Artificial intelligence (AI) has been increasingly deployed in various sectors and numerous ways. This detailed overview will highlight the multiple uses of AI in service industry applications. It can sometimes take a while for company decision-makers to figure out the most appropriate ways to rely on AI. However, the best course of action is often to see what peers do when deploying AI for service-oriented applications.
Source: Unsplash
Retail
Retail is using AI for guided shopping, product recommendations, and stock/location lookups, and 63% of retail companies now use AI to streamline service workflows. Spending through retail chatbots specifically is projected to reach $72 billion by 2028, up from about $12 billion in 2023.
Banking and finance
This is one of the most mature sectors. Banking and finance shows roughly 92% AI adoption, and the shift is from basic FAQ bots to agents handling real transactions — balance alerts, fraud flags, onboarding, document verification.
Bank of America’s Erica is a standout example, resolving 98% of queries within 44 seconds. Analysts estimate AI could raise banking productivity 3–5% and cut sector costs by roughly $300 billion.
But banking is also the sector most shaped by regulation: a banking AI chatbot in 2026 has to resolve customer questions, avoid giving regulated advice it isn’t qualified to give, and produce an audit trail that survives compliance review, since the CFPB, OCC, and FFIEC have all issued explicit guidance on how AI can and cannot interact with consumers in regulated financial contexts.
Telecom
Telecom leads all sectors with about 95% of providers integrating AI into customer support — outage updates, troubleshooting, plan changes. Telstra, for example, is using AI to streamline customer interactions and move toward autonomous network operations.
Source: Unsplash
Healthcare and insurance
Adoption here is more operational — appointment scheduling, claims, digital assistants for routine questions. One notable data point: NIB Health Insurance saved $22 million through AI-driven digital assistants, cutting customer service costs by roughly 60%.
There’s also research suggesting AI models can score as more empathetic in text-based interactions than some human reps in controlled studies — worth taking with some skepticism since empathy metrics are notoriously hard to measure well.
Watch how AI is transforming physical therapy — personalized, engaging, and scalable rehab, powered by real-time progress tracking clinicians can trust.
Travel and hospitality
This sector is moving fastest toward full agentic behavior. Analysts expect that by 2027, AI agents will increasingly mediate travel and dining decisions — searching, comparing, evaluating options against a traveler’s preferences, and even completing bookings on the traveler’s behalf.
That’s a real change in who the “customer” of a hospitality brand even is: increasingly an agent acting on a person’s behalf rather than the person directly, which is pushing hospitality brands to make their offerings and pricing machine-readable so AI agents can find and represent them accurately.
The travel and hospitality sector still lags other industries in AI maturity, largely due to siloed data and outdated legacy systems, even as adoption continues to grow across the space.
Restaurants
Many restaurant brands worldwide have started using AI to improve customer interactions. An AI chatbot might recommend menu items, answer routine questions, or help someone book a table — often through the restaurant’s website, WhatsApp, or even a phone line staffed by voice AI. That frees up the people working at the restaurant to spend more time with customers face-to-face, or on other tasks.
These tools aren’t perfect. They can’t handle every query, but they’re well-suited to the questions that come up again and again: opening hours, whether there are gluten-free or kids’ menu options, or how to book a table. A chatbot can usually answer these faster than a person can — and unlike staff, it’s available at 2 a.m. as easily as at 8 p.m., which matters since a large share of inquiries and missed reservation calls happen outside normal hours or during the dinner rush.
Using machine learning to improve results
Applications of machine learning and artificial intelligence for business have also impacted the food sector and the quality of service customers receive. For example, Domino’s Pizza used data from millions of orders to create a prediction model to help customers better understand when they’d receive their food.
The algorithms took numerous aspects into account, including what someone ordered and the number of employees and customers in a given restaurant when the person placed an order. In such cases, Big data platforms can help decision-makers at restaurants understand which information they have might be helpful for a future artificial intelligence application.
AI voice agents step in
Some AI applications in the service sector are becoming more interactive because they can understand what customers say and respond in the moment. Rather than just running scripted prompts, these AI voice agents interpret speech in context — much like the natural back-and-forth of a real conversation — and act on it directly, whether that means placing an order, adjusting a recommendation, or handling a follow-up question without a staff member stepping in.
Fast-food brands Checkers and Rally’s rolled out an AI ordering agent that could accurately understand what customers wanted with minimal staff involvement. The agent also handled upselling on its own, prompting customers toward combos instead of single items.
Source: Unsplash
As a restaurant accumulates data from an agent like this, leaders can use it to better understand their customers:
- What do people order most often?
- Do preferences shift by day of the week or time of day?
- How do orders differ when someone’s buying for their family versus just themselves?
McDonald’s took a related approach with its outdoor menu boards, using AI to make them adaptive — surfacing different items based on trends, weather, and time of day. This built on an earlier, simpler version of the idea: years before the AI rollout, McDonald’s tested weather-based menu changes, featuring ice cream on hot days and coffee on cold ones, and internal data showed the approach drove real sales spikes.
AI as a supplement to human support
Many people are interested in using AI in service-based industries because they believe it can help human agents work more competently and efficiently. Technological advances can sometimes save people from engaging in many manual tasks. For instance, artificial intelligence in field service industry platforms can assist administrators with technician scheduling or free them from some data entry tasks.
These offerings don’t take human support staff members out of the picture. However, they often make transactions and engagements more efficient for customers. Then, those people have more favorable experiences with companies overall.
The hospitality sector can also enhance customer experience with artificial intelligence. A hotel chatbot could help people make initial room reservations or change ones previously made. It could also make it easier to add specific requests to a booking, such as that the room should have a baby crib or a couch that converts into a bed.
Such solutions could make things easier once guests arrive, too. Whether they need extra towels or another ice bucket, a chatbot can field those requests and send them to the correct hotel workers. Companies specializing in AI problem-solving solutions can give clients more ideas about how the technology could help them meet needs and support profits.
What industry uses AI the most?
Source: Unsplash
Many companies are in the early stages of their AI adoption, making it hard to pinpoint which industry is most dependent on AI. However, artificial intelligence service industry applications are increasingly used.
Telecom and banking lead, with roughly 95% and 92% adoption respectively — both handle huge volumes of routine, repetitive interactions (bill questions, outage updates, fraud alerts), which makes them ideal for automation and gives them a head start.
Retail is close behind (63% of companies using AI to streamline service), with healthcare and travel/hospitality further behind, the latter still held back by legacy systems and fragmented data.
Worth noting: these numbers shift depending on how “AI adoption” is defined across reports — some count any AI tool, others only generative or agentic systems.
Other opportunities exist outside the customer service industry, too. Combining machine learning and marketing can help professionals understand the messaging most likely to appeal to certain user groups.
Algorithms can perform sentiment analyses to understand what people like and dislike most about specific products. Then, marketing team members could pass those insights on to customer service representatives to get them more prepared for future interactions.
Things to know before using AI in service-oriented roles
Using AI in a business for any reason represents a significant decision. That means there are a few things to keep in mind before proceeding with further research.
What are the three types of AI?
As people learn more about AI, they discover there is more than one type. Artificial narrow intelligence — or weak AI — is a solution that excels in a single task. It simulates one human behavior.
There’s also artificial general intelligence — or strong AI. It can think and act the same way humans do. However, it’s only a theoretical concept for now. People have made progress in related areas, though.
Source: Unsplash
One recent achievement allows a person to see why a machine learning model made a particular conclusion. Such information is critical when an algorithm’s result could dramatically impact someone’s future.
Finally, there’s a third type of AI called artificial superintelligence. It doesn’t exist in real life, either. This category of artificial intelligence surpasses human capabilities, so it’s still the stuff of science-fiction novels and books for now.
Is AI a service or product?
People often wonder if AI technology is a product or service. The answer depends on how clients use it. Some might develop in-house algorithms that give valuable insights about customer experience statistics or other trends relevant to service industries. Other companies won’t have the resources for custom-built solutions, but decision-makers there may still want to experiment with AI options.
In the latter case, machine learning-as-a-service — sometimes referred to as ML-as-a-service — can help.
What is ML-as-a-Service?
Machine learning-as-a-service enables people to pay flat rates for their usage and deployment of artificial intelligence and machine learning solutions. This approach allows clients to eliminate the often-high upfront costs of technological products and services. It also makes it easier for them to fit machine learning into their budgets and business models without worrying about how the adoption could result in financial strain.
After seeing how it works in the early stages, decision-makers can determine if they want to use AI long term. If so, it may make more sense to transition from the ML-as-a-service model to something more permanent.
Reaching a well-informed conclusion is often easier if people collect specific metrics. For example, how many customer service inquiries did a chatbot handle versus a human last month? Is the number of people interacting with an AI product going up or down compared to the previous quarter?
Source: Unsplash
AI and service industry use cases make sense
The examples here show why it’s often so compelling to bring artificial intelligence technology to the service industry. Customers’ interactions with service-oriented companies can forever change their options and impact the likelihood of them doing business in the future with those options. If artificial intelligence can cause positive experiences while reducing service provider workloads, there’s no reason not to consider it.
Author bio
April Miller is a senior writer with more than 3 years of experience writing on AI and ML topics.
