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.

The concepts of artificial intelligence (AI) and machine learning (ML) are not new. They are relatively new to Finance and FP&A leaders, and that means many may have an aversion to leaning more about and leveraging them in delivering value from FP&A. 

The Global Artificial Intelligence / Machine Learning FP&A Committee was created in March 2018 with the aim to see how the latest developments in those technologies can influence modern Financial Planning and Analytics. On the 14^th of November, the Committee held its third meeting. 

Disclaimer: Financial Modelling has no strict “right” or “wrong” method of application. It does, however, have forms of best practice and this what this article attempts to highlight