AI Impact on Jobs: How can Workers Prepare?

In a previous blog, I explored the main findings from a recent MIT paper on AI’s impact on work. In this blog, I want to offer practical advice for workers worried about their jobs future. There is a lot automation anxiety surrounding the topic which often gets amplified through click-bait sensational articles. Fortunately, the research from the MIT-IBM Watson paper offers sensible and detailed enough information to help workers take charge of their careers. Here are the main highlights.

From Jobs to Tasks

The first important learning from the report is to think of your job as group of tasks rather than a homogenous unit. The average worker performs a wide range of tasks from communicating issues, solving problems, selling ideas to evaluating others. If you never thought of your job this way, here is a suggestion: track what you do in one work day. Pay attention to the different tasks you perform and write down the time it takes to complete them. Be specific enough in descriptions that go beyond “checking emails.” When you read and write emails, you are trying to accomplish something. What is it?

Once you do that for a few days, you start getting a clearer picture of your job as a collection of tasks. The next step then is to evaluate each task asking the following questions:

  • Which tasks brings the most value to the organization you are working for?
  • Which tasks are repetitive enough to be automated?
  • Which tasks can be delegated or passed on to other in your team?
  • Which tasks can you do best and which ones do you struggle the most?
  • Which tasks do you enjoy the most?

As you evaluate your job through these questions, you can better understand not just how good of a fit it is for your as an individual but also how automation may transform your work in the coming years. As machine learning becomes more prevalent, the repetitive parts of your job are most likely to disappear.

Tasks on the rise

The MIT-IBM Watson report analyzed job listings over a period of ten years and identified groups of tasks that were in higher demand than others. That is, as job change, certain tasks become more valuable either because they cannot be replaced by machine learning or because there is growing need for it.

According to the research, tasks in ascendance are:

  • Administrative
  • Design
  • Industry Knowledge
  • Personal care
  • Service

Note that the last two tend to be part of lower wage jobs. Personal care is an interesting one (i.e.: hair stylist, in-home nurses, etc.). Even with the growing trend in automations, we still cannot teach a robot to cut hair. That soft but precise touch from the human hand is very difficult to replicate, at least for now.

How much of your job consists of any of the tasks above?

Tasks at risk

On the flip side, some tasks are in decline. Some of this is particular to more mature economies like the US while others have a more general impact due to wide-spread adoption of technologies. The list of these tasks highlighted in the report are:

  • Media
  • Writing
  • Manufacturing
  • Production

The last two are no surprise as the trend of either offshoring or mechanizing these tasks has been underway for decades. The first two, however, are new. As technologies and platforms abound, these tasks either become more accessible to wider pool of workers which makes them less valuable in the workplace. Just think about what it took to broadcast a video in the past and what it takes to do it now. In the era of Youtube, garage productions abound sometimes with almost as much quality as studio productions.

If your job consists mostly of these tasks, beware.

Occupational Shifts

While looking at tasks is important, overall occupations are also being impacted. As AI adoption increases, these occupations either disappear or get incorporated into other occupations. Of those, it is worth noting that production and clerical jobs are in decline. Just as an anecdote, I noticed how my workplace is relying less and less on administrative assistants. The main result is that everybody now is doing scheduling what before used to be the domain of administrative jobs.

Occupations in ascendance are those in IT, Health care and Education/Training. The latter is interesting and indicative of a larger trend. As new applications emerge, there is a constant need for training and education. This benefits both traditional educational institutions but also entrepreneurial start ups. Just consider the rise of micro-degrees and coding schools emerging in cities all over this country.

Learning as a Skill

In short, learning is imperative. What that means is that every worker, regardless of occupation or wage level will be required to learn new tasks or skills. Long gone are the days where someone would learn all their professional knowledge in college and then use it for a lifetime career. Continual training is the order of the day for anyone hoping to stay competitive in the workplace.

I am not talking just about pursuing formal training paths through academic degrees or even training courses. I am talking about learning as a skill and discipline for you day-to-day job. Whether from successes or mistakes, we must always look for learning opportunities. Sometimes, the learning can come through research on an emerging topic. Other times, it can happen through observing others do something well. There are many avenues for learning new skills or information for those who are willing to look for it.

Do you have a training plan for your career? Maybe is time to consider one.

AI Impact on Work: Latest Research Spells Both Hope and Concern

In a recent blog I explored Mckinsey’s report on the AI impact for women in the workplace. As the hype around AI subsides, a clearer picture emerges. The “robots coming to replace humans” picture fades. Instead, the more realistic picture is one where AI automates distinct tasks, changing the nature of occupations rather than replacing them entirely. Failure to understand this important distinction will continue to fuel the misinformation on this topic.

A Novel Approach

In this blog, I want to highlight another source that paints this more nuanced picture. The MIT-IBM Watson released a paper last week entitled “The Future of Work: How New Technologies Are Transforming Tasks.” The paper was significant because of its innovative methodology. It is the first research to use NLP to extract and analyze information on tasks coming from 170 million online job postings from 2010-2017 in the US market. In doing so, it is able to detect changes not only in the volume but also in job descriptions themselves. This allows for a view on how aspects of the same job may change over time.

The research also sheds light on how these changes translate into dollars. By looking at compensation, the paper can analyze how job tasks are valued in the labor market and how this will impact workers for years to come. Hence, they can test whether changes are eroding or increasing income for workers.

With that said, this approach also carry some limitations. Because they look only at job postings, they have no visibility into jobs where the worker has stayed consistently for the period analyzed. It is also relying on proposed job descriptions which often time do not materialize in reality. A job posting represents a manager’s idea for the job at that time. Yet circumstances around the position can significantly change making the actual job look very different. With that said, some data is better than perfect data and this researches open new avenues of understanding into this complex phenomenon.

Good News: Change is Gradual

For the period analyzed, researches conclude that the shift in jobs has been gradual. Machine learning is not re-shaping jobs a neck-breaking speed as some may have believed. Instead, it is slowly replacing tasks within occupations over time. On average, the worker is asked to perform 3.7 less tasks in 2017 as compared to 2010. As the researchers dig further, they also found a correlation between suitability to machine learning and faster replacement. Tasks more suitable to machine learning do show a larger average of replacement, at around 4.3 tasks while those not suited for machine learning show 2.9 average replacement. In general, jobs are becoming leaner and machine learning is making the process go faster.

This is good news but not necessarily reassuring. As more industries adopt AI strategies the rate of task replacement should increase. There is little reason to believe what we saw in 2010-2017 will repeat itself in the next 10 years. What the data signal demonstrates is that the replacement of tasks has indeed started. What is not clear is how fast it will accelerate in the next years. The issue is not the change but the speed in which it happens. Fast change can be de-stabilizing for workers and it is something that requires monitoring.

Bad News: Job Inequality Increased

If the pace is gradual, its impact has been uneven. Mid-income jobs are the worst hit by task replacement. As machine learning automate tasks, top tier middle income jobs move to the top income bracket while jobs at the bottom of the middle income income move to the low income jobs. That is, occupations in the low tier of the middle become more accessible to workers with less education or technical training. At the top, machine learning replace simpler tasks and those jobs now require more specialized skills.

This movement is translating into changes in income. Middle jobs has seen an overall erosion in compensation while both high and low income jobs have experienced an increase in compensation. This polarizing trend is concerning and worthy of further study and action.

For now, the impact of AI in the job market is further exacerbating monetary value of different tasks. The aggregate effect is that jobs with more valued tasks will see increases while those with less value will either become more scarce or pay less. Business and government leaders must heed to these warnings as they spell future trouble for businesses and political unrest for societies.

What about workers? How can these findings help workers navigate the emerging changes in the workplace? That is the topic for my next blog