Automated self-service is Silicon Valley’s trick for automating the service economy. By tapping end users as a new workforce, it also fuels machine learning and the knowledge economy.
Automation has dramatically reshaped the contours of our agricultural and manufacturing sectors. Now, with a boost from machine learning, it is remaking the service economy. Its footprint, recognizable in all the service markets it touches, shows up as a disruptive new ability for end users to serve themselves. Intelligent, automated self-service is what automation looks like when it hits the service economy. It will revolutionize the way we understand work and is paving the way for the knowledge economy.
Automating the Service Economy
Just like agriculture and manufacturing companies, service firms automate by replacing employees with machines. The difference is that most of the work of service firm employees ultimately entails directly serving—and interacting with—end users. When service companies automate work, that contact between end users and employees is reduced or eliminated, so end users interact with a company through its various technological interfaces. Examples include bank ATMs, automated gas pumps, self-checkout counters at supermarkets, e-commerce websites, and even publishing tools like the one I am using to share these words with you.
When we learn to see it, most of what we think of as the “high-tech sector” is actually just the service economy becoming automated. Firms like Google, Amazon, and Uber use self-service technologies to disrupt one market after another. The underlying premise behind most Silicon Valley startups, too, usually boils down to “Use technology X to disrupt service market Y before anyone else.”
The New Work of Serving Ourselves
Many of us have mixed feelings about self-service. As Craig Lambert points out in his book Shadow Work (Counterpoint, 2015), companies frequently use self-service technologies to reduce labor costs by dumping tasks such as grocery checkout onto end users. With that said, nearly two-thirds of consumers respond that, when it comes to interacting with companies, they prefer to answer a question or solve a problem on their own. We like the flexibility and speed of performing our own searches rather than working through a human service provider, such as a librarian or travel agent. We usually find it faster, more convenient, and less expensive.
As machines enable us to do more things for ourselves, they eliminate service jobs without necessarily eliminating service work. Automated self-service technologies enable us to work directly with a company without interacting with its employees. The travel agent no longer books my flight; I do. That job disappears, but not necessarily the work. These interactions may not feel like work, since we engage in it voluntarily and aren’t compensated for doing it. Shopping on Amazon or booking flights on Expedia feels more like consumption than actual work, but that is only because our notion of work has yet to catch up with an automated service economy.
All service businesses—whether automated or not—generate value through a co-creative partnership with their end users. Whether it’s an airline, a streaming music service, or a restaurant, the value of a service emerges as we engage with it. Self-service technologies just replace employees as the interface through which end users and the company co-create that economic value. Self-service technologies act as a medium or membrane through which companies automate engagement with end users. They are the new engines of value creation, employing the workers of the new economy: end users.
Intelligent Automated Self-Service
Tools such as speech recognition, speech synthesis, machine vision, and emotion recognition lessen the burden these new “workers” face in doing self-service work. In the process, machine learning makes it possible to automate more and more services that once relied on the subtlety of the human touch. Machine learning thus acts as a catalyst for an automated service economy.
This relationship between service automation and machine learning is reciprocal, however, because self-service technologies also play a critical role in harvesting the end user data that fuels much of today’s machine learning. One of the fundamental design principles of such technologies is that our interactions with them automatically generate a stream of behavior data, and so, while high-tech service disruptors empower billions of people around the globe to serve themselves, they simultaneously generate vast quantities of data that allow the likes of Google, Facebook, and Amazon to dominate the field of machine learning.
As automated self-service technology gets smarter through machine learning, it becomes intelligent automated self-service. The automation drives down costs while the intelligence improves the usefulness and attractiveness of the service. Better service generates more usage, more data, and ongoing rounds of increasing intelligence, all of which feeds back on itself. This virtuous cycle is the engine that high-tech disruptors use to devour one service market after another. The result is an upward spiral of domain-specific, artificially intelligent, automated self-service.
Growing the Knowledge Economy
From the perspective of economic productivity, these increases in automation and intelligence aim to increase the efficiency of end users, not of employees. The question is, to what end?
Google does not pursue its mission—“To organize the world’s information and make it universally accessible and useful”—out of the goodness of its corporate heart. Instead, Google (and Facebook) have convincingly demonstrated the huge profit potential of extracting knowledge from end users. Amazon and Uber operate with business models that differ from either of these two advertising giants, but what these companies—and in fact, all companies now leading the automation of the service economy—share in common is the ability to transform the information gleaned through end user engagement into useful, and extremely valuable, knowledge. Indeed, their profits ultimately hinge on this ability.
Machine learning transforms raw data into information that, when combined with automation, is in turn transformed into applied knowledge. The automation of the service economy is thus the precursor of the rise of a new, knowledge-based economy, and automated self-service technologies are becoming the primary interface through which we embed human knowledge into machine intelligence. In this new knowledge economy, our myriad contributions of data are transformed into a kind of coding—a synthetic intelligence, part human and part machine—that directs the automated operations of machines. Our interaction with automated self-service technologies is the new work of humans, and through these contributions we embed human volition and meaning-making into a new, synthetic intelligence—and into the automated knowledge economy in which it thrives.