Since its emergence back in the 1950s, artificial intelligence (AI) technology has penetrated all four main business sectors, including the tertiary one. Today, thousands of organizations in the service sector have adopted AI to transform their back-office operations, enhance customer service and facilitate decision-making. Although this shift can be stressful, like any major transformation, it enables businesses to achieve significant efficiency gains.  

In this article, experts from Itransition, an AI consulting agency, showcase how AI transforms the service sector, using three major industries as examples.   

Transportation 

Looking to improve transportation safety, cost-efficiency and speed, transportation service providers are adopting AI at an accelerated pace. According to Precedence Research, the global AI in the transportation market has surpassed $4.50 billion in 2024. Experts now project it to reach $34.83 billion by 2034, showing a CAGR of 22.70% during the forecast period. 

FedEx, a U.S.-based package shipping company, is one of the leading AI adopters in transportation, mainly using the technology for order distribution and routing. One of its AI ​​systems, the shipment eligibility orchestrator, dynamically dispatches orders among couriers based on their location, vehicle capacity and professional skills, therefore ensuring timely and risk-free delivery. For example, it can allocate high-priority medical shipments to a driver trained to execute such orders, thus reducing the risk of failed delivery. Another AI tool used by the company, FedEx dynamic route optimization (DRO), adjusts driver routes in real-time based on traffic counts and historical delivery data to increase shipping speed. 

Driver monitoring is also a notable AI use case in transportation. Integrated with sensors and cameras inside the vehicle cabin, driver monitoring systems (DMSs) can analyze driver behavior in real-time to detect early signs of distraction or fatigue and trigger appropriate safety measures.  

For instance, the DMS embedded in GM Super Cruise cars monitors drivers’ eye and head positions and alerts them to keep attention on the road or even forcefully stops the car if required, thereby increasing safety for the company’s customers. This perspective on technology’s positive impact on driver safety is widely shared among transportation experts. According to Teletrac Navman’s 2025 survey, 83% of respondents believe that AI is the future of driver safety, with 32% saying that AI helps efficiently monitor driver behavior in real-time, which can reduce the number of driving incidents. 

Finance and Banking 

The financial services market is another domain where AI continues to gain steady traction. The global AI in finance market is expected to grow from $38.36 billion in 2024 to $190.33 billion by 2030, which represents a CAGR of 30.6%, according to research by MarketsandMarkets. 

One of the most common technology use cases in this area is fraud detection, which involves using AI algorithms to analyze large sets of transactional data and accurately identify fraudulent activity to prevent it. For instance, Capital One, a well-known American bank, complemented an internal fraud detection tool with machine learning (ML) technology, which enabled it to improve detection efficacy and reduce false positive declines by up to 40%. As Nitzan Mekel-Bobrov, vice president of ML at Capital One, said, “With machine learning, we’re protecting our customers by preventing fraud. On the one hand, this is an essential component of our defensive strategy. But on the other, it’s preventing customers from having a negative experience where they’re being declined when they shouldn’t be.” 

AI can also significantly improve financial reporting — another critical aspect of any financial services business, which can be too challenging to handle manually or with traditional reporting tools. According to a KPMG survey, 46% of finance professionals have already implemented AI to improve financial reporting, with 25% planning to increase their AI investment in the next three years.  

ICBC Argentina, a subsidiary of a major Chinese bank, has deployed AI to automate the creation of quarterly forecasts and other types of reports that were previously generated manually using more than 100 spreadsheets. As a result, the company was able to reduce the time required for financial reporting and forecasting from two days to just a few seconds. 

Healthcare 

Given its long-standing embrace of innovation, it is no surprise that the healthcare industry has rapidly adopted AI technologies. According to McKinsey, more than 70% of respondents from healthcare institutions said they have already implemented generative AI (Gen AI) capabilities or are planning to do so, while 60% noted that AI yields a positive return on investment (ROI). 

Common AI use cases in healthcare include ML-based disease diagnosis and prediction, with ML helping in the early detection of various diseases. ML algorithms can analyze vast amounts of medical data, including laboratory test results and patient histories, to identify and predict heart disease, Alzheimer’s, cancer and other conditions more accurately and quickly than human diagnosticians. For instance, a joint study by several British medical universities found that ML algorithms can predict lung cancer more effectively than radiologists (76% diagnostic accuracy against 65%). 

Mayo Clinic, a private U.S.-based medical center, is among the institutions using AI to diagnose and predict heart diseases such as intracerebral hemorrhage, weak heart pump and atrial fibrillation. In most cases, the AI-based screening tool successfully identifies patients at risk for these and other conditions after analyzing diagnostic images and other types of test results — for example, it identifies people at risk of left ventricular dysfunction with 93% accuracy. By recognizing these diseases early, the clinic can provide more personalized and efficient treatment for patients. 

Another prominent application area for AI in healthcare is patient flow forecasting, which enables healthcare institutions to estimate future patient inflows based on the analysis of internal data, such as patient health records, and external data, such as regional healthcare statistics. This helps hospitals ensure they have enough workforce and beds available to meet increasing patient demand.  

Fraser Health, a public health organization operating in Canada, has deployed an AI model that predicts how many patients will arrive in emergency departments, helping to adjust physician schedules at multiple hospitals across British Columbia. The model can predict patient surges with 91% accuracy for the coming day and 81% accuracy for the week ahead. 

Final Thoughts  

Today, it is hard to find an industry unaffected by AI technology, as it has deeply penetrated all four major business sectors. Businesses operating in the services sector are among those that have successfully implemented AI and are gaining tangible benefits from it. Route optimization and driver monitoring in transportation, fraud detection and reporting in finance and disease and patient flow forecasting in healthcare are just a few examples of real-life AI use cases.  

If you are considering implementing AI but do not know where to start or how to approach the technology properly, you can always resort to professional AI consultants. AI experts can analyze your business needs to identify how AI can be applied in your unique business case, help you choose the right AI algorithms and tech stack and design a robust AI solution architecture to ensure smoother technology adoption.