Artificial intelligence and big data for operational decision-making: A systematic review of trends and governance frameworks (2020–2025)

Abstract

Purpose: This systematic literature review examines how integrating Artificial Intelligence (AI) and Big Data Analytics (BDA) transforms organizational operational decision-making.

Methodology: Following PRISMA guidelines, a systematic search of the Scopus database initially retrieved 341 records. After removing duplicates, applying timeframe filters, and conducting title/abstract and full-text screening, 15 peer-reviewed studies (2020–2025) were included in the final synthesis.

Findings: The review identifies three themes: (1) intelligent decision augmentation through real-time analytics and predictive models; (2) infrastructure, skills, and governance requirements; and (3) sectoral transformation and ethical implications. AI and BDA improve decision accuracy, forecasting precision, and transparency while reducing reliance on human intuition.

Implications: The findings highlight the need for synergy among AI systems, human judgment, and institutional controls, providing managers and policymakers with practical benchmarks to assess organizational readiness and to design governance frameworks that balance innovation with accountability. These insights are especially relevant to developing economies such as Cambodia, where the Cambodia Digital Economy and Society Policy Framework (2021–2035) explicitly prioritizes data-driven digital transformation across government and business sectors.

Originality: This research offers a contemporary synthesis of AI and Big Data applications for operational decision-making across diverse industries, with explicit attention to governance frameworks, an area underrepresented in prior reviews.

Limitations and directions for future research: Adoption success depends on the quality of infrastructure, organizational readiness, and governance robustness. Future research should investigate strategies to strengthen organizational preparedness and develop governance frameworks that address privacy, algorithmic bias, and accountability.