Artificial Intelligence as Disruptive Technology in Accounting: A Qualitative Study of Practitioner Perceptions on Automation, Judgment, and Decision Support

This study investigates how practicing accounting professionals in the Philippines interpret the adoption, usefulness, constraints, and governance implications of artificial intelligence (AI) in accounting work. Using a qualitative descriptive case-oriented approach, semi-structured interviews were conducted with 45 practitioners across multiple sectors (e.g., manufacturing, services, retail, logistics, banking, and professional services) from September to December 2025. Interviews were audio-recorded, transcribed using AI-assisted transcription, translated to English where needed, and analyzed inductively through thematic analysis supported by NVivo. Findings indicate that AI is currently adopted most comfortably in low-risk assistive uses—particularly summarization, document clarification, and preliminary review of lengthy narratives—rather than as a stand-alone engine for core accounting decisions. Deeper integration into routine accounting processes (e.g., posting, classification, reconciliation, forecasting, and assurance) remains conditional and uneven due to limited top-management sponsorship, weak policy institutionalization, and significant data-readiness constraints. A central mechanism emerging from the data is verification overhead: where outputs lack traceability or auditability, AI can add an additional validation step, reducing net efficiency gains. External compliance realities—especially manual, template-bound regulatory reporting—further constrain end-to-end automation. Governance concerns (confidentiality, cybersecurity exposure, error opacity, and non-transferable professional liability) operate as decisive adoption barriers and reinforce boundary conditions in which AI may assist but cannot replace human judgment, contextual interpretation, and accountable sign-off. Participants anticipate workforce recomposition rather than immediate displacement, emphasizing upskilling in AI literacy, analytics/forecasting, cybersecurity awareness, and AI governance.