Among the most prominent companies and organizations in the world leading the thinking around the Future of Work, McKinsey may be at the very top. The firm has released numerous primary research-fueled reports in recent years that relate to issues covering automation, job displacement, the skill gap, and income equality — their most recent long-form report in 2017 titled Jobs Lost, Jobs Gained: What the future of work will mean for jobs, skills, and wages.
One of their more interesting additions to their Future of Work portfolio, though, started late last year and has continued into this year: an 8-episode (so far) podcast series titled The New World of Work that brings together McKinsey experts (notably James Manyika, who is one of the most well-known thought leaders in the world on the subject) and industry players to discuss various aspects of the changing workforce landscape and its implications on society. I had a chance to listen to the first 8 episodes and offer my takeaways below.
Overall: How McKinsey Views the Future of Work
The phrase “The Future of Work” gets thrown around an increasing amount as a hot topic to pay attention to, and there are actually a handful of sub-categories within it that people may be referring to when they use it. McKinsey breaks it down into four:
- Jobs Lost & Gained — what the impact of the evolution of the workforce is on the number of jobs that exist
- Type of Work — how the rise of the gig economy and other independent work are changing the nature and set-up of the work that people are performing
- Organization of Work — how adoption of AI and other technology by companies will lead to a different distribution of work between man and machine within a company, and how the two interact with each other
- Income Inequality — how the evolution of the workforce will risk accelerating income inequality and the implications for society
The first category “Jobs Lost & Gained” is often the most discussed — with speculation into which jobs will most certainly be automated, and which jobs may be “safe”. The unique lens that McKinsey brings to the discussion is less of a focus on the jobs, and more of a focus on the activities within them — that is, you must first analyze which activities within different jobs are susceptible to automation before speculating if the job itself may be at risk of phasing out of the economy.
Takeaway #1: There may be more jobs that change than are displaced
McKinsey’s above premise that activities within jobs should be the initial focus has led them to extensive primary research on what the specific activities are that make up each job in the economy and how “at risk” each of those activities is to automation.
Their most notable finding is that there are only about 5% of jobs in the economy that are made up of 100% activities that are highly automatable, but a whopping 60% of jobs in the economy are made up of at least 30% activities that are highly automatable. These two stats drive one of their primary conclusions that there may be more jobs that change than jobs that are displaced entirely.
With this conclusion in mind, they argue that moreso than focusing on the risks around mass unemployment, we should worry and look for solutions to the risk of mass transitions in the workforce that may need to occur from these “changes” in jobs. Amidst every transition will be underlying questions such as:
- Can an individual find a new job?
- If so, can they move to where that job is?
- If so, do they have the necessary skills for that job?
- If so, is the income sufficient for them?
These and other questions will drive a challenge (but also a market opportunity) of how to best offer job dislocation support to employees as more and more find themselves in the position of making previously unanticipated workforce transitions.
While I appreciate and agree with McKinsey’s push to go deeper on jobs to examine the true nature of activities that might lead to automation, I think there is substantial risk of job loss that could be interpreted from their statistics that they aren’t taking into account. That is, inherent in McKinsey’s conclusions is an assumption that when a job has a set of activities that is only partially automatable, the most likely outcome is for the nature of the job to change, but for the number of jobs available to stay the same. I would argue that there are two other outcomes that are at least equally likely: (1) that the company needs to retain employees to perform the activities that are not automatable, but that they are able to significantly reduce the amount of employees necessary to do so, and (2) that the companies simply abandon the activities that are not automatable, in favor of simplifying their workforce and leveraging technology to increase the cost effectiveness of the set of activities that were automatable.
Thus, I agree with McKinsey’s conclusion that there will be a significant amount of job “change” and that a focus on transitions in the labor market is absolutely crucial, but I also believe that they may be understating the amount of jobs that could be displaced.
Takeaway #2: Regardless of displacement, there are obstacles ahead for the middle class employee
Regardless of where you come down on how many jobs will be displaced in the economy, McKinsey points out multiple fundamental shifts that put a middle class employee in the workforce in a more challenging position.
While the rise of the “gig economy” (and other independent work) is often described with a certain degree of excitement, as indeed many individuals are driven into working in the gig economy because of their own interest in it, it’s important to observe that there are also many who are driven to it because they either can’t find work elsewhere or can find work elsewhere, but aren’t making enough money and need to use it to supplement their income.
It’s helpful to start with this understanding because upon analysis of the standing of workers in the gig economy, it becomes evident that there are drastically fewer rights for workers (and significantly less power for labor unions). Often times workers have less access to benefits, less guarantee of stability, and less legal recourse if something goes wrong. As a prime example, Uber is entrenched in legal debates in multiple geographies arguing that their drivers are not employees of the company because acknowledging so would require the company to provide them with more rights, benefits, and stability.
In addition to the decrease of the leverage that some workers may experience, there is also already a challenge of income inequality that many in the economy are facing. McKinsey cites a variety of statistics that all lead to this same conclusion, including declining labor share of GDP, stagnant wages for the middle class, and the increasing gap between rich and poor. And the important layer on top of these statistics, of course, is that they are all at risk of getting significantly worse as workforce automation continues to rise.
Many of these observations lead to the natural question of what the response is from a policy perspective. In the age of automation and an evolving workforce, there is undoubtedly an opportunity to examine policy levers related to upskilling, labor market mobility, labor regulations, and income support. But with all that said, for me it remains unclear how governments will develop the agility by which to not only solve the current challenges, but to continue to rapidly solve challenges on a continuous basis in the ever-changing workforce of the future — an agility that has not often been demonstrated thus far.
Takeaway #3: There is a market opportunity to impact the new economic landscape (and LinkedIn is in a leading position), but open questions remain
As the future of work gets closer to becoming the present, there emerges a set of new market opportunities as dynamics and needs change in the overall economic landscape. In particular, there is an opportunity for information flow between job seekers, employers, and learning programs in an effort to connect people to skills and then to jobs that match those skills.
One especially interesting conversation in the podcast series was between James Manyika and LinkedIn CEO Jeff Weiner, in which the two discussed the remarkable position that LinkedIn is in to add value in that new landscape. One initiative LinkedIn is working on is the Economic Graph, in which they are attempting to create a digital representation of the economy that includes: 560 million individuals in the global economy, 20 million companies, 15 million open jobs, 60 thousand schools, and thousands of underlying skills that connect all of these groups. In addition to the innate value of having a large set of digitally organized information and connecting some of these different stakeholder groups, LinkedIn (or whichever company of set of companies ends up succeeding) is also in a position to proactively identify where skills gaps exist in specific industries or geographies.
All that said, my perspective is that still remains a difference between universal knowledge of this information (which is no doubt a major step in progress) and actual action on it in a way that makes the economy more efficient. While it is reasonable to understand how an individual job seeker, or individual company, or individual learning program would take such information on skills gaps in the economy and use it to better their own individual position, I think the larger question is how such a data set could be used on a systematic and collaborative level for the betterment of the economy overall (included, but not limited to from a policy level).
On top of all of these outstanding questions, I believe the most crucial one to answer is around financing. The most important activity in the connection between the above stakeholder groups is the upskilling of an individual in a specific set of skills that will prepare them for a specific job — but who is paying for that upskilling? The job seeker? The employer? The taxpayer? An investor who shares future income? There are plenty of innovative possibilities that can be considered, but it’s imperative that we begin to lay the foundations for those possibilities now, as they will be essential to quickly deploy as the pace of change in the workforce continues to accelerate.