Beyond Payroll Automation: A Descriptive Study of AI-Enabled Payroll Adoption in Indian SMEs
DOI:
https://doi.org/10.29070/db9sgz23Keywords:
AI-enabled payroll, payroll automation, artificial intelligence, SMEs, digital HRM, organisational capability, HR–Finance integration, IndiaAbstract
Digital payroll has transitioned payroll administration from a manual process to a combination of attendance, salary computation, statutory deductions and reporting systems. The advent of Artificial Intelligence (AI) is an additional shift from rule-based automation to anomaly detection, validation, analysis and decision support. The current gap in the research is that the existing literature on AI/ digital HRM in Indian SME literature has grown significantly at a macro-level focusing on the adoption of AI, with a minimal focus on payroll as a specific HRM–Finance workflow, or on how the adoption of AI-led functionality differs from developing the capability to use it effectively. Thus, the issue that this study addresses is the possible adoption–capability gap in AI-supported payroll. The study is descriptive in nature, aimed at understanding the current AI revolution in the payroll realm, identifying the key payroll use cases of AI and exploring the organisational context that affects its impactful application. The design is a qualitative and descriptive design based on documents. Secondary evidence is obtained from peer reviewed research papers, government and institutional publications, industry reports and publicly available documentation of payroll technologies that are operational in the Indian market. The evidence is discussed and themed on the following areas: AI-powered payroll functions, integration of data and processes, employee capability and human oversight. The analysis suggests that AI-powered payroll is shifting from transactional to exception identification, validation, and analytical assistance roles, but this shouldn't be interpreted as proof of organisations' overall capabilities. Effective use requires good data quality, workflow integration, employee skills, and managerial oversight. The study also brings in a focused management perspective by differentiating between adopting AI-enabled payroll and the organizational capability needed to value it.
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References
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