3 Examples of Machine Learning in the Workplace

Home / Industry Insight / 3 Examples of Machine Learning in the Workplace

The rise of robots has become a hot topic lately. Workers fear machines will steal their jobs, employers look to artificial intelligence to improve efficiency, and businesses of all shapes and sizes investigate machine learning as a way to deliver innovative and exciting solutions to their customers. It’s a bold new world. While robot butlers aren’t yet bringing us our morning coffee, Machine Learning is quite prevalent with many everyday examples that exist at home and in the office.

What is Machine Learning?

Machine learning is a component of artificial intelligence where a system automatically learns and improves on its own by identifying patterns and accessing available data. Today, there are many real life examples of machine learning in action, including social media experiences,  speech recognition software, and even within Netflix.

Another common application of Machine learning can be found within Uber’s UberEATS app. UberEATS, the popular and convenient food delivery app has been improving the customer experience through their own machine learning platform, Michelangelo. The primary use of Michelangelo is with UberEATS estimated time of delivery model. While most hungry UberEATS customers take the estimated delivery time feature for granted, it is actually a super complex, multi-step process that is the result of Michelangelo “learning” how long various restaurants take to prepare food, estimating how busy the restaurant is based on other orders and the time of day, how long it typically takes to get from point A to point B, and other historical data stored on Uber’s systems.

Machine learning applications such as UberEATS are designed to improve your experience based on the analysis of available data. Below are three examples of machine learning applications that currently exist in the workplace.


As the most common and well know office application, there are many use cases for machine learning for your email. One established use of machine learning for your email application is with SPAM and malware filtering. Designed to keep your inbox clear of inappropriate and bothersome emails, modern SPAM filters are designed to evolve and learn what should be automatically filtered out of your inbox. Machine learning takes into account that spammers and cyber criminals are evolving and using new techniques to get inside your inbox. Since a lot of malware uses code that is similar to previous versions, modern spam and malware filters can use machine learning to detect these patterns and offer protection from new threats. Here, the application of machine learning is designed to save you valuable time, instead of getting caught up in email you didn’t want to receive in the first place or that could introduce malware into your systems.

Other machine learning possibilities for email include automated replies. In 2015, Google introduced a feature for Gmail that offers possible replies based on the content of the message. Imagine if this could be applied to common emails at work?

Information Security

With security threats getting harder and harder to prevent, it seems obvious that we would look to machines to help fight cyber crime. Since it can be difficult (or impossible) for your information security team to keep an eye on all aspects of your network at once, Security Information and Event Management (SIEM) platforms are designed to do that for you. Machine learning is deployed with SIEM platforms to analyze incoming data from several different systems at once to identify potential security breaches or ongoing attacks.

Smart Personal Assistants

Speech recognition personal assistants such as Amazon Alexa and Google Home have become common place at home and are poised to make a move into the workplace. Google machine learning techniques are currently being used in marketing departments around the world in the form of chatbots. Online chat and conversation bots are designed to learn from a database and gather information from potential customers in a pleasant, conversational way. Here, conversation bots are designed to save valuable pre-sales time by prequalifying potential leads and gathering information needed to make a sales pitch or direct the customer in the right direction.

The true benefits of machine learning are just now being realized, and in the near future, at least part of most jobs may be offset by system learning to help you.

Related Posts