A Layman’s Guide to Overcoming the AI/ML Black Box Problem in Planning

When:  Apr 8, 2021 from 2:00 PM to 3:00 PM (ET)

All businesses plan. Planning has multiple purposes including setting targets, monitoring performance, and anticipating hypothetical future shocks. At one time, spreadsheets were the primary planning tool at most companies, but as technology use and data collection have increased, so has the complexity and sophistication of planning tools. Current trends include planning at greater levels of detail, shorter planning cycles to support more agility in decision making, and the use of Artificial Intelligence and Machine Learning (AI/ML) tools to automate forecasting. Furthermore, the notion of eXtended Planning and Analysis (xP&A) is taking hold, which is about integrating planning processes across functions including Finance, Sales and Operations to increase accuracy and eliminate inconsistencies, while saving time and effort. After all, you cannot shorten planning cycles if every cycle involves time-consuming reconciliation meetings between Finance, Sales and Operations!

This all sounds wonderful, and it can be, but there is a catch: mistrust of the “Black Box”, also known as “Algorithm Aversion”. The best model in the world will get you nowhere if nobody trusts it. This needs to be front-of-mind right from the start of any modeling effort. In this month’s Advanced Topics webinar, QueBIT Master Consultant and Data Scientist, Christine Schoenen, will share practical tips on how to mitigate mistrust of the Black Box in your next planning initiative, with a special focus on AI/ML use-cases which is where this problem is most acute.