Artificial Intelligence (AI) and Machine Learning (ML) have been integrated seamlessly into our daily lives, everything from tailored Netflix suggestions to the cutting-edge facial recognition technology on our smartphones. Because planning and forecasting are based on large amounts of historic data, finance organizations are in a strong position to reap the benefits of the same type of acceleration and automation.
Embarking on a successful implementation journey demands meticulous preparation and should be accompanied by supportive measures including practical use cases and tested strategies. The Business Application Research Center (BARC) emphasizes that for an effective and efficient integration of predictive planning and forecasting, it is more beneficial to choose platforms with built-in ML capabilities. This is preferable to establishing separate data storage within specialized data science tools.
For a comprehensive understanding of how finance teams can tactfully implement predictive planning and forecasting within their organizations, we invite you to delve into this insightful BARC report.
