How Using Machine Learning Can Add Value to Your Internal Operations

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?

Optimise Processes

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.

illustration of landscape of tablets I phones, screen with human figures placed sitting, standing and working in it

Improve Employee Engagement

One of the great benefits of ML is that it takes over the dull, monotonous and resource-intensive work previously carried out by employees – thereby freeing them up to focus on more engaging and productive tasks.

Take data entry. This manual and often tedious process is not only boring, but is prone to error. You can use ML to reduce or eliminate errors, while giving your employees more valuable tasks to do.

You can even use ML to help you choose better employees in the first place. For example, going through hundreds of CVs to create a shortlist is time-consuming and difficult. You can use ML to pick out a shortlist instead, speeding up the recruitment process. While you wouldn’t use it to make the hiring decision, it can dramatically speed up the process.

Increase Customer Satisfaction

You can also improve your customer service and cut costs at the same time by using chatbots – which you probably know use machine learning.

Chatbots analyse historical data and use intelligent algorithms to provide high-quality answers immediately. Many consumers prefer chatbots to get direct answers quickly, and while these cannot take over from humans entirely, for basic questions and answers, they are incredibly effective.

You can also use ML to get customer insights using large amounts of data. This can enable you to predict customer behaviour and purchasing patterns to improve personalisation. As a result, you can send offers that are more suitable for them and recommend products.

This is a great way to improve the customer experience as well as make more sales. E-commerce websites use ML algorithms to take data from purchase history and match it to the inventory so they can then group products together that are likely to be of interest.

Are Your Ready for Machine Learning?

Multiple industries are benefiting from machine learning by improving the customer experience, boosting employee engagement and increasing sales. Over the coming years, advancements in the technology is certain to play an ever-increasing role for organisations of all sizes.

The question is: are you ready for it? Now is the time to invest in ML if you want to gain an advantage over your competitors before they step up ahead of you. ML is making its way into the workplace, so start making plans to take full advantage of it in your organisation.

If you would like to discuss how machine learning can benefit your business contact us here.

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