Artificial intelligence (AI) and machine learning are rapidly reshaping the business landscape. In 2025, 88% of organizations reported using AI in at least one business function, up from 78% the year prior, according to McKinsey & Co.’s report on the state of AI in 2025.
Reflecting this growing adoption, the use of machine learning—a subset of AI—is expected to expand dramatically in the coming years. Fortune Business Insights projects that the global machine learning market will rise from about $65 billion in 2026 to more than $430 billion by 2034—an increase of more than 500%.
This growth underscores the increasing importance of machine learning in business analytics, which enables organizations to extract actionable insights from data to be able to make more accurate, informed decisions. Organizations across industries are finding new ways to effectively leverage machine learning to enhance their overall performance and drive stronger growth.
What Is Machine Learning in Business Analytics?
Businesses have long relied on data—in areas such as sales, inventory, cash flow, revenue, and expenses—to guide their operations and strategic decision-making. For centuries, the work of analyzing and interpreting this information was done manually by humans, often using ledgers and spreadsheets.
Today, organizations are increasingly using machine learning to collect and analyze large volumes of data more efficiently in order to uncover insights in patterns and trends that can be used to make more informed business decisions.
Machine learning uses algorithms and statistical models to enable computers to learn from data and improve their performance over time without being explicitly programmed. Machine learning models can be used to detect patterns, recognize relationships, and make predictions or recommendations based on the information they analyze.
As these models are exposed to more data, their accuracy and effectiveness improve. In business analytics, machine learning is used to enable organizations to better understand their operational and financial data, which can help guide their strategic planning and decision-making.
Where Is Machine Learning Used in Business Analytics?
The use of machine learning to enhance business analytics spans sectors and functions. It’s used in healthcare, finance, cybersecurity, manufacturing, and retail, among many other industries, to facilitate forecasting, fraud detection, risk assessment, customer behavior analysis, and operational planning.
Organizations also apply machine learning to financial modeling, pricing optimization, and performance monitoring, helping leaders make data-driven decisions that improve efficiency and overall business performance.
Types of Machine Learning
Machine learning models can be trained using different methods depending on the type and amount of data available. Business analytics commonly uses five types of machine learning:
- Supervised learning: Supervised learning trains models using labeled data, where the outcome is already known. This enables models to learn relationships between inputs and outputs and make predictions based on new data.
- Unsupervised learning: Unsupervised learning uses unlabeled data to identify relationships, patterns, or groupings in large datasets. This approach helps organizations discover trends and better understand their data.
- Semisupervised learning: Semisupervised learning combines labeled and unlabeled data to improve a model’s accuracy and performance. The model begins by analyzing the labeled data first, then takes what it learned from that initial analysis to guide its exploration of the unlabeled data, which it uses to refine its outputs.
- Reinforcement learning: Reinforcement learning trains models by allowing them to explore data and adjust their behavior based on feedback in order to achieve a specific outcome. Over time, models learn to improve their performance by optimizing their actions.
- Self-supervised learning: Self-supervised learning uses unlabeled data to train models without requiring manually labeled examples. The model learns by using one part of the data to predict another part, allowing it to improve its performance. Unlike unsupervised learning, which focuses on identifying patterns and relationships, self-supervised learning trains models to make predictions.
Machine Learning Applications in Business Analytics
Using machine learning in business analytics processes can give organizations a distinct competitive advantage. The insights generated by machine learning models help companies better understand their customers’ behavior and market dynamics, make strategic decisions that enhance growth, improve operational efficiency, and detect risks.
Machine learning’s impact spans business functions, including operations, marketing, logistics, information security, and customer service. The following are some of the most common applications of machine learning in business analytics.
Predictive Analytics and Forecasting
One of machine learning’s most beneficial contributions to business analytics is its ability to identify patterns and make predictions based on large datasets, allowing human analysts to move beyond descriptive analytics (understanding what has happened) to predictive analytics (projecting what could happen).
Machine learning models use historical data to generate forecasts that help organizations anticipate market shifts and plan more effectively. This capability enables organizations to be more accurate in their forecasting and planning in business areas such as sales, demand, and financial performance.
Customer Segmentation and Personalization
Machine learning enables organizations to analyze customer data (demographic information, purchase histories) and identify patterns in customers’ behavior, preferences, and interactions. These insights allow businesses to group their customers based on shared characteristics and better understand their unique needs. Organizations can create more personalized experiences and products for their customers, which helps them boost engagement and align their offerings with their customers’ expectations.
Automated Data Analysis
With machine learning, organizations can analyze massive amounts of data much faster and with greater accuracy than with traditional manual methods, saving them time and reducing errors. Instead of relying solely on human analysis, machine learning models can be used to automatically identify key insights within complex datasets and make certain decisions with minimal human intervention.
However, while automation can streamline operations and enhance decision-making, human oversight is still crucial to ensure responsible outcomes.
Anomaly and Fraud Detection
Machine learning models can monitor data to recognize unusual patterns or deviations from expected behaviors. This helps organizations detect anomalies that may indicate security threats or fraudulent activity. By identifying these issues early, machine learning enables organizations to better manage risk and helps them respond to threats more effectively.
Price Optimization
Machine learning can sift through data related to market conditions, spending habits, and inventory levels to provide organizations with valuable assistance in making pricing decisions. As with other machine learning applications in business analytics, the patterns and relationships that are found within the data help businesses adjust their pricing strategies to align with demand and their broader business goals.
Machine Learning Use Cases
Machine learning is a versatile tool in business analytics, performing numerous tasks—data analysis, forecasting, risk assessment—more efficiently and often with greater accuracy than manual analysis done by humans.
While these applications represent the core analytical capabilities of machine learning, their true value becomes clear when applied to real-world business challenges across sectors and industries. Machine learning use cases span manufacturing, retail, finance, healthcare, and beyond. Whatever the industry, machine learning is being used to power data-driven decisions that help organizations run more efficiently and stay competitive in today’s business environment.
Finance
Machine learning is widely used in finance for everything from real-time fraud detection to stock market transactions. Machine learning models can analyze decades of stock market data to forecast trends and make recommendations about whether to buy or sell. According to IBM, between 60% and 70% of stock trading is done by machine learning algorithms.
Banks and other financial institutions train machine learning models using historical data so they can recognize suspicious transactions and other anomalous activity that may require investigation. Mastercard reports that the AI-enhanced solutions it uses to analyze transactions can boost fraud detection rates by an average of 20%.
Retail and E-Commerce
Retail and e-commerce companies increasingly use machine learning in business analytics to gain deeper insights into their customers’ behavior and to optimize their business operations. By analyzing customers’ purchase histories, browsing activity, and engagement data, machine learning models can assist retailers in delivering more personalized product recommendations and marketing messages to each customer, while helping retailers anticipate demand and manage their inventory more effectively.
According to a 2023 survey by Nvidia, 42% of retailers were already using AI, while an additional 34% were assessing or piloting AI initiatives. Among larger companies—retailers with annual revenues over $500 million—adoption is even more widespread: 64% reported already using AI.
Manufacturing and Supply Chain
Machine learning is a valuable tool in manufacturing and supply chain management, where organizations use it to strengthen their forecasting, identify disruptions, and streamline their operations. Machine learning models analyze data from suppliers, logistics providers, production schedules, and equipment, which can help organizations anticipate delays and identify inefficiencies.
Machine learning also enables predictive maintenance—by analyzing sensor data from factory equipment—allowing manufacturers to identify signs of equipment failure before breakdowns occur, minimizing downtime and improving their productivity.
Digital technology is expected to reshape manufacturing in the coming years. According to Deloitte’s 2024 manufacturing industry outlook, 83% of manufacturers said smart factory solutions—such as machine learning and generative AI, 5G, smart equipment, and cloud computing—will transform production within five years.
Healthcare
Healthcare organizations use machine learning to enhance both their operations and their delivery of patient care. Through analysis of electronic health records, medical devices, and facility data, machine learning models aid providers in their diagnoses and treatment planning, as well as help them make more timely clinical decisions, such as when to book certain health screenings for patients. It also is used to anticipate staffing needs.
Adoption has accelerated in recent years: The American Medical Association (AMA) revealed that 66% of physicians were using AI in 2024, up from just 38% in 2023.
Marketing
Marketing is among the sectors with the highest rates of AI and machine learning adoption. A 2026 Salesforce report found that 75% of marketers use AI for tasks such as personalizing content and predicting the performance of campaigns.
Marketing teams also use machine learning to analyze consumer data from emails, social media platforms, websites, and purchase histories, helping them map out customers’ behavior patterns and build more detailed customer profiles. Machine learning’s ability to guide customer segmentation and offer insights on how to deliver more relevant customer experiences is particularly valuable as consumers increasingly expect better personalization from companies.
Additional Resources on Machine Learning and Business Analytics
AI and machine learning technology are advancing at an exponential rate, expanding how businesses use data to improve their performance and guide their decision-making.
For professionals seeking to leverage machine learning in business analytics to drive greater efficiency and business growth, staying on top of the latest trends, applications, and innovations is essential. The following resources can help business leaders gain a deeper understanding of how AI and machine learning are being applied across industries and where they can create value in business analytics.
- Stanford University HAI, “Artificial Intelligence Index Report 2025”: Provides a comprehensive overview of how AI is currently being used in business and in everyday life, including an analysis of investment trends and adoption patterns, along with its wider economic impacts
- International Research Journal of Economics and Management Studies, “The Impact of Artificial Intelligence and Machine Learning on Business Analytics”: Explores how AI and machine learning are revolutionizing business analytics, with a discussion of predictive accuracy, process automation, cross-industry applications, and ethical considerations
- PLOS One, “Application of Machine Learning in Predicting Consumer Behavior and Precision Marketing”: Discusses how machine learning can be used to identify potential customers, predict customers’ behavior, improve conversion rates, and design precision marketing tools
- Decision Analytics Journal, “An Analysis of Critical Factors for Adopting Machine Learning in Manufacturing Supply Chains”: Looks at what shapes successful machine learning adoption in manufacturing supply chains, with an emphasis on implementation conditions and organizational readiness
- IBM, “A Guide to AI in Marketing”: Explains how AI and machine learning contribute to marketing analytics, including in customer insights, forecasting, segmentation, and automation
Machine Learning’s Expanding Business Impact
The growing role of machine learning in business analytics reflects a broader shift in how organizations use data to guide their strategy. As machine learning models become more advanced, they can help organizations refine their decision-making and adapt more effectively in a rapidly changing marketplace.