Morphological Review of AI Applications for Budgeting

Artificial intelligence (AI) advancements are compelling management accountants to explore budgeting applications. However, the absence of an exhaustive classificatory framework makes it difficult to identify established AI applications and innovation opportunities. I address this gap by systematically reviewing AI applications within a morphological box and highlighting common configurations. Using an input-process-output approach, I examine how data (input) can power AI to perform value-enhancing manipulations (process), resulting in diverse benefits (output) for budgeting. My findings indicate that, for example, using internal data for cost forecasting is a typical application of AI, whereas automated applications are still in their early stages of development. Moreover, I identify gaps in the current literature and neglected AI applications, pointing to important areas for future research.