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.
AI in food service industry applications
Many restaurant brands known worldwide have started using AI to improve customer interactions. For example, an AI chatbot could give menu recommendations, answer customers’ questions or help them make dinner reservations. Then, people working at the restaurant can devote more time to assisting customers face-to-face or engaging in other duties. AI chatbots can’t respond correctly to every query. But they often work well for the things people ask most often.
Those questions might include things related to a restaurant’s opening hours, whether they serve gluten-free items or if there’s a kids’ menu. A chatbot can often answer those things faster than people.
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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.
Combining AI and Voice-Recognition technology
Some examples of AI in service sector applications are even more interactive for customers because they can understand what they say. Their functionality works similarly to the natural language process that occurs when people speak. Specifically designed AI algorithms can interpret human speech in context, then provide accurate responses.
One example from fast-food brands Checkers and Rally’s was an AI-ordering system that accurately understood what people wanted with minimal interactions from staff. The solution also included upselling elements, such as encouraging people to order combos instead of single menu items. Once the restaurant has a large amount of data from such solutions, restaurant leaders could use that information to understand customers better.
What do people order most often? Do their preferences change based on the day of the week or time? What differences exist when someone buys food for their family versus themselves? McDonald’s began using artificial intelligence in the service industry in its outdoor menu boards to make them more adaptable.
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People see different menu choices featured depending on factors such as trends, the weather and the time of day. Several years before using this AI solution, McDonald’s executives applied more basic technology to show people different menu options based on the weather. For example, consumers saw ice cream featured on hot days and coffee when it was cold. Internal data suggested this approach caused sales spikes.
These are some of the many examples of how people could use AI in food service industry applications. However, decision-makers should strongly consider hiring people from a machine learning consulting company if they need further guidance. Those professionals can evaluate the specific needs of a business and investigate how AI could help. That may mean deploying AI assistants for customers or using artificial intelligence to improve internal business processes.
Artificial Intelligence in financial services industry applications
Some industries use artificial intelligence to find the most suitable candidates in an applicant pool. For example, research indicates 7 in 10 schools have dedicated enrollment teams. AI tools help people find the best-suited candidates. That’s one of the reasons why it’s becoming more popular to use AI in the financial services sector, too.
Consider if someone applies for a loan or credit card. Algorithms can assess data from various sources and evaluate the aspects that could make an applicant more or less risky for a bank or other financial institution.
AI services and solutions can also work well to help customers plan their financial futures. If they enter details such as when they hope to retire and how much they have saved for retirement, a solution could advise on what steps they can take to meet that goal. The customer data accumulated through such interactions can also become extremely valuable for a financial services company.
It could uncover trends indicating people retiring younger or older than they previously did. Similarly, the information collected by an AI system might show people are more or less prepared for retirement than they were a decade or so ago. In such cases, the people working at financial services companies can use those takeaways to shape their in-person conversations with customers, making them more relevant.
The use of artificial intelligence in financial services industry applications is not as widespread as in some other industries. However, it’s starting to gain momentum. People interested in pursuing the possibilities can use machine learning consulting companies as a starting point.
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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.
Artificial intelligence in service industry businesses can make humans more available to troubleshoot customers’ complicated requests, while AI tools deal with simpler needs. Imagine if a person contacted a mobile phone service provider and wanted to know the issue date of their next bill. An AI platform could likely give that information after getting basic details from the customer, such as an account number and billing address.
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?
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.
More specifically, statistics from 2021 showed that 25% of all industries worldwide use AI for service-related needs. The percentage also rises in specific industries.
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.
Working with a data science company is an excellent way to get clarification on the most advantageous ways to use AI within a business. That might mean deploying it in the customer services department or developing an AI-based tool to allow clients to get the information they need faster.
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.
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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. 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.
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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?
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.
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