Top machine learning use cases 
and industry applications

Top machine learning use cases and industry applications

June 9, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence

ML technologies are fundamentally transforming the way businesses work. With the advancement of ML algorithms, computers can process data sets quickly and efficiently, enabling organizations to better process large amounts of information faster. In addition, the advancements in machine learning solutions and expertise have allowed for more accurate predictions of customer behavior and market trends. So it is only natural that the global ML spending is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029, at a CAGR of 38.8%, according to Fortune Business Insights. 

The growth in ML usage has resulted in various ways organizations can implement these technologies. From healthcare to retail to finance, machine learning unlocks new insights and improves operational performance. With so much potential and opportunities to leverage ML, let’s explore some of the most common examples of its use cases and applications across industries.

Scheme title: Worldwide AI software by category value, 2020-2021

Data source: — Artificial Intelligence Spending Grew 20.7% Worldwide in 2021, According to IDC

Key ML applications

Key ML applications

Computer vision is a subfield of ML that focuses on enabling computers to understand the content of digital images, videos, and other visual data. Computer vision use cases include facial recognition, object detection/classification, image segmentation, etc. and is relied upon across retail, healthcare, cybersecurity, and other sectors.

    • Automated visual inspection and defect detection in manufacturing
    • Biometric authentication for security
    • MRI analysis for the detection of tumors, internal bleeding, and other diseases
    • Social distancing tracking
    • Real-time traffic monitoring
    • Object detection for autonomous vehicles and robotics


Natural Language Processing (NLP) is a subset of machine learning that allows computers to comprehend and interpret spoken and written human language. It is increasingly implemented in various sectors, including customer service, healthcare, and marketing automation, for tasks such as sentiment analysis, text classification, document summarization, and more.

    • Sentiment analysis of customer feedback, reviews, and social media posts
    • Text translation
    • Generation of product information for ecommerce
    • Speech to text, text to speech
    • Automatic extraction of relevant information from text
    • Chatbot and voice assistants for customer support
    • Meeting notes generation

Data mining

Data mining uncovers patterns and discovers correlations, trends, and outliers in large data sets. Data mining use cases can be seen in various industries such as finance, healthcare, and retail for tasks like fraud detection, medical diagnostics, customer segmentation, and more.

    • Financial fraud detection
    • Demand forecasting in retail and logistics
    • Anomaly detection in cybersecurity
    • Medical treatment recommendations
    • Risk estimation in banking
    • Purchase trend analysis
    • Consumer sentiment analysis from social media data

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38 ML use cases by industry

1 Inventory management

Processing customer purchase patterns and a range of external factors like seasonality and market trends, machine learning in retail helps companies optimize and streamline their inventory management, preventing stockouts and overstocking.

2 Shelf and store optimization

ML can be used to optimize the layout of stores and shelves and improve customer experience and maximize sales. The algorithm can do it by analyzing customer traffic patterns, product placement, and signage.

3 Workforce optimization

Retail-specific ML solutions can help more efficiently distribute shifts and workload by analyzing customer traffic patterns, sales data, and employee availability.

How to identify machine learning use cases for your business

To successfully implement ML, companies need to carefully consider a number of factors and have an in-depth understanding of the available options. To help with that, we have developed a step-by-step guide:


Pinpoint the improvement area

The idea to implement ML usually stems from problems or inefficiencies in established business processes or the desire to improve workflow speed or outcomes. So companies that have their sights on machine learning adoption should begin by answering a seemingly basic question “What are we adopting ML for?”. For this, they need to review the process intended for ML-based improvement and try to identify exact business pain points and areas for improvement.


Decide whether ML is the best solution

Machine learning can be a very effective solution, but its implementation isn’t reasonable in every case. ML algorithms require a lot of time, money and effort to build and train, as well as a significant revamp of the company’s IT infrastructure and ongoing maintenance post-deployment. So businesses need to make sure that no regular software solution is already able to deliver the expected results or solve the existing problem. 


Review the existing ideas

If you’ve decided in favor of ML adoption, you next need to evaluate your rough ML solution ideas more closely in terms of implementation complexity, data availability and quality, and the value they can bring to your business. Moreover, companies should assess whether they can scale up the ML-powered application in the future and what resources they would need for its maintenance. Next, they should assess the regulatory, risk, and transparency aspects of the potential use cases.


Assess your ML maturity

To identify suitable use cases for machine learning, one should also assess the organization’s current level of maturity with respect to ML implementation. Companies need to 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. Assessing your ML readiness is critical to understand whether you can realistically implement complex use cases.


Prioritize use cases

At this final stage, companies single out specific tasks or issues the ML solution will be built for. More often than not, this stage can take multiple iterations due to the discovery of hidden implementation bottlenecks. For example, companies can come to the conclusion they should implement several smaller, more flexible ML tools than a single complex solution. This is why effective use case prioritization should be approached gradually, with each potential ML application re-evaluated at every step.

5 benefits of successful ML use cases

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

1 Improved efficiency

When ML algorithms and models are properly applied, they can automate mundane tasks and reduce the amount of time and effort employees spend on a complex task. This way, ML allows companies to allocate resources on strategic initiatives and high-value projects.

2 Enhanced customer experience

By leveraging ML technology 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 sufficiently increase customer loyalty and satisfaction.

3 Streamlined decision-making

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

4 Reduced costs

As ML technology automates mundane tasks and eliminates manual processes, it can significantly reduce operational costs and free up resources for other, more important projects. This can drive bottom-line savings and enable businesses to focus on higher-value activities.

5 Improved predictability

ML algorithms are particularly powerful when applied for predictive analytics, allowing businesses to gain wider insights into future trends and better anticipate customer behavior. This can enable companies to plan ahead with greater accuracy, ensuring they’re always one step ahead of the competition. For example, by utilizing ML models for forecasting sales or predicting customer preferences, organizations can develop more accurate strategies and increase their chances of success.

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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 technologies 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.