Source | https://hbr.org |Ravin Jesuthasan & John Boudreau
Today, executives have to cut through a lot of hype around automation. Leaders need a clear-eyed way to think about how these technologies will specifically affect their organizations. The right question isn’t which jobs are going to be replaced, but rather, what work will be redefined, and how? Based on our work with a number of organizations grappling with these issues, we’ve found that the following four-step approach can help.
1. Start with the work, not the “job” or the technology. Much work will continue to exist as traditional “jobs” in organizations, but automation makes traditional jobs more fluid and an increasing amount of work will occur outside the traditional boundaries of a “job.”
Optimally integrating humans and automation requires greater ability to deconstruct work into discrete elements — that is, seeing the tasks of a job as independent and fungible components. Deconstructing and then reconfiguring the components within jobs reveals human-automation combinations that are more efficient, effective, and impactful. AI and robotics increasingly take on the routine aspects of both blue and white collar jobs, leaving the non-routine to humans. That challenges the very essence of what organizations retain as human work. The reconfiguration of these non-routine activities will yield new and different types of jobs.
2. Understand the different work automation opportunities. AI can support three types of automation: robotic process automation (RPA), cognitive automation, and social robotics.
RPA automates high volume, low complexity, routine administrative “white collar” tasks — the logical successor to outsourcing many administrative processes, further reducing costs and increasing accuracy. Optimizing RPA can only be done when the work is deconstructed. For example, RPA will seldom replace the entire “job” of a call center representative. Certain tasks, such as talking a client through their frustration with a faulty product or mishandled order will, for now, remain a human task. Others, such as requesting customer identification information and tracking the status of a delivery are optimally done with RPA.
Cognitive automation takes on more complex tasks by applying things like pattern recognition or language understanding to various tasks. For example, the Amazon Go retail store in Seattle has no cashiers or checkout lanes. Customers pick up their items and go, as sensors and algorithms automatically charge their Amazon account. Automation has replaced the work elements of scanning purchases and processing payment. Yet other elements of the “job” of store associate are still done by humans, including advising in-store customers about product features.
Social robotics involves robots moving autonomously and interacting or collaborating with humans through the combination of sensors, AI, and mechanical robots. A good example is “driverless” vehicles, where robotics and algorithms interact with other human drivers to navigate through traffic. Deconstructing the “job” reveals that the human still plays an important role. While the human “co-pilot” no longer does the work of routine navigation and piloting, they still do things like observing the driverless operation, and stepping in to assist with unusual or dangerous situations. Indeed, it is often overlooked that the human co-pilot is actually “training” the AI-driven social robotics, because every time the human makes a correction, the situation and the results are “learned” by the AI system.