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5 major benefits of machine learning in the enterprise

Businesses are inserting machine learning into processes wherever possible. Here are a few of the ways machine learning users are benefiting from machine learning.

Enterprises are adopting machine learning at a rapid rate and the reasons why are clear. From improved customer relations to enhanced productivity, machine learning is unlocking new ways of getting business done and helping enterprises find their competitive edge.

Machine learning is the practice of examining data for hidden patterns that might be useful for developing predictions about future performance. Here's a look at a few of the primary benefits of machine learning in business.

Customer support

Customer support is one of the primary areas where machine learning is benefiting business operations. Enterprise are now using machine learning to monitor data about their customers, develop more accurate pictures of consumer preferences and inform customer support efforts.

By enabling businesses to understand their customers at a deeper level, machine learning helps support teams address customer concerns in a more proactive way. Today, many organizations are using machine learning to identify customers who are likely to experience issues and recommend ways to address those problems before the customer even reaches out. Many online retailers are using machine learning bots to help customers through the purchasing process with the aim of reducing abandoned purchases.

List of call center technologies.
Machine learning informs many of the tools used in call centers.

Predictive maintenance

Predictive maintenance is one of the most powerful benefits of machine learning in business. Today companies in sectors ranging from manufacturing to oil and gas are using predictive maintenance to keep their machinery humming. This equipment is the lifeblood of these types of enterprises, so any downtime can be costly. That's why they've invested so much in keeping machinery up and running.

Predictive maintenance works by monitoring data streams generated by equipment. This sets a baseline profile describing normal operations. When the data starts to vary from this baseline, it could be indicative of a looming problem. Given enough data and time, predictive maintenance algorithms can learn to spot specific mechanical issues, prompting work crews to fix problems before operations grind to a halt.

Industrial automation and process automation

Automation is often seen as one of the main reasons to adopt machine learning. By automating tasks, enterprises can reduce human errors and free their workforce up to focus on more valuable tasks.

In industrial settings, this often means programming physical machines to perform a task. Increasingly, these robots are being given a layer of machine learning to help them perform the task more intelligently. Image recognition and computer vision are enabling bots to navigate the physical world, helping them perform things like sort shipments and move pallets.

Machine learning is also helping bots automate tasks in the virtual world. Right now, robotic process automation, or RPA, is one of the hottest trends in the automation market. These bots, which can be programmed to perform specific tasks, are increasingly adding a layer of machine learning, allowing them to learn better, faster ways to accomplish tasks with fewer errors. Tasks that have clear inputs and outputs, like data entry, are prime candidates for automation using RPA tools. But smart algorithms are enabling RPA to take on more complicated tasks, too. Image recognition tools now make it possible for bots to scan unstructured data, like pictures of receipts, to do things like automatically generate expense reports.


Machine learning can sharpen forecasting in a range of industries. Retailers are using machine learning forecasting tools to improve customer demand predictions; financial companies are using it to predict the future performance of stocks; supply chain companies are using it to predict the fastest way to get goods to consumers.

This benefit of machine learning is clear. If you know what conditions are likely to come your way, you can respond in a way that gives you a competitive edge over other businesses. That's why forecasting tools have become so common in the enterprise.

Improved work conditions

There's been a lot of talk as more companies have embraced AI and machine learning about the loss of jobs. Many people believe these technologies are going to automate jobs on a massive scale. Yet, while some types of jobs have been lost, on the whole machine learning is changing jobs, rather than eliminating them. And this may be a good thing for workers.

Jobs safe from automation.
These jobs are likely to survive machine learning automation.

Machine learning is best at automating rote tasks. These are processes that have clearly defined steps and expected outcomes. These are also processes that human workers tend to dislike. They are boring, repetitive and time-consuming. When enterprises turn these tasks over to machines, they don't typically eliminate their human workers. Instead, they allow their staff to spend their time on more strategic tasks that add more value. So while the specter of job losses stemming from machine learning hasn't vanished completely, it has yet to make a very substantial impact.

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