FP&A Board Maturity Model: Best-in-class FP&A and how to get there
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By Olga Rudakova, Finance Professional and Data Skills Trainer
Without a doubt, Artificial Intelligence can improve the speed and quality of our work. To what extent is it valid for Financial Planning, Budgeting and Forecasting? Can we completely delegate those jobs to modern technology, or is the human being still an irreplaceable part of the equation?
The Digital Middle Eastern FP&A Circle has attempted to answer key questions related to deploying AI/ML in FP&A: does it improve accuracy? What is inside the black box? How does it affect FP&A team size and skillset?
This article provides an overview of the topics and cases presented and discussed by the expert panellists in the "Deploying AI/ML for Planning, Budgeting & Forecasting" webinar, as well as the results of our polling questions.
Wibe Spekking, CFO at Microsoft Netherlands, has shared a Microsoft case study and has demonstrated how the product revenue forecasting process has changed after Machine Learning Technology implementation.
Wibe has started by outlining the main challenges before ML and the opportunities new technology brings:
Building an ML-based forecast takes much time, but the results are certainly worth it:
There are several steps Microsoft has learned on the way, and they are certainly with to consider when you start implementing ML in your organisation:
During the webinar, attendees from the FP&A industry participated in the survey and stated how the size of their FP&A team had changed after the introduction of AI/ML. 48% of respondents said team size had stayed the same, while 34% had decreased.
Gizelda Ekonomi, Financial Planning Transformation Senior Manager at Vodafone, shared a practical case study from Vodafone on a Hybrid Product Management Approach to Planning.
Gizelda has shared with us common challenges in Financial Planning:
AI can aggregate and process data with far greater speed, accuracy, and scalability than humanly possible. However, it is important to understand the "What", the "How", and the "Why" of AI-based forecasts.
The Product development approach helped the Vodafone team identify the Top 5 feedback themes of the FP&A community on the implementation of ML in FP&A:
Collecting that information helped the team to create a Hybrid Product Management Approach To Planning which combines automation and human input:
Muhammad Zeeshan Taqi, Managing Partner, FP&A at Hub of Finance Transformation, presented a series of case studies where planning accuracy was tremendously improved thanks to AI-based analytics.
Muhammad has summarised his learnings from his vast experience with AI-based forecasting tools in the following five points:
In which FP&A areas do we use AI/ML?
Budgeting and forecasting are leading areas of FP&A, where most of our respondents (42%) use AI or ML. Our panellists also see this as the area where FP&A can benefit most from implementing AI/ML. AI and ML also have great potential in scenario planning and business analytics.
Andreas Simon, Regional Director MEA at Jedox, has shared quite a practical 6-step approach to AI implementation with us.
Step 1. Data Modeling & Specification involves:
Step 2. Data Analysis requires:
Step 3. Design Decision means:
Step 4. Design Implementation is focused on:
Step 5. Test & Refine involves:
Step 6. Production means applying results to the production level.
In addition to providing us with a step-by-step guide, Andreas shared a practical case study of a hotel chain. This approach was implemented and resulted in a forecast accuracy of over 95%.
AI and ML technologies improve our budgeting, planning, and forecasting efficiency and accuracy. But before we can enjoy such a wonderful result, we must invest time and effort to build these models. This can only be achieved through a long process of trial and error.
ML implementation is a marathon, not a sprint. Don't quit this race midway.
We would like to thank our global sponsor, Jedox, for their great support with this FP&A Circle.
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