I have written much about the type of skills that 21^st century knowledge workers will require in an era shaped by four forces:

• Technology, which is eliminating growing number of traditional jobs and fundamentally changing the tools that will be available to (and the skills that will be required of) knowledge workers;
• Globalization, where increasingly sophisticated knowledge-based jobs can be performed by increasingly highly-educated knowledge workers in lower-cost countries around the world;
• The “New Normal” employment environment in which companies are reducing hiring and reducing benefits and job security by using contingent workforces—freelancers, contract workers and part-timers—to perform many functions that formerly were done in-house; and
• Extreme volatility, where sudden, often unanticipated socio-political and economic events prompt rapid changes in our lives and work environments.

Knowledge workers who hope to thrive in this environment will require very different skills and a very different approach to and philosophy of work than their parents. They will, of course, continue to need deep functional skills in their chosen discipline, whether that be business, engineering, law or sociology. However, they’ll also require a broad range of complementary skills—what I call foundational skills—that will be required of people in all occupations. These skills which, as described in my October 30 article on Core Skills, include what I generally describe as high-level thinking, “Integrative imagination,” quantitative analytics, IT fluency and a range of soft skills, particularly around communications, teamwork and inter-personal sensitivity.

This month’s article draws on the work of three economists, MIT’s David Autor and Frank Levy, and Harvard’s Richard Murnane, who look at the role of two types of skills that will be particularly critical in helping knowledge workers protect themselves from, and capitalize on the effects of two of the most profound of the forces transforming the 21^st century work environment—technology and globalization. These skills are:

• Complex communication skills; and
• High-level cognitive skills.

The Skills Matrix

Three primary articles by this trio of economists provide a framework for interpreting the very different ways in which the forces of technology and globalization will transform the U.S. Workforce. These articles are: Autor and Levy’s 2003 The Skill Content of Recent Technological Change; Levy and Murnane’s 2005/2006 How Computerized Work and Globalization Shape Human Skill Demands; and Autor’s 2010 The Polarization of Job Opportunities in the U.S. Labor Market.

 

The authors divide work tasks into five categories:

• Routine Cognitive Tasks: Mental tasks that are well-defined by deductive or inductive rules. Examples include dealing with simple customer service questions, many kinds of administrative tasks and formulaic tasks such as evaluating applications for mortgages.
• Non-Routine Cognitive Tasks (Expert Thinking): Solving problems for which there are no rule-based solutions. Examples include the practice of law and medicine, scientific research, architecting software, managing complex organizations, as well as some non-professional careers such as diagnosing tough auto repair problems.
• Routine Manual Tasks: Physical tasks that can be described though the use of deductive or inductive rules. Examples include all types of assembly line jobs and the counting and packaging pills into containers.
• Non-Routine Manual Tasks: Physical tasks that cannot be well described by a pre-defined set of If-Then-Do rules, or that require optical recognition and fine muscle control. Examples include driving a truck or taxi, cleaning a building, gardening and serving as a health care aide.
• Complex Communication: Interacting with humans to acquire information, to explain it, or to persuade others of its implications for action. Examples include a manager motivating the people whose work she supervises, a salesperson gauging a customer’s reaction to a piece of clothing, a biology teacher explaining how cells divide and an engineer describing why a new design for a microprocessor is an advance over previous designs.

Routine cognitive tasks (which can be accomplished by applying defined rules) and routine manual tasks (that can be defined in terms of a specific set of movements) are most subject to computerization and, in many cases, outsourcing. Jobs based on these tasks, therefore, will increasingly disappear, at least in the U.S. and other high-wage countries. The vast majority of those that remain will provide little job security and will be subject to intense price pressures.

Non-routine manual tasks, meanwhile, are not generally subject to computerization. And since most of these services are site-specific, they cannot be readily outsourced. Most of these jobs, however, can be performed by people with relatively modest degrees of education and training and do not require particularly high levels of strength, stamina or hand-eye coordination. They, like those for routine tasks, will be subject to much competition and will provide low salaries and often, little job security.

Some of these jobs face an even greater threat in the future—information technology. Robots, for example, can already accomplish some basic non-routine tasks (such as vacuuming rooms while avoiding walls, furniture and pets). Google’s prototype self-driving car, meanwhile, has already driven several hundreds of thousands of miles with a driving record blemished only by a single minor accident (which was, reportedly, caused by human error). Although it will likely take years for future intelligent devices to achieve significant market presence, the future is already in the process of being outlined, if not actually written.

This being said, a few non-routine tasks do require special training and skills and produce particularly high-value results—think for example, of gem cutters and professional performers and athletes. The relative handful of people who qualify for such jobs will continue to enjoy high levels of differentiation and will often be able to command high salaries. Indeed, globalization and the rapid growth of middle classes in developing countries, has the potential of increasing the demand and compensation for such services and, in some cases, of creating globally-branded superstars.

The Job Opportunities of the Future

Although a tiny handful of non-routine physical workers have the potential of earning high incomes and gaining good job security, they will be the exception. For the vast majority of people, the higher-probability route to a rewarding career will come from the other two job categories:

• Non-routine cognitive tasks; and
• Complex communications.

Non-routine cognitive tasks go far beyond the type of problem-solving skills that are typically taught in middle- and high-school classes. Most such teaching involves problems with rules-based solutions, which, as the authors explain, are relatively easy to teach and to test. These are the types of cognitive skills that IT-based tools are most capable of addressing. The challenge is to teach the types of higher-order cognitive skills for which computers are less well-suited—those for addressing problems for which “the rules are not yet known”

These, as explained by Irving Wladawsky-Berger, include two types of problem. Those for which:

• The information is hard to represent in a form that computers can use, such as feelings or impressions derived from viewing body language; and
• Rules are difficult to articulate. This can include “complex processes” (such as those required to learn to ride a two-wheel bicycle), “pattern recognition” (the solving of problems that cannot be expressed in deductive or inductive rules), “divergent thinking” (as in starting from existing knowledge to develop new concepts and to ask new questions); and the ability to exercise “good judgment” in the face of uncertainty.

Complex communications also includes a broad range of capabilities. At the most basic, it entails the ability to describe (in speaking and/or writing) complex phenomena and patterns in ways in which people can understand, the ability to ask questions in ways that prompt people to think of issues in new ways, and the ability to listen to and/or read and comprehend concepts. At a higher level, it involves interaction (simultaneously communicating, receiving and processing), empathy (as in understanding and addressing the feelings and motivations of others) and persuasion (especially in selling your ideas and motivating others to action).

How will these skills be incorporated into, and in some cases, redefine tomorrow’s jobs? How do employers communicate the need for such skills? Most importantly, how will these high-level skills be taught (not to speak of measured) in a society that is finding it so hard to teach even basic skills?

Then there is the longer-term question. Will/when/how information technology is likely to impact, complement or transform these high-level conceptual and communications functions—and what will this mean for individuals’ ability to use these tools to differentiate themselves and deliver high-value services?

Up to a few years ago, such questions would appear to be little more than remote speculation. Then came IBM’s Watson—the computer system that handily beat the reigning Jeopardy champions.

Although it will take years for “intelligent” machines to effectively displace humans in non-routine cognitive tasks, Watson has already demonstrated its ability to work across both domains—complex communications and high-level conceptual analysis. It, for example, not only showed that it could recognize natural language, but also interpret idioms, parse puns and to do it all in fractions of a second.

As for its role in conceptual tasks, one of the first commercial implementations of the new system is likely to be as a diagnostic tool to help (although certainly not replace) doctors in the diagnoses of illnesses. Rather than displace doctors, however, the diagnostic system will initially be used to complement them—reducing their need to research obscure combinations of symptoms, prioritizing diagnostic options and presenting doctors with better information from which they can make their final decisions. The same is true in the second major commercialization initiative, in customer service for financial services companies where it will initially support human agents, helping them anticipate customer needs and ask more probing questions.

But, as explained in my February 20 article on Watson, the role of Watson and its successors will only grow, as they prove their capabilities, as software is tuned and as adoption spreads into additional fields, such as financial analysis, supply-chain management and technical support. Consider, for example, the number of customer support functions that are already handled without human intervention, even without the help of Watson.

I will examine these and many other questions surrounding the skills required for the high-value jobs of the future in subsequent articles.