In part two of our blog series about machine learning in the enterprise, we talk briefly about some of the most common use cases for machine learning. Larger companies produced the widest variety of use cases, however, there was no one single area of focus. Despite such varied answers on where companies were centralizing their attention, we noticed some common trends that we’ll discuss below.

Get the Full Report “The State of Enterprise Machine Learning” here.

Intro to enterprise machine learning

Machine learning is rapidly improving in compute power, data availability, and storage capabilities. These advances encourage companies to race to leverage machine learning within the enterprise at full speed. There are still uncertainties and risks associated with experimenting with and implementing artificial intelligence in the enterprise, but most large companies have entered the machine learning space regardless. 

Benefits but also challenges of enterprise machine learning are coming to the surface as more enterprises begin to utilize it as a solution. Artificial intelligence will eventually automate, augment, or replace many human business processes. In our report, “The State of Enterprise Machine Learning,” we explore the benefits, use cases, challenges, and trends in enterprise machine learning. This article will explore the use cases.

Which industries use machine learning?

Enterprise machine learning use cases span many industries including healthcare, financial services, retail, automotive, government, transportation, and utilities. Some examples of use cases within each industry are:

  • Healthcare – Machine learning is being used to develop better processes for diagnosis. 
  • Financial Services – ML is used to prevent fraud, know when to trade, and identify high-risk profiles.
  • Retail – ML can capture, analyze, and use customer shopping data to personalize the shopping experience.
  • Automotive – Machine learning is used to improve operations, marketing, and customer experience, as well as quality control vehicle parts.
  • Government – ML can help mine data from multiple sources in order to increase efficiency, save money, detect fraud, and protect against identity theft.

These are just a few examples, because there is use for AI within every industry. Whether it is simply to improve data analytics or to assist a larger business process, data scientists can build ML models to increase efficiency in any company, in any industry.

What are the use cases of machine learning?

Data science teams have discovered many use cases of machine learning that benefit industries across the board. Here are a few of the most common enterprise machine learning use cases.


Enterprise Machine Learning Use Cases

Big emphasis on the customer

Among all our respondents, there was clear attention to how machine learning capabilities would help them interact with and retain their customers. Some of the highest selected use cases identified were: generating customer insights and intelligence (#1), improving the customer experience (#2), interacting with customers (#5), increasing customer satisfaction (#6), and retaining customers (#7).

Among the largest companies, the most common use case reported was increasing customer loyalty (59%), followed by increasing customer satisfaction (51%), and interacting with customers (48%). Similarly, among the smallest of responding companies, increasing customer satisfaction (36%) was the second most identified use case behind reducing costs (43%).

Larger organizations use data science to identify areas of cost savings

For larger organizations, cost savings seems to be an increasingly important area of focus. This is due to the fact that it is easy to tie ROI to cost savings programs and showcase success.  43% of companies with 1,001 to 2,500 employees put it as a use case, as well as 41% of companies between 2,501 and 10,000 employees, and 48% of companies with more than 10,000 employees.


Enterprise Machine Learning Maturity

The focus on reducing costs is higher among sophisticated adopters

Sophisticated adopters have put the time and effort into developing their machine learning capabilities, with larger companies more likely to do so with greater resources. These larger and more sophisticated companies are investing more across a broader range of use cases. They are also the most focused on how they can use machine learning to reduce costs; 44% mentioned it as one of their use cases.

Early stage adopters are mainly focused on improving their customer retention through the application of machine learning (60%), with the middle stage adopters split between increasing customer loyalty (38%) and a growing interest in reducing costs (39%).

In general, larger and more sophisticated companies filled in more use cases overall than smaller and less mature companies: as you put resources toward and get better at ML, you get smarter about where to apply it and gain clarity on how it can help your business.

With these in mind, how are you utilizing your company’s machine learning capabilities, and how can Algorithmia help?

Algorithmia’s AI Layer provides a serverless microservices architecture which allows your team to deploy models as independent services rather than one large monolithic structure. This infrastructure productionized models easily, cutting down unnecessary time spent on deployment and allowing the team to spend more time building and training models. Algorithmia created this ML platform because we understand the challenges enterprises have to face to implement machine learning, and we want to make it easier to benefit from the technology.

Help your organization chart the path to machine learning maturity by gaining an understanding of the benefits, challenges, and solutions of the current uses of enterprise machine learning.

Get the Full Report “The State of Enterprise Machine Learning” here.

Lucy Targett