Exploring Machine Learning and AI Applications in Business

As artificial intelligence (AI) applications advance, businesses are making changes to their organizations and structures to derive more value from them. According to a survey by the global consulting firm McKinsey & Co., 78% of companies reported using AI in at least one business function, with the average organization incorporating AI applications in at least three business functions.¹

While generative AI is most often used in marketing and sales, it’s also frequently employed in product development, information technology (IT), and software engineering. Machine learning (ML) and generative AI applications are expanding beyond text-based output, with many businesses experimenting with multi-modal AI and agentic AI applications as well.¹

This article will explore AI applications in business and how they deliver value through increased productivity and risk mitigation.

Understanding AI and ML

While people sometimes use the phrases "ML" and "AI" interchangeably, ML is technically a subset of AI, which is an umbrella term for many different technologies.² Other widely used forms of AI include natural language processing, computer vision, and deep learning.³

ML tools can "learn" from the data you feed them. The more data they analyze, the more accurate they become.⁴ The volume of business information in different industries has become too big for humans to manage.⁵ Data professionals rely on ML technology to process vast amounts of data and quickly generate actionable insights.⁶

Generative AI, a type of AI that can create new content based on existing data, integrates many different subsets of AI. Multimodal generative AI models use ML to process and generate multiple types of data, including text, speech, images, and videos.

Agentic AI applications are complex models capable of working to achieve a goal, such as generating a presentation about quarterly earnings, without direct human oversight.⁷

Core AI Applications in Business Processes

AI business applications span a wide range of use cases. The following are some of the most common.

1. Predictive Analytics in Forecasting

Predictive analytics focuses on what might happen in the future. This involves analyzing data to identify patterns and using 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.

With traditional predictive analytics methods, analysts may need hours to extract insights from small datasets. By using AI predictive analytics, they can generate actionable details from millions of data points in just a few minutes.⁸

AI predictive analytics is common in many industries:

• The healthcare industry uses it to make clinical management decisions⁹
• Finance leverages AI to predict creditworthiness¹⁰
• Marketing and sales rely on ML algorithms to predict customer behavior¹¹

2. Fraud Detection and Risk Management in Finance

ML tools can identify fraud by sifting through large quantities of data to identify unusual patterns and learn from the analysis. ML 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.¹²

Financial organizations also use ML algorithms to process data points so they can better 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.¹²

3. Enhancing the Customer Experience with AI

Businesses can use AI and ML applications to streamline and elevate the buying process. With the right ML solutions, organizations can harvest, organize, and analyze vast amounts of customer data—from purchasing history and demographic information to browsing habits and engagement across digital platforms. These insights allow companies to deliver highly personalized recommendations that match individual preferences, removing friction from the buying process and helping customers find the right products faster.

AI also enables omnichannel personalization, ensuring that customers enjoy a consistent, tailored experience whether they interact with a brand through its website, mobile app, email, or in-store. For example, a customer who browses a product online might later receive a personalized promotion through email or see related recommendations in the company’s app, creating a seamless journey across multiple touchpoints.¹³

In addition, businesses are turning to AI-powered self-service tools to enhance customer support. Chatbots can answer common questions, guide prospects to relevant resources, and provide 24/7 assistance without increasing staff workload.¹⁴ Similarly, voice AI technologies—such as virtual assistants and interactive voice response (IVR) systems—are improving customer engagement by handling service requests, offering product information, and even assisting with transactions through natural, conversational interactions.¹⁵ Together, these innovations free up human agents to focus on complex issues while ensuring customers receive faster, more personalized support.

4. Marketing and Sales Optimization 

Marketing is one of the most widely used applications of AI in business. Teams rely on AI tools to zero in on the ideal customer and automatically optimize advertising campaigns while they’re running.

AI ad-targeting tools analyze user behavior, demographics, and engagement to determine when and where to deliver promotional content for the best results. Lead-scoring automation uses predictive analytics to rank potential customers on the likelihood that they’ll make a purchase in order to prioritize outreach efforts.

Marketing professionals also use AI to automate and scale A/B testing, which involves comparing variations of marketing campaigns to determine which is more effective. These tools can test multiple versions of promotional material simultaneously, analyze the results, and optimize the campaign in real-time.¹⁶ Popular AI tools marketers use for these tasks include HubSpot Marketing Hub and Salesforce Marketing Cloud.¹⁷

5. AI in Supply Chain and Operations

AI supply chain management tools help optimize planning, production, and distribution throughout the supply chain. These tools process data from multiple sources to make predictions that improve operational efficiency.

Today’s supply chains are extremely complex. AI models help with demand-planning by forecasting production needs and warehouse capacity based on datasets such as customer demand or sensors embedded in devices across the supply chain.¹⁷

This application of AI in business also extends to inventory optimization, allowing businesses to meet customer expectations without taking on excess stock. These models provide a clear view into inventory and deliver predictions based on historical data and current lead times.

Manufacturers use AI and ML applications to find the most efficient delivery routes and optimize truckloads to save time and money delivering products.¹⁸

Emerging Applications of AI

Among the many tangible business benefits of AI and ML solutions, one of the most significant is their ability to process massive amounts of complex data. This allows organizational leaders not only to respond quickly to challenges but also to make well-informed, evidence-based decisions that align with long-term goals.¹⁹

Increasingly, companies are weaving AI directly into business strategy. Beyond routine analytics, AI tools now support high-level planning by identifying market trends, forecasting demand, and simulating potential outcomes of strategic moves. For executives, this means being able to spot risks earlier, test scenarios before committing resources, and reduce bias in decision-making.²⁰ These insights extend across functions—from finance to supply chain to marketing—creating stronger alignment between departments and enabling organizations to operate with greater agility.

Rather than replacing human talent, AI is increasingly seen as a complement to professional expertise, giving employees tools to enhance creativity, efficiency, and decision-making.¹⁹ Demand for AI expertise is growing at an unprecedented pace. Job postings requiring AI skills have more than doubled between 2023 and 2025, and postings continue to rise month over month.²¹ This surge highlights the value of continuous learning and upskilling: professionals who stay current with AI and ML capabilities will be well-positioned to thrive as businesses continue to expand their use of advanced technologies.

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