What accountants need to know about artificial intelligence and machine learning: a review and call for future research

Purpose This paper provides a comprehensive and conceptually grounded review of how artificial intelligence (AI) and machine learning (ML) are transforming professional judgement in accounting. It clarifies the epistemic foundations of AI and ML, synthesises the expanding accounting literature employing these techniques and provides an agenda for future research. Design/methodology/approach This study reviews AI and ML applications across auditing and assurance, financial reporting, management accounting, taxation, ESG measurement, financial distress and earnings prediction, and public-sector analytics. Applying Abbott's (1988) system-of-professions framework, it connects methodological developments to broader institutional questions about expertise, authority and governance in an AI-enabled accounting environment. Findings Three insights emerge. First, ML models consistently outperform traditional statistical approaches across prediction-intensive accounting domains by capturing nonlinearities, interactions and high-dimensional structures that conventional methods overlook. Second, ML expands the evidentiary boundaries of accounting by incorporating unstructured, textual, behavioural and alternative data, reshaping what counts as relevant and credible evidence. Third, as ML systems increasingly rival or exceed human predictive judgement, particularly in areas such as fraud detection, accounting estimates and going-concern prediction, they challenge the profession's epistemic authority, necessitating new expertise in model interpretation, governance and error evaluation. Research limitations/implications AI and ML fundamentally reshape the evidentiary basis of accounting, creating new forms of machine-generated knowledge that challenge traditional professional judgement. As predictive models increasingly surpass human experts, research must investigate how authority, responsibility and trust shift within hybrid human–AI decision systems. Future research should examine how algorithmic evidence is validated, governed and integrated into audit and reporting frameworks, and how professional identities, skill sets and jurisdiction evolve as accountants transition from primary judgement-makers to interpreters and overseers of AI-driven inference. Practical implications AI can enhance audit quality through automated anomaly detection, continuous monitoring and ML-driven risk assessment. Firms can use ML to improve accounting estimates, fraud detection, misstatement prediction and ESG analytics. Management accountants can deploy AI for forecasting, planning and real-time cost optimisation. Regulators and tax authorities can apply ML to detect non-compliance and prioritise audits. Across all settings, accountants increasingly focus on interpreting, validating and governing AI outputs rather than generating predictions themselves. Originality/value This paper demystifies AI and ML concepts, mapping empirical developments across the field and offering a theoretically grounded account of how AI reshapes professional judgement and epistemic authority. It also identifies opportunities for future research.