Analytic models are rarely static. Their aim is to model the organisation in such a way as to allow managers to investigate what is actually going on and to assess changes to the way it operates.

FP&A practitioners rely on raw materials, i.e. data, to conduct their work. In order to conduct their work a variety of data types should be utilized. There are three types of data that FP&A practitioners should utilize. The first type of data is financial numbers. Financial numbers must be utilized due to the goal of FP&A. The goal of FP&A is to assess whether wealth is or will be created based on decisions within businesses. These decisions can be assessed through elements within financial statements.  Income, stockholders’ equity, and cash are the elements most commonly used to assess wealth. These elements are expressed through financial numbers so it seems foolish to describe their importance but it is necessary to do so. This is due to the role of other data types.

In July 2017, I presented at the 2017 AICPA FP&A Conference in Las Vegas over AI (Artificial Intelligence) & machine Learning impact on FP&A. The session received great feedback from attendees and other speakers. So, I wanted to share an article summarizing the presentations main points. The goal of this article is to provide insights into the impact AI & machine learning will have on people, processes and technologies in FP&A. Hope you enjoy!

In the period from 2001 to 2006, the Retail division of Swiss Post transformed itself from a public service company into a business driven sales organization. Crucial drivers of change were, on the one hand, the adaptation of the steering systems up to the final implementation of a "Beyond Budgeting" philosophy and, on the other hand, a humanistic attitude of management and finance department against all employees. The success was amazing: The division succeeded in increasing the sales of new retail products from 0 to over 400 million CHF, optimizing the bottom line by more than 100 million CHF or more than 5 percentage points and becoming the first state-owned company in Switzerland to win the EFQM- Award for Business Excellence.