Automation reaches the cubicle
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.  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 – thanks to Moore’s Law – 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. 
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.  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.  Typical customer inquiries often concern account status (e.g., checking balances, noting address changes, requesting cancellations), technical help (e.g., “my wifi isn’t working”), purchase advice (e.g., “which of these phone plans best fits my needs”), and complaints and returns (e.g., “my delivery never arrived and I want a refund” or “your employee was rude to me”).  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.
Virtual assistants in, human agents out
Given their superior value creation at lower cost, virtual assistants are likely to increasingly replace human agents in the call center. 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’s 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.
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.  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’s human agents. 
Virtual assistants have the potential to create superior value for customers and the companies they frequent across nearly every industry. Virtual assistants can perform tasks (like checking accounts or diagnosing technical issues) faster than human agents can, as the algorithm can process data more quickly than an agent thinking through a problem or typing a search query into a database, reducing the throughput time of each call and therefore taking up less of the customer’s time, improving customer service. Virtual assistants can also reduce customer wait times to essentially zero (besides connection and processing times), as the capacity of the virtual assistant to simultaneously handle inbound volume is limited only by server capacity rather than number of hired bodies currently available (and not on break) in the call center: this further reduces the total throughput time for each customer from the time s/he dials the support number or opens the chat box to issue resolution improving customer service. Gone as well is any need for the dreaded transfer ping pong between agents as the customer need only deal with one virtual assistant with instant access to all relevant data. 
Virtual assistants enable not only higher quality service, but also more consistent service. Virtual assistants are always available, enabling companies to provide constant coverage over weekends, holidays, and even adverse natural events like snowstorms and hurricanes. Virtual agents are also inherently more consistent than human agents in their customer interactions: level of experience, personal motivation, and current mood are no longer variables affecting the quality of service provided.  In particular, virtual assistants never experience emotional fatigue, a common problem among human agents, who are often faced with repeated hostile interactions with dissatisfied customers. Through virtual assistants, best practices are also immediately universally available to all customers, rather than slowly disseminating through meetings or training sessions. 
Ultimately, all this improvement in the quality and speed of service for customers has the potential to substantially boost brand value for companies: by lowering customers’ reluctance to connecting to customer support, virtual assistants could increase the volume of inbound queries and thereby provide the company a more accurate portrait of actual, real-time customer needs and pain points while solving latent issues customers may have previously decided were too much trouble to request the company’s help in solving.  Further, by constantly analyzing inbound requests and customer responses, virtual assistants facilitate more continuous and consistent data collection than a human agent could, enabling companies to identify and more quickly address those issues that are most common or critical to customer satisfaction (among other possibilities). Machine learning also has the potential to uncover optimal solutions humans had never before considered, providing opportunity for novel and superior problem solving. More effectively addressing more customer needs through virtual assistants has the potential to create substantial brand value. Human agents, then, risk being outdone by virtual assistants on quality, speed, and availability of service: these bots create superior value directly and indirectly for the companies currently using human-staffed call centers.
The value capture model for virtual assistants is also more attractive to enterprise clients.  We’ll use Amazon Lex pricing as an example, which charges $0.004 per speech request and $0.00075 per text request processed.  For a call center representative overseas, in India, for example, (making ~$3,000 per year) to be more cost effective than a virtual assistant, the agent would have to answer over 75 calls a day, 365 days per year (assuming 4:1 speech to text requests, 30 requests per call, and zero holidays or weekends).  For a US call center representative, who makes an average of ~$31K per year, that number is over 800 calls per day, every single day of the year. 
Moreover, Amazon’s pay as you go model requires no upfront or minimum fees, making scaling up or down virtually costless for the company ; hiring, training, and firing human agents, on the other hand, is quite expensive, as is asking them to work overtime. Even when volumes aren’t highly variable, call centers in the US experience attrition rates of 30-45% (versus a US average of 15%) , meaning virtual agents eliminate a costly and logistically challenging element of the current call center model. Moreover, the enterprise no longer needs to pay for managers to supervise and motivate agents and likely needs only retain a smaller team of quality control agents. A third-party solution like Amazon Lex means enterprises don’t even need to invest much long-term in coders or data scientists, outsourcing that development to a tech giant.
While the value creation described above largely takes a future state of near to full automation of call center requests for granted, there are certainly tasks that virtual assistant can’t handle today or potentially ever. When a home insurance customer calls to report that her/his house has burned down, for instance, today’s virtual assistant is simply not equipped to respond in an appropriately empathetic way.  Call center agents, however, are still ultimately likely to lose for two major reasons. First, even in the near term when virtual assistants can only handle the most routine, predictable tasks, this will inevitably siphon some of the volume from today’s human agents, meaning call centers will need fewer FTEs. As the algorithms take in increasingly quantities of data, it’s reasonable to assume that virtual assistants will progress up the complexity curve. Just look at Google’s Duplex versus the infuriating rigidity of yesterday’s endless interactive voice response (“For your account status, please press 1”).  Second, longer term, consumers are likely to become increasingly comfortable with digital interactions (and those interactions may feel increasingly natural as the technology improves), and they may simply no longer expect companies to provide individuals 24/7 for customers to chat with personally. 
The assistant will become the master
Call centers are likely just the beginning of the automation of service tasks. We may well see these virtual assistants assisting and then increasingly replacing medical professionals, marketers, as well as human personal assistants.
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Article video: https://aws.amazon.com/lex/