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Machine learning use cases:
applications, industries & trends

October 2, 2025

Key applications of machine learning

Key applications of machine learning

Computer vision systems use machine learning and deep learning to understand the content of digital images, videos, and other visual data. Computer vision tasks include object detection and classification, image recognition and segmentation, and pose estimation.

Natural Language Processing

NLP solutions use machine learning to interpret and mimic spoken or written human language. Common NLP tasks are speech recognition, text classification, natural language generation, document summarization, sentiment analysis, and real-time translation.

Conversational AI tools rely on NLP algorithms to replicate human language patterns and seamlessly interact with users. They can be deployed to communicate with customers, provide them with information or assistance, and collect information from them.

AI agents are ML-powered software systems designed to autonomously handle complex tasks on behalf of users. They can help automate business operations to boost efficiency or perform analytical tasks and share their findings in natural language to democratize data analysis.

Data mining

Data mining is a branch of data science that focuses on applying ML algorithms to identify patterns, trends, or outliers across big data and extract meaningful insights. Tools that use data mining include recommendation engines, anomaly detection software, and predictive analytics systems.

Generative AI

GenAI systems are advanced ML solutions that leverage multimodal language models to generate original content based on user instructions or “prompts”. They can be used to quickly create new text, visuals, and code or summarize existing content.

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Real-world ML use cases by industry

Inventory management

Processing customer purchase patterns and a range of external factors like seasonality and market trends, machine learning solutions help retail companies predict future demand and timely replenish their stock, preventing stockouts and overstocking.

Shelf & store optimization

ML can be used to optimize the layout of stores and shelves, improving customer experience and maximizing sales. For this, retailers need to apply dedicated computer vision algorithms that analyze customer traffic patterns and product and signage placement.

Workforce optimization

Workforce management solutions equipped with ML algorithms can help distribute shifts and workload by processing sales data, employee availability and performance, labor laws, and overall labor budget and automatically generating shift schedules and task assignments.

Recommendation systems

Recommendation engines provide personalized product and content recommendations to customers on ecommerce sites. They use machine learning algorithms to analyze a customer’s past purchases, browsing behavior, and reviews in real time to create highly tailored recommendations.

Pricing optimization

Embedded into ecommerce sites, machine learning algorithms can dynamically optimize the prices of products and services. By analyzing real-time competitor pricing, historical sales data and sales trends, demand predictions, and customer demographics, companies can adjust their pricing models to maximize revenue and stay competitive.

Customer assistance

Retail companies can integrate chatbots into their online stores to act as shopping assistants and provide more interactive, life-like shopping experiences for customers. These bots can understand customer queries and respond to them, provide product recommendations, and even process orders.

Medical image analysis

Computer vision algorithms can analyze large volumes of medical images such as X-rays, CT scans, and MRI scans in a matter of minutes, identify anomalies in them, and provide diagnostic feedback to physicians, significantly reducing diagnosis time and improving its accuracy.

Medical transcription & documentation

Machine learning solutions with NLP capabilities can be used to transcribe audio recordings of medical consultations, extract key patient data, and save it in the respective EHRs, minimizing manual data entry and preventing human error.

Hospital management

Implemented into hospital management systems, machine learning algorithms can analyze historical and real-time data to optimize hospital operations, such as patient scheduling and resource allocation, and automate routine administrative processes, helping hospitals run more efficiently.

Diagnostics

Machine learning algorithms can detect anomalies in patient data, such as a nodule on a lung X-ray or cardiac arrhythmia recorded via wearables, faster and more accurately than humans. By processing large datasets, deep learning models identify outliers that may be signs of diseases, helping physicians address them at early stages before they become too severe.

Credit underwriting

Machine learning systems can analyze large volumes of customer data, such as credit score, income, and spending history, and accurately determine a customer’s creditworthiness to help banks make better underwriting decisions.

Fraud detection

Many financial institutions currently rely on machine learning solutions to identify anomalies that could indicate fraudulent activity, such as large transactions between new companies registered in tax havens which is a common sign of money laundering.

Personalized recommendations

Machine learning algorithms can be used to examine customers’ spending habits, trading histories, risk propensity, or other factors and generate personalized recommendations for financial products and services, improving engagement and loyalty.

Learning path personalization

Implemented into learning management systems, machine learning algorithms can customize the learning experience for each student, offering personalized courses and learning paths based on a student’s performance, interests, and goals. This way, educational institutions can help students learn more effectively and reach their academic goals faster.

Dropout mitigation

Machine learning can detect patterns in students’ performance data and identify those at risk of dropping out, allowing educators to intervene before it is too late and provide the necessary support for those in need.

Student assistance

Education institutions can use AI tutors and chatbots to support students 24/7 and help them learn more effectively. These AI tutors can understand student queries and give answers to them, and even grade assignments autonomously.

Portfolio management

ML-powered fintech solutions help private investors and wealth managers identify the most promising stocks, bonds, or other assets to build more balanced and diversified portfolios. Some of these systems also integrate robo-advisors to automatically create personalized financial plans and investment portfolios aligned with clients’ goals and risk tolerance.

Sentiment analysis

Machine learning algorithms can analyze news and social media data in order to identify market sentiment and provide insights into upcoming market trends.

Algorithmic trading

Algorithmic trading systems apply machine learning algorithms to streamline buying and selling financial instruments. By analyzing large amounts of data, ML algorithms can identify potential trading opportunities and execute them faster than humans.

Stock ranking

Traders can use machine learning algorithms to quickly rank stocks based on various factors such as historical data, market trends, and economic indicators and generate values for K, quality, growth, and other score types.

Content creation

Integrated into CRM systems, natural language processing and machine learning algorithms can analyze customer profiles and generate relevant marketing messages and content to drive purchasing decisions and boost engagement.

Predictive customer analysis

Machine learning systems can analyze customers’ browsing behavior, buying patterns, and other relevant factors to predict future business outcomes. This includes forecasting customer lifetime value to help sales reps prioritize high-value clients or identifying customers at risk of churn to retain them with targeted offers.

Content optimization

Marketing teams can apply machine learning to optimize their content for search engines and social media. For instance, ML systems can execute automated A/B tests and display the most effective content version to a certain audience segment, or provide marketers with keyword placement recommendations. This helps improve visibility and engagement and deliver more relevant content with less effort.

Occupancy prediction

Real estate agencies can implement predictive models to analyze data on property usage and forecast occupancy rates. This helps property managers adjust rental rates to maximize profits, as well as optimizing heating and ventilation to reduce energy costs and ensure a comfortable tenant experience.

Real estate appraisal

Using machine learning techniques for pattern recognition allows real estate companies to predict how a property’s features, location, surrounding infrastructure, and other metrics can impact its value. This helps realtors set a suitable price to maximize both the chances of sale and profit.

Tenant risk assessment

Realtors can use ML-powered software to analyze potential tenants’ credit histories, tax returns, eviction records, and other data and determine their ability to make regular rent payments. This enables agencies to make informed decisions on whether to approve or reject rental applications.

Property search personalization

A common feature of the world-leading real estate marketplaces, ML algorithms optimize the property search experience by intelligently assessing a customer’s preferences based on their search history and web profiles, significantly narrowing down the number of offers and increasing engagement.

Predictive maintenance

Using predictive analytics, manufacturers can detect patterns in equipment performance data to anticipate potential failures and timely address emerging issues before they escalate, reducing downtime and maintenance costs.

Product development

With the help of ML, manufacturers can accelerate development cycles and create products optimized for both performance and cost. ML-driven generative design tools consider many parameters, including material properties and design constraints, to generate the most suitable design faster.

Demand forecasting

Machine learning-powered analytics systems can analyze large sets of sales data and identify patterns in customer demand, helping manufacturers optimize production cycles and reduce inventory costs.

Quality control

With the help of computer vision, production floor control systems can automatically inspect products and detect defects. Such ML solutions can help manufacturers ensure that their products meet quality standards and reduce the need for manual inspections.

Route optimization

By analyzing real-time and historical traffic data, machine learning-powered routing systems can identify the most efficient routes, reducing delivery times and saving transportation and labor costs. Such systems can also predict delivery times more accurately by taking into account loading and unloading times at each route point.

Warehouse management

ML algorithms can be used to optimize warehouse operations by analyzing data on inventory levels, order volumes, and other factors. By identifying opportunities for inventory management optimization, machine learning can help logistics providers reduce costs and improve efficiency.

Carrier performance analytics

Logistics companies can use machine learning to analyze carrier performance by processing data on on-time delivery, shipment damages, and other metrics. This way, machine learning solutions can help logistics providers identify the best carriers for each shipment, negotiate better rates, and improve customer satisfaction.

Crop management

Getting data from drones, IoT sensors, and computer vision solutions, ML tools can monitor crop health, detect pest infestations, and predict yield. Such a system can help farmers optimize their irrigation and fertilization practices, reduce pesticide use, and improve yields.

Water management

With the help of ML algorithms, agriculture data analytics solutions can analyze moisture data collected by sensors and accurately predict water needs for different crops. Farmers can then use this information to optimize their irrigation schedules and reduce water waste.

Harvest prediction

ML can be used to predict the timing and quantity of a crop harvest by assessing data on weather patterns, soil conditions, and other factors. Delivering accurate harvest predictions, machine learning models can help farmers plan their operations more efficiently, including yield storage, processing, and distribution.

Trends & stats on ML use cases

Retail & ecommerce

The top AI use cases that retailers are currently investing in include store analytics and insights (53%), personalized customer recommendations (47%), and adaptive advertising, promotions, and pricing (40%)

NVIDIA

34% of retailers have adopted AI in at least one process area, with demand forecasting, personalization, chat, and threat detection representing the most popular areas

Gartner

Healthcare

The robot-assisted surgery segment dominated the AI in healthcare market in 2024 with the largest revenue share of over 13%. However, fraud detection is expected to be the fastest growing segment between 2025 and 2030

Grand View Research

81% of US patients surveyed have used an AI-powered healthcare support chatbot or voice assistant in the past year

Hyro

Financial services

In 2024, 57% of financial services companies leveraged artificial intelligence for data analytics, making it the leading AI application in this industry

Statista

In 2024, customer experience and engagement emerged as the top application of generative AI in financial services, with 60% of respondents prioritizing this area. Other key use cases included reporting, investment research, and document processing (implemented by over 50% of firms)

Statista

Education

In 2024, machine learning was the leading technology segment in the AI in education market, with a 64.7% revenue share. Key ML use cases included learning content personalization, educational chatbots, student performance forecasting, and automated assessments

Grand View Research

The most common AI tools used by teachers are AI-powered educational games (51%), adaptive learning platforms (43%), automated grading and feedback systems (41%), and chatbots for student support (35%)

Forbes

Marketing

In 2025, the most popular AI use case among marketers globally was content creation and optimization (mentioned by 37% of respondents). Other key applications included email marketing optimization, social media management, and ad targeting

Statista

In 2024, the top AI use cases among US B2B marketers included content-related tasks (52%), coding (39%), and presentations (35%)

Statista

Manufacturing & supply chain

Industry 4.0 pioneers have achieved a two- to three-fold increase in productivity and a 30% reduction in energy consumption by implementing AI in use cases such as heavy-transport equipment routing and demand forecasting

McKinsey

74% of surveyed manufacturers plan to use or are already using GenAI to enhance their customer experience. Key GenAI use cases in this regard include virtual chatbots providing customers with product specifications and service manual generation

Deloitte

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Benefits of successful ML use cases

Successfully implemented machine learning solutions can bring a range of benefits to organizations, including cost savings, improved efficiency, and better decision-making:

Improved operational efficiency

ML solutions enable businesses to automate a range of administrative processes and reduce the amount of time and effort employees spend on mundane tasks. This way, companies can cut operational costs and free up employees’ time for more strategic and higher-value activities.

Enhanced customer experience

By leveraging ML models to analyze customer data, businesses can gain unique insights into customer needs and preferences. With this data on hand, they can create more personalized experiences and boost customer loyalty and satisfaction while minimizing customer churn.

Data-driven decision-making

ML algorithms can help organizations make better business decisions faster by processing data from multiple sources and providing valuable insights into complex and large-scale processes. By providing decision-makers with the right information in a timely manner, businesses can make smarter decisions that have far-reaching effects.

Reduced business risk

Organizations can leverage the forecasting capabilities of ML systems to predict potentially harmful future events or scenarios, such as fraud, customer churn, and supply chain disruptions, and take timely steps to prevent them.

Development

Development

We help you develop ML-powered software fully aligned with your business goals, corporate workflows, and industry specifics or modernize your current ML solution to keep pace with your evolving needs and new tech trends.

Consulting

Our consultants offer expert advice at every stage of your ML project to help you overcome emerging technical challenges, speed up software launch, and maximize the adoption benefits of your solution.

Unlock the benefits of machine learning

Businesses worldwide have already unlocked the benefits of machine learning and proved its infinite potential. From enhanced customer experiences and improved efficiency to better decision-making and reduced costs, the potential of ML is unparalleled compared to other technological initiatives currently on the market. As such, it is wise for businesses to start taking advantage of this technology sooner rather than later to gain a competitive edge.

However, companies must remember that the success of an ML project heavily depends on the unique business case of each organization, its goals, and available data. By taking all these factors into account before launching an ML project, businesses can ensure they get the most out of their investment.

FAQs

  1. Pinpoint the improvement area
    Identify problems, inefficiencies, and areas for improvements in established business processes that can be addressed with machine learning.
  2. Decide whether ML is the best solution
    ML implementation requires time, effort, and resources, especially for ML model training. Make sure that no regular software solution can deliver the expected results or solve your business problem.
  3. Review existing ideas
    Evaluate your ML solution ideas more closely in terms of implementation complexity, data availability and quality, and the value it can bring to your business. Then, assess whether you can scale up the solution in the future and what resources you would need for its maintenance. Next, assess the regulatory, risk, and transparency aspects of the potential use cases.
  4. Assess your ML maturity
    To identify suitable ML use cases, assess your organization’s current readiness for ML implementation. Consider factors like the established degree of automation in processes and systems, the quantity and quality of data available for training ML models, and the availability of relevant talent like ML engineers and data scientists.
  5. Prioritize use cases
    Single out specific tasks the ML solution will handle. This stage should be approached gradually, with multiple iterations to reconsider potential use cases based on the discovery of hidden implementation bottlenecks. For instance, you may realize that it’s worth implementing several more flexible ML open-source tools rather than a single custom solution

Machine learning can be divided into three categories based on the approach used to train a ML model:

  • Supervised learning
    The model is trained with labeled data, namely data points tagged with labels that describe what this data represents. For instance, a labeled dataset can contain thousands of emails tagged as “spam” or “not spam”. This helps the algorithm understand the relationship between input (emails) and output (spam/not spam) and apply such findings to spot future cases of email spam. Common supervised learning algorithms include CNNs (convolutional neural networks), kNN, random forest, support vector machines, and linear regression.
  • Unsupervised learning
    The ML algorithm processes unlabeled data, autonomously identifies shared features or differences, and groups data points accordingly (e.g., customers clustered into segments based on similar purchasing patterns). K-Means clustering, PCA, autoencoders, and Apriori are popular unsupervised learning algorithms.
  • Reinforcement learning
    Machine learning systems undergo multiple training iterations using a trial-and-error approach. In each iteration, the system attempts to perform a task (e.g., an autonomous vehicle reaching a certain location), receiving rewards for correct actions (including avoiding obstacles) and penalties for poor performance (such as collisions). Key algorithms used in reinforcement learning are Q-learning, policy gradient, and SARSA

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