How AI and Machine Learning Changes Modern-day Industries by@mikhailkirilin

How AI and Machine Learning Changes Modern-day Industries

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Mikhail Kirilin

Copywriter, community manager, editor. Interested in fintech, investing, fund management.

Artificial intelligence and machine learning are two phrases that everyone nowadays seems to use. Lately, it’s on the hype across most industries where technological advancements are crucial to survival and growth, e.g., agriculture, healthcare, and manufacturing. The AI and machine learning fields have seen tremendous growth that warrants some hype, as they’re becoming critical to improving the industries that most affect us. But it’s important to know exactly how AI/machine learning is transforming the industries and their role in our technological advancements. To start, let’s briefly define both terms.

  • Artificial intelligence is the simulation of human intelligence processes by machines, mainly computer systems. It’s a wide-ranging branch of computer science that entails training computers to do tasks usually done by humans.

  • Machine learning is a subset of artificial intelligence that deals with building computer systems to learn and adapt automatically without human input, by using algorithms to analyze and draw conclusions from data.

AI and machine learning are the foundation for using computers to automate ordinary human tasks, so we can focus on other important tasks that only humans typically perform. They’re helping to make things easier across many industries in the following ways:

Manufacturing

Automating mundane tasks is the first and foremost way that AI is helping transform the manufacturing industry. Thanks to AI improvements over the past decade, robots can now perform many manufacturing tasks that humans typically perform, e.g., assembling final products, moving heavy tools across large manufacturing spaces, welding, drilling, and quality control checks.

AI is also adopted during product development to simulate products in virtual environments to know how they’ll perform in the real world. For example, automobile designs are run through aerodynamics simulation software to measure performance before manufacturing starts in real life. Though such simulation software already exists, the simulation process is far from perfect because it requires many hours of calculations and estimations. Computer systems are learning to simulate real-world scenarios with more accuracy and less time with machine learning.

Also, AI helps with the predictive maintenance of complex machines, giving a clue of when parts are at their breaking point and need care.

Healthcare

AI is helping transform the healthcare industry in ways including:

Drug development

Many biotech and pharmaceutical companies now use AI to aid drug discovery. Drug discovery involves complex processes at all steps, such as predicting chemical properties, identifying molecules, and image analysis. For example, drug discovery research usually requires hundreds of laboratory hours to analyze microscopic images and observe how diseases unfold and how drug candidates affect them. This task requires experts to spend valuable time studying the images and taking down their findings, but they could spend that valuable time on other crucial tasks that computers typically can’t perform. Pharmaceutical companies now adopt machine learning to help analyze microscopic images and free valuable time for biochemists to work on other things.

Medical Imaging (Testing)

Medical imaging is another area where AI and machine learning are making major healthcare improvements. For example, when radiologists perform x-ray scans on patients and send the results to doctors for analysis, doctors typically try their best to infer the medical problems affecting a patient so they can begin effective treatment. However, doctors don’t have infinite time and often miss subtle problems like cancers and blood vessel blockages.

With machine learning, computers can be trained on a dataset of millions of images and use that experience to point out medical conditions from x-ray images. Already, hospitals are spending millions of dollars to develop artificial intelligence software that reads x-rays and other medical scans with hopes that it’ll spot symptoms that doctors typically miss.

Cybersecurity

AI is creeping into the cybersecurity field with very positive results. Many companies have propped up in the niche of developing cybersecurity software that uses artificial intelligence and machine learning to analyze networks and fish out cyber threats, e.g., CrowdStrike, SentinelOne, and Cylance. AI works better when analyzing cyber threats because machine learning systems can learn much more over time than humans, whose time is often limited. Also, hackers could launch countless types of attacks on a network, and AI techniques can detect them better than typical antivirus software when enormous data is involved.

Cyber security is one of the critical fields which benefits from AI and machine learning advancements. With AI applied in cybersecurity, it’s easier for IT personnel working at large enterprises to take note of the typical backdoors and attack vendors hackers take advantage of and work to patch them up. AI in the cyber field is just at the beginning stages and still has much potential ahead.

Retail

AI brings many benefits to the retail industry by automating mundane tasks usually performed by humans, e.g., cashiers. Many big retailers now adopt AI-based checkout processes, e.g., Amazon Go, because they benefit from it. For example, a company named Caper AI makes smart shopping carts and cashier-less checkout systems for small convenience stores.

AI’s impact on retail doesn’t stop at the consumer side but also affects the back-end operations such as warehouse management and logistics. AI has given birth to robots that move goods within warehouses with ease and autonomous truck driving that’ll help move goods from one place to another at lower costs.

Transportation

Autonomous driving accomplished with AI will make a big dent in the transportation industry. Many transportation companies, especially trucking and public transport, have judged autonomous driving to be the industry’s future and are spending big to make it happen. Automobile companies alike are also investing billions of dollars in developing autonomous driving software for their customers. If all goes as planned, we’ll have suitable autonomous driving within the next 100 years that’ll revamp the transportation industry as we know it.

Finance and Investments

Yes, AI is also transforming the finance industry. Hedge funds, mutual funds, venture capital firms, high-frequency trading firms, and the likes are increasingly adopting AI and machine learning to help make investment decisions. To make profitable investments in the securities market, investors analyze large swaths of data to predict which securities will perform well in the future. This analysis is time-consuming and exhaustive, making it difficult for humans to do. It’s no surprise that financial firms have adopted machine learning and artificial intelligence to help with their analysis.

For example, Stripes, a venture capital firm, employs a virtual robot that trawls for information around the web on over 13 million private companies and looks out for potential investments for the VC firm. The robot has helped Stripes’ 0% net of fees, according to Bloomberg.

Investors also employ AI and machine learning for automated trading to a good point. Examples of software they use include the Bloomberg Terminal, MetaTrader 5, and Alphasense. There are countless other industries that AI is transforming by the day, such as agriculture, education, construction, cloud computing, etc.

Challenges of AI in the Enterprise

Though AI brings about much-needed advancements across industries, there are various challenges in implementing them to satisfaction. They include:

Ethics

Automation stemming from AI can lead to significant job losses without viable alternatives in the short term. AI is gradually replacing jobs such as truck driving, customer service, receptionists, and book-keeping, which are jobs held by many people. There’ll be adverse economic consequences if we don’t create alternative employment for these people.

Cost

Developing and implementing AI and machine learning systems requires a large number of computing resources that translate to high costs. High cost is a barrier for small businesses to partake in the benefits of AI advancements.

Training

Obtaining the vast data set needed to develop machine learning and AI techniques can be difficult for enterprises. Also, the data obtained may be corrupt or falsified and distort an AI system’s workability.

All in all, the rise of AI and machine learning is a net benefit for our world. It’s bound to improve the economy and the utility of many industries. McKinsey, a leading management consulting firm, estimates that AI may deliver an additional economic output of $13 trillion by 2030. It’s evident at this point that AI is here to stay, and it’s only up to us to adapt to it or stay behind.


Artificial intelligence and machine learning are two phrases that everyone nowadays seems to use. Lately, it’s on the hype across most industries where technological advancements are crucial to survival and growth, e.g., agriculture, healthcare, and manufacturing. The AI and machine learning fields have seen tremendous growth that warrants some hype, as they’re becoming critical to improving the industries that most affect us. But it’s important to know exactly how AI/machine learning is transforming the industries and their role in our technological advancements. To start, let’s briefly define both terms.

  • Artificial intelligence is the simulation of human intelligence processes by machines, mainly computer systems. It’s a wide-ranging branch of computer science that entails training computers to do tasks usually done by humans.

  • Machine learning is a subset of artificial intelligence that deals with building computer systems to learn and adapt automatically without human input, by using algorithms to analyze and draw conclusions from data.

AI and machine learning are the foundation for using computers to automate ordinary human tasks, so we can focus on other important tasks that only humans typically perform. They’re helping to make things easier across many industries in the following ways:

Manufacturing

Automating mundane tasks is the first and foremost way that AI is helping transform the manufacturing industry. Thanks to AI improvements over the past decade, robots can now perform many manufacturing tasks that humans typically perform, e.g., assembling final products, moving heavy tools across large manufacturing spaces, welding, drilling, and quality control checks.

AI is also adopted during product development to simulate products in virtual environments to know how they’ll perform in the real world. For example, automobile designs are run through aerodynamics simulation software to measure performance before manufacturing starts in real life. Though such simulation software already exists, the simulation process is far from perfect because it requires many hours of calculations and estimations. Computer systems are learning to simulate real-world scenarios with more accuracy and less time with machine learning.

Also, AI helps with the predictive maintenance of complex machines, giving a clue of when parts are at their breaking point and need care.

Healthcare

AI is helping transform the healthcare industry in ways including:

Drug development

Many biotech and pharmaceutical companies now use AI to aid drug discovery. Drug discovery involves complex processes at all steps, such as predicting chemical properties, identifying molecules, and image analysis. For example, drug discovery research usually requires hundreds of laboratory hours to analyze microscopic images and observe how diseases unfold and how drug candidates affect them. This task requires experts to spend valuable time studying the images and taking down their findings, but they could spend that valuable time on other crucial tasks that computers typically can’t perform. Pharmaceutical companies now adopt machine learning to help analyze microscopic images and free valuable time for biochemists to work on other things.

Medical Imaging (Testing)

Medical imaging is another area where AI and machine learning are making major healthcare improvements. For example, when radiologists perform x-ray scans on patients and send the results to doctors for analysis, doctors typically try their best to infer the medical problems affecting a patient so they can begin effective treatment. However, doctors don’t have infinite time and often miss subtle problems like cancers and blood vessel blockages.

With machine learning, computers can be trained on a dataset of millions of images and use that experience to point out medical conditions from x-ray images. Already, hospitals are spending millions of dollars to develop artificial intelligence software that reads x-rays and other medical scans with hopes that it’ll spot symptoms that doctors typically miss.

Cybersecurity

AI is creeping into the cybersecurity field with very positive results. Many companies have propped up in the niche of developing cybersecurity software that uses artificial intelligence and machine learning to analyze networks and fish out cyber threats, e.g., CrowdStrike, SentinelOne, and Cylance. AI works better when analyzing cyber threats because machine learning systems can learn much more over time than humans, whose time is often limited. Also, hackers could launch countless types of attacks on a network, and AI techniques can detect them better than typical antivirus software when enormous data is involved.

Cyber security is one of the critical fields which benefits from AI and machine learning advancements. With AI applied in cybersecurity, it’s easier for IT personnel working at large enterprises to take note of the typical backdoors and attack vendors hackers take advantage of and work to patch them up. AI in the cyber field is just at the beginning stages and still has much potential ahead.

Retail

AI brings many benefits to the retail industry by automating mundane tasks usually performed by humans, e.g., cashiers. Many big retailers now adopt AI-based checkout processes, e.g., Amazon Go, because they benefit from it. For example, a company named Caper AI makes smart shopping carts and cashier-less checkout systems for small convenience stores.

AI’s impact on retail doesn’t stop at the consumer side but also affects the back-end operations such as warehouse management and logistics. AI has given birth to robots that move goods within warehouses with ease and autonomous truck driving that’ll help move goods from one place to another at lower costs.

Transportation

Autonomous driving accomplished with AI will make a big dent in the transportation industry. Many transportation companies, especially trucking and public transport, have judged autonomous driving to be the industry’s future and are spending big to make it happen. Automobile companies alike are also investing billions of dollars in developing autonomous driving software for their customers. If all goes as planned, we’ll have suitable autonomous driving within the next 100 years that’ll revamp the transportation industry as we know it.

Finance and Investments

Yes, AI is also transforming the finance industry. Hedge funds, mutual funds, venture capital firms, high-frequency trading firms, and the likes are increasingly adopting AI and machine learning to help make investment decisions. To make profitable investments in the securities market, investors analyze large swaths of data to predict which securities will perform well in the future. This analysis is time-consuming and exhaustive, making it difficult for humans to do. It’s no surprise that financial firms have adopted machine learning and artificial intelligence to help with their analysis.

For example, Stripes, a venture capital firm, employs a virtual robot that trawls for information around the web on over 13 million private companies and looks out for potential investments for the VC firm. The robot has helped Stripes’ 0% net of fees, according to Bloomberg.

Investors also employ AI and machine learning for automated trading to a good point. Examples of software they use include the Bloomberg Terminal, MetaTrader 5, and Alphasense. There are countless other industries that AI is transforming by the day, such as agriculture, education, construction, cloud computing, etc.

Challenges of AI in the Enterprise

Though AI brings about much-needed advancements across industries, there are various challenges in implementing them to satisfaction. They include:

Ethics

Automation stemming from AI can lead to significant job losses without viable alternatives in the short term. AI is gradually replacing jobs such as truck driving, customer service, receptionists, and book-keeping, which are jobs held by many people. There’ll be adverse economic consequences if we don’t create alternative employment for these people.

Cost

Developing and implementing AI and machine learning systems requires a large number of computing resources that translate to high costs. High cost is a barrier for small businesses to partake in the benefits of AI advancements.

Training

Obtaining the vast data set needed to develop machine learning and AI techniques can be difficult for enterprises. Also, the data obtained may be corrupt or falsified and distort an AI system’s workability.

All in all, the rise of AI and machine learning is a net benefit for our world. It’s bound to improve the economy and the utility of many industries. McKinsey, a leading management consulting firm, estimates that AI may deliver an additional economic output of $13 trillion by 2030. It’s evident at this point that AI is here to stay, and it’s only up to us to adapt to it or stay behind.

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