How Artificial Intelligence Is Changing Traditional Industries

In the last 30 years, the pace of technological development has increased at an exponential rate. Since the advent of the Internet in the 90s, the world has changed in fundamental ways. Everything—from the way we communicate with one another to the way we pay for our groceries or work—has been reinvented. 


Fueled by a set of critical technologies, the pace of technological progress is only picking up speed. Some of those technologies are machine learning, deep learning, and neural networks, which fall under the umbrella term ‘Artificial Intelligence.’ 


At the forefront of the current technological revolution is Artificial Intelligence, also known as AI. AI is a field in computing that studies how the human brain operates, seeking to apply those principles to programming. In AI, the machine is fed only an input; it is then left to its own devices to figure out the rest of the equation. 


How Artificial Intelligence Is changing Traditional Industries


AI is not some future technology concocted by a science-fiction writer. It is very much real, and it’s already here. Netflix, Facebook, and Amazon rely heavily on AI to deliver their services and improve the experience of their customers. You probably use AI-based tech every day, even if you are not aware.


AI is disrupting the world as we know it in profound ways—from changing the way, products are manufactured to how they are shipped, marketed, and used by the consumer. The impact of this tech upon the world can not be overstated.

Predicting Consumer Behavior

The evolution of e-commerce gives us a good idea of how AI is changing the world. In a matter of years, shopping online has become an essential part of everyday life for many, with e-commerce going from mere novelty to becoming a multi-billion dollar industry. 


The United States’ Amazon and China’s Alibaba, the biggest players in the industry, are some of the most valuable companies in the world. These firms use potent algorithms based on AI tech to predict consumer behavior accurately. These algorithms tell them what you want to buy even before you know; they can then sell their products with a personalized ad. 


Netflix, the US-based streaming firm, is another example. It uses AI-based algorithms to predict viewer behavior and make personalized suggestions.

Conversational Intelligence

Conversational AI, more commonly known as chatbots, is another technology that has taken off in the past few years, particularly in the e-commerce and retail industries. These chatbots allow retailers to quickly and efficiently answer customers’ main doubts and problems. 


In some cases, these systems have become so sophisticated that users cannot tell them apart from their human counterparts. The most advanced chatbots can already answer open-ended questions in an eerily human-like way.


H&M, the Swedish clothing retailer, uses these bots to provide a pleasurable shopping experience to visitors on their website. Based on the customer’s preferences, H&M’s chatbots are able to make suggestions on what to try on next.


These chatbots are saving companies millions of dollars in salaries by allowing firms to reduce their staff. At the same time, they are improving customer service and creating a more personalized experience for visitors to their website.

Machine Learning

Machine learning is an AI technique that seeks to develop systems that learn and grow based on experience. It allows computers to improve and adapt their processes automatically without any targeted programming. Its applications are wide-ranging: from e-commerce to banking and medicine, every industry can benefit from machine learning.


In e-commerce and retail, machine learning is used to create targeted campaigns that attract more buyers. Marketers use machine learning to collect and parse vast amounts of data with the goal of providing an experience similar to what a customer would have in-store.


Machine learning is likewise used to implement dynamic pricing. Businesses can change and readjust prices by taking into account various factors all at once, such as competitor pricing, product demand, and day of the week.


According to a study by Boston Consulting Group, retailers who use machine learning for personalization have seen sales increases by 6 to 10 percent compared to companies that do not use these techniques.


Cogito is a prominent example of a company utilizing machine learning to improve customer experience. The company combines machine learning and behavioral science to facilitate the job of phone professionals and make voice calls a more enjoyable experience for customers.

Deep Learning

Deep learning is based on the concept of letting systems teach themselves. This AI technique enables computers to perform high-level thought and abstraction, including image recognition. It seeks to mimic how human brains function. To that end, it uses deep neural networks in which data is passed along. These networks adapt according to whatever data they are processing, always becoming more efficient.


Deep learning has applications in every industry. In the medical field, for example, it is being used to help doctors. The Chinese startup Infervision is using deep learning and image recognition to diagnose possible lung cancer with X-rays. With Infervision, radiologists can diagnose cancer more accurately and efficiently than ever before.


Financial technology, or fintech, refers to a suite of technologies that have streamlined banking and facilitated financial operations. Fintech is changing the way we bank, make payments, and even invest, making banks more connected and enabling the transfer of money from one corner of the world to another in seconds.


Payment technology showcases just how far fintech has evolved. From credit cards—once the state-of-the-art payment technology—the industry moved on to online payments. Now mobile payments via smartphone apps and QR codes are commonplace.


Even investing has been fundamentally altered by AI. In 2009, Bob Goodson, the Director of Quid AI, was challenged to create software that predicts which 50 companies would become successful startups in the near future.


Eight years later, Businessweek magazine reviewed the list. The results surprised even Goodson himself—about 20 percent of the companies the computer chose were valued at a billion dollars! It included names like Evernote, Spotify, Etsy, Zynga, Palantir, Cloudera, and OPOWER.


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