past telecom interactions, a virtual assistant could to suggest to an irate broadband customer a number of potential fixes for a non-functional router, adjusting each subsequent suggestion based on the caller\u2019s input. While diagnosing technical fixes and advising on purchases may seem more complex than checking account balances, the call center has encountered most inquiries hundreds if not thousands of times, making even complex asks in fact repetitive and predictable. Virtual assistants, then, are well positioned to perform such tasks.<\/p>\n

While it is still early to call a clear winner in the virtual assistant space, Amazon and Google provide compelling examples of what a winner in this space might look like given their aggressive push to-date into the home assistant space. [10] Indeed, both tech giants have developed call center virtual assistant products (Lex and Contact Center AI, respectively), and we can use their models as comparison points to today\u2019s human agents. [11][12]<\/p>\n