Machine Learning is improving business work processes in 8 ways

230

Today’s leading organizations are using machine learning–based tools to automate decision processes, and they’re starting to experiment with more-advanced uses of artificial intelligence (AI) for digital transformation. Corporate investment in artificial intelligence is predicted to triple in 2017, becoming a $100 billion market by 2025. Last year alone saw $5 billion in machine learning venture investment. In a recent survey, 30% of respondents predicted that AI will be the biggest disruptor to their industry in the next five years. This will no doubt have profound effects on the workplace.

Machine learning is enabling companies to expand their top-line growth and optimize processes while improving employee engagement and increasing customer satisfaction. Here are some concrete examples of how AI and machine learning are creating value in companies today:

  • Personalizing customer service. The potential to improve customer service while lowering costs makes this one of the most exciting areas of opportunity. By combining historical customer service data, natural language processing, and algorithms that continuously learn from interactions, customers can ask questions and get high-quality answers. In fact, 44% of U.S. consumers already prefer chatbots to humans for customer relations. Customer service representatives can step in to handle exceptions, with the algorithms looking over their shoulders to learn what to do next time around.
  • Improving customer loyalty and retention. Companies can mine customer actions, transactions, and social sentiment data to identify customers who are at high risk of leaving. Combined with profitability data, this allows organizations to optimize “next best action” strategies and personalize the end-to-end customer experience. For example, young adults coming off of their parents’ mobile phone plans often move to other carriers. Telcos can use machine learning to anticipate this behavior and make customized offers, based on the individual’s usage patterns, before they defect to competitors.
  • Hiring the right people. Corporate job openings pull in about 250 résumés apiece, and over half of surveyed recruiters say shortlisting qualified candidates is the most difficult part of their job. Software quickly sifts through thousands of job applications and shortlists candidates who have the credentials that are most likely to achieve success at the company. Care must be taken not to reinforce any human biases implicit in prior hiring. But software can also combat human bias by automatically flagging biased language in job descriptions, detecting highly qualified candidates who might have been overlooked because they didn’t fit traditional expectations.

Read the source article at Harvard Business Review.