use cases for machine learning adoption in global industries

Key Takeaways

  • Industries that use ML benefit from improved decision-making, increased efficiency through task automation, and enhanced customer experiences via personalized services.
  • The global ML market, which reached $204.30 billion in 2024, is projected to achieve a CAGR of 17.15%, resulting in a market volume of $528.10 billion by 2030.
  • With machine learning algorithms, businesses can sharpen their competitive edge, drive innovation, and achieve sustainable growth.

Machine learning (ML) stands as a potent instrument transforming numerous industries. For businesses, exploring machine learning use cases within their organization yields significant benefits, including cost savings, efficiency improvements, competitive edge, and enhanced customer and employee satisfaction.

With decades of implementation experience, machine learning has firmly established itself. Today, we will explore the benefits of embracing machine learning for industry top operations in global businesses with real-life examples of machine learning and expert insights.

How Does Machine Learning Work?

Machine learning is an artificial intelligence (AI) subset that involves the development of statistical models and algorithms. The algorithms enable computers to learn from data and make predictions without explicit programming. Key steps include data collection, preprocessing, feature selection, model training, evaluation, and deployment.

But what is machine learning used for business-wise? From a business perspective, machine learning automates tasks, gains insights from data, and enables data-driven decisions. Common ML use cases apply to customer relationship management, financial services, healthcare, supply chain management, hotel operations automation, and more.

Machine Learning Industry Projections

machine learning use cases statistics 2024-2030

Having reached $204.30 billion in 2024, the global ML market is expected to grow at an anticipated CAGR of 17.15%. Such dynamics will lead to a market volume of $528.10 billion by 2030.

Benefits of Employing Machine Learning

benefits of employing machine learning in industry operations

The adoption of machine learning offers businesses a wide range of benefits, from enhanced decision-making and increased efficiency to personalized customer experiences and improved security. By using machine learning in industry operations, businesses can improve their decision-making, increase productivity and security, and offer better experiences to their clients.

Enhanced Decision-Making

Machine learning uses algorithms that analyze vast amounts of data to uncover valuable insights, enabling businesses to make informed decisions quickly and accurately. By identifying patterns and trends in data, ML empowers organizations to anticipate market changes, identify potential risks, and capitalize on emerging opportunities.

Increased Efficiency and Productivity

Automation of repetitive tasks and workflows through machine learning streamlines processes, leading to greater efficiency and productivity. By automating mundane tasks, employees can focus on more strategic business activities.

Personalized Customer Experiences

Most machine learning applications in business focus on delivering personalized experiences to customers by analyzing their preferences, behaviors, and interactions. By leveraging customer data, ML algorithms can tailor recommendations, promotions, and content to individual preferences, enhancing customer satisfaction and loyalty.

Improved Fraud Detection and Security

As part of secure software development, ML algorithms can process large volumes of data in real-time to detect fraudulent activities and security threats. By identifying unusual patterns and anomalies, ML helps businesses proactively mitigate risks and safeguard sensitive information, protecting both customers and the organization.

Predictive Maintenance

Machine learning algorithms analyze equipment sensor data to predict when machinery or assets are likely to fail. By proactively identifying potential issues, businesses can schedule maintenance proactively, minimize downtime, and optimize asset performance.

Such maintenance-related use cases of machine learning lead to cost savings and improved operational efficiency.

ML Use Cases in Major Industries

applications of machine learning in business by industry, according to Statista

As ML continues to evolve, its applications are transforming major industries across the globe.

From revolutionizing patient care to optimizing fintech operations, ML is reshaping the way businesses operate and innovate in these key sectors. Let’s explore the impactful applications of machine learning in businesses globally, delving into the following industries:

  • Healthcare
  • Fintech
  • Logistics and transportation
  • Travel and hospitality
  • Retail and e-commerce
  • Manufacturing and supply chain
  • Media business
  • Energy, oil and gas

ML in Healthcare

ml use cases in healthcare operations

Machine learning in healthcare is revolutionizing patient care, diagnosis, and treatment planning, allowing medical institutions to elevate the core operations:

  • Patient outcomes predictions. ML analyzes electronic health records (EHRs) to predict readmission likelihood, identify high-risk patients, and optimize treatment protocols. Google’s DeepMind Health utilizes this health-tech trend to analyze EHR data and predict patient deterioration, facilitating early intervention and improved outcomes.
  • Medical imaging analysis. ML algorithms detect abnormalities in radiology images like X-rays, MRIs, and CT scans. For example, IBM Watson Health’s ML platform aids clinicians in diagnosing diseases such as cancer and recommending personalized treatment plans.
  • Patient experience. Digital experiences in hospitals offer more convenience to patients, as they can make appointments remotely through chatbots and web or mobile applications. Such solutions also contribute to staff workload optimization, where automation of patient interaction allows medical workers to focus on their core activities.

At Acropolium, we have our own machine learning use case examples for healthcare businesses. When cooperating with a proteomics company, our dedicated team integrated ML technology into biomaterial analysis processing as part of big data processing app development. As a result, our partner reported a 40% increase in data processing accuracy.

ML in Fintech

In fintech, machine learning drives fraud detection, risk assessment, and personalized financial services. Many financial institutions planning to have their legacy software reengineered are focused on AI and ML-based solutions. In fact, 80% of banks recognize the potential benefits of AI and machine learning, with 75% of respondents already incorporating AI strategies.

  • Fraud detection. PayPal employs ML to detect and prevent fraudulent transactions by analyzing transaction patterns and user behavior.
  • Financial service personalization. Robo-advisors like Wealthfront and Betterment use ML to provide automated investment advice tailored to individual financial goals and risk tolerance.
  • Trading analytics. ML algorithms process large amounts of financial data to optimize trading strategies and predict market trends, enabling informed decisions in investors.

One of our clients has also uncovered the benefits of machine learning in business process automation. We developed a data profiling and quality monitoring tool for a financial company that wanted to automate data processing and extract high-quality insights. Operating with ML-based software, the client saw data processing time reduced by 30%, with the data quality rate reaching 95%.

ML in Logistics and Transportation

In logistics and transportation, machine learning and artificial intelligence lead the list of top tech trends. By adopting machine learning in logistics operations, companies optimize route planning, predict delivery times, and manage inventory efficiently.

  • Route optimization. UPS exemplifies successful applications of machine learning in business: the shipping company uses ML algorithms to optimize delivery routes, reducing fuel consumption and delivery times.
  • Demand forecasting and inventory management. Amazon employs ML for demand forecasting and inventory management, ensuring timely product delivery to customers.
  • Fleet management. ML enhances fleet management by analyzing vehicle performance data for optimized maintenance scheduling. When integrated into transportation or supply chain management systems, ML-based trackers significantly improve fleet transparency and warehouse visibility.

ML in Travel and Hospitality

Implementing ML in the travel sector enhances customer experiences through personalized recommendations and efficient operations. While automating routine tasks and offering unique services to guests, this hospitality tech trend also ensures stronger data security, lowering the risk of hotel data breaches.

  • Personalized service. Airbnb utilizes ML algorithms to recommend personalized accommodations to users based on their bookmarks and browsing activity.
  • Automated pricing management. Additionally, Hilton employs ML for dynamic pricing and revenue management, optimizing room rates based on demand and market conditions.

ML in Retail and E-commerce

By presenting the right audience with personalized offers, ML helps businesses improve marketing ROI, increase customer engagement, and drive sales. According to Harvard Business Review, 49% of organizations use ML and AI to identify sales prospects, and 48% employ these technologies to gain deeper insights into their prospects and customers.

  • Customer behavior analytics. Machine learning algorithms analyze customer data and behavior to optimize marketing and advertising campaigns. Amazon, for instance, uses machine learning for industry and internal analytics. The company analyzes the market along with customer behavior and recommends products, driving sales and customer engagement.
  • Tailored recommendations. Companies like Stitch Fix apply ML for personalized styling recommendations, improving customer satisfaction and retention.

ML in Manufacturing & Supply Chain

machine learning application examples in manufacturing and supply chain management

Machine learning algorithms analyze supply chain data to optimize inventory levels, forecast demand, and improve logistics planning. By optimizing inventory levels and streamlining logistics processes, ML enables businesses to reduce costs, improve delivery times, and enhance overall supply chain efficiency.

  • Predictive maintenance. Predictive maintenance is utilized by General Electric through ML-based systems to analyze sensor data from industrial equipment, anticipating maintenance requirements and averting expensive breakdowns.
  • Inventory optimization. Walmart harnesses machine learning for demand forecasting and inventory optimization, guaranteeing the timely availability of products in the desired locations for customers.

ML in Media Business

ML applications in the media business include content recommendation, sentiment analysis, and personalized advertising. Similar to e-commerce and retail machine learning use cases, the technology helps media companies identify behavioral trends in users. Next, this data turns into insights for personalization.

  • User retention. Netflix utilizes ML algorithms to recommend personalized content to users based on their watching history, thus enhancing user engagement.
  • Tailored user experience. Spotify applies ML for personalized music recommendations, improving the user listening experience.

ML in Energy, Oil and Gas

In oil and gas software, ML technologies are used for predictive maintenance, asset optimization, and risk management. In such industries, the use of AI/ML technology centers around smart resource allocation and, subsequently, cost savings.

  • Downtime prediction. Chevron employs ML algorithms to analyze oil wells and pipeline sensor data to predict equipment failures and prevent costly downtime.
  • Energy optimization. NextEra Energy applies ML for wind and solar forecasting, optimizing energy production and distribution.

ML Applications Across Industries

machine learning use case examples by industry

The number of machine learning use cases varies from industry to industry and comes down to automation, which saves time and costs. While there are many ways to make ML work for you, integrating the technology with a certified vendor is vital to succeed. By addressing specific industry needs to AI/ML consulting agencies, businesses can:

  • Improve patient outcome predictions through EHR analysis in medical operations.
  • Strengthen fraud detection for secure transactions in financial activities.
  • Leverage real-time route optimization, demand forecasting, and inventory management in logistics and transportation.
  • Offer personalized accommodation recommendations and enable dynamic pricing for revenue management in hotels.
  • Analyze customer behavior analytics for targeted marketing and tailored product recommendations for improved retail sales.
  • Embrace predictive maintenance for machinery and inventory optimization for cost reduction in manufacturing operations.
  • Drive content recommendation for user engagement in media business strategies.
  • Elevate downtime prediction for preventive maintenance and promote energy optimization for efficient oil and gas production.

Final Thoughts

Machine learning is a transformative trend across global industries, revolutionizing traditional practices and unlocking unprecedented opportunities for growth and innovation. From healthcare to retail, logistics to finance, modern use cases of machine learning have reshaped operations, empowered decision-making, and enhanced customer experiences.

The journey of ML adoption is ongoing, with endless possibilities awaiting those who embrace its potential to drive business success in the digital age. Acropolium is an experienced outsourcing vendor with a proven track record of delivering secure projects across diverse industries. And we are here to share the ML journey with you!

With robust expertise in GDPR-compliant software, we follow ISO-certified processes to implement cutting-edge solutions for your success. Contact us to see how your business can transform with machine learning and more!

Sources of Information