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NACM National

Artificial Intelligence (AI) and data analytics are becoming more common place in the credit profession because of their ability to improve accuracy and efficiency. However, the technology must be used correctly to get the most bang for your buck.

“One of the big challenges for companies today is that you have processes for nearly everything,” Kellogg marketing professor, Eric Anderson, said in a recent Kellogg Insight article. “You have a process for … financial reporting, for managing a supply chain, for dealing with marketing. But if you go back and ask yourself, ‘Do we have a well-established process for doing AI and analytics in the company?’ the answer [at] most places is ‘no.’”

Anderson explained three key steps to ensure you get the most out of AI and data analytics so you can make a real difference in your work. First, develop a working knowledge of data science. This doesn’t mean you have to be an expert on all of your data technology, but you should be able to hold a conversation about the topic, Anderson said. “You would never be caught dead saying ‘I don’t know anything about finance, but I’ve got this really smart CFO that knows everything about finance,’” he told Kellogg Insight. You must have the confidence to ask questions about AI and analytics so that you can make the right decisions.

The second trick to leveraging your AI is by supporting communication between business leaders and data scientists. As previously established, most people are not going to be a data analyst expert on top of their regular jobs. It is important to find a data analyst who is able to speak in common language so there are no communication gaps regarding the goal for your company.

Lastly, don’t mess up what is working. As many processes as AI and data analytics improve, there are just as many that it can mess up. Instead, use technology to solve small problems. “If you start with problems, you can identify what your needs are and hire against them,” Anderson explained. But if you start implementing AI where it is not needed, you run the risk of making work more complicated.