Paper Type

Complete

Abstract

The increasing adoption of Artificial Intelligence (AI) in accounting promises efficiency gains, improved data quality, and enhanced decision-making. Yet organizations often struggle to assess which accounting processes are suitable for AI automation. Existing approaches either focus on rule-based automation or adopt generic AI criteria, overlooking the multidimensional nature of accounting processes. To address this gap, this study introduces the A^4 (AI Automation Potential Assessment of Accounting Processes) framework, developed following the Design Science Research methodology by Peffers et al. (2007). The framework enables a structured, scoring-based evaluation (0–100) of accounting processes based on literature-derived criteria, supporting comparative assessment and prioritization of automation candidates. Empirical evaluation, following Sonnenberg and vom Brocke (2012), demonstrates the framework’s theoretical rigor and practical applicability. This study contributes to the literature on AI-driven accounting automation by introducing a systematic, scoring-based evaluation framework that enables practitioners to prioritize accounting processes based on their AI automation potential.

Paper Number

1222

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Aug 15th, 12:00 AM

Assessing the AI Automation Potential of Accounting Processes: The A4 Framework

The increasing adoption of Artificial Intelligence (AI) in accounting promises efficiency gains, improved data quality, and enhanced decision-making. Yet organizations often struggle to assess which accounting processes are suitable for AI automation. Existing approaches either focus on rule-based automation or adopt generic AI criteria, overlooking the multidimensional nature of accounting processes. To address this gap, this study introduces the A^4 (AI Automation Potential Assessment of Accounting Processes) framework, developed following the Design Science Research methodology by Peffers et al. (2007). The framework enables a structured, scoring-based evaluation (0–100) of accounting processes based on literature-derived criteria, supporting comparative assessment and prioritization of automation candidates. Empirical evaluation, following Sonnenberg and vom Brocke (2012), demonstrates the framework’s theoretical rigor and practical applicability. This study contributes to the literature on AI-driven accounting automation by introducing a systematic, scoring-based evaluation framework that enables practitioners to prioritize accounting processes based on their AI automation potential.