The new worker: Specialist or Generalist?

The economy is changing around us needs and uses a very different type of worker. That is why so many people struggle in the traditional work environment with work and life balance, the eight-to-five work day, with over credentialism, and with processes that are inefficient and inadequate.

Over the past decade, the most asked for new benefits were flexible working hours and to be able to work remotely. The Coronavirus has done us a favour by forcing us to completely change how we work and therefore has accelerated what was already happening by maybe five to 10 years.

The Half-Life of Information

Workplace unrest may be at least partly caused because of the half-life of information. A recent article looks at this issue from a variety of angles: the half-life of facts, the half-life of secrets, and the half-life of knowledge.

It is often said in Silicon Valley that the half-life of an engineer is around five years – maybe less. This is because we are learning more from what we already know and gathering new facts and data each year.

Many older beliefs and facts are either disproved or our understanding changes. New discoveries and research add to the body of knowledge and push out older or outdated information. New technologies require new skills. Experienced people often feel left behind or are struggling to understand how to use the new tools that younger people are comfortable with.

The Perils of Being a Specialist

In an industrial era, specialists thrive. Data and facts remain largely the same for years. Rules, methods, formulas, and procedures provide guidance and lead to success. This stability was comfortable but did not push people to learn or to be agile.

And, increasingly these facts, rules, and formulas will be contained in algorithms and in A.I. system that augment our decision making, lessening the need for experts.

With specialisation comes the danger of not seeing the forest for the trees. With narrow fields of view, everything is viewed through the lens of the area of expertise while often missing the bigger picture.

Limitations of Specialists

Many of the issues around privacy, ethics, diversity, and other issues that organisations are facing can be partially traced back to hiring specialist employees who have not been trained to imagine the possible outcomes or implications of their work.

They have been trained to rely almost exclusively on data and facts.

In this chaotic era, we have seen the limitations of that strategy and the result is individuals who do not make the best decisions and overly rely on data without understanding human psychology.

Without a foundation in ethics, culture, psychology, or a sense of history, they may misunderstand or not be aware of the side effects of their actions.

The Rise of the Generalist: The New Worker

Steve Jobs once said, “Technology alone is not enough. It is technology married with the liberal arts, married with the humanities, that yields us the results that make our hearts sing.”

To thrive is to become as broadly and generally educated as possible. A thorough grounding in basic subjects such as language, history, government, science, physics, chemistry, mathematics, philosophy, ethics, geography, music and art will provide a foundation for making better decisions. The so called “renaissance person” will be in demand.

The new worker is more likely to pursue self-employment or work for a small firm where the atmosphere is collaborative and there is open communication.

A few firms, such as Google and Zappos, are willing to hire people with no degrees, but with passion, interest, and life experience. They invest in mentor-based training. They put them into situations where they are forced to learn rapidly. They have large portions of the workforce who have chosen to work as temporary staff or on a contract. The manufacturing portion of work is done more and more by automated tools and software or by robots. While it does require some engineers and technicians to build these, it is far fewer than in the old economy.

Expert systems and machine learning will not be able to help us do what is right or fair. They cannot help us make decisions, judge how to deal with situations or even help us decide who might be the better candidate for a job.

These decisions require the wisdom that is created through having broad and general knowledge and experience.

Cover image: Shutterstock

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