In the video, Asif Khan, Global FP&A Lead at PayU, shares 5 steps of implementing ML for fore

Takeshi Murakami, Group Controller at Microsoft, shares an interesting case study on leveraging AI/ML in decision-making. Microsoft Finance enhanced forecast accuracy by using ML instead of the traditional bottom-up process.

FP&A teams are using AI to drive step changes in business performance, pushing their influence beyond their traditional areas of analyses.

The pressure of globalization and agile decision-making requires companies to improve their business modeling.  They must integrate big data in real-time, synthesize that data to identify causal relationships and value-drivers, and ultimately use the findings to make high-impact business decisions.

The democratization of technologies is underway. Tools like machine learning (ML), which were confined to universities, hedge funds or investment banks just until a decade ago, are now finding their way into industry-wide applications. The finance function is set to reap the benefits of this democratization wave.

4-5 years ago in Microsoft, Cloud business was a small portion of the overall business, but now is the key business for the company, with new and diverse purchase options like subscription model on office 365, pay as you go model on Azure cloud platform... boosting and creating an avalanche of data every day.