Business challenges change quickly.  Business tools change quickly. As an executive, you need to become familiar with the potential of the latest tool: Machine Learning. You need to know what it can mean for your business’s bottom line – and that it has the potential to overhaul your business in the coming years.

Machine learning (ML) is an automatic process designed to improve business outcomes by interpreting collections and streams of corporate data. An ML system consists of software (and sometimes hardware) components tailored to discern patterns in the data.  From these patterns ML provides insights that would not otherwise be apparent. Properly designed ML will enhance its results are more data becomes available, appearing to “learn” over time.


While this technology has a multitude of applications across today’s digital environment, it’s especially applicable for modern corporations. Here’s what you need to know.

How Enterprises Employ Machine Learning

Taken without context, ML can be difficult to evaluate. Many executives understand that this technology aggregates and “learns from” information, but very often question how to apply it to a specific business.  This is both usual and understandable. Buzzwords are commonplace; evaluating the benefits in practical application can be difficult.


Despite this, ML has enjoyed widespread adoption in recent years. Today, 49% of organizations are exploring or considering deploying machine learning. Many others have already adopted it or are sophisticated users.


The question, then, is how these companies are using machine learning.

One sector that has benefited from machine learning is delivery services. UPS and other large logistics companies use ML to streamline their fulfillment. Today, UPS can see how long a truck is idle, its average speed, the complexity of its route, how often it gets delayed by traffic or unscheduled stops on the route, etc. Machine learning takes that data and re-arranges the route, optimizing travel so that the driver can be as efficient as possible. This ultimately saves time, gas, and money, and provides a better customer experience.


Machine learning has also made considerable impact in the world of manufacturing automation. Early adopters of ML have better automated machines and embedded processes into operational workflows, lowering manufacturing costs and improving efficiency.  This directly benefits business value and ROI.

Enterprise Machine Learning to Improve Revenues

With the help of ML corporations can take past purchase data and use it to predict future activity.  Machine learning can search back in time through structured corporate databases, identifying correlations and making predictions. Machine learning can also infer associations and hidden structures in unorganized sets of data. Understanding purchase patterns and correlations between purchases can guide customer interaction, improving both revenues and the customer experience.


We know that knowledge is power. This is especially true for anyone in an executive role: the more knowledge, the better. Machine learning presents an excellent opportunity for you to gather and analyze information both statically and in real time. This, in turn, increases operational productivity, revenues, and business value.