Machine learning (ML) is getting a lot of headlines in the news. Not only is artificial intelligence becoming more advanced, it is also being used by more organisations to make significant improvements to their internal operations.
The paradigm shift that is currently underway, together with billions of $’s worth of investment, is increasing as the advantages of ML become clear – from better automation to improved personalisation.
Despite the obvious benefits, businesses struggle to take full advantage of ML. Some are even unaware of the benefits of investing in the technology at all.
In this article, we take a look at how ML is making its way into the workplace, and how businesses can start making plans to take full advantage of this new and exciting technology.
Overview of Machine Learning
Firstly, what exactly is ML? While not exactly artificial intelligence in the sense of creating computers that can think for themselves, ML is an AI technique. For more information, check this overview of the differences.
ML essentially allows computer systems to use advanced algorithms to learn behavioural outcomes and predict them based on huge data sets so they can make better decisions (as opposed to rule-based systems).
So, how can it help your business?
ML provides businesses with many ways to optimise operational processes. One of the most important is through predictive maintenance. By crunching large amounts of data and discovering patterns hidden within, ML can detect when problems are occurring before they become serious. This can help you to fix problems early, reducing the risk of failures and saving money.
ML can also help to speed up processes like analysing sales data. Sales teams often have huge amounts of data to look through, from social media, website activity and more, but often there is simply too much. ML makes it possible to automate processes and get the insights you need fast.
Dynamic pricing is another area ML will help. Dynamic pricing has been around for a while and allows businesses to change pricing based on demand, but can be hard to implement across a large business. ML makes it easy. Uber is a good example of how to use dynamic pricing on a massive scale.
ML can even be used for the automation of fraud detection. Using the technology, you can build models that take a range of data into account including social media, transactions and external data. ML can then use pattern recognition to pinpoint behaviour that is out of place.