The machine learning revolution: ML innovation in 8 industries

These days, there is a lot of confusion surrounding the term “artificial intelligence,” with people attributing it at different times to different technology domains. Machine learning and deep learning are on that list, too, though they’re distinct AI approaches. Both refer to techniques by which computer programs become “smarter” as they process more information. Entefy produced a primer on key terms in AI in our article Essential AI: A brief introduction to terms and applications.

From a business perspective, what is most interesting about machine learning is not the mechanics of how it works but the vast array of revolutionary applications being developed using it. To show just how impactful this technology already is, we put together this roundup of machine learning projects in 8 different industries. 


Machine learning could make flying safer and more user-friendly. Pilots and flight crews operate around the world, and each of them generates significant amounts of data. In addition to information that’s automatically recorded via the aircraft’s computers, human personnel catalog their notes and observations as well. But there aren’t necessarily standard documentation processes for the latter, which means valuable information might slip through the cracks. 

Machine learning may soon be able to parse these varied forms of data, interpreting different types of shorthand and slang and organizing all available information into a central database. In the future, an AI pilot may well draw on that database to respond to in-flight challenges and ensure flight safety.   


The more data companies gather about driver behaviors, the better they understand why accidents happen and how to prevent them, which is why machine learning may well make our roads safer. Businesses that operate vehicle fleets have begun using telematics to collect information about all aspects of driver performance and deploy machine learning algorithms to do just that.

Every time an employee over-accelerates, takes a turn too sharply, or fails to buckle their seatbelt, sensors and tracking systems detect and record it. This information enables companies to better train drivers by homing in on common driving problems. They can compare numbers of accidents against how many hours drivers are working and adjust employee schedules accordingly. Of course, improving human driving skills may be a stopgap on the path toward even greater road safety, as autonomous vehicles could prevent 90% of accidents


Manufacturing companies use machine learning algorithms to cut waste and other expenses in their processes. Smart programs analyze existing workflows and key in on areas that can be improved. In a report on artificial intelligence in the industrial sector, the authors found that machine learning programs could process thousands of data points gathered from multiple machine types and subprocesses. The results of such analyses could lower expenses in semiconductor production alone by 30% and could boost manufacturing productivity more generally by 20%. 

Transportation & Logistics

Forecasting is incredibly important in supply chain logistics. A sudden storm, market upset, or rise in transportation costs could severely impact logistics companies and their clients. But machine learning is helping these businesses become both more agile and resilient. Smart programs can use contextual data to predict potential problems so companies can create contingencies. The more accurate and current their data, the better equipped they are to respond to crises. 


Imagine going to work in the morning and being faced with millions of options for solving a given problem. Being spoiled for choice may sound good, but human beings simply cannot process that amount of information, especially on a short and urgent timescale. Yet that’s the situation plant breeders encounter all the time. Determining which breeds are most likely to thrive in a given climate, region, or season is no small feat, and the outcomes of their decisions impact the food supply for millions of people.

Fortunately, machine learning is up to the demands of modern agriculture and can analyze historical datasets to identify which breeds are suited to different circumstances. The technology can also be used to spot diseased crops through pattern recognition, so growers can intervene and save the rest of their yields. This type of precise science will be increasingly important as the global community copes with food insecurity and the growing impact of climate change. 

Consumer Goods & Services

Effective use of artificial intelligence depends on data, and retailers gain access to more customer information every day. Companies that combine consumer profiles with behavioral data and market trends can create powerful sales strategies, and machine learning programs can analyze wide-ranging data sets to identify optimal selling conditions. A business that deploys a dynamic pricing model powered by machine learning insights can promote its products “at the right price, with the right message, to the right targets,” according to the McKinsey Global Institute’s discussion paper, Artificial Intelligence: The Next Digital Frontier? Companies that get this right could see up to a 30% increase in online sales. 

Hospitality & Travel

Travel companies use machine learning to identify behavioral trends among consumers so they can tailor their booking experiences accordingly. Machine learning enables travel industry brands to extract trends in which factors influence travelers’ decisions most and which devices and search methods they use for different types of queries. For instance, the number of reviews a property garners matters more to consumers than the actual number of stars it receives. Knowing this, hotels may try to incentivize people to leave reviews so they can get on other travelers’ radars. 


Approximately 9 in 10 American adults use the Internet, and usage among the 18-29 age group stands at 98%. At that level of Internet penetration, telecommunication companies can’t afford downtime or infrastructure lapses. But their vast networks of cell towers, satellites, and fiber optic cables ae difficult to monitor manually.

Now, one major telco is using cameras on wings, or COWs, which are drones that capture images of their cell towers and commercial installments and diagnose problems in real-time. The company anticipates a future in which it runs the drone-captured data through a machine learning algorithm that pinpoints problems and fixes them automatically. Not only would this mean faster problem resolution, it would also create safer working conditions since there would be less need for technicians to climb to the tops of towers and telephone poles to make the fixes themselves.