<![CDATA[Purpose: This study aims to analyze the role of digitalization and artificial intelligence (AI) in improving productivity in industrial and construction sectors, identify the supporting and inhibiting factors influencing their implementation, and formulate research gaps and novelty opportunities from previous literature. Methods: This study employed a Systematic Literature Review using the PRISMA 2020 framework. The literature search was primarily conducted through Scopus using keyword clusters related to AI-driven automation, skills mismatch, and vocational workforce, complemented by additional relevant scholarly sources. From 272 initial records, the selection and eligibility process resulted in 66 reports, which were analyzed through narrative and thematic synthesis. Findings: The findings indicate that digitalization and AI contribute to productivity through process automation, information integration, work accuracy, quality control, resource optimization, and data-driven decision-making. However, their effectiveness depends on human resource readiness, digital literacy, interoperability, system security, data governance, standardization, and organizational support. Research Implications: The study implies that productivity improvement through digitalization and AI should not be approached solely as technological adoption. Industrial and construction organizations need to strengthen workforce competencies, data quality, governance mechanisms, system integration, and organizational readiness to ensure that digital transformation produces sustainable productivity outcomes. Originality: This study offers an integrative socio-technical perspective that connects AI-driven automation, skills mismatch, vocational workforce readiness, governance, and multidimensional productivity. Its originality lies in positioning productivity as the result of interaction among technology, people, processes, data, and organizations, rather than as a direct consequence of digital technology adoption.]]>
