Leveraging Causal Models to Craft AI Strategy.

In the dynamic field of Artificial Intelligence (AI), strategic integration is crucial to avoid the pitfall of optimizing peripheral processes at the expense of organizational goals. This paper presents a nuanced AI strategy framework that combines Causal Loop Diagrams (CLD) with Agent-Based Modeling (ABM) to address the intricacies of complex, growth-oriented organizations. By marrying empirical data with theoretical constructs and broad meta-knowledge, this approach enables the practical application of common sense to AI planning. The use of CLDs helps reveal and tackle ingrained patterns that could hinder organizational progress, while ABM facilitates the testing of AI strategies through extensive simulations within varied market and product scenarios. This study exemplifies the benefits of a causal-model-enabled AI strategy for strategic alignment and sustainable growth, suggesting a shift from mere efficiency-driven AI applications to those grounded in a thorough comprehension of complex system interdependencies. The proposed framework paves the way for organizations to develop a robust, anti-fragile AI ecosystem that is attuned to the nuances of their operational environment. [ABSTRACT FROM AUTHOR]