Understanding AI/ML Use Cases: 12 Examples that Drive ROI

Artificial intelligence and machine learning (AI/ML) are widely used to analyze large datasets, automate processes, and improve business decisions. However, not every AI application makes an equal business impact.

TierPoint helps organizations evaluate AI initiatives through the lens of business alignment, along with governance, readiness, and infrastructure. We’ve identified 12 industry examples that commonly deliver operational, financial, and customer experience improvements and how to maximize their benefits.

Why Do AI and ML Matter for Organizations?

AI and ML can help businesses meet customer expectations, automate repetitive tasks, and gain a competitive edge. With data growth exploding, organizations can use AI technologies to extract valuable information from large amounts of data, including opportunities for innovation and personalized experiences. AI-powered business process automation also can free up employees for more creative and strategic projects.

However, choosing the right AI and ML use cases is critical to realizing these benefits. McKinsey found that most organizations are experimenting with or piloting AI, despite 88% reporting regular use. When AI initiatives don’t align with target business outcomes, organizations struggle to prove or achieve strong ROI. This alignment can be the difference between AI implementation failure and success.

What Are 12 Common AI/ML Use Cases?

Some of the most well-known AI/ML use cases revolve around generative AI assistants like ChatGPT and Claude. However, business applications can also include fraud detection in finance, predictive maintenance in manufacturing, and imaging analysis in healthcare. Here are real-world AI applications for 12 different industries that can drive positive business outcomes.

1. AI/ML in Banking and Finance

In banking, rapid fraud detection protects assets, reduces monetary losses, and maintains customer satisfaction and trust. AI tools can monitor transactions in real-time, accelerating detection and response when suspicious activity occurs. This can help prevent regulatory compliance issues, financial scams, and customer churn.

Financial institutions can also use AI for intelligent risk assessment. AI solutions can conduct large-scale data analysis, looking at payment histories, market conditions, transaction scores, and non-traditional factors to drive smarter lending decisions. Gaining a more comprehensive picture of someone’s financial status can lower default rates and improve profit margins, while providing opportunities for borrowers who haven’t had the chance to build credit through traditional means.

AI-powered risk assessment can also reduce subjectivity in the trading process. AI algorithms can identify patterns and execute trades at times that are calculated to be the most beneficial. They can even incorporate other data points, such as historical trends or current news sentiment, to support more well-informed decisions.

On the customer side, AI models can be used as robo-advisors. These virtual assistants offer automated investment advice and portfolio management for customers who want additional guidance without the higher touch of working with a human advisor.

2. AI/ML in Manufacturing and Supply Chain

One of the most valuable AI/ML use cases is seen in the manufacturing industry, in which equipment failures and downtime can lead to devastating revenue losses. AI predictive maintenance can analyze sensor data and predict when equipment may be on the verge of failure, allowing for proactive, scheduled repairs and much shorter periods of downtime.

AI systems can also support quality assurance, identifying minute issues that can’t be seen by the human eye. Manufacturers can train AI image recognition tools to identify small product defects 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.

Organizations can also improve demand forecasting by analyzing historical trends, economic shifts, and even weather patterns to predict customer needs and interests. McKinsey reports this can reduce inventory levels by 20-30% while also preventing undersupply. AI/ML can also support capital allocation and resource optimization across the supply chain.

3. AI/ML in Transportation

Some self-driving cars are already operating across the U.S. These autonomous vehicles use AI to process a massive amount of data from cameras and Light Detection and Ranging (LiDAR) sensors to navigate roads, make decisions, and understand their surroundings. Driverless trucks are also expected to enter major markets by 2027, though the transportation industry is already adopting AI/ML in other areas.

Advanced driver-assistance systems (ADAS) offer adaptive cruise control, automatic emergency braking, and lane departure warnings to improve vehicle safety. Plus, much like on the factory line, AI can predict failures in both consumer and commercial cars and suggest preventative maintenance measures. ADAS can reduce insurance premiums and costs from accidents. 

Transportation companies can also use AI for fleet management and route optimization. While inefficient trips can eat away profit margins by increasing fuel consumption and transit times, AI can analyze real-time traffic and fuel data to find the most cost-effective paths and improve customer satisfaction. Uber Freight estimates that AI can reduce empty-trailer trips by 10%.

4. AI/ML in Retail and Ecommerce

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.

Inventory management is a delicate balance for all retail businesses. AI/ML solutions, like predictive analytics, 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.

High-demand items may also sell too cheaply without frequent pricing updates. AI can implement dynamic pricing to ensure that businesses maximize their revenue per SKU. The pricing can be set based on competitor activity, stock levels, and current activity compared to historic norms. 

Customer service can also make or break a business, so routing their concerns quickly and appropriately is essential. Retailers can use AI agents to answer routine inquiries, providing instant response times for straightforward questions. Digital assistants also free up time for customer support professionals to handle more complex questions, reducing wait times. 

5. AI/ML in Tech

The technology sector has adopted AI agents faster than any other industry, according to McKinsey. In particular, they’re driving software engineering automation throughout the development process, including in code testing and debugging.

Large language models (LLMs) can be used to autocomplete code or suggest code snippets for specific applications. AI can also aid in the development pipeline, monitoring system logs to anticipate potential bottlenecks before they become a problem.

6. AI/ML in Healthcare and Pharmaceuticals

In healthcare, AI/ML enables real-time imaging and historical data analysis that can help expedite and improve the accuracy of the medical diagnostics for patients. Medical imaging analysis can detect early signs of disease, improving patient outcomes and reducing long-term healthcare costs.

Timothy Chou, founder of Pediatric Moonshot, shares how using AI alongside secure, inter-hospital data sharing can support diagnostics of rare conditions:

After diagnosis, AI can help healthcare professionals generate more precise treatment plans. 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 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.

7. AI/ML in Insurance

While insurance was once a notoriously paper-dependent industry, more firms are moving away from manual processes in favor of intelligent claims and document processing. Using computer vision and natural language processing (NLP), AI can review accident photos and claim forms, expediting the time to payment for the client. 

Risk assessment can also be greatly improved with ML models that can analyze individual behaviors to personalize premiums, instead of relying on broader demographic categories. 

8. AI/ML in Digital Marketing

AI tools can improve personalization in digital marketing efforts. They allow organizations to target ads based on user behaviors and preferences, increasing engagement rates and conversions while improving the customer experience. These tools can even suggest the best next action that should be taken to close every lead, based on past customer interactions. 

AI-powered customer segmentation and behavioral analytics can use current data to create lookalike audiences that are most likely to become new customers. This can improve return on ad spend (ROAS) and reduce the burden on internal team members to recruit new business. 

Retention is one of the most valuable parts of most service-based businesses, including digital marketing. Churn prediction models can determine which client or customer is most likely to leave based on their behavioral patterns, and can alert team members when it’s best to reach out and offer additional incentives or address any issues that have arisen. This proactive approach can reduce churn rates, thereby increasing customer lifetime values (CLV). 

Creative assets can take time to generate, especially when they need to be slightly varied based on where they are being published. Generative AI can create thousands of asset variations instantly, lowering the cost of content creation and making it easier for teams to implement large-scale A/B testing.

9. AI/ML in Cybersecurity

The longer suspicious activity goes undetected, the worse the results can be for an organization. AI threat detection and response solutions can identify and mitigate cyber threats in real time, allowing cybersecurity teams to mount faster responses. Teams can spot zero-day attacks and prevent expensive data breaches, with AI helping with prioritization to cut through the noise.

10. AI/ML in Telecom

Telecom organizations can optimize network performance by using AI/ML solutions that can route resources in an instant. This can reduce congestion in peak hours, making for a better user experience.  

Service interruptions can be extremely detrimental to telecommunications customers. AI can monitor signal degradation and pinpoint failing components so companies can act quickly before service is interrupted.

11. AI/ML in Education

Every student learns differently, and AI/ML solutions can make the learning experience truly individualized, personalizing educational materials to different learning styles, paces, and levels of proficiency. This can also be applied to tutoring settings, where AI can provide automatic 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.

Educational institutions can also use AI to analyze patterns in student grades, attendance, and other key metrics to identify at-risk students more quickly, providing more time for one-on-one interventions.

12. AI/ML in Energy

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.

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. This can include load balancing and renewable energy integration. Grids that leverage renewable energy can 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.

Outages can be frustrating at their mildest and life-threatening at their most severe. AI-enabled sensors can be used to precisely identify grid failure sources, helping dispatch crews arrive at the scene faster. AI can also enable some automated self-healing processes.

What Are the Benefits and Challenges of AI/ML Implementation?

As organizations increasingly look to embrace the use of AI and ML technologies, they must consider certain benefits and challenges before adoption and implementation.

AI/ML Benefits

AI/ML technologies can help businesses improve efficiency, lower costs, and improve the customer experience, and enhance decision-making.

By automating tasks, businesses can save time and money, reinvesting their resources for high-value business projects. Reducing the number of manual tasks also reduces human errors, increasing accuracy.

AI can also enhance the customer experience in many different ways, increasing retention and customer satisfaction. Hyper-personalization can tend to extremely tailored customer interests and needs. Chatbots can provide round-the-clock support for customer concerns. Recommendation engines can suggest what a customer will want based on historical data.

Since AI/ML can 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, also increasing customer satisfaction.

AI/ML Challenges and Risks

AI/ML technologies come with challenges that can even contribute to widespread implementation failures. Often, these failures stem from businesses trying to apply AI to use cases that aren’t aligned with their core objectives. However, issues can also arise when leadership is not on board, organizations lack the resources to scale a project past the pilot phase, and when teams haven’t addressed key governance and infrastructure issues. 

Data governance is essential to combat security, privacy, compliance, data quality, and bias issues that can come with new AI/ML implementations.  Businesses need to be mindful of data privacy and security implications that come with AI, both for the safety of the organization and of the end users, and have a plan to mitigate and eliminate security risks. Depending on the industry, organizations must also be compliant with regulations such as GDPR, CCPA, or HIPAA.  

Businesses also need the right infrastructure to handle AI. Training complex models requires deep learning and specialized hardware installed in scalable environments. Real-time value only comes from well-supported environments.

How to Select the Right Use Case for an AI/ML Pilot

AI strategy and experimentation should have a well-defined focus, and the use cases you consider should align with your organization’s goals, operational challenges, and long-term growth strategy. Rather than adopting AI for the sake of innovation alone, prioritize projects that can deliver measurable value, whether through cost savings, improved efficiency, enhanced customer experiences, or faster decision-making.

Partnering with experienced providers can help businesses navigate these considerations, accelerate deployment, and develop a more strategic approach to AI adoption and implementation.

Key Takeaways

Drive ROI with Your AI/ML Initiatives

Artificial intelligence and machine learning solutions can greatly benefit your business and end users. However, effective implementation depends on a number of factors that affect AI readiness. Organizations must focus on AI/ML use cases that align with measurable business objectives, prepare data for AI solutions, and establish the right infrastructure to support long-term success.

Start building a clear strategy and scalable foundation. Complete the AI readiness self-assessment in our latest whitepaper, The Mid-Market AI Readiness Blueprint: A Guide to an AI Readiness Advantage.

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