Applications of Machine Learning and AI in Business

The adoption of machine learning (ML) and artificial intelligence (AI) in business is expanding quickly. According to a McKinsey survey, 50% of businesses in 2020 had implemented AI in at least one area of their operations. This figure jumped to 56% in 2021 and reached 60% in 2023.^{1, 2}

As artificial intelligence goes mainstream, it’s gaining multiple use cases in organizations.³ Read on for a machine learning overview and to discover some of the most common uses of ML and AI in business.

Machine learning tools can learn from the data you feed them. The more data they analyze, the more accurate they become.⁴ However, the ability to self-improve is not the only reason data analysts implement ML.

The volume of business information in different industries has become too big for humans to be reasonably expected to manage it.⁵ Therefore, data professionals rely on machine learning technology to process vast amounts of data and quickly generate actionable insights.⁶

ML and AI are often discussed together. Sometimes, people use the terms interchangeably, but the two technologies are different. Artificial intelligence is the ability of machines to solve problems or do tasks that need human intelligence, such as predicting the future using patterns from past events. Machine learning is a subset of AI.⁷

A clearer distinction between the two is that artificial intelligence is an umbrella word for different technologies, including machine learning. Other forms of AI include natural language processing, computer vision, and deep learning.⁸

AI and ML can help organizations save time, but where exactly should businesses implement these technologies? Consider the following AI applications and ML use cases.

Predictive analytics focuses on what might happen in the future. It involves analyzing data to identify patterns and use those insights to predict possible business outcomes, such as demand and sales.

While analysts have used predictive analytics for as long as companies have collected data, generating timely insights from large amounts of information has always been difficult. Implementing AI in predictive analytics is a game-changer. It’s the key to more reliable forecasting.

Using traditional predictive analytics methods, analysts may need hours to extract insights from small datasets. With AI predictive analytics, they can generate actionable details from millions of data points in just a few minutes.⁹

AI predictive analytics is common in the following industries:

• The healthcare industry uses it to make clinical management decisions¹⁰
• Finance leverages AI to predict creditworthiness¹¹
• Marketing and sales rely on machine learning algorithms to predict customer behavior¹²

ML tools can identify fraud because they sift through large quantities of data, identify unusual patterns, and learn from the analysis. Financial institutions commonly use machine learning in these business operations:¹³

Machine learning models can identify suspicious activities in transactional data. The systems are trained to recognize normal payment processes and flag uncommon ones that may indicate fraud.

Machine learning algorithms process data points to evaluate the risk level of user accounts or transactions. They assign risk scores based on transaction amount, an account user's location, and the person's transactional habits. The higher the score, the greater the possibility of fraud.

ML solutions can learn and adapt. When fraudsters change tactics, the systems automatically retrain themselves to recognize emerging fraud patterns. That way, organizations stay on top of new malicious acts.

Businesses can use AI and machine learning applications to streamline the buying process. With the right ML solutions, organizations harvest, organize, and analyze a large volume of customer data. This may include purchasing history, demographic information, and the individual customer’s typical behavior, such as online browsing habits. Companies can use insights from these datasets to recommend relevant products tailored to a customer’s purchasing preferences. Personalized suggestions remove friction in the buying process because people spend less time looking for products that suit their needs.

In addition, businesses use AI self-service tools to enhance customer experience. For example, chatbots can answer basic questions and guide prospects to relevant content on a company’s website. Since the bots can work around the clock, the company can offer 24/7 customer support without hiring more agents or increasing staff workload.¹⁴

Among the many tangible business benefits of AI and ML solutions, they process large quantities of complex data faster than humans. As a result, organizational leaders can rapidly make better-informed, data-driven decisions.¹⁵

In the future, companies will increasingly integrate AI and business strategy. That’s because AI systems can help executives extract insights from big data, avoid biases in their choices, and make strategic business decisions quickly.¹⁶

Experts predict that AI will disrupt almost all industries. According to McKinsey, AI is expected to automate up to 70% of business activities in nearly every occupation by 2030.¹⁷ Some people may think that robots are coming for their jobs, but that isn’t necessarily the case. For professionals who will update their skills to use technology effectively, AI will complement their work instead of replacing them.¹⁵ Additionally, there will be a high demand for skill with artificial intelligence and machine learning capabilities. Statistics say jobs that require AI and ML expertise will increase by 71% in the next five years.¹⁸ Continuous learning can be a powerful way to upskill or reskill, ensuring that the increasing use of AI in business is an opportunity rather than a threat.

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