Machine learning plays a hugely important role in increasing efficiency and analytics. Many markets are changing, including traditional markets such as insurance, banking, government, manufacturing, as well as the pharmaceutical industry. Based on the fact that Machine Learning has a promising potential for the future, it is significant to clearly identify the market opportunities for Machine Learning. Is there a corresponding growth potential? The answer is definitely "yes." Let's look at some numbers that confirm this:
- nearly 50% of respondents to a 2020 McKinsey study affirmed the use of AI in at least one business function.
- according to a Wall Street Journal report, improvements in AI and machine learning could boost GDP growth by as much as 14% by 2030.
- Companies can achieve around 54% of a productivity increase by using AI.
- reportedly 91.5% of the world's leading companies currently have active projects in AI, machine learning and Blockchain.
Source: McKinsey study from 2020
With machine learning, different computer systems can use all customer data. They work with what has been programmed and simultaneously adapt to new conditions or changes. Corresponding algorithms adapt to the data and develop behaviors that were not programmed in advance. Deep learning is a subset of machine learning in this regard. Essentially, it involves an artificial neural network with three or more layers. Neural networks with only one layer can make estimated predictions. We have two examples of how machine learning can be used to improve business processes in the insurance and telecommunications industries:
Most insurance companies are able to process about 10% of the data they have access to. Much of the data being accessed today is largely in structured data in traditional data warehouses. Logically, insurance companies are failing to unlock the value of structured data while overlooking the enormous value hidden in their unstructured data. The benefits of machine learning with new analytics could help insurance companies gain valuable business insights for all processes across the value chain.
For example, one of the most prominent examples of practical use cases of machine learning in insurance is real-time insurance consulting.
Find the Anomaly - Machine Learning algorithms have also proven to be extremely valuable to the telecom sector. Key use cases of machine learning in the telecom sector include anomaly detection, network optimization, root cause analysis and managed services.
Thanks to years of experience in the telecommunications industry, Telekom, for example, has a holistic overview of the practical use cases of machine learning in this sector, with anomaly detection being among the most important.
Machine learning can help increase the efficiency of monitoring systems by significantly improving them to detect anomalies in telecommunications networks. The systems can then help proactively detect performance issues as well as inconsistencies in network behavior. Real-time applications of machine learning in telecommunications include analyzing data for trouble ticket management. Machine learning algorithms can enable targeted classification, prioritization and escalation of incidents. At the same time, insightful churn prediction facilitates improved customer retention and capacity planning.
We can all agree that Machine Learning is a cutting-edge technology in the field of Artificial Intelligence (AI). Even with its first attempts, Machine Learning has already improved the way we act and make decisions, changing the way we imagine the future. Currently, Machine Learning is transforming many important infrastructures, such as the energy sector, from power grids to oil and gas. However, the application areas of Machine Learning are not limited to the markets and industries mentioned above. Some exciting implementations come from the finance and controlling industry. At CoPlanner, we help companies realize the potential of Machine Learning for their businesses by addressing real-world use cases, be it in controlling or other functional departments, to add value to the entire value chain.
The subfield of machine learning that consists of algorithms that allow software to train itself to perform tasks such as speech and image recognition by exposing multilayer neural networks to large amounts of data.