AI in the workplace: The practical magic of machine learning
September 12, 2019

The introduction of AI in the workplace offers huge potential to boost productivity, efficiency, and accuracy for businesses of all sizes. And while the technology remains in its nascent stages of development, forward-thinking businesses are starting to see the potential it holds for growing their organization and boosting the productivity of their employees.

  • 42% of executives said they believed AI in the workplace would be of “crucial importance” within two years
  • The International Data Corporation predicts that investment in cognitive and AI systems will grow to $52 billion by 2021
  • PwC predicts that AI could contribute $15.7 trillion to the global economy by 2030

Not all AI is created equal

It’s important to understand that artificial intelligence is a broad term that covers a huge field of technologies, and most are not necessarily concerned with improving how businesses work.

However, machine learning, a practical application of artificial intelligence, is transforming businesses, improving the workdays of their employees, providing a better customer experience and ultimately helping to boost the bottom line.

Machine learning is a form of AI in the workplace

Machine learning and AI can easily be confused, with the terms being commonly used interchangeably. Machine learning, though, has to do with an application performing a specific task by making predictions or decisions based on the data it has at its disposal, based on learned behavioral patterns.

Innovations in practice

Loan Approvals

When you give the machine learning algorithm a task, such as deciding on the outcome of a loan application, it pores through huge amounts of pre-existing data, and delivers a prediction—approval or denial—based on the results of previous loan applications. One of the most important characteristics of machine learning is that it continuously works to improve its operation as to make the optimal prediction.

Customer service

Plenty of businesses are turning to machine learning algorithms, under the guise of chatbots, to improve customer service—you’ll find them on websites and may even speak to a “chatbot representative” from a call center. A chatbot is often just a communications layer built on top of a machine learning engine. As such, the chatbot interacts with people to build training data sets that grow and develop their understanding, which enables them to make prediction-based replies and actions.

Machine learning chatbots can take the feedback received from customers and turn that information into tangible improvements in the business. For example, based on customer interactions via a chatbot, a company might see how to redesign its website to provide a customer journey that generates a higher conversion rate. This kind of data is invaluable.

Security

Financial institutions constantly face the threat of fraud. With experts predicting online credit card fraud soaring to $32 billion by 2020. Banks are tackling this problem with machine learning.

The technology’s ability to intelligently digest vast amounts of data—something the finance industry has plenty of—and learn how to perform tasks, has helped shape the way customers are protected.

These systems can scan through reams of historic data, detect anomalies such as unusual transactions and deliver a prediction in the form of flagging it as suspicious. Which obviously improves customer experience and relieves humans of what would be an insurmountable workload – instantly.

Marketing

Another good example of the practical application of machine learning is how it can help inform digital marketing strategies – by taking advantage of the streams of data businesses get when customers land on their website. Based on this information, marketing teams can tailor different campaigns and digital advertising to particular demographics.

Let’s take email campaigns as an example. When a prospect spends time scrolling through your webpage that sells ski wear, but leaves before purchasing, a machine learning algorithm can be applied to recommend the most appropriate offer; the one that has the highest chance of conversion, based on past experiences with ‘identical’ prospects. Machine learning enables businesses to add more customer touch points to continue the buyer journey. 

Transforming business

AI in the workplace gets a lot of the limelight when it comes to technological innovation but it’s machine learning and its practical applications that are creating the innovations set to transform business for the better. We can already see how different industries are using the insights and predictions machine learning provides to empower employees, boost sales and enhance customer experience. Stay tuned to see how else machine learning changes the way we do business.

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