Artificial intelligence is revolutionizing life as we know it. Whether prepared for these changes are not, global industries from agriculture to medicine to finance have all restructured to incorporate machine learning tactics.

In agriculture, AI platforms can help farmers utilize resources more efficiently by tracking minute environmental changes. In medicine, AI can help diagnose patients quicker and tailor rehabilitation. In finance, predictive algorithms are rewiring how Wall Street functions.

In general, AI will help make life more efficient across the board—no more waiting, no more wondering, no more flippant usage of resources. Today, there may be an app for any topic under the sun, but in the future, there’s also likely to be an algorithm at our disposal.

However, not every industry is responsive to AI. Though most facets of life will incorporate meaningful data in some way or another, certain areas resist predictive observation (see: quantum mechanics). So, which industries are less likely to permanently change in respect to machine learning?

Sports Betting

Today, some of the world’s largest sports betting groups are racing to create a predictive algorithm for major leagues. Punters want access to reliable outcomes, while pundits benefit from understanding micro and macro patterns.

Even so, the human element of each individual athlete can’t be quantified precisely. For some punters, having a ‘computer pick’ option is more about knowing a sportsbook has covered their bases than having flawless analysis.

As the sector expands to more US markets, the likes of New Jersey and Pennsylvania sports betting sectors must cater to all types of bettors. In other words, computer picks and AI won’t be going anywhere—but neither will human pundits capable of delving deep into player psychology.

Customer Service

When faced with an automated response sequence from a customer service line after hours of toiling over a cable box or an IRS receipt, most people will eventually project their frustration at the recording. Having to deal with an emotionless drone while in a heightened state is the stuff of gurus, not the average person.

Customer service benefits from certain AI functions, like categorizing and sorting complaints and requests. However, there’s a certain point at which humans demand interaction with other living, breathing organisms—and will take to Yelp and other review sites when denied this courtesy.


Most people travel to get away from the everyday stressors in their lives. They choose a beachside location or a unique culture that sparks their interest. Invariably, people travel to experience new things.

AI, at its core, makes things easier and more efficient. It can predict which flights are the cheapest and cross-reference this information with nearby hotels, car rentals, day trips, and more. But AI won’t delve further into the industry to take over hospitality or guided excursions, especially in areas where cultural activities are the main attraction for tourists.


Retail Clothes

Like the other industries listed here, retail has also benefitted from the rise of machine learning algorithms. There are countless apps available today that promise a better fit by providing tailoring services, while others let shoppers try on clothes virtually.

In the future, it’s possible most of the world’s consumers will be shopping online. However, there are a few aspects of retail clothes shopping that aren’t likely to become automated, namely related to fashion.

First, there’s high fashion and its associated catwalk, which is a decidedly live experience. Second, there are certain outfits that must be worn and altered before a big event, like wedding gowns. And lastly, there’s preference. Some prefer to try on pants or shoes before buying, no matter how trusted a site’s predictive algorithm. In other words, AI won’t change what’s done in retail spaces, just how we do it.

Creative Arts

AI and machine learning are structured to do anything but get creative. They follow sets of rules that are designed to yield certain outcomes. Any deviation is handled immediately, as it compromises an AI’s goal. For this reason, creative arts won’t see any meaningful influence from AI.

But that doesn’t mean artists are afraid of working with machine learning platforms. In fact, some artists have taken to creating their own algorithms by feeding programs images of a certain aesthetic. From there, the program recreates ‘art’ as it has been instructed to.


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