5 Practical Business Applications for Machine Learning

5 Practical Business Applications for Machine Learning

5 Practical Business Applications for Machine Learning Today’s forward-thinking organizations are leveraging the power of artificial intelligence to automate the decision making process. In fact, corporate investment in AI is predicted to reach $100 billion by the year 2025. As a result of this rapid digital transformation, many changes are underway in the workplace. In particular, there are a number of ways that machine learning is already making an impact for companies in every industry. Here are a few to consider.

Personalizing the customer experience.

One of the most exciting benefits machine learning can have for businesses is the fact that it can help improve the customer experience while also lowering costs. Through things like deep data mining, natural language processing and continuous learning algorithms, customers can receive highly personalized support with little to no human intervention. And people are warming to the idea. In fact, 44% of US consumers say they actually prefer chatbots to human agents.

Improving loyalty and retention.

With machine learning, companies can do a deep dive into customer behavior to identify those who are at a higher risk of churning. This enables organizations to develop and implement next steps designed to target and retain those high-risk customers. The more proactive a company is in this area, the more profitable it will be over time.

Enhancing the hiring process.

When asked about the most difficult part of their job, corporate recruiters and hiring managers almost unanimously list the task of shortlisting qualified candidates for job openings. With dozens and sometimes even hundreds of applicants, sifting through and narrowing down the options can be a monumental task. Machine learning is fundamentally changing the way this process is handled by letting software do the dirty work, quickly identifying and shortlisting those candidates that are the best fit.

Detecting fraud.

Did you know that the average organization loses up to 5% of their total revenues each year due to fraud? Machine learning algorithms can be used to track data and apply pattern recognition to identify anomalies. This can help risk management detect fraudulent transactions in real-time so they can be prevented. This type of “algorithmic security” can also be applied to cybersecurity, leveraging AI to quickly and accurately pinpoint threats so they can be addressed before they are able to do damage.

Streamlining IT operations.

Another way AI and machine learning are revolutionizing how organizations operate is through intelligent IT automation. Powered by machine learning algorithms, agentless automation and orchestration platforms become force multipliers, driving efficiency and helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure.

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