Most companies claim to use artificial intelligence (AI), but what does that really look like in practice?
We pull back the curtain on AI use cases by industry to show you exactly how companies are using this emerging and quickly developing technology to solve real-world problems.
From hospital to logistics hubs, banks to travel apps, you’ll see how AI quietly powers smarter decisions, smoother operations, and better customer experiences across five major industries:
- Healthcare
- Finance
- Logistics
- Travel
- SaaS
Let’s dive into the details that matter, one industry at a time.
AI use cases in the healthcare industry
Hospitals and clinics generate vast amounts of data from medical scans to electronic health records, and AI systems excel at spotting patterns in this data that humans might miss. Through this capability, AI in healthcare finds its use in augmenting diagnostics, patient monitoring, and even administrative tasks. Let’s take a look.
Predictive diagnostics from imaging and patient history
AI-driven big data solutions for healthcare analytics are helping doctors detect diseases more accurately and earlier by carefully analyzing medical images and patient histories.
Advanced machine learning (ML) models closely examine X-rays, MRIs, and CT scans, catching subtle irregularities that human radiologists might miss. By mid-2024, the U.S. FDA had already authorized 950 AI-powered medical devices, three-quarters of them tailored for radiology imaging.
In a mammography trial conducted in Sweden, an AI system detected 20% more breast cancer cases compared to traditional methods, while simultaneously reducing radiologists’ workload by 44%.
But predictive diagnostics extends beyond imaging. AI tools also analyze patient medical histories, including symptoms, lab reports, and lifestyle information, to predict diseases such as diabetes, heart disease, or kidney issues.
Some algorithms can even predict critical conditions such as sepsis or heart failure several hours in advance, allowing doctors to intervene early.
In short, AI enables medical teams to identify health issues sooner, leading to personalized care and improved patient outcomes.
Intelligent patient monitoring and alert systems
AI is providing doctors with real-time insights into patient health, making both in-hospital and remote care safer and more effective.
In hospitals, AI-powered monitoring systems constantly track vital signs, lab tests, and other data. These intelligent tools catch early warning signs before standard alarms notice them.
Consider a pediatric ICU in California: an AI system called Sickbay continuously tracks vital signs. It recently flagged subtle changes in a baby’s heart rate that standard monitors missed, allowing doctors to intervene minutes before a crisis.
In such scenarios, AI acts like a digital “smoke alarm,” alerting staff to subtle changes so they can act before a full-blown emergency occurs.
AI doesn’t just help in intensive care. Across hospitals, early-warning tools analyze vital signs and lab results to predict critical conditions, such as sepsis, cardiac arrest, or respiratory failure, hours in advance.
For example, Johns Hopkins University's AI solution, TREWS, detects sepsis nearly two hours sooner than traditional methods, resulting in an 18% reduction in sepsis deaths, a reduction in hospital stays by half a day, and a 10% reduction in ICU time.
AI monitoring isn’t limited to hospitals. For chronic patients at home, AI tools analyze data from wearable devices and automatically alert medical teams when readings fall out of the safe range. It can prompt a quick check-in or emergency action if needed.
Therefore, AI enables healthcare providers to:
- Identify potential emergencies earlier
- Allow for faster response times
- Provide targeted care
- Improved patient outcomes
Automated administrative workflows and documentation
Clinical care often receives the most attention, but behind the scenes, doctors are often overwhelmed by administrative work. Documentation alone accounts for 34% to 55% of their day, costing the U.S. healthcare system up to $140 billion annually.
AI is cutting through that backlog.
Ambient scribe systems are already being used during consultations to draft clinical notes without any need for typing and toggling between screens. Voice recognition and natural language processing (NLP) technology enable doctors to focus their complete attention on their patients, while AI transcribes conversations and converts them into notes, prescriptions, or referrals. Almost 68% of physicians report using these AI tools more frequently than they did last year.
But documentation is just one part. AI is also automating admin tasks like:
- Patient scheduling
- Insurance checks
- Claims processing
Instead of relying on manual data entry or long phone calls, these tasks now run quietly in the background, reducing delays, cutting errors, and freeing up staff for patient care.
Whether it’s drafting discharge summaries or speeding up MRI approvals, AI is helping healthcare run more smoothly behind the scenes.
AI use cases in the finance industry
In finance, AI backs up security, decision-making, and compliance efforts. Banks and financial firms handle massive transaction volumes and complex regulations, which becomes an ideal environment for intelligent algorithms.
Let’s look at three common key use cases of AI in finance.
Real-time fraud detection and anomaly scoring
Fraud is a constant threat in the finance industry, and AI is now one of the most reliable tools to combat it.
Unlike traditional rule-based systems that flag transactions based on fixed conditions, AI models scan billions of transactions in real time. They determine if a transaction appears suspicious or not by examining:
- Spending patterns
- Device data
- Login behavior
and more.
This helps banks detect fraud more quickly and with fewer false alarms. Companies that have implemented AI fraud detection tools have seen a 40% increase in accuracy, resulting in a reduction of both missed fraud and incorrectly blocked legitimate payments.
Big players like American Express and PayPal are already seeing results. Amex improved its detection rates by 6%, while PayPal reported a 10% lift in identifying fraudulent activity with real-time monitoring powered by AI.
What makes AI so effective? It doesn’t just flag known fraud techniques. It can also spot unusual patterns that don’t match past behavior — the kind of threats human analysts might miss. These systems react in milliseconds, minimizing losses and disruption.
Today, 73% of banks use AI and ML models. Of those, 84% apply them to fraud detection, making it the most widely adopted use case in banking AI.
AI is now the quiet layer of defense examining every swipe, transfer, and login, helping financial institutions act faster and smarter.
Automated underwriting and credit risk modelling
Lending used to be slow and sometimes limited by the information on hand. Now, AI is helping banks make faster, more informed credit decisions by analyzing much more than a simple credit score or income statements.
Modern AI underwriting systems consider a wide range of factors, including payment history, income patterns, utility bills, and online business reviews. This helps lenders better assess who’s a good risk and do it in a fraction of the time.
Thirty-two percent of banks are already utilizing AI to automate certain aspects of their underwriting processes, and what sets these models apart is their ability to uncover trends that can be difficult for humans to spot. For example, a combination of shifting spending habits and broader economic changes might predict a small business default before trouble actually hits.
Some lenders even report that smarter AI risk models help them approve more creditworthy customers, while keeping defaults in check.
Of course, speed and efficiency aren’t the only priorities. With the increasing availability of data and automation comes a growing need for transparency. Banks are now expected to explain how credit decisions are made and to ensure their AI isn’t introducing bias. Regulators are increasingly requiring that AI systems can justify their outcomes and treat applicants fairly, regardless of their background.
While AI does the heavy lifting, financial institutions still need people to guide complex decisions and ensure the process stays fair. Therefore, AI doesn’t replace underwriters; it acts as a tool for:
- More responsible lending
- Broader access to credit
- Smoother and quicker process for everyone involved
Compliance monitoring through natural language processing
Finance firms are subject to numerous regulations, ranging from anti-money laundering to sanctions screening. Staying compliant is a daily challenge, but AI, especially the NLP technology, is making the job easier.
One key use for it is scanning communications. Instead of teams combing through thousands of emails, chat logs, or call transcripts, NLP systems quickly flag suspicious phrases or patterns that could hint at insider trading, policy violations, or other risks.
The same technology scans reports, news, and even social media to spot potential compliance issues early.
AI could spot signals of money laundering by:
- Reviewing transactions and customer data
- Checks for links to sanction lists
- Uncovers complex connections between the two
The result is far fewer false alarms and less manual work. Some banks have seen up to a 70% reduction in false-positive alerts after deploying AI for monitoring and sanction screening. The U.S. Treasury recovered $375 million in 2023 by utilizing these advanced systems to identify improper payments.
In addition to monitoring activities, financial institutions can also use AI to analyze new regulations:
- NLP tools track updates from regulators
- Sort new requirements
- Summarize what teams need to know
This way, compliance staff spend less time wading through dense legal texts and more time focusing on real risks.
AI use cases in the logistics industry
The logistics and supply chain sector demands efficiency. Be it moving goods via optimal routes, maintaining equipment, or balancing inventory. AI has found a natural home here, optimizing complex operations that involve numerous variables, such as:
- Which is the best route
- What impacts vehicle health
- What are the stock levels
- When would the delivery be made
Companies like UPS, FedEx, Amazon, and numerous others are heavily investing in AI to reduce costs and expedite deliveries. Let’s examine three primary AI use cases in logistics.
Dynamic route optimization and delivery scheduling
Finding the best routes for thousands of drivers moving millions of packages is a daily challenge for logistics companies. AI-route optimization (AIRO) now takes on this challenge, and the results are impressive.
- UPS leverages a custom AI solution, ORION, that crunches real-time data, including traffic, weather, package locations, and delivery windows, to guide each driver on the most efficient route. It has saved UPS 10 million gallons of fuel per year and helped the company avoid up to $320 million in costs.
- Amazon uses similar AIRO tools to meet the demands of same-day and next-day shipping, constantly adjusting routes and schedules behind the scenes. It let them fulfill 2 billion same-day or next-day deliveries in Q1 of 2024.
Even smaller delivery fleets are leveraging AI-powered solutions to reduce the number of miles driven, fuel consumption, and time spent on the road.
What sets these systems apart is their flexibility. If a road closes or traffic builds up, AI reroutes drivers in real-time, ensuring deliveries remain on schedule. It can also group nearby stops and prioritize urgent packages by reshuffling the sequence as needed.
For customers, this means faster, more reliable deliveries and a smaller environmental footprint. For companies, it’s a major boost in efficiency and significant savings that add up every day.
Predictive maintenance for fleet and warehouse equipment
Nothing’s worse than unexpected breakdowns in logistics, because they bring the operations to a standstill. With an AI-powered approach, companies are now leveraging predictive maintenance to keep vehicles and equipment running smoothly.
Here’s how it works:
- Sensors collect real-time data on aspects such as engine performance, vibration, and temperature
- AI systems study this data and spot subtle warning signs that a part may soon fail (e.g., a slight shift in engine vibration might indicate a fuel pump issue)
- Automatically schedules maintenance downtime for the vehicle to prevent breakdowns mid-route
The results are impressive, with companies reducing maintenance costs by up to 25% and keeping their vehicles on the road approximately 20% longer. A Deloitte study even shows a 70% reduction in equipment breakdowns and much less unexpected downtime.
Predictive maintenance isn’t just limited to trucks. Warehouse equipment, such as conveyor belts and robotic arms, can also benefit from it.
DHL Express Germany piloted an AI-powered predictive maintenance system for its sorting machines at major hubs, such as Munich Airport. The technology proactively detected issues such as critical vibrations and equipment misalignment before they caused breakdowns, allowing the team to fix problems early and avoid unplanned outages.
As a result, DHL was able to keep its parcel operations running smoothly even during periods of high demand.
By shifting reactive maintenance to a predictive, planned approach, logistics companies can save significant money, avoid delays, and maintain customer satisfaction. And as AI systems learn from more data over time, they get even better at predicting problems before they turn into costly disruptions.
Inventory forecasting and smart replenishment systems
Keeping the right products in stock without overfilling warehouses is a delicate balancing act, and AI-powered inventory forecasting is making that task easier for logistics teams.
Instead of relying on gut feelings or basic projections, AI analyzes historical sales data, seasonal patterns, promotions, and even external factors such as weather trends. With this data, it can predict demand for every product and location more accurately.
As a result, companies can reduce excess inventory by 20% to 30% while avoiding empty shelves. That means:
- Less money tied up in unsold stock
- Lower storage costs.
- More product availability
Some distributors are reportedly using AI to improve their fill rates by 5% to 8% and reduce the number of “out of stock” notices for customers.
In addition to forecasting, AI can also trigger automatic restocks when inventory levels are low or a surge in demand is anticipated. For example, if a blizzard is expected in a particular region, an AI system working for a hardware store can trigger restocks of snow shovels.
Retailers like Walmart and Amazon already use these systems, and even smaller businesses are adopting AI-driven software to keep shelves stocked.
Amazon also uses AI-powered “smart” warehouses that organize products for quicker picking and route orders based on shipping times and inventory levels. Here’s what it helped them achieve:
- Identify and store inventory 75% faster
- Process orders 25% quicker
- Deliver 9 billion orders the same or next day in 2024
- Reduce incident rates by 28% between 2019 and 2023
- Saved customers $500 on average in delivery fees
Therefore, for businesses, using AI in logistics represents a significant step toward reducing waste, lowering costs, and achieving more reliable delivery. For consumers, it means better product availability at the right prices.
AI use cases in the travel industry
AI is quickly becoming a behind-the-scenes helping hand in the travel and hospitality industry, making every step smoother for both companies and customers.
- Airlines use it to adjust fares in real time
- Hotels and online agencies deploy chatbots to answer questions instantly
- During flight delays, AI helps rebook passengers and get operations back on track faster
Let’s break down exactly how with these three common AI use cases for travel companies.
Personalized dynamic pricing and fare optimization
If you’ve noticed flight or hotel prices jump up and down as you browse, that’s dynamic pricing in action. AI now drives this process, allowing airlines and hotels to set prices that reflect:
- Real-time demand
- Remaining seats or rooms
- Competitor rates
- Even your own browsing habits
AI-powered pricing engines can adjust fares or rates on a minute-by-minute basis. They examine everything from booking channels to customer loyalty status, finding the “just right” price for each situation.
For airlines, that means filling more seats at the best possible rate. For hotels, it means maximizing the value of each room and adjusting extras, such as spa packages or late checkout fees.
Hotels using AI for dynamic pricing have increased room occupancy by 9.1% and boosted revenue by 13.7%. The same applies to airlines. For example, Delta Air plans to scale the use of AI to factor in variables such as trip purpose and booking channel. Their pilot program, which allowed AI to determine 3% of the airline’s flight prices, resulted in “amazingly favorable unit revenues,” as reported by the company’s president, Glen Hauenstein.
Online agencies also use AI to personalize offers. For example, when two travelers search the same route on Expedia, they’ll see different prices based on their search history or customer profile.
And no, AI doesn’t just raise prices. If demand suddenly drops, it can instantly trigger discounts or flash deals, making sure seats and rooms aren’t left empty. This flexibility becomes especially valuable when travel patterns shift, such as when they did during the pandemic. Companies using AI can react in real-time, much faster than any human team.
In short, AI-backed dynamic pricing helps travel businesses stay competitive and boost occupancy, and occasionally pass savings to customers so no seats or rooms remain go to waste.
Automated itinerary planning and customer chat support
Planning a trip can feel overwhelming. You've got to think about flights, hotels, tours, and all the small details in between.
Companies using Generative AI can build AI agents that create personalized travel plans and provide round-the-clock support. You simply share your destination, dates, and interests, and the AI suggests a multi-day itinerary, complete with recommended sights, restaurants, and activities.
For example, Expedia’s AI travel planner lets users ask for personalized plans like, “I have five days in Paris and love art and food. What should I do?” The AI responds with a custom schedule, saving travelers hours of research. You can also tweak the itinerary on the fly if your plans change. Additionally, the company took a step further and created a feature that utilizes AI to transform an Instagram travel reel into a comprehensive itinerary.
On the support side, AI chatbots are everywhere, also working 24/7 for airlines, hotels, and booking sites. They handle everyday questions right inside apps or websites, such as flight status, reservation changes, baggage rules, or hotel requests.
Expedia’s virtual assistant manages over 143 million conversations each year and resolves more than half of all traveler requests without human help. Travelers who use the AI bot often report double the satisfaction compared to those who call customer service.
These bots aren’t just for FAQs. They can help:
- Rebook flights after a cancellation
- Upsell extra services
- May fulfill on-property requests for hotels
- Suggest add-ons, like early check-in
And much more.
Travelers love this because it provides instant answers, reduces hold time, and offers support at any hour. For travel companies, AI chat tools reduce phone calls, cut costs, and free up staff.
Operational disruption management and recovery
Flight delays, weather disruptions, and last-minute cancellations disrupt travel plans, and customers hate them. Before the development of AI in the industry, recovery meant hours lost with staff manually calling passengers, rearranging crews, and piecing together backup schedules.
However, now, AI is changing that story. Take Air Canada, for example. When a flight gets canceled, their AI system instantly reviews all possible rebooking options across partner airlines, crew schedules, and gate availability. What once took up to 12 hours now takes about 10 minutes. Travelers get a text or app notification with their new flight before they even leave the airport.
- The system takes into account loyalty status, costs, and missed connections.
- Offers each passenger a practical solution
- It minimizes airline expenses in all possible ways
AI systems also enable proactive management of disruptions. It can help run simulations before a storm, showing the impact of early cancellations or crew reassignments, so companies can act before travelers end up stranded. Even complicated logistics, such as finding legal-duty replacements for a crew that has timed out or reshuffling planes after a large-scale event, can now be handled in minutes.
Other travel sectors are also jumping on board:
- Train operators reallocate track slots with AI
- Cruise lines use smart tools to adjust itineraries on the fly
- Urban transit and rideshare services rely on AI to reroute buses or vehicles when unexpected events occur
The real benefit is that travelers receive clear answers and rebooking options without having to wait endlessly, and companies can recover operations and goodwill much faster. For airlines like Air Canada, 90% of cancellations are now handled automatically, with most passengers sorted out in record time.
AI can’t change the weather, but it’s making travel hiccups much less stressful for everyone involved.
AI use cases in the SaaS industry
Software-as-a-Service (SaaS) companies reside in the digital realm, and they naturally leverage AI to enhance their products and operations. In a highly competitive market, SaaS providers use AI to:
- Enhance customer support
- Retain users
- Streamline their development processes
We talk about key areas where AI is making a difference in this industry below.
Intelligent in-app support and email triaging
SaaS products typically have a massive user base, and support needs can quickly accumulate as the business expands. AI is now built into many platforms as an in-app assistant, ready to answer questions or solve problems as they arise. Here’s how it works:
- User gets stuck or isn’t sure how to use a feature
- They open an AI chatbot inside the app and ask a query
- The AI assistant pulls from documentation, FAQs, and even the user’s current screen to offer step-by-step solutions
- For trickier problems, it can hand off the conversation to a human agent with full context intact.
Common questions, such as “How do I reset my password?” receive instant, helpful replies, often personalized and always in the company’s voice. More than a third of user inquiries are now resolved without human involvement. For example, ClickUp scaled its support during periods of rapid growth by using an AI assistant to field routine questions, keeping live agents available for complex issues. They automated 40% of customer inquiries with AI without human intervention.
Multilingual support is another bonus. At Anglara, our experts can build AI chatbots that translate customer messages and respond in real-time. Therefore, our clients no longer need a giant team of language specialists.
In addition to users getting quick, on-demand support, AI agents can also extend help to support teams with email traiging. When someone sends an email or support ticket, AI can instantly read and sort it. If it’s urgent, such as a system outage, the AI sends it directly to the right engineers. If it’s a new feature idea, it goes to the product team.
With these systems in place, SaaS providers can ensure that fewer messages get stuck waiting in the wrong place, important requests receive attention quickly, and customers get answers 69% faster.
Customer churn prediction and engagement scoring
For SaaS businesses, retaining customers is just as important as acquiring new ones, if not more so. According to Kovai.co’s CEO Saravana Kumar, “it can cost four to five times more to acquire new customers versus retaining current ones in the SaaS industry.”
With AI, SaaS companies can identify which users might be considering leaving long before they actually do. These systems look at things like:
- How often people log in
- Which features they use
- How many support tickets they file
- Their feedback scores
If someone’s usage drops off or they haven’t explored new features in a while, AI can flag them as a possible churn risk. With this early warning, the customer success team can reach out with training, help, or special offers to prevent the customer from canceling their subscription.
One SaaS company experimented with AI to target at-risk users and saw their churn rate drop by 18% in just a few months. Because the AI pinpoints only those who need attention, teams targeted them with personalized communication.
AI can also provide engagement scoring — a related use case. AI sorts users into groups, such as “power users,” “casual users,” or “at-risk users,” so account managers know who needs a check-in and who might be open to an upsell.
Some companies even use AI to estimate customer lifetime value, guiding them on where to allocate extra effort. With these tools, SaaS businesses can retain more of the customers they’ve worked hard to acquire and be proactive by stepping in early to resolve issues.
Automated code review and deployment pipelines
AI is enabling SaaS companies to ship software quickly and reliably by handling much of the tedious work associated with code review, testing, and deployment. They can scan new code in seconds and catch possible bugs, security risks, and style issues before they reach production.
These systems have learned from millions of code examples, enabling them to identify patterns or common mistakes that might slip past even experienced developers. Many teams that utilize AI for code reviews have seen their review cycles decrease by up to 40%, resulting in fewer bugs being introduced into live software.
But even with these smart systems in place, it’s your developers who set the standards, design architectures, and solve complex challenges. AI can handle the repetitive checks and flag risky changes, but only skilled engineers can make judgment calls, build robust systems, and innovate on product features.
Modern tools like GitHub Copilot can suggest code snippets, reduce repetitive work, and help avoid simple errors. The team gets more time to focus on creative problem-solving and the decisions that define product quality and user experience.
Ready to put AI to work in your industry?
AI’s biggest wins happen when technology is matched to real business needs, whether you’re in healthcare, finance, logistics, travel, SaaS, or an entirely different industry. Even if your sector isn’t on this list, the same principles apply —- the right AI approach can unlock efficiency, speed up operations, and create new value.
At Anglara Digital Solutions, our team specializes in practical, hands-on AI business consulting tailored to your specific needs. We work closely with clients to identify high-impact AI opportunities, develop innovative solutions, and drive measurable outcomes.
Curious how AI could work for you? Schedule a free consultation with Anglara today and discover what’s possible for your business.
