2023B05 - Unlocking the Potential of Artificial Intelligence in Auditing with Effective Management Control Practices (Dr. L. Bellora-Bienengraber)

Why?

The auditing community has partially welcomed the use of artificial intelligence (AI) as a promising way of increasing audit quality and the efficiency of the audit process. Among AI’s many cited benefits are more effective audits and greater speed of the audit process. Consequently, the potential benefits associated with AI use in auditing are high. However, the use of AI may severely disrupt the socio-technical fabric of audit firms. Besides its obvious technical attributes, the introduction of AI is an inherently social process of auditors’ personal and professional adaptation that must be managed carefully by audit firms for AI to realize its promised potential.

What?

The objective of this project is to support audit firms in managing auditors’ process of personal and professional adaptation to AI in ways that ensure the realization of the potential associated with AI. Our approach is rooted in self-regulation theory. In line with this, we propose that the introduction of AI in audit practice requires processes of self-regulation by auditors, i.e., attempts to (1) alter the AI use to re-establish a fit with their personal and professional standards, (2) alter these standards to re-establish the fit with their AI use, or (3) a combination thereof. The outcomes of self-regulation can be both behaviors that contribute and/or impede the realization of the potential of AI in auditing.

We argue that the audit firm can mobilize management control practices to assist auditors in their adjustment (as it affects both auditors’ AI use and their personal and professional standards). Specifically, we ask which characteristics these management control practices need to have and in which combinations they will meaningfully contribute to auditors’ processes of adjustment to AI’s use. Moreover, we ask how these processes relate, ultimately, to audit quality and the efficiency of the audit process.
In recognition of the relative novelty of AI use in audit practice, we propose to answer the research questions with a multimethod approach in which the findings of the initial interview study inform and recalibrate the subsequent survey and experimental study. Answering our research questions will inform audit practice about (1) how auditors self-regulate to AI; (2) how the audit firm’s existing management control practices impede or support these processes of self-regulation; and (3) how the audit firm’s management control practices can be optimized to support auditors’ self-regulation in ways that ensure the realization of the potential of AI.