Corporate Reporting and Informatics: A Revisitation of Traditional Business Decision Models

Abstract

This literature review examined the nexus between corporate reporting and informatics technologies with particular reference to the transformation of traditional business decision models. A comprehensive content analysis supported by empirical data from 65+ peer-reviewed academic sources and industry research spanning 2000-2026 was employed. The dataset covered multiple industries, including financial services, manufacturing, healthcare, retail, and energy sectors. Results revealed that weak data governance frameworks, poor analytical capability, and organizational resistance to algorithmic decision-making are key factors limiting AI adoption in corporate reporting systems. The study found that the absence of standardized governance protocols for algorithmic decision-making remains a significant challenge to organizational confidence and regulatory compliance in implementing autonomous decision systems. However, empirical evidence documented substantial business value from successful implementation: organizations achieved 32-45% improvement in forecasting accuracy, 25-47% enhancement in operational efficiency, and 38-43% reduction in decision-making latency. The review emphasizes that realizing transformative benefits requires systematic investment across technology, governance, organizational development, and culture dimensions over 3–7-year implementation horizons. Success demands addressing critical challenges related to data quality, legacy system integration, algorithmic bias, regulatory compliance, and organizational change management.