Artificial intelligence is fast becoming a valuable enterprise technology that can revolutionize the way firms do business. According to a 2024 report published by McKinsey, AI adoption doubled in the period between 2017 and 2024, with an estimated 50% of all organizations incorporating some form of AI into their business processes.

The global AI market is expected to continue growing in the near future, too, with a yearly growth rate predicted to be around 37%. This impressive market growth demonstrates quite clearly how transformational AI applications can be if and when deployed effectively.

AI is being leveraged across multiple sectors to improve efficiency and reduce operational overheads in many ways. In this article, we're focusing on how businesses use AI in their operations. We'll look at how AI can be applied by enterprises in a range of different sectors, and show the benefits that different types of AI applications can deliver.

What is AI, and why is it crucial for business?

The term AI is becoming somewhat generic, covering a range of technologies from machine learning to generative AI. In this article, we will focus less on the underlying technologies that make up the greater AI sphere. Instead, we’ll look at how AI applications can support businesses in crucial roles ranging from pattern recognition and automation to supporting the decision-making process.

Many enterprises have already proven how impactful such technology can be by deploying AI applications that enhance productivity, automate repetitive tasks, and make crucial information more readily available and easier to understand. Some of the main successful ways that AI applications have been used so far include:

Automating tasks that are repetitive in nature.
Analyzing large, often unstructured datasets rapidly to uncover insights and aid decision-making.
Delivering a personalized customer journey, often using AI-driven virtual assistants to improve the customer experience.
Using AI applications to streamline critical processes such as supply chain management and complex scheduling.

In the following sections, we will take a closer look at how AI applications are being used in a range of sectors to deliver proven results.

AI applications in business

No longer the domain of tech giants and large enterprises, AI business applications have become much more accessible, both in affordability and effectiveness. It is now possible to deploy a business AI application without internal development by using a third-party provider offering SaaS or a similar service. These providers can help a business achieve various AI-related goals, from automating repetitive tasks to analyzing data to make complex predictions, putting AI applications within reach of even small operations.

AI has been used successfully across many sectors, including manufacturing, finance, marketing, and healthcare. In the following sections, we will examine several sectors and explain how AI has already proven its value.

AI applications in healthcare and life sciences

One key example of the application of AI in business is in the healthcare sector. AI and healthcare operations, both frontline and back office, have proven how this technology can benefit humanity.

AI has been used for a range of healthcare-related tasks, from assisting doctors in diagnosing patients to discovering new drugs. Some of these use cases include:

Medical imaging and diagnostics: AI-powered tools have already proven their worth by helping medical professionals analyze images produced by radiology machines to diagnose diseases such as cancer much earlier.
Predictive analytics in healthcare: AI has been used to analyze patient data and predict the likelihood of disease outbreaks. For example, Johns Hopkins University has successfully used AI to predict the deterioration of patients in the ICU.
Virtual health assistants: AI-powered virtual assistants have been deployed to provide patients with a quick and accessible way to check their symptoms and, if necessary, schedule an appointment with the relevant medical practitioner.
Robotic surgery: Possibly the most impressive example of AI in healthcare to date is using AI to help surgeons complete complex operations. The additional precision this provides ensures the best chance of a positive outcome and shorter recovery times.

AI in education

There are several applications of AI in education. AI is being used in the classroom to enhance the learning experience and by school administrators to lower the overhead associated with keeping a school running smoothly. Some of the proven ways that AI has been used in education are:

Adaptive learning: A number of AI platforms have been used in classrooms to deliver customized, personalized learning schedules for students. These systems consider the student’s performance, strengths, and weaknesses to produce tailored lesson plans.
Automated marking and grading: Teachers have access to AI tools that can help them evaluate the work a student submits. These tools can find patterns and also uncover common mistakes. Technology such as natural language processing (NLP) enables these tools to understand even the most complex text.
AI-powered teaching: Virtual assistants can be used in the classroom as teaching tools. They can answer domain-specific questions, provide guidance, and act as one-on-one tutors regardless of where the student is located, whether in the classroom or remotely.
Enrollment and administration: AI has been used to streamline the admissions process for educational establishments. These tools can analyze past data related to a student and evaluate whether they are suitable for a place. AI can also be used to plan class scheduling and monitor the overall performance of students to help teachers highlight any who may be struggling.

AI in marketing

Marketing professionals have found many ways to leverage the value of AI, particularly generative AI, to expedite the marketing process and improve results. Marketers have successfully used AI to predict the behavior and needs of customers, as well as to create marketing content and materials. For example:

Personalized recommendations: Many e-commerce sites now incorporate an AI-driven recommendation engine, which informs visitors about products or services they might find helpful based on buying patterns within historic customer data.
Virtual assistants and conversational AI: Marketers commonly use AI virtual assistants as the first point of contact for sales inquiries. These AI applications are able to answer customer questions about products, make product recommendations, and also provide initial customer support.
AI-powered content generation: The use of generative AI in marketing has become very popular. It enables marketers to quickly produce high-quality, targeted content such as blog posts, social media posts, and marketing copy. More recently, marketing teams have started using AI tools focused on producing videos, too.
Programmatic advertising: Online advertising platforms have adopted AI as a technology to improve how ads are placed, helping to ensure that only the most relevant ads are shown to a website visitor. This can maximize the ROI of online advertising as it tends to get better engagement.

AI in sales

Sales teams have deployed AI applications to help increase sales and ROI in many markets—including leveraging generative AI for sales professionals to help create sales copy and tools for lead generation and forecasting. Some examples include:

Lead scoring and customer insights: Several customer relationship management systems (CRMs) now incorporate AI applications to help identify high-value leads. These systems evaluate purchasing patterns and customer engagement to determine which leads have the highest chance of converting.
Virtual sales assistants: Deploying AI in customer service can enable the use of a virtual assistant to help the customer decide which product best suits their needs, and to answer basic customer support questions. Virtual assistants can intelligently forward the customer to a live sales representative when the customer has more complex questions or problems that need human input.
Sales forecasting: AI can be very effective at analyzing vast volumes of data, uncovering actionable insights, and making predictions. Sales forecasting is a great example here. By looking at past sales history, market trends, and other external factors, AI applications can predict which products will be in demand and when.

AI in banking and finance

AI has become a valuable tool for companies that operate within the finance sector, including banks, insurance providers, and trading firms. The benefits of AI in finance delivered so far include risk assessment, automated trading, and improved decision-making. Some other examples include:

Fraud detection: One of the best examples of an application of AI in banking is how it has been used to improve the way financial institutions can spot fraudulent trades and activity. Transaction data can be monitored in real time, and when a suspicious pattern in transactions emerges, it can be flagged as potential fraud for investigation. This is more reliable and reduces the number of false positives that banks and finance houses act on.
Improved reporting: Generative AI in finance has been successfully employed to produce reports, graphs, and charts much quicker than humans can. AI applications can analyze much larger datasets than human analysts, too. In the past, these kinds of reports took time to create, but AI has changed the landscape, enabling them to be produced in real time or near real time.
Credit scoring and loan approvals: AI applications have been deployed to assist finance companies in assessing the risk that customers pose. By examining past credit data from diverse sources, as well as additional records such as employment history, credit scoring can become much more accurate. This means finance firms can more accurately judge a customer's risk, and make the process of applying for finance more efficient for both the borrower and the lender.

AI in energy and utilities

There are a number of key uses of AI in energy and utilities. Energy providers take on the responsibility of ensuring uninterrupted supply for customers, an area in which AI applications are beginning to have a major impact.

By analyzing massive amounts of real-time data from sensors, as well as historical maintenance and performance data, energy providers can leverage AI to deliver a more robust service. Some good examples of how AI has been used in the energy and utilities sector include:

Smart grid management: Smart energy grids use AI algorithms to analyze energy consumption and predict demand based on usage patterns. This results in a more agile approach to service provisioning and can help make service outages less likely. AI can also help optimize the use of energy storage and renewable energy sources alongside traditional power generation methods, relying on wind and solar when they are available and intelligently shifting demand to fossil fuel power plants when demand rises.
Predictive maintenance: AI applications can predict when infrastructure is likely to fail based on prior performance data and maintenance records. By examining data from sensors, AI can predict that a piece of equipment requires maintenance or is likely to fail. The result is a more proactive approach to maintaining equipment rather than simply reacting when repairs are needed. This helps to make infrastructure more reliable and less prone to unexpected failure.
Renewable energy optimization: AI can be used to analyze weather patterns and predict when wind and solar generators will work most efficiently. This enables energy providers to tap into these renewable resources when the conditions are viable, rather than run the equipment when conditions are less favorable, which can then result in wear and tear and less-than-optimal energy production.

AI in supply chain and manufacturing

There are many ways to use AI for manufacturing that bring improvements in efficiency and productivity. It can be used to help smooth logistics, automate complex manufacturing processes, and enhance quality control protocols. A combination of real-time sensor data analysis and prior logistics data can be analyzed to predict supply and demand problems before they happen, as well.

A few ways that AI has already been used in manufacturing include:

Warehouse automation: Large retail enterprises need to maintain a huge volume of stock, which needs to be stored somewhere. Efficient warehouse management is key to ensuring perishable goods are rotated out in age order and that related goods are stored close together to make picking more efficient. AI can help with these problems by completely automating a warehouse. This can be done by using autonomous mobile robots, which take over moving goods around the warehouse.
Supply chain optimization: The just-in-time method of supply chain management has become very popular. This means a company only keeps the stock level it needs rather than sitting on a high-value inventory. AI can assist with this methodology by analyzing past sales data, market trends, and other factors that implement the supply and demand for goods a company sells. These insights can be used to smooth out bumps in the supply chain and ensure products do not go out of stock.
Quality control: AI-driven vision systems can inspect products from a production line for faults. These kinds of systems can be taught to recognize common faults to find defective products or highlight problems with the manufacturing infrastructure. This helps to reduce the chance of human error and ensure the highest product quality is maintained. The result can be fewer returns and recalls, and maybe even higher profits.

AI in government and public services

Governments, local authorities, and public service providers have already successfully deployed AI to help improve citizen services, increase public safety, and optimize operations. These organizations can use AI and analytics to follow a more data-driven approach to decision-making, which can ultimately lead to lower operational costs alongside improved service offerings.

AI has been used in the government and public services sector in ways including:

Traffic and urban planning: Local authorities have successfully implemented AI-driven traffic management systems to reduce congestion. These systems often incorporate smart traffic signals controlled by analyzing historical traffic data to predict possible traffic congestion hotspots and alleviate them. Furthermore, town planners have extended this functionality by using AI to analyze the movement of commuters and highlight bottlenecks in public transport that need to be eliminated. A similar process can be used to predict the impact of new construction and infrastructure projects on traffic patterns, as well as other considerations such as air quality and environmental impact.
Better public safety: Some law enforcement and emergency services have already successfully deployed AI systems to help them keep the public safe. AI applications that use facial recognition have been used to identify and respond to potential security threats more rapidly. Additionally, police forces have used AI-powered predictive analytics to find patterns and highlight areas at a higher risk of crime so that police resources can be used and deployed more efficiently. AI has also been used to help plan for disasters by optimizing plans for evacuating residential areas and ensuring that help and support are available for victims.
Digital government services: Members of the public often have to navigate complex, bureaucratic processes when dealing with their local government—such as applying for permits or dealing with bills for local public services. By deploying AI-powered virtual assistants, local governments can deliver answers and assistance to citizens dealing with these situations at the point when they need it, whether or not the office is open.

AI applications examples in other areas

We’ve covered some of the main examples of AI applications in specific sectors, but this is far from an exhaustive list. Some additional noteworthy uses include:

Real estate: Machine learning models can analyze property markets to predict trends in price and demand for specific types of accommodation in a certain area. Predictions can take into account a property's features, location, and data on past values of similar properties to identify potentially profitable property investments.
Legal sector: Law firms have already used AI in a few ways, such as providing a simple virtual assistant that can answer basic legal questions. However, more advanced AI platforms can analyze legal contracts and highlight any gaps or potential legal pitfalls, suggesting key clauses to help minimize risk.
Retail stores: AI has been embraced by the e-commerce sector, but retail stores can still leverage AI in other ways, such as AI computer vision systems supporting checkout-free shopping in some stores. On top of this, AI can be used for inventory management, to implement smart pricing, and to optimize the logistics and supply chain.

AI continues to expand its influence across diverse industries, driving innovation, automating processes, and improving customer experiences. As AI technology evolves, its applications will further redefine how businesses and professionals operate in a wide range of fields.

What AI application could benefit your business?

AI is fast becoming a must-have technology for many businesses, and there are use cases for building AI applications in just about every sector. As AI technology matures and organizations find new ways to leverage its value, any company that wishes to maintain an edge will need to implement capable AI applications.

Your business can likely benefit from adopting AI, so now is the time to consider deploying AI applications to help lower operating costs and increase productivity and efficiency. Depending on the market sector your business operates within, these use cases could range from simple GenAI applications that enhance workflows to full-blown automation of a robotic workforce.

There is no doubt that in the coming years, new and novel ways to use AI applications in business will be discovered.

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