The Impact of Automation on Jobs and the Future of Work
In a recent Digital HR Leaders podcast, John Boudreau joined David Green to explore some of the themes and the concepts from his recent book, Reinventing Jobs. John is a renowned expert on the Future of Work and is the Research Director for USC's Centre for Effective Organisations and Professor of Management and Organisation at the Marshall School of Business.
His large-scale studies and focused field research address the future of the global Human Resources profession, HR measurement and analytics, decision-based HR, executive mobility, HR information systems and organisational staffing and development. John has published more than 50 books and articles and his research has been featured in Harvard Business Review, The Wall Street Journal, Fortune and Business Week.
In this extract taken from their conversation, John and David discuss the four-step framework for reinventing jobs and optimising work from his book Reinventing Jobs, co-authored with Ravin Jesuthasan.
The topic of automation and the impact it will have on the future job market, is one that is currently front and centre. Some argue that AI and automation will replace approximately 40% of jobs over the next 10 to 15 years, while others are certain that it will open the flood gates of opportunity allowing us as humans to engage in innovative and impactful work. In John Boudreau and Ravin Jesuthasan’s book Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, they look at the impact that automation might have not on replacing humans but rather the potential to create new, more valuable and fulfilling roles for humans.
John explains that when thinking about the role of automation the idea would be that we have “systems that think about human workers and we could say, well, let's expand that beyond just employing a human in a job. Let's think about human workers engaged in many different ways. That's "Lead the Work". As Ravin and I were talking about Lead the Work and what our next book might be, we realised that automation had a great deal of similarity, that it also was a boundary breaking idea, that it also required dis-aggregating the work to see the patterns. So, we began to play with that idea, and the result was this book.”
Ultimately automation should be considered not as a way of replacing humans but actually as a part of your workforce. It can be thought of in a way as part of your talent pool. There are number of examples that highlight the way in which we work with automation, leverage it as a collaborator and often consider it part of the team. John explains
“This full language expert that happens to be sitting in a computer like Alexa or Siri or something like that, that can answer my questions when I say, has anyone ever done this before? That can suggest options based on what it sees. All right, so now how do we create a framework to understand that.”
He describes his four-step framework as a way to understand and come up with solutions for automation. The first step in this framework is disaggregating the work. It’s important to start with the work, not the job or technology.
Step One: Deconstructing the job
Traditionally organisations have built team structures broken down into and aligned to ‘jobs’, however what we’ve already begun to see is that automation makes traditional jobs somewhat more flexible, meaning that a lot more ‘work’ will take place outside of the traditional constructs of a job.
As John explains throughout the podcast, during their research for this book they found that when attempting to analyse exactly how many jobs will go away, that in fact it’s incredibly difficult to answer this question at the ‘job level’. In order to truly analyse how automation would impact the workforce, jobs needed to be broken down into tasks. By breaking jobs down into tasks it was possible to determine that
“Say 70% of the tasks that are part of this job are, let's say, easily automated, but 30% are not. So the real issue is that we don't end up with jobs going away, we end up with parts of jobs going away, and then you have a need for humans, but you only need them for whatever it might be 30% or 70% of the work they did before.”
While it’s thought that automation will remove a percentage of the work we do today, it’s important to remember that new tasks and skills may arise as a result of this deconstruction of jobs.
“The interesting thing is it is not unusual for that new 60% to be so much more valuable because of automation. That you actually could do better economically by just paying the same people more to do 90% of the work they did before because it's so much more valuable. So we talk about generally, the idea that once you deconstruct and see these tasks, you may indeed replace humans at the task level, you may augment them, meaning that they're pretty much doing the same thing, but we've made it more efficient, faster, etc.”
Often you get situations whereby automating the tasks, you are in fact reinventing them. In these situations, humans are doing something that no human being could possibly do without automation. When win-win situations such as this arise, humans end up actually being compensated more because they have reinvented the job.
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Step Two: Assess the relationship between job performance and strategic value
When thinking about the payoff of automation there are four things to consider. The first payoff is the reduction in errors. Unlike machines humans make mistakes and the value of automation is really recognised when human error is removed, and mistakes or accidents no longer take place.
The second payoff of automation can be seen in performance. As John explains the differences in the way people perform don't produce a lot of difference in value. “So, you could think about the many different performance levels and they all produce the same value, so we may, make them more uniform.” The third payoff is this idea of augmenting. Whereby automation allows us to augment our output thus increasing the value of our performance. Sometimes automation can take all of us ordinary humans, and it can make us into the most extraordinary version of workers. In an example John explains that:
“The best surgeons in the world have certain things that they do. Some of them are frankly avoiding mistakes. The best surgeons in the world will think of ideas or they've read the literature so that they know what to do in a certain situation. With automation, AI can inform a surgeon about the best procedure. Now we've taken all those surgeons that were frankly, somewhat poor performers because they'd never think of that. And we have made them all superhuman.”
For HR, the question arises as to what is the payoff we're trying for?
“Is it reducing the stakes? Is it augmenting the human performance? Or is it some sort of exponential reinvention of the work where we make everyone a superhuman with this tool?”
These are discussions that we need to have, to try and answer these questions.
Step Three: Identify options
Once jobs have been analysed and broken down into tasks the next step in the process is to determine what type of automation should be leveraged in each situation, whether that be robotic processes, artificial intelligence or deep learning. At the job level identifying the answer to this question is practically impossible, however at the task level it becomes somewhat clearer. John highlights that while “that’s still tricky, it also reflects the future of HR. This step must happen in conjunction with people who know technology well.”
What we often experience is HR knows the work in great detail but isn’t very technology savvy or involved in much of the innovation taking place in the technology arena. You then have IT, who are great at understanding the technology and speculating at how it might affect the work, but they don’t have a deep enough understanding of that actual work. John explains when situations like this arise “If it's driven by technology, you'll get lots of rather naive solutions about the work, but very deep on the technology side. If it's HR, you get very deep understanding of the work and the workers, but maybe some naivety about technology. So again, we come back to that idea of a boundary-less HR function, where if you look around HR, it may look like a technology design organisation in many parts of it, you might really not see a difference there.”
Step Four: Optimise work
Once you’ve broken the job down to the various tasks that it encompasses and have conducted all of the necessary analysis to understand what elements will be automated, the final step in the process is to reinvent the job. “You put it back together optimally, and you have the courage to say, if it turns out that we need only 50% of the work being done by people, we're prepared as an HR organisation to help our leaders understand that and work with it.”
It is definitely fair to say that AI and machine learning will significantly disrupt the workforce but equally it has the potentially to empower employees. Despite popular belief, automation won’t happen all at once in every role, it’s set to be a gradual implementation but will most definitely happen. As a HR function it’s our responsibility to support our leaders in both understanding the impact of automation and in developing a strategy that ensures we’re reaping the benefits automation has to offer.
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ABOUT THE AUTHOR
Manpreet Randhawa is the Head of Digital Content for myHRfuture.com. In her previous role as the Change Management Lead for People Planning, Design & Analytics at Cisco Systems, she was responsible for defining and executing on the change management strategy to successfully implement and sustain the digital and cultural transformation across the enterprise. Manpreet is very passionate about change management and technology and how to use both to transform the employee experience and prepare companies for the Future of Work.