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