We all know that artificial intelligence is a powerful thinking machine, which makes us all concerned about whether AI will take knowledge work away from us humans. Indeed, we have seen mechanical AI replace people in factory jobs, so it’s natural to worry about the future of knowledge work.
Pessimists think that AI can take over much of knowledge work. A 2018 Journal of Service Research article on AI in the service field seems to bolster their outlook. Researchers had used analytical modeling to show that as AI advances from mechanical to thinking to feeling, AI will assume more jobs. In their scenario, eventually even highly skilled workers in jobs focused on interpersonal relationships and empathy might be replaced by true emotional machines.
Optimists, meanwhile, cling to a belief that AI exists to help humans and will never be able to replace them. Rather, AI will augment human work. And sure enough, it already has. But the notion that AI cannot replace human thinking (and feeling) is perhaps a myopic and parochial human-centric view. AI is already in the process of advancing toward a thinking level, so the rosy picture that AI will augment rather than replace human thinking may not hold.
So how can humans survive and thrive in the Thinking Economy? Fortunately, there are two areas to focus on.
Be Intuitive, Not Analytical
Thinking AI can be analytical or intuitive, with the latter not being very mature yet. AI seems certain to assume an increasing percentage of analytical thinking tasks, but humans may be able to hold off AI (for a while) with respect to intuitive thinking tasks. By distinguishing the two types of thinking intelligences, we have a clearer picture of what machines are good at.
Current machines are powerhouses of analytical thinking. Thus, as humans we should not compete head-to-head against them by striving to think like a computer. Nevertheless, current machines are still not good at intuitive thinking, which suggests that we can enjoy thinking intuitively (even not 100% rationally) and refer to data analytics to support our intuitions. For example, developing an effective marketing strategy requires intuitions about knowing what would work best, knowledge that’s accumulated from experience.
Let AI Personalize for You
The benefit of consumer personalization has long been recognized, but we don’t have the means and resources to achieve total personalization, mainly due to the high costs involved. Such expenses can be due to the difficulty of identifying each consumer’s preference or the difficulty of offering individually different products.
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Analytical AI, however, is very good at recognizing patterns and categorizing things from big data. Therefore, given the power of analytical AI in pattern recognition, personalization becomes its major benefit. For example, Netflix relies on machine learning to recommend movies to its customers. Such personalization involves two systems of machine learning: one system analyzes the movie-watching pattern (which types of movies a viewer likes and dislikes), and one system comes up with the recommended movies.
In the Thinking Economy, almost everything can be personalized based on big-data input and machine-learning algorithms and models. For instance, when we go to Amazon.com, the site recognizes us immediately, either because we sign in or based on our browsing behavior.
What all this ultimately means is that in today’s Thinking Economy, human workers still have an advantage in two areas: thinking intuitively and maximizing AI as a tool for providing personalized customer experiences…for now.
As AI rapidly advances, those advantages will soon become obsolete. People in all industries and professions should start thinking ahead and developing new skill sets for the coming Feeling Economy, in which human workers focus more bolstering interpersonal and empathetic tasks. For example, even for data scientists, the nature of their jobs will need to focus more on how to communicate methods and results with colleagues and clients, instead of continuing to master the analytic techniques. Companies that start planning for this shift are more likely to succeed.