IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution


  • Mark Feblowitz IBM
  • Oktie Hassanzadeh IBM
  • Michael Katz IBM
  • Shirin Sohrabi IBM
  • Kavitha Srinivas IBM
  • Octavian Udrea IBM



Scenario Planning, Neuro-Symbolic Systems, AI Planning, Causal Extraction From Text


Scenario Planning is a commonly used Enterprise Risk Management (ERM) technique to help decision makers with longterm plans by considering multiple alternative futures. It is typically a manual, highly labor intensive process involving dozens of experts and hundreds to thousands of person-hours. We previously introduced a Scenario Planning Advisor prototype (Sohrabi et al. 2018a,b) that focuses on generating scenarios quickly based on expert-developed models. We present the evolution of that prototype into a full-scale, cloud deployed ERM solution that: (i) can automatically (through NLP) create models from authoritative documents such as books, reports and articles, such that what typically took hundreds to thousands of person-hours can now be achieved in minutes to hours; (ii) can gather news and other feeds relevant to forces in the risk models and group them into storylines without any other user input; (iii) can generate scenarios at scale, starting with dozens of forces of interest from models with thousands of forces in seconds; (iv) provides interactive visualizations of scenario and force model graphs, including a full model editor in the browser. The SPA solution is deployed under a non-commercial use license at and includes a user guide to help new users get started. A video demonstration is available at




How to Cite

Feblowitz, M., Hassanzadeh, O., Katz, M., Sohrabi, S., Srinivas, K., & Udrea, O. (2021). IBM Scenario Planning Advisor: A Neuro-Symbolic ERM Solution. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 16032-16034.