Algorithmia Blog - Deploying AI at scale

Most Common Use Cases for Enterprise Machine Learning

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.

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 are putting significant efforts into using 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.

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?

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