AI is replacing jobs, changing traditional roles, applying pressure on knowledge workers, creating new fields of work, and raising the demand for certain skills.
Allegis Group,  August 23, 2017

AI’s Impact on Jobs: Reading the Indecipherable Tea Leaves

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(Editor’s Note: The following is an excerpt from “AI and the World of Work: Embracing the Promises and Realities,” a free white paper available on the Allegis Group website.)

Thanks to artificial intelligence (AI), the jobs landscape will be vastly different in the years to come. Businesses and governments are worried about the shift as machines take on complex, thought-driven tasks and take away work from people traditionally considered irreplaceable. And for business or talent decision makers, the impact of AI on the way work gets done may change the nature of workforce strategy in the very near future.

What will that future be? When it comes to jobs, the tea leaves are indecipherable as analysts grapple with emerging technologies, new fields of work, and skills that have yet to be conceived. The only certainty is that jobs will change. Consider the conflicting predictions put forth by the analyst community:

  • According to the Organization of Economic Cooperation and Development, only 5-10% of labor would be displaced by intelligent automation, and new job creation will offset losses.
  • The World Economic Forum said in 2016 that 60% of children entering school today will work in jobs that do not yet exist.
  • 47% of all American job functions could be automated within 20 years, according to the Oxford Martin School on Economics in a 2013 report.
  • In 2016, a KPMG study estimated that 100 million global knowledge workers could be affected by robotic process automation by 2025.

Despite the conflicting views, most analysts agree on one thing: big change is coming. Venture Capitalist David Vandergrift has some words of advice: “Anyone not planning to retire in the next 20 years should be paying pretty close attention to what’s going on in the realm of AI. The supplanting (of jobs) will not happen overnight: the trend over the next couple of decades is going to be towards more and more automation.”

While analysts may not agree on the timing of AI’s development in the economy, many companies are already seeing its impact on key areas of talent and business strategy. AI is replacing jobs, changing traditional roles, applying pressure on knowledge workers, creating new fields of work, and raising the demand for certain skills.

Replacing Human Workers: A Closer Look at RPA

While it’s easy to focus on particular industries and skills impacted by AI, the larger story may prove to be the impact of AI on how work gets done. Robotic process automation (RPA) is one example of a fundamental advance in applying technology as part of the workforce. This area of AI is driven by an intelligent agent that functions very much like a virtual employee, automating many office functions with a minimal investment in IT and support.

Notably, the virtual employee can be trained by any human knowledgeable on the process, rather than an IT specialist. RPA is expected to significantly impact business process outsourcing (BPO) as this type of technology can be easily implemented within existing client systems and processes. At present, RPA has found widespread traction in the financial services industry and can be expected to expand from there. Key advantages include:

  • Scalability and Cost: RPA is projected to have a particularly strong impact on the BPO model. It is capable of managing many routine tasks performed by humans at a lower cost while offering greater flexibility and scalability. A key advantage of RPA is that it can scale up or down without additional human resources or IT infrastructure.
  • Ease of Deployment: RPA is expected to achieve rapid adoption because it can deploy onto an enterprise technology platform without a substantial investment in hardware, specialized personnel, or application development.
  • Adoption: Both Accenture and KPMG now have dedicated RPA practice groups. Other professional services providers will likely follow suit as the technology becomes widespread.
  • RPA Versus True AI: A 2016 CIO magazine article points out that RPA is not true AI. RPA uses traditional computing technology to drive its decisions and responses, but it does this on a scale large and fast enough to roughly mimic the human perspective. AI, on the other hand, applies machine and deep learning capabilities to go beyond massive computing to understand, learn, and advance its competency without human direction or intervention — a truly intelligent capability. RPA is delivering more near-term impact, but the future may be shaped by more advanced applications of true AI.

Transforming Roles: New Pressures on the Knowledge Worker

RPA is an excellent example of how companies are leveraging AI to fulfill needs today, but that is only the beginning of the story. For many, the real question about AI relates to its impact on the jobs of the future. For example, talent industry analyst Josh Bersin says AI is positioned to change how work is done and create new jobs: “What we concluded is that what AI is definitely doing is not eliminating jobs; it is eliminating tasks of jobs, and creating new jobs, and the new jobs that are being created are more human jobs.”

Deloitte’s Human Capital Trends Report also recognizes a changing demand for skills among organizations due to the growing footprint of AI technology. The future will increase the value of workers with a strong learning ability and strength in human interaction. On the other hand, today’s highly paid, experienced, and skilled knowledge workers may be at risk of losing their jobs to automation. Ironically, the best qualities for tomorrow’s worker may be the strengths usually associated with children.

“Learning has been at the center of the new revival of AI. But the best learners in the universe, by far, are still human children,” notes University of California, Berkeley Psychologist Alison Gopnik. “At first, we thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems — the corridas of nerd machismo — would prove to be hardest for computers. In fact, they turn out to be easy. Things every dummy can do like recognizing objects or picking them up are much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby.”

The emphasis on learning is a key change from previous decades and rounds of automation. Advanced AI is, or will soon be, capable of displacing a very wide range of labor, far beyond the repetitive, low-skill functions traditionally thought to be at risk from automation. In many cases, the pressure on knowledge workers has already begun.

Consider recent developments:

  • Doctors: AI technology has started to impact skilled professions such as physicians, as IBM’s Watson has demonstrated superiority to humans across a growing range of medical functions.
  • Lawyers: The legal profession is in a state of flux as powerful machine learning systems supplant the traditional roles filled by paralegal professionals and, increasingly, attorneys themselves.
  • Accountants: New machine learning-based applications are rapidly automating core corporate treasury and finance functions while accounting firms are developing technology to supplant their own CPAs and tax specialists.
  • Journalists: AI is impacting the traditional role of newspaper reporters as existing technology writes financial and sports reporting for many major media outlets.
  • Information Security Technicians: DARPA is investing heavily in automating network security maintenance, in part out of a belief that threats and changes occur too fast for a human to manage. Other organizations are also involved in similar efforts.
  • Pilots: AI applications are increasingly being used to do the job of airline pilots. While the ability to fully replace commercial pilots is debatable, AI may reduce the number of pilots or co-pilots needed to fly a plane.

In addition to its effect on traditional knowledge workers and skilled positions, AI may influence another aspect of the workplace: gender diversity. According to Stanford AI expert Jerry Kaplan, “Men hold 97 percent of the 2.5 million U.S. construction and carpentry jobs. … [These] male workers stand more than a 70 percent chance of being replaced by robotic workers. By contrast, women hold 93 percent of the registered nurse positions. Their risk of obsolescence is vanishingly small: .009 percent.”

He continues, “Many occupations that might appear to require experience and judgment — such as commodity traders — are being outdone by increasingly sophisticated machine-learning programs capable of quickly teasing subtle patterns out of large volumes of data. … If your job involves distracting a patient while delivering an injection, guessing whether a crying baby wants a bottle or a diaper change, or expressing sympathy to calm an irate customer, you needn’t worry that a robot will take your job, at least for the foreseeable future.”

The impact of AI on specific types of knowledge workers is difficult to predict as AI is only just beginning to tackle the types of work that doctors, engineers, lawyers, and writers do. Regardless of industry, however, AI is a real challenge to today’s way of thinking about work, value, and talent scarcity. AI will expand and eventually force many human knowledge workers to reinvent their roles to address issues that machines cannot process.

At the same time, AI will create a new demand for skills to guide its growth and development. These emerging areas of expertise will likely be technical or knowledge-intensive fields. In the near term, the competition for workers in these areas may change how companies focus their talent strategies.

New or Expanding Fields of Expertise and Growing Skills Demands

What types of skills will be needed to fuel the development of AI over the next several years? The answer is subject to debate, but most observers agree on several fields of new opportunity. These prospects include:

  • Ethics: The only clear “new” job category is that of AI ethicist, a role that will manage the risks and liabilities associated with AI, as well as transparency requirements. Such a role might be imagined as a cross between a data scientist and a compliance officer.
  • AI Training: Machine learning will require companies to invest in personnel capable of training AI models successfully, and then they must be able to manage their operations, requiring deep expertise in data science and an advanced business degree.
  • Internet of Things (IoT): Strong demand is anticipated for individuals to support the emerging IoT, which will require electrical engineering, radio propagation, and network infrastructure skills at a minimum, plus specific skills related to AI and IoT.
  • Data Science: Current shortages for data scientists and individuals with skills associated with human/machine parity will likely continue.
  • Additional Skill Areas: Related to emerging fields of expertise are a number of specific skills, many of which overlap various fields of expertise. Examples of potentially high-demand skills include modeling, computational intelligence, machine learning, mathematics, psychology, linguistics, and neuroscience.

A New “Old” Challenge in the Job Market

Concern about automation and its impact on jobs is nothing new. In 1928, a New York Times headline declared, the “March of the machine makes idle hands.” That article referenced the reduction in work in agriculture while output subsequently rose due to new farm machines.

Long after that observation on the eve of the Great Depression, observers continued to worry over the impact of automation. According to a recent Economist article, “Panics about ‘technological unemployment’ struck in the 1960s (when firms first installed computers and robots) and the 1980s (when PCs landed on desks).” As advances in technology posed a perceived threat to many workers, numerous companies also viewed innovation as a threat to business. Rather than enter the innovation discussion, companies would take a “wait and see” approach, adopting new solutions when they were proven and safe. The risk today is that when innovation happens quickly, a “wait and see” mindset will leave a company well behind its competitors.

Andrew Ng, former chief AI scientist at Baidu Research, co-chairman and co-founder at Coursera, and Stanford University adjunct professor, points out, “In the past, a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their internet strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they’d started thinking earlier about their AI strategy. AI is the new electricity. Just as 100 years ago electricity transformed industry after industry, AI will now do the same.”

For companies considering their talent needs, these lessons of past innovations should loom large. The development of the internet showed how new technologies could take away jobs and create new ones. It showed how the companies that ignore change find it difficult to catch up. No business wants to fail. No one wants to be the Blockbuster Video of the AI era, but few companies are stepping up to stay ahead of the curve. The lesson: as the jobs that support AI evolve, companies must be prepared to secure new skills. A proactive approach to the AI-driven landscape of work can be a key ingredient to a talent advantage in the future.

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