Artificial intelligence (AI) is advancing rapidly. We see it everywhere from the Roomba vacuuming our floor to Alexa answering our questions. Even driverless cars are on the way, and some cool robots are already here.

Other than making our lives more like science fiction, what does all this mean for everyday people? As it turns out, the implications are profound.

AI has already pretty much captured the routine, repetitive tasks. For example, car factories today mostly use robots rather than people. This has resulted in a severe dislocation, as many of those employed in physical and mechanical work (e.g., factory workers, farmers, miners, etc.) became obsolete.

The economy today has mostly transformed from a Physical Economy to a Thinking Economy, in which thinking ability is prized. Focusing on thinking-intensive work, such as work that requires STEM skills, has been seen as the way to avoid obsolescence.

Today, that conventional wisdom is under threat. A new generation of AI, employing machine learning methods, is now assuming many of the thinking tasks formerly performed by humans.

This threatens the same kind of dislocation that we saw previously with physical jobs, only this time it is the thinking workers who are threatened.

For example, IBM’s Watson system is assuming many thinking-intensive tasks, such as providing medical diagnoses, playing chess and go, and even defeating the best human players on the TV show, Jeopardy.

Such thinking applications are being actively developed today, and will become even more ubiquitous over time. The first such thinking intelligence AI applications are mostly predictive and analytical, but work on AI common sense and intuition (“general intelligence”) is advancing rapidly.

Thinking workers who move into roles that require more common sense and intuition will hold out longer than the stereotypical tech geek, but eventually even the general intelligence thinking tasks will fall to AI.

Such AI applications change work, but they also change the consumer, who now depends on Siri and Google for knowledge and information. If we want to multiply two numbers together, we no longer have to know our multiplication tables.

We can find the answer very quickly online. The result is that people (both workers and consumers) are getting worse at thinking, because AI is taking over a lot of it.

With thinking no longer as important, people need to gravitate toward feeling. Feeling intelligence is the hardest thing for AI to attain, which means it is humans’ best opportunity to stay relevant.  The “soft” skills, like empathy, emotional intelligence, and communication skills, are still areas of advantage for humans.

To stay relevant, workers should gravitate toward interpersonal roles such as management or sales. Jobs that focus on relationships, such as business-to-business marketing, are likely to require human guidance for decades to come. We refer to this era as the Feeling Economy.

One interesting implication of the Feeling Economy is that women are likely to play a more important role. The Physical Economy was male-dominated, because men are bigger and stronger, on average, than women.

Women did better in the Thinking Economy, because their physical disadvantages no longer mattered. In the Feeling Economy, the evolutionary advantage of women for nurturing and for empathy, is likely to make them the leaders.

In fact, we already see this in some of the world’s most advanced economies.  Women today lead many advanced economies, such as Germany, and the 10 countries that are most favorable for women have a GDP that is several times the world average.

So, how quickly will all this happen? As it turns out, the shift toward the Feeling Economy is already well underway. Feeling jobs are seeing the greatest employment increases and pay increases.  Based on empirical data, we project that the Feeling Economy (when feeling intelligence becomes more important, on average, than thinking intelligence) will arrive in about 15 years.

What can be done? A human worker who currently focuses on thinking intelligence, but who wants to stay relevant, is well-advised to re-train to develop his/her feeling skills.

In the classes that I teach, I emphasize group work more than anything else, to get people functioning effectively in an interpersonal environment.  I also require group written assignments and group oral presentations.

I require very little in the way of technical calculations, because AI will do those, but I make sure everyone understands the role of AI, and what it will mean for their jobs.

Roland T. Rust is Distinguished University Professor and David Bruce Smith Chair in Marketing at the University of Maryland. Ming-Hui Huang is Distinguished Professor, Department of Information Management, National Taiwan University. 

Their new book, The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy (with Ming-Hui Huang, National Taiwan University) is published by Palgrave Macmillan, a division of Springer nternational Publishing.