Requirements Engineering for Generative AI Adoption in Enterprise Financial Reporting: A Framework for Governance, Reliability, and Business Alignment
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P126Keywords:
Generative AI, Requirements Engineering, Financial Reporting, AI Governance, Reliability, Internal Control, Mixed Methods, Business Alignment, AuditabilityAbstract
Generative artificial intelligence (GenAI) entered enterprise finance faster than the control architectures normally used to govern financial-reporting technologies. This study investigates a requirements-engineering problem rather than a technology-adoption problem alone: which requirements must be specified, prioritized, verified, and governed before GenAI can be embedded in financial reporting without weakening reliability or business accountability? An explanatory sequential mixed-methods design (QUAN→QUAL) integrates quantitative secondary evidence from 2024 finance and financial-reporting surveys with qualitative content analysis of public regulatory, professional, standards, and enterprise-governance materials available by 30 November 2024. The quantitative phase treats published survey samples separately and reconstructs adoption, benefit, and control-readiness patterns rather than pooling incompatible instruments. The qualitative phase codes archival materials for requirement mechanisms involving data provenance, human review, traceability, model/output validation, access control, accountability, materiality, workflow fit, and value realization. Integration is performed through a joint display linking observed adoption-readiness gaps to the mechanisms that explain them. The evidence indicates that adoption intent substantially exceeds implemented GenAI use, while perceived value is conditional on reliable data, professional judgment, governance maturity, and auditability. The resulting Governance–Reliability–Alignment Requirements Engineering (GRARE) framework specifies four requirement gates: admissibility, evidentiary reliability, accountable workflow integration, and business-value alignment. The study contributes an empirically grounded bridge between requirements engineering, responsible AI governance, and financial-reporting control design. It also shows why GenAI requirements in regulated reporting should be expressed as verifiable socio-technical obligations rather than feature requests. Because no original interviews were conducted, qualitative claims are restricted to traceable public records, and no respondent quotations or fabricated statistical estimates are introduced.
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