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GE, Shell and Uber are Using AI and Machine Learning to Manage Staff

GE and Shell are using AI and Machine Learning to Manage Staff
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Various big companies across the world including Shell and Uber are turning to artificial intelligence (AI) and machine learning (ML) in which to manage their staff.

To begin with, Uber simply used AI to help with things such as tasking assignments, but now it’s doing so much more for the company.

Other companies such as GE and Shell are also beginning to embrace the likes of AI when it comes to tasks such as scheduling and other data-focused tasks.

Human resources are also working with AI in certain management aspects.

Gartner predicts that by 2020, there will be a 25% growth in overall workforce management and human resources software as more companies turn to AI for help in these areas.

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The reason why AI is so successful in these areas is that its so very good at making decisions based on the large amounts of data it’s presented with.

For example, AI can use data to assign tasks, build teams, provide feedback, identify potential, and measure performance, the same way in which a human would through observations over time.

There are some areas that AI excels in when it comes to making certain management decisions. However, it’s still possible that biases are there as they were created by humans in the first place.

According to Tomas Chamorro-Premuzic, a professor of business psychology at University College London that may one day lead to us not needing human managers at all.

However, just because AI is capable of these things, doesn’t mean to say that companies will use it in this way.

Many businesses choose to use AI just for help with simple administrative tasks. Either way, at least they’re beginning to recognize how essential the implementation of AI is to survival in the world of commerce today.

Tasks that AI are particularly good at could be seen as middle management tasks.

In getting AI to carry out these tasks opposed to humans, firms can produce more accurate results in less time, saving them a great deal of money in the long run.

Source TechRepublic