In a technology-driven economy, skills like software design, database engineering, or computer science might seem indispensable.
Yet, today’s employers are not necessarily prioritizing technical experience and knowledge in their recruitment process. As companies’ information systems constantly evolve, they increasingly seek adaptative, collaborative, and creative skills.
The jobs of the future will center around mindsets like continuous learning, critical thinking, and intellectual agility. Or what we call meta-learning capabilities.
Here’s what defines these long-lasting skills.
Critical and system thinking
Despite its obvious supremacy today, technical expertise is no longer enough to compete in the job market. This is especially true in a time of ever-evolving technological developments.
The head of train maintenance at GE knows this more than everyone else. Instead of hiring mechanics and technicians to diagnose damage and repair parts, he now uses data engineering teams to predict faults and underperformance before they happen.
These individuals, whether engineers, software programmers, or data scientists, don’t rely only on their knowledge of tools like databases and software. They can also apply models from other fields and think about problems in a systemic way. For example, a common problem encountered by trains in the US is the strong winds blowing across the Great Plains of the Midwest. They inevitably derail train cars.
To anticipate these events and reduce their impact, GE’s operations team came up with the idea of using a model from another industry to predict falling trees for transport trucks. By applying probability equations based on wind strength and speed, they could predict the days when winds are most likely to cause damage to rail carriers.
In the same way, the aerospace industry no longer values closed expertise. With the vast amount of information available today, it has never been easier to predict the operation and life expectancy of a particular component, whether it’s an engine, wiring, or a landing wheel. Each engineering department doesn’t have to consider how their specific component function, but the overall value the system brings to consumers.
In other words, each engineer must be able to conceive an interdependent system that fulfills a common purpose and function. And this is not a natural way of thinking for engineers or mathematicians used to analytic thinking.
Creative problem-solving skills
For many reasons, Google is a dream workplace for future knowledge workers. The company provides a challenging and stimulating environment that attracts top talent.
But Google, like many innovative companies, is no longer looking for purely technical skills. In their recruitment process, they are increasingly leaving room for improvised problem-solving exercises, which involve group thinking. Candidates are invited to work together to solve problems that are far from their original field of expertise. The most successful are those that can find the rule or model that best applies to the use case.
This process is similar to Google’s internal process, which values sharing times called Scrums. Every day, employees from the same organization get up and explain the missions they will conduct during the day. This way, everyone can know which problem everyone is facing and provide their perspective on the matter.
Similarly, at the end of the week, everyone says what they have accomplished or failed to accomplish. These moments stimulate the pride and creativity of each person to share their idea, to assess those of others, or to even outbid them.
In this way, Google seeks to train creative and collaborative problem-solver employees. They teach them to listen to the ideas of others and add their insights in a game of intellectual stimulation.
This divergent and generative thinking is certainly one of the keys to Google’s great innovation in software design (Gmail, Gmap, Google Photo, Google Keep, Google News). And it will be decisive in the years to come to enlighten new technologies with powerful ideas.
Meta-learning and far transfer
In their educational journey, most teachers expect their students to make good use of theoretical concepts. They want them to learn a concept and be able to apply it to a similar problem. Educators may then feel happy when their students have reached this level of understanding. What we call a near transfer of knowledge.
But some teachers also hope to secretly impart what education experts name far transfer. In this case, students show sufficient mastery of the concept to apply it to a priori unrelated fields.
Although these abilities seem rare, they can be taught, especially by applying experiential learning. Educational researchers have conducted an experiment that assesses the result of such a learning method.
They gave two groups of participants the task of throwing darts at a target drowned under 1 meter of water. Each group increased its success rate on this task by practicing several times. But to break the tie, the first group was then instructed about the principle of light refraction in water. As the targets were sunk even deeper, this group became therefore significantly more accurate in their throws than the other group.
The conclusion to take from this experiment? Learners usually tend to perform far transfer when they receive theoretical knowledge related to the tasks at hand.
What it means also is that people who are used to putting their knowledge into action increase their meta-learning abilities. The feedback from experience stimulates their understanding of their theoretical concepts.
And this is a huge advantage over algorithms that struggle to perform transfer learning at more complex tasks.
Social and cultural fluidity
What does a waiter understanding the intentions of foreign customers and a salesman adapting to local business practices have in common?
They are both professions that involve a subtle comprehension of cultural differences, and a fluidity in moving from one world to another. These skills interactions are in high demand among companies as professional exchanges and interactions are increasingly globalized.
Every year hundreds of millions buy foreign products online and millions work in foreign companies, so every corporate department needs workers with fine social and cultural understanding.
This is especially the case, as these are capabilities beyond the reach of machines. While sentiment analysis and facial recognition algorithms can pick up on users’ emotions and senses, they cannot yet incorporate in their language more subtle differences. They can’t either create connections via body language. This can mean adapting the tone of voice to fit an emotional situation or subtly mimicking the gestures of the person you’re trying to convince.
Similarly, they cannot automatically learn the cultural codes of a country or the social context of a local environment. Each interaction involves assumed knowledge, and machines struggle to process these undertones that define human conversations.
Open-minded workers are instead able to investigate beforehand the culture they are interacting with and to connect with different values. And the best ones have sufficient introspection and hindsight to reconsider their cultural preconceptions.
Here are 4 meta-learning mindsets that will undoubtedly give you a head start over computer automation. Feel free to get familiar with them!