2020 Artificial Intelligence Industry Trends

As of date some industries have been doing better than others at implementing AI. AI is lifting efficiency and performance across many diverse industries in 2020, with an array of benefits sweeping across multiple categories. As AI matures, these 9 areas and industries look like they are poised to benefit the most:

Financial Services

Banking and financial services are among the key industries benefiting from AI. The structured nature of financial data and the industry's past experiences with analytics may have made it easier for companies in this sector to implement artificial intelligence.

Many legacy organizations [in other industries] are still learning how to move from pilot projects to full operational deployments since the underlying data and architecture is very different in a pilot versus being operationalized. Financial services and insurance firms have been better at the advanced analytics on transitions since that data is well understood and managed.

In the future, the industry could see additional gains by applying AI to other areas beyond analytics, such as customer service.


Cybersecurity is likely to see big gains from AI in 2020. Already, many vendors are adding AI capabilities to their products. In its Top 10 Strategic Technology Trends for 2020, Gartner noted, "ML-based security tools can be a powerful addition to your toolkit when aimed at a specific high-value use case such as security monitoring, malware detection or network anomaly detection."

However, enterprises are likely going to need AI-based cybersecurity in order to counter new threats, which might make use of AI and machine learning themselves. As Forrester noted in its Predictions 2020, "The unfortunate reality will come to light that evil forces can adopt technologies such as AI and machine learning faster than security leaders can."

Predictive Maintenance

Many different industries, such as manufacturing, transportation, oil and gas, utilities, and even cloud computing data centers rely on complex machinery in order to stay in business. And any downtime of critical resources quickly results in significant financial losses. As a result, many organizations are investing in a combination of IoT sensors, computer vision, and/or machine learning technology to help them improve uptime by proactively identifying potential risks and scheduling maintenance in advance.

AI is improving business performance with predictive maintenance, where deep learning analyzes large amounts of high-dimensional data to detect anomalies in everything from factory assembly lines to building HVAC systems to commercial aircraft engines.


Manufacturing companies are beginning to dip their toes in the AI waters. In addition to predictive maintenance, manufacturing firms are using artificial intelligence and subsets like machine learning to better manage their supply chains, forecast demand, improve quality, deliver products, and increase customer satisfaction.

However, manufacturing firms will need to improve their underlying information architecture and data management practices if they want to be successful with their AI efforts. Even when machine learning algorithms do not need any reference architecture to function (visual identification of part defects for example), application of the results of the analysis does require that knowledge and information architecture.

Logistics and Transportation

Within the transportation industry, enterprises are using artificial intelligence to help them shave precious minutes off their delivery times and dollars off their costs. Spread across a large fleet, these small gains can result in millions of dollars of savings per year, alongside increases in customer satisfaction. In logistics and transportation, AI optimizes the routing of delivery traffic, improving fuel efficiency and reducing delivery times.

In addition, the transportation industry is also turning to AI to help with the creation of advanced safety systems, semi-autonomous vehicles, and eventually, fully autonomous vehicles. The result could be safer travel for everyone.


Like the transportation industry, the travel industry is also using machine learning (a subset of AI) to enhance logistics, which can allow them to reduce prices for customers. The travel industry is seeing positive results from AI in the area of fraud detection.

Criminals often target airlines and hotels to convert stolen credit card numbers into cash. They will book travel on the stolen card, and then attempt to get a refund back to their own personal cards, pocketing the difference. Others set up far more elaborate schemes where they book travel and then attempt to resell it on the black market. AI can help identify both kinds of fraudulent transactions, resulting in cost savings for the travel industry and their customers, as well as reducing the inconvenience for people with the stolen credit cards.


Another prime area for AI implementation right now is B2B, particularly B2B sales. B2B sales are benefiting from AI as well, with speech recognition making it possible to track and optimize every customer interaction, from research to early engagement to closing the sale.

In addition to speech recognition, AI firms are also making use of advanced machine learning and analytics for a variety of purposes. For example, within B2B, pricing often differs significantly from customer to customer, and machine learning can help them better segment their customers and price more effectively. Of course, they can also use machine learning for forecasting, supply chain management, and for uncovering other insights that can help them become more competitive.


With coronavirus on the news every day, everyone is interested in ways to improve healthcare, and artificial intelligence seems like one promising way to speed innovation in the field. Healthcare continues to be a prime application for AI. Algorithms can assist with tasks as diverse as analysis of scans, development of vaccines, interpreting research results, and improving patient care.

Again, experts caution, however, that in order for AI to be effective within healthcare, organizations need to have good data, solid training models, and the right IT infrastructure in place to both conduct the analysis and secure sensitive data.


For online stores like Amazon, AI has become such an expected part of the sales process that people don't even notice it anymore. In retail sales, combining customer demographic and past transaction data with social media behavior observation helps generate individualized 'next product to buy' recommendations, which is now routine for many retailers. Through 2020, look for these recommendation engines to continue to improve and for retailers to find new ways to implement AI.

One interesting data point is how democratized AI adoption is. AI flattens the competitive landscape, empowering smaller businesses to leapfrog and outmaneuver much bigger ones. Expect enterprises in retail and other industries to attempt to use AI technology to better compete against larger firms.