The latest breakthroughs in the field of Machine Learning have sparked a revolution that no one could’ve ever imagined. As an emerging technology, it is disrupting many traditional practices followed by businesses. In fact, according to a recent report, the global market size of Machine Learning was valued at 15.44 billion USD in 2021 and it is predicted to surge from 21.17 USD billion in 2022 to 209.91 USD billion by 2029 with a compound annual growth rate (CAGR) of 38.8%. From the statistics, it is clear that the future of Machine Learning looks quite promising since it is showing no signs of slowing down in the years to come. This may be one of the reasons why Machine Learning in businesses has been the most debated topic among CIOs, CTOs, and CXOs.
Despite being a popular topic of discussion among the C-suite audience, Machine Learning is often used interchangeably with AI. However, Machine Learning is a subset of AI that enables systems to learn from data and previous experiences and make accurate predictions with little or no human intervention. Today, many big names in the industry have already started leveraging the potential of Machine Learning in their businesses. Besides industry titans, a large number of Startups and SMEs are also embracing the application of Machine Learning in business to bring transformative changes.
Machine Learning comes with an unmatched potential to analyze large chunks of data, which in turn enables businesses to make data-driven decisions with better accuracy. Besides data analysis, Machine Learning models can help businesses streamline complex workflows and automate manual and repetitive jobs resulting in improved workforce productivity and efficiency. Simply put, Machine Learning for enterprise is actively making its way into businesses’ core operations.
Key Benefits of Machine Learning in the Business Landscape
Many enterprises have already adopted AI and Machine Learning for business operations since it offers a host of benefits. Let’s take a quick look at Machine Learning benefits for businesses.
1. Predicting Customer Lifetime Value
Many businesses struggle when it comes to predicting customer lifetime value since they are inundated with massive volumes of data. However, using Machine Learning models, businesses can not only process huge amounts of data they have but also extract meaningful insights out of it. Based on the derived insights, businesses can conduct customer segmentation and predict purchasing patterns and customer behaviors based on their browsing experience and buying history.
2. Work Process Automation
Another major challenge faced by businesses is data duplication and inaccuracy. With Predictive Modeling and Machine Learning models, enterprises can automate manual and repetitive tasks performed by humans resulting in reduced errors and improved work efficiency. By implementing Machine Learning, businesses can free up employees from tasks that require intense manual labor and let them invest their productive time in the activities that add value to the business.
3. Product Recommendation
Today, many eCommerce store merchants use the application of Machine Learning in business to offer the most relevant products to customers by analyzing their historical behavior. Machine Learning helps merchants improve cross-selling opportunities, which in turn, improves customer experience and sales.
4. Enhanced Customer Service
Machine Learning improves customer service by introducing intelligent automation through virtual assistants and chatbots. These ML-driven tools not only provide quick and personalized support but also address customer issues and queries in real-time. The automated nature benefits businesses from improved response times, ensuring efficient and round-the-clock customer support, regardless of the volume of inquiries.
5. Cost Savings
Machine Learning uncovers cost-saving opportunities by improving resource allocation, reducing waste, and optimizing processes. Enterprises can leverage Machine Learning across various business functions to identify inefficiencies, analyze operational workflows, and streamline processes for maximum efficiency.
Industries to Look Out for Machine Learning Disruption
Businesses are actively adopting Machine Learning (ML) models to improve customer experience, drive innovation, increase ROI, and gain a competitive edge. Let’s take a quick look at how Machine Learning will shape businesses across industry verticals in the days to come.
Businesses operating in the finance domain are already using Machine Learning for fraud detection and offloading repetitive tasks to digital workers (intelligent bots). However, in the coming years, more mature use cases of Machine Learning will be explored in other critical areas such as customer sentiment analysis, investment modeling, trading, risk prevention, and more. Osman Ali, a Portfolio Manager, Quantitative Investment Strategies at Goldman Sachs says that Machine Learning will be a robust tool in asset management. Furthermore, Machine Learning when combined with predictive analytics, will also help financial institutions improve transaction efficiencies.
The Role of Machine Learning in Fraud Detection
The healthcare industry churns out large volumes of data and nearly 80% of data in the healthcare vertical is unstructured. By applying advanced Machine Learning models, healthcare providers can not only streamline the data but also make data-driven decisions to diagnose health conditions and explore treatment options. In fact, physicians are actively collaborating with statisticians, data analysts, and data scientists to engineer advanced tools that will accurately predict diseases. Besides this, ML models will be used in automating the drug discovery process, resulting in reduced time-to-market.
More and more manufacturers are leveraging the potential of data to improve their production and assembly line – all thanks to Machine Learning. This emerging technology not only helps manufacturers in analyzing large volumes of data generated but also serves as the foundation of Industry 4.0 coupled with data connectivity and automation.
In the days to come, manufacturers will look at more mature use cases that are beyond Predictive Maintenance such as Digital Twins, Real-time Error Detection, Cost Reduction, Warehousing Efficiency, Cognitive Supply Chain Management, and Smart Manufacturing. According to a report, the global smart manufacturing market size is predicted to grow at a CAGR of 17.2% from 2023 to 2028 and will reach $241 billion by 2028.
Machine Learning emerges as a catalyst in disrupting industries across verticals. As we navigate through this landscape of disruption, it becomes critical to recognize the ethical and biased considerations that accompany the adoption of this emerging technology. If you are also planning to embrace Machine Learning in your business to leverage its limitless potential, you should consider seeking consultation from a reliable Machine Learning development solutions provider that can help you successfully implement Machine Learning models tailored to your business requirements. Click here to learn more about our services.