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Examples of Artificial Intelligence (AI) in 7 Industries
Artificial intelligence (AI) is a hot topic. But for many, the application of AI beyond Apple’s Siri and Amazon’s Alexa remains murky.
New and emerging use cases for AI are expected to transform nearly every industry in the coming years and decades. And savvy companies and business leaders stand to benefit.
What Is Artificial Intelligence (AI)?
Broadly speaking, AI refers to computer systems that can perform problem-solving and decision-making tasks normally associated with human intelligence. These can include:
- Recognizing images and speech
- Making decisions
- Translating languages
- Providing recommendations
- And more
Examples of Artificial Intelligence Across Industries
So, how is artificial intelligence used?
AI applications range from consumer-oriented solutions (such as chatbots) to highly complex industrial use cases, like predicting the need for manufacturing equipment maintenance. Examples from seven industries provide a glimpse into the breadth and depth of the possibilities for AI.
1. Financial Services
AI has numerous applications in both consumer finance and global banking operations. Examples of artificial intelligence in this industry include the following.
Financial fraud attempts, whether on a massive scale or through day-to-day crimes (like credit card skimming), continue to rise rapidly in frequency and cause major disruptions to both organizations and individuals. According to Business Insider, banks like J.P. Morgan Chase use proprietary artificial intelligence algorithms to flag transactions that don’t fit normal patterns for further inspection.
Gone are the days of traders shouting on the stock market floor. Today, most major trading transactions are handled by algorithms that react and make decisions far faster than humans ever could. In fact, by 2024, the algorithmic trading industry is expected to reach $19 billion annually.
Within the broader landscape of financial services, insurance stands out for its unique applications of artificial intelligence. These include:
For decades, underwriting decisions have relied on highly manual processes and data inputs, as well as invasive processes like medical exams. Today, insurance companies use AI to assess risks using massive data sets that draw on factors ranging from prescription drug history to pet ownership.
Today, artificial intelligence can handle much of the claims process for simple claims. Examples of artificial intelligence range from handling client interactions through chatbots (like Progressive’s Flo) to using machine vision to assess auto damage. As machine vision and AI capabilities increase, human involvement will likely play less of a role in claims decisions.
While healthcare has traditionally relied heavily on human labor and care, an increasing number of tasks can now be outsourced to AI. Below are two examples of AI in healthcare.
Precision Medicine and Algorithms
An individual’s health outcomes, or even their response to a certain treatment, can vary significantly based on numerous factors, from lifestyle to genetics. These factors are difficult for human doctors to parse. AI can take in huge amounts of data to identify optimal treatments for patients, or even to identify emerging health concerns before they rise to a level that a human might notice.
Computer Vision for Diagnosis and Surgery
Increasingly, computer vision and machine learning are showing promise for diagnosing conditions such as skin cancers, and even for assisting during complex surgeries. For instance, AI can ensure physicians perform all necessary steps correctly during an operation.
4. Life Sciences
Since the life sciences by nature involve large data sets generated by experiments, it’s not surprising that artificial intelligence has numerous potential applications in the field. Artificial intelligence examples in life sciences include:
The search for novel treatments still requires large-scale experiments and confirmation of hypotheses. However, machine learning has been used since the 1990s to speed the process considerably. It can predict how certain compounds will interact with each other and even how a drug will work against its target.
Predicting Disease Spread
Throughout the COVID-19 pandemic, experts have used AI and machine learning extensively to predict the spread and impacts of the virus, particularly as it has mutated. Data from these models has allowed public health and healthcare leaders to adopt policies and prepare resources to minimize spikes and reduce stress on the broader healthcare system.
While many of us often take internet and communications availability for granted, the telecommunications industry depends on a series of highly complex processes and constant adjustments. AI can address these needs in several ways.
To maintain flawless operations, networks need to adapt to changing traffic and quickly address anomalies. Currently, 63.5% of telecommunications providers are using AI to monitor and improve their networks and deliver the best possible performance for their end customers.
Telecommunications networks rely on broadly distributed sets of hardware. And problems within this infrastructure can ripple throughout the network. Artificial intelligence offers telecommunications companies the opportunity to use predictive algorithms to identify when problems are most likely to arise, allowing
6. Oil, Gas, Energy
Oil, gas, and energy is an increasingly complex field, and one with little room for error given safety and environmental considerations. Artificial intelligence is allowing energy companies to increase their efficiency without increasing costs. Applications include:
Image-Processing to Identify Maintenance Needs
AI’s increasing ability to process images and recognize patterns is making it possible to use drones and other image sources to check power infrastructures for equipment breakdowns or even downed wires. This is a tactic that has already been implemented across the United Kingdom’s power grid.
Anticipating Energy Demand
As the transition to renewable energy continues, predictive data on energy demand and availability will be essential for energy providers as they make decisions about storage and utilization. This can include identifying how much solar power needs to be stored for nighttime or rainy days. AI will help companies parse the factors that impact demand and make informed decisions for the future.
Safe, efficient aviation, especially in the context of rising fuel prices, depends on the careful use of data to optimize both individual flights and the broader aviation infrastructure. AI applications in this sector include:
Predicting Route Demand
To maximize profits while retaining customer loyalty, airlines must strike a careful balance between providing enough flights between specific destinations without flying more routes than is economical. AI models can take factors like internet traffic, macroeconomic trends, and seasonal tourism data into account to help airlines make informed decisions about their route offerings.
Providing Customer Service
During major disruptions, such as those caused by massive weather events, few airlines have the staffing capacity to handle individual customer queries and needs one-on-one. In addition to automated messaging, airlines increasingly rely on AI to extract key pieces of information from customer messages and provide an appropriate response. For example, this may involve directing a customer inquiring about their luggage to information about reporting lost baggage.
The Future of AI Across Industries
As the breadth and depth of AI applications demonstrate, applications of big data, machine learning, and more have major implications across industries. While some of these applications are nascent, they are likely only the tip of the iceberg as this technology matures. Organizations, therefore, should consider acting quickly to scale up their internal capacity to investigate and apply AI.
By Rachel Hastings
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