Automation's dual impact on blue and white-collar employment

By Jill Caren
Wealth of Geeks
 
As of 2022, the global industrial robot market’s valuation was $48.50 billion. Experts expect it to soar to $142.8 billion by 2032. The trajectory of this development promises significant gains in productivity. But, a shadow looms just behind it.

What is the price for this rapid leap in automation and productivity? To what extent will this drive for automation and digitalization affect workers? Will it render some roles and occupations obsolete?

—————

The current landscape of automation in various industries

Industrial robots are already making their way into factories worldwide. According to the World Robot Report 2021, the average robot density in manufacturing has surged to 126 robots per 10,000 employees. This is twice the level seen in 2015, when there were only 66 robots per 10,000 employees.

In the United States, for every additional robot introduced per 1,000 workers, wages take a 0.42 percent dip. At the same time, the employment-to-population ratio drops by 0.2 points.

And it’s not just menial jobs that are undergoing significant alteration. Automation impacts both blue-collar and white-collar sectors.

Ramanan Krishnamoorti, University of Houston’s VP for Energy and Innovation, observes growing artificial intelligence (AI), machine learning (ML), and robot use in routine tasks and white-collar roles. ML is beginning to streamline decision-making, automating everyday decisions akin to its applications in finance and medical imaging-related decision-making. These roles involve performing the same actions or steps regularly and consistently, often requiring more significant variations or more decision-making.

In the context of these changes, Krishnamoorti also highlights a growing fascination with hazardous environment robotics, improving safety and accessibility in sectors like energy, manufacturing, and maintenance.

Drones are increasingly used for tasks like inspecting wind blades and cleaning solar panels, automating previously dangerous or labor-intensive work. Using machines for these tasks removes the risk and danger to employees.

—————

Examining automation vulnerabilities

Although experts can generally understand the broad consequences of automation across the board, pinpointing the sector most significantly impacted remains to be determined due to the diverse outcomes presented in various studies.

A study by Blue Collar Brain shows that the manufacturing and construction sectors face the highest likelihood of complete automation, with a 53% chance of jobs being fully automated, surpassing the average risk of 34% observed across all sectors.

In stark contrast, a collaborative research effort between the University of Pennsylvania and OpenAI has unveiled a distinct vulnerability among educated white-collar workers regarding workforce automation. Workers in the white-collar sector making up to $80,000 per year are most likely vulnerable to labor automation. Even sectors typically deemed “secure” are susceptible to the effects of automation.

Krishnamoorti mentions a pivotal differentiation regarding the factors contributing to specific white-collar jobs’ vulnerability to automation. He posits that roles characterized by repetitive tasks lacking a significant need for information synthesis are particularly at risk. A prime example is the transformation witnessed in legal briefs, which previously demanded extensive research and analysis. The advent of AI, illustrated by tools like ChatGPT, has revolutionized the speed and efficiency of tasks such as rapid library searches and summarizing background information.

Krishnamoorti elaborates, “The availability of information and the ability to generate algorithmically-driven summaries have become relatively effortless. While jobs reliant on such skills as analysts, journalists, law assistants, and researchers will undoubtedly benefit, they won’t be entirely replaced. These roles necessitate not only gathering foundational information but also adding and delivering a fresh perspective through information synthesis, a process that current automation still struggles to replicate.”

—————

Short-term and long-term perspectives for blue collar jobs

The short-term outlook for automation presents a nuanced perspective for the manufacturing and construction sectors. Krishnamoorti acknowledges that, momentarily, manufacturing and construction could experience a rise in robot-assisted roles rather than fully autonomous ones.

This growth is based on the idea that supervised automation is more suitable for repetitive and risky tasks. This approach reduces unintended consequences and integrates human control rather than machine control.

Krishnamoorti further highlights two significant shifts that are unfolding. The rise of machine-based learning and on-the-spot decision-making through edge computing both curbing the necessity for supervised automation and driving unsupervised automation. These advancements, still in the early stages with demonstrated applicability in research and high-value test cases, are gaining momentum for long-term growth in manufacturing and construction roles.

—————

Blue collar jobs with the lowest risk of automation

Not all roles within this sector will experience identical outcomes due to automation, and the change rate may also differ. According to the Blue Collar Brain analysis of more than 700 blue-collar jobs, some occupations are safer from automation than others.

The study, conducted using a probability scale ranging from 0 to 1, reveals that first-line supervisors of blue-collar jobs face the lowest risk of automation. Police officers, electricians, and construction trades follow closely behind.
The following skilled trades are listed with their corresponding probabilities of automation:

• First-Line Supervisors of Mechanics, Installers and Repairers: 0.003

• First-Line Supervisors of Fire Fighting and Prevention Workers: 0.0036

• First-Line Supervisors of Police and Detectives: 0.0044

• Supervisors Transportation: 0.029

• First-Line Supervisors of Personal Service Workers: 0.076

• Electrical Power-Line Installers and Repairers: 0.097

• Police and Sheriff’s Patrol Officers: 0.098

• Electricians: 0.15

• First-Line Supervisors of Construction Trades and Extraction Workers: 0.17

The inevitable evolution of workplaces and jobs stresses the necessity of continuous adaptation. In line with this, Krishnamoorti advises mid-skill workers to prioritize ongoing learning. This recommendation is crucial because training approaches are transforming, integrating augmented reality, virtual reality, and digital twins. He foregrounds the enduring significance of collaboration, communication, and critical thinking in white-collar work.

These attributes drive automation’s potential. Furthermore, the growing importance of cyber technology and cybersecurity proficiency spans all job levels, demanding a continuous learning approach.