Transforming Information Systems into Intelligent Socio-Technical Ecosystems via Machine Learning and Explainable AI: A Systematic Review

Misti Jahrani Putri (STMIK IKMI Cirebon, Indonesia)
Martanto Martanto (STMIK IKMI Cirebon, Indonesia)
Fathurrohman Fathurrohman (STMIK IKMI Cirebon, Indonesia)
Edi Tohidi (STMIK IKMI Cirebon, Indonesia)
Umi Hayati (STMIK IKMI Cirebon, Indonesia)

Immortalis Journal of Interdisciplinary Studies
Vol. 2 No. 3 (2026): July - September

Rapid developments in Machine Learning (ML) and Data Analytics (DA) are reshaping Information Systems (IS) from transactional tools into intelligent, socio-technical ecosystems. However, existing literature remains fragmented between purely algorithmic focus and organizational adoption. This Systematic Literature Review (SLR) synthesizes 48 high-impact Scopus-indexed studies published between 2020 and 2026, following the PRISMA 2020 framework, to map the architectural and organizational integration of ML and DA in IS transformation. Our findings reveal a paradigm shift across five dominant thematic clusters: predictive forecasting, intelligent automation, big data integration, Explainable AI (XAI), and Human-AI collaboration. Rather than pure automation, the literature strongly underscores a transition toward Human-Centered AI, where model interpretability and socio-technical governance are critical to user trust and system performance. Furthermore, we identify core deployment bottlenecks—specifically regarding algorithmic transparency, cross-system interoperability, data privacy, and ethical governance. This study contributes a novel conceptual framework illustrating the interplay between technical ML capabilities, cross-cutting enablers, and organizational value creation, offering actionable guidelines for designing sustainable, transparent, and adaptive AI-driven IS.