Even though we have so much advanced technology surrounding us, we still cannot just ask, “Hey Siri, what’s my forecasted EBITDA look like?” There are many reasons why such technology isn’t available yet—insufficient data, unstructured data and some human knowledge that is not yet transferable to machine. However, there are many available technology tools that can simplify planning tasks and make planning and budgeting easier and far more accurate for finance professionals.
Is AI truly experimental technology? In most cases, the answer is no. Many of the algorithms used for budgeting, planning, and forecasting are already in use and were proven decades ago. The innovation is around the availability of such technology. Just a few years ago, you had to purchase expensive hardware and software and hire a data scientist to build a model that today is most likely available “out of the box.”
Common FP&A Use Cases
While the usage of AI technology became common in many operational procedures (credit scoring, navigation, fraud detection), there are several areas where AI algorithms may help FP&A professionals with their planning and budgeting. The first, and probably most popular area, is time-based forecast and prediction. Those algorithms analyze historical data (weekly sales, monthly electricity costs, etc.) and calculate future periods prediction.
Additional FP&A Support
- Data cleansing: AI algorithms may indicate a potential abnormality on data trends (i.e., sales on a specific month are double the usual trend for that month).
- Planning drivers: AI algorithms can help to identify the most significant factors in data trends (i.e., season may be a significant factor but the seniority or the loyalty of a customer may not).
Tips for a Quick Start
When: For those who worry about the challenges of unknown territory or just like the idea of admiring fancy new technology tools, my recommendation to you is start now and lead with this technology. It is important to understand that AI is not a project. It is a learning process and an ongoing tool. The earlier you start, the more benefit your organization will be able to obtain.
How: Start small with a specific data source that can be validated. This is not just risk mitigation. It helps you and your organization learn and understand the tools and its benefits.
Where: Start with an area that brings added value to your organization. It could be data sets that are usually very time consuming and can be automated or areas where prediction is not accurate enough and AI algorithms can make a visible, measurable impact.