Machine Learning Will Drive Economic Growth in Every Industry


by Marc Fischer, Co-Founder and CEO of Dogtown Media

Brace yourselves for impact. Humanity is on the brink of a fourth industrial revolution thanks to machine learning. This subset of artificial intelligence (AI) is unlocking unprecedented value across every industry, and how you embrace it will determine which side of the disruption equation you’re on.


Corporate investment in AI is increasing exponentially. According to a forecast from the International Data Corporation, $12 billion was spent on AI and machine learning development in 2017, and this will reach $57 billion by 2021. Research from Deloitte Global returned similar insights: machine learning programs doubled from 2017 to 2018 and are expected to double again by 2020.


But to truly understand machine learning’s economic effects, we must expand our horizons further into the future. PricewaterhouseCoopers expects AI to be responsible for $15.7 trillion of global GDP growth by 2030 — more than the current GDP output of China and India combined. By 2035, Accenture estimates AI will increase productivity and profitability by 40 percent and 38 percent, respectively.


These figures are undoubtedly impressive, but not surprising when you consider the main benefits machine learning offers for every industry: reduced costs, increased profits, and a drastic improvement in customer satisfaction. Together, these advantages yield better business outcomes for all sectors.


Let’s look at how machine learning is already affecting a few key industries.


Machine learning is giving healthcare providers groundbreaking tools to diagnose various irregularities like tumors from X-rays and MRIs more effectively than any human radiologist could. For example, Stanford researchers have created a machine learning algorithm capable of detecting 14 types of medical conditions from a single chest X-ray.


This is a big reason why McKinsey predicts machine learning, coupled with big data and healthcare app development, could be worth $100 billion per year in healthcare alone. Imagine what insights machine learning could offer by analyzing big data in medicine. The Georgia Institute of Technology is already utilizing deep learning algorithms to predict heart failure before it happens. Machine learning is ushering in a new era for preventative healthcare.


Warehouses and factories are racing to leverage machine learning’s capabilities in their supply chains, and it’s easy to see why. By eliminating bottlenecks, streamlining inventory management, and optimizing production and logistics, machine learning is minimizing waste and driving unparalleled efficiency.


McKinsey expects machine learning to eliminate 50 percent of supply chain prediction errors, reduce transportation costs by 10 percent, and cut administrative expenses by 40 percent. Much of this will be due to the fact that machine learning will make it possible for facilities to operate 24 hours a day, 365 days a year. But quality plays just as big of a role as quantity does. Through the technologies that are currently available, 78 percent of physical work could be automated. This will allow workers to take on safer, less physically-demanding roles that provide more value.




Perhaps nothing has revolutionized our roads more in recent years than ridesharing services like Lyft and Uber. However, this will pale in comparison to the changes self-driving cars will bring. Uber earns 20 percent of revenue per ride, but when the company transitions to autonomous vehicles, this will rise to 100 percent.


Cars are far from the only means of transit being disrupted by machine learning. Airplanes are also receiving a substantial boost in intelligence. Boeing is focused on utilizing AI to decrease pilot input, and thus human error, on commercial flights. If this alarms you, well, here’s a surprise; AI in the air is already occurring. Major South Korean airline Asiana forbids pilots from manually flying the airplane once it reaches an altitude of 3,000 feet. Above this threshold, AI takes you where you need to go.


Diving into each industry that will be impacted by machine learning app development would be impossible; this technology’s cross-industry impact cannot be overstated. Through the examples above, I hope you’ve gained a better understanding of how machine learning will impact your industry.


Maybe you’ve even thought of how this disruptive technology can be applied to your company today. With this being said, only one question remains: are you ready for machine learning?


Please note: This article contains the sole views and opinions of Marc Fischer and does not reflect the views or opinions of Guidepoint Global, LLC (“Guidepoint”). Guidepoint is not a registered investment adviser and cannot transact business as an investment adviser or give investment advice. The information provided in this article is not intended to constitute investment advice, nor is it intended as an offer or solicitation of an offer or a recommendation to buy, hold or sell any security. Any use of this article without the express written consent of Guidepoint and Marc Fischer is prohibited.


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