Intelligent Automation and Machine Learning as Key Drivers of Digital Transformation in SMEs under Emerging Economic Risks

This article investigates how intelligent automation (AI) and machine learning (ML) act as key enablers of digital transformation in small and medium-sized enterprises (SMEs) in the face of emerging economic risks. Based on a critical review of recent literature and a bibliometric mapping conducted on data from the Web of Science Core Collection, the study shows that the research agenda is strongly focused on the AI/ML–SME triad and on risk-related predictive applications, e.g., financial fragility, insolvency, credit risk, but the mechanism by which ML capabilities are transformed into end-to-end operational outcomes and economic resilience remains poorly explained. The article proposes a “risk-aware” conceptual framework that treats AI and ML as an integrated system: ML generates cognitive signals, such as predictions, recommendations, anomaly detection, including GenAI/NLP, and AI operationalizes these signals into orchestrated, monitored, and auditable processes. Implementation typologies are identified (from point automation in back-office and augmented analytics, to end-to-end hyperautomation and GenAI-based front-office automation) and the mechanisms through which AI–ML can reduce vulnerabilities (efficiency, response time, resource optimization, continuity) or introduce new risks (drift, bias, security, compliance, vendor dependency) are discussed. The results highlight the decisive role of organizational mediators, such as data quality, skills and AI literacy, governance, auditability and vendor management, in differentiating between value creation and risk amplification. The contribution of the study lies in the explicit integration of the “risk-aware” economic perspective in the assessment of AI–ML adoption in SMEs and in the formulation of application directions for responsible and scalable implementations.