Understanding AI/ML Use Cases: 12 Industry Examples

Artificial intelligence and machine learning (AI/ML) technologies are reaching ubiquity in both business and personal life settings. Companies like OpenAI and Google have made generative AI tools accessible to the average consumer, while tech companies, healthcare facilities, and manufacturers are using AI/ML solutions to create innovative new products and services. We’re in a new world with the current state of AI/ML and the next few years can set the stage for what’s to come. Businesses don’t only have to think about the AI/ML use cases for their industry, but also how to apply the technology in a responsible way for both consumer protection and business sustainability. The World Economic Forum predicts that we may see adverse outcomes from AI technologies in the next decade, but businesses can counter these adverse effects by being mindful of potential risks, including misinformation, disinformation, bias, displacement, job loss, and increased risk of cyberattacks.

We’ll cover why AI/ML technologies are important in the world today, including benefits, challenges, and common AI/ML use cases in 12 different industries.

Why Are AI and ML Important in Today’s World?

AI/ML can help businesses meet customer expectations, automate repetitive tasks, provide a competitive edge, and make sense of the massive amounts of data now being generated. In 2025, the amount of data that is being made and consumed is projected to reach over 180 zettabytes. Both artificial intelligence and machine learning technologies can extract valuable information from this data, unearth opportunities for innovation, and transform businesses to provide more personalized and on-demand experiences. Plus, rules for automation can free up human workers so they can work on more creative and strategic projects.

Benefits vs. Challenges of AI/ML

As organizations increasingly look to embrace the use of AI and ML technologies, as with anything, there are certain benefits and challenges to consider before adoption.

Business Benefits of AI/ML

Adopting new technologies can be a significant upfront task, but incorporating artificial intelligence and machine learning into your business processes can come with several benefits. Because AI/ML can be used to analyze large sets of data, unearthing new patterns and trends can lead to more informed and efficient decision-making. The data from AI/ML, as well as tools featuring the technology, can help businesses design new products and services.

By automating tasks, businesses can save time and money, investing their efforts on business-building projects. Providing a more personalized experience through AI/ML can make the end-user experience more positive, increasing retention and customer satisfaction.

These benefits can apply to just about every industry in some way.

Business Challenges of AI/ML

Just like the benefits of AI/ML can apply to almost any industry, so can the challenges. Because much of the applications for AI/ML are on the rise and in development, businesses need to be mindful of data privacy and security implications, both for the safety of the organization and of the end users. The FTC recently announced that businesses cannot quietly update their privacy policies to include disclosures about AI/ML data mining. While AI/ML can be used for product development, the organization has made it clear that consumers need to be informed on what data is being used and how.

That’s not the only ethical consideration businesses have to make. AI/ML algorithms are not perfect or objective – their training can include or grow biases that organizations need to keep in mind when using the tools.

While some AI/ML technologies have been used for years, generative AI using large language models, natural language processing, and robust data sets have been on the rise. In fact, there has been a 20-fold increase in demand for generative AI skills for workers, with 50% of employees believing that having these skills will be important for their roles – and this belief isn’t limited to IT. Despite this perceived demand, only 13% in the past year have been offered AI training. Without more training opportunities, businesses will continue to see a skill shortage.

Depending on the type of functionality businesses want AI/ML solutions to fill, implementation can be expensive. Some of this can come from the level of customization needed, or the effort it will take to integrate AI/ML with existing systems.

12 Industry Examples of AI/ML Use Cases

The benefits of AI/ML can be seen in almost any industry, and common use cases can apply established and emerging technologies in a way that’s well-suited for the nature of the business.

Generative AI can create images, text, and music for organizations, whereas explainable AI (XAI) can provide a transparent view of the decision-making process behind AI algorithms. Edge AI can deploy models at the edge of networks to reduce latency for end-users, and responsible AI can work to address the potential implications of new technology for operators and users.

While some of these conversations are still unfolding, here are some common use cases for AI/ML in 12 different industries.

AI/ML in Healthcare

The real-time imaging enabled by AI/ML in healthcare can help expedite and improve the accuracy of the diagnostic process for patients. Plus, being able to use data sets to diagnose with greater precision can also help clinicians tailor a more personalized treatment plan for patients, which can lead to improved outcomes and patient experience.

AI/ML can also play a significant role in drug development. AI can simulate molecules and predict the effectiveness of drugs during the development process before anything is physically produced.

Patients can also enjoy AI/ML in routine medical settings through AI-powered chatbots that can answer common questions and provide basic guidance.

AI/ML in Finance

Humans can bring their subjectivity into the trading process. AI algorithms can be trained to take a more objective view of the market by identifying patterns and executing trades at times that are calculated to be the most beneficial. These algorithms can also be used on the customer side as robo-advisors, offering automated investment advice and portfolio management for customers who want additional guidance without the higher touch of working with a human advisor.

Detecting fraud quickly in banking is key to keeping costs low and keeping customers protected and happy. AI tools can analyze transactions and quickly detect suspicious activity, preventing and combating fraudulent activities in real time.

AI/ML in Retail

Customers are more likely to leave a retail site if they’re not seeing the products that fit their interest. AI can personalize the customer experience by providing product recommendations based on past buying behaviors and previously indicated customer preferences.

Managing inventory is a delicate balance for all retail businesses. AI/ML solutions, like predictive AI, can help minimize waste and meet demand by predicting which product lines will be strong sellers, optimizing levels to keep both overstock and selling out at a minimum.

AI/ML in Manufacturing

One of the most important AI/ML use cases comes from the manufacturing industry. Equipment failures and downtime can lead to devastating revenue losses. Predictive maintenance powered by AI/ML can analyze sensor data and predict when equipment may be on the verge of failure, allowing for much shorter periods of downtime, if any.

While humans can see obvious quality issues, there may be pieces that come down the factory line with minute issues that can’t be seen by the human eye. AI image recognition can be trained to identify small defects in manufacturing that may cause big problems for end users. When AI is used to automate repetitive tasks in quality control or data entry, workers can be used for more creative tasks, including developing new products or working on strategic improvements.

AI/ML in Automotive

Some self-driving cars are already on the market. These vehicles process a massive amount of data and require strong 5G connections to navigate the roads, make quick decisions, and understand their surroundings.

However, more common AI/ML features can be present in non-self-driving cars as well. Advanced driver-assistance systems can offer adaptive cruise control, automatic emergency braking, and lane departure warnings. And, much like on the factory line, AI can predict failures in consumer cars and make suggestions for preventative maintenance measures.

AI/ML in Education

Every student learns differently, and AI/ML solutions can make the learning experience truly individualized, providing the right educational materials at the right pace for different learning styles and levels of proficiency.

This can also be applied to tutoring settings, where AI can be used to automatically provide feedback and guidance to students learning new subjects.

While teachers play a vital role in grading and feedback to help students grow and learn in the classroom, they can also be supported through automated grading. Educators can create rules based on a rubric and allow for automated grading of essay-based assignments, giving them more time to focus on other in-class tasks.

AI/ML in Telecommunications

Optimized network performance can be greatly improved through the use of AI/ML solutions that can route resources in an instant.

Cyberattacks can throttle the lines of communication in key moments, but AI can be used to identify and mitigate network and infrastructure cyberattacks before they’re noticed by humans.

Issues common to telecommunications can be answered by AI chatbots, leaving human interaction for more critical issues and outages that don’t have easy fixes.

AI/ML in Marketing and Sales

Personalization can be used in marketing and sales similar to its application in retail settings. AI can target ads based on user behaviors and preferences, increasing engagement rates and conversions while improving the consumer experience.

High-potential leads for sales teams can be identified using lead scoring and qualification tools powered by AI, while chatbots can assist with both lead generation and customer satisfaction by answering questions and routing prospects through part of the sales process.

AI/ML in Human Resources

Recruiters looking for specific skills and experience in new employees can use AI tools to analyze resumes and cover letters, cutting down on the number of reviews they have to perform.

Once an employee is hired, AI/ML solutions can also aid in the onboarding process with personalized training experiences.

AI/ML in Cybersecurity

The longer a cyber threat goes undetected, the worse it can be for an organization. AI/ML solutions can often find cyber threats in real time, allowing cybersecurity teams to mount faster responses. This can be done through anomaly detection – finding unusual activity that might be indicative of an incoming cyberattack.

AI can also protect consumers through fraud prevention measures, identifying and preventing fraudulent activities and transactions.

AI/ML in Real Estate

Virtual tours for homes have become more common, and AI-powered virtual tours can create a more dynamic experience for potential remote buyers. Markets can change quickly, but AI algorithms can be used to estimate property values based on up-to-the-minute market data, helping sellers and buyers get a more accurate read on pricing.

Real estate agents can improve their productivity through AI-enabled lead generation and qualification, which can be used to identify potential buyers and sellers based on their demographics and recent online behavior.

Finally, predictive maintenance can also be used in real estate for rental properties or single-family homes, giving maintenance workers and homeowners a heads up on when certain updates are needed or issues are likely to arise.

AI/ML in Energy

Optimizing energy distribution according to predicted consumption across a power grid through the use of smart grid technology can improve efficiency and reduce costs for a local power utility company.

Grids that leverage renewable energy can also use AI/ML to predict the output they should expect to see from sources such as solar and wind farms, making the integration with conventional energy sources in the grid more seamless.

While some energy demand can be predictable, many factors can change that demand quickly, including weather, historical data, and certain events. AI can perform demand forecasting to more accurately meet these fluctuations.

Improving Business Outcomes with AI/ML Adoption

Adoption of artificial intelligence and machine learning solutions can be a blessing to your business and end users, but knowing how to apply the solutions and best leverage the data you already have can feel like a big weight on your shoulders. At TierPoint, our consulting services can help you determine where and how AI/ML tools should be implemented to improve business processes across a broad spectrum of industries.

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