T a large Indian hospital group had a problem. To optimize spending and enable the organization to provide the highest levels of patient care, she had to analyze hundreds of indicators: patient information, such as age and insurance; clinical data, such as treatment and laboratory tests; operational data, such as food quality and the responsiveness of medical personnel; and financial data, such as costs and revenues. The hospital's management software was limited in its ability to handle this complex analysis. To address this challenge, the CFO installed a new decision-support system powered by machine learning. After action was taken on the resulting insights, patient satisfaction scores increased by 12%, and the hospital group enjoyed significant savings by eliminating activities that didn't add value.
Like the hospital CFO, finance leaders in a wide range of industries are trying to integrate data and analytics into finance office processes to improve operations. However, not all CFOs have cracked the code for capitalizing on the promise of these tools, in particular new machine learning applications. In a 2022 study by Gartner, 80% of CFOs said they believe that finance needs to significantly accelerate implementation of machine learning and artificial intelligence in order to effectively support and protect the business.¹ Why are so many CFOs slow to take full advantage of these technologies, and what can be done to bring them up to speed?
This story is from the Summer 2023 edition of MIT Sloan Management Review.
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This story is from the Summer 2023 edition of MIT Sloan Management Review.
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