The No. 1 Job Skill Needed for the Smart Machine Age: Knowing How to Iteratively Learn

We as a society are on the leading edge of a confluence of technology advances that will dramatically change how we live and work. It’s likely that this change will be as disruptive for us as the Industrial Revolution was for our ancestors. This change will be led by artificial intelligence, which will become ubiquitous in our lives, and by increased global connectivity and computing power, the Internet of Things, biotechnology, genetic engineering, nanotechnology, advanced robotics, and augmented and virtual reality. Technology will globally transform the workplace, education, government, health care and every business industry.

These technological advances will be ongoing and will have several likely impacts:

1. We will live longer.
2. Lifelong jobs will become rare.
3. Most working people of the future will be part-time freelance workers.
4. Technology will automate tens of millions of service and professional jobs in the United States.
5. Many people will not be able to find work.

Living longer and working less will create big educational and social issues around income inequality, lack of upward social mobility, democratic governance, social cohesiveness and finding meaning in life. We will live in continuously changing and uncertain times. That raises the question: How do we as individuals navigate that kind of life?

Hunting and Gathering for Meaningful Work

An analogy can be found in our history — the lives of our hunter-gatherer ancestors. They generally lived a nomadic life in search of food and safety. They excelled at iterative learning. They learned how to go into new environments and thrive without getting eaten or killed by predators. They excelled at iteratively learning with all their senses about their animal and plant environments. And they learned that they would more likely find food and safety if they joined with others in small teams and tribes. Collaboration and sharing the bounty of the hunt and gathering were keys to their survival.

From a work perspective, many of us will become modern day hunter-gatherers. We will be called entrepreneurs, freelancers, independent contractors and members of microsharing economies. Or if we are fortunate enough to find full-time work, we will likely have several different jobs in our career, requiring us to continuously learn new skills. Most of us will be constantly “hunting and foraging” for meaningful work.

Just as in the hunter-gatherer days, the most vital skill for finding meaningful work and building a meaningful life in the Smart Machine Age will be knowing how to iteratively learn — knowing how to go into new situations and learn by trial and error. Iterative learning is not foreign to us. Trial and error is how we learned as young children. It is how, for example, many of us learned to ride a bicycle. We started with training wheels to avoid taking big personal risks. We learned by doing and adapting to the results.

Curiosity and Courage

It was also helpful that we as young children were curious and unafraid to explore new things. Unfortunately, for many of us that curiosity and courage to explore and learn from making mistakes was “schooled” out of us by the dominant education model created to meet the needs of the Industrial Revolution workplace: to perform repetitive tasks in a standardized, error-free manner. We took tests in school to determine whether we were “smart,” which meant making better grades by making fewer mistakes. Years of that snuffed out the curiosity and the courage to explore and iteratively learn from results different than the ones we expected or desired. We learned to avoid mistakes — and that makes iterative learning very difficult.

In the Smart Machine Age, many of us will have to relearn the process of how to iteratively learn. And we will have to relearn how to be curious like a child and to be courageous like an explorer.

Best Learning Practices

Who is trained to excel at iterative learning?

Research scientists are trained to excel at iterative learning. A good scientist approaches the unknown with an open mind, humility, and the curiosity and courage to iteratively test ideas and beliefs (not values) by performing experiments. Good scientists are not afraid of failing because they know failing is a necessary part of the process of figuring out what is true. And they will actively try to find data that disconfirms their ideas. Their iterative learning process is called the scientific method. We all will need to learn to think more like scientists.

Some of us already use iterative learning processes in our workplaces today. If you use lean startup, effectuation theory, the scientific method, design thinking, A/B testing, constant improvement processes or any adaptive learning process, then you are trying to iteratively learn. Unfortunately, not enough of us do. In the Smart Machine Age, everyone will need to use an iterative learning process. And we will need to have the discipline to use other learning processes, too. Such as: Root Cause Analysis, Unpacking Assumptions, If-Then-Then Thinking, Critical Thinking Questions and Checklists, Rapid Experimentation, After-Action Reviews and the PreMortem.

The Smart Machine Age will require another big leap for many of us. It will require us to reject our individualistic, self-centered, self-protective, survival-of-the-fittest approach to life because just as in the hunter-gatherer days, we will need to collaborate with others in order to thrive.

The Smart Machine Age is upon us. Do you have the most important job — and life — skill to thrive in this new environment?

Ed Hess is professor of business administration and Batten Executive-in-Residence at Darden and co-author of Humility Is the New Smart: Rethinking Human Excellence in the Smart Machine Age (Berrett-Koehler, 2017).