The intersection of artificial intelligence (AI), machine learning technologies, and human resource management (HRM) represents a transformative shift in organisational development. This paper presents a systematic literature review (SLR) examining peer-reviewed evidence on human-machine collaboration in HRM within social enterprises and mission-driven organisations. Using the SPIDER framework and PRISMA guidelines, the review conducted a systematic search on Scopus retrieving 96 peer-reviewed articles, supplemented by backward snowballing.
A critical methodological finding emerged during string construction: incorporating AI-related terms (Cluster C) into the automated TITLE-ABS-KEY search reduced the corpus by 97%, from 245 results to fewer than 12. This reflects a publishing convention whereby AI is discussed substantively in article bodies without being foregrounded in searchable metadata. Cluster C was therefore applied as a manual inclusion criterion during screening rather than as an automated filter.
The review’s primary substantive finding is structural: the three-way intersection of AI, HRM, and the third sector is demonstrably scarce in the indexed peer-reviewed literature. The single article meeting all inclusion criteria — Alnamrouti, Rjoub and Ozgit (2022) — provides empirical evidence that AI positively influences sustainable organisational performance in an NGO context, functioning primarily as a mediator amplifying the effect of organisational learning. Backward snowballing on seven references yielded zero additional inclusions, independently corroborating the structural scarcity finding.
Three major research gaps are identified: (1) the absence of domain-specific empirical evidence on AI adoption in social enterprise HRM functions (recruitment, performance management, learning and development, workforce planning, employee engagement); (2) the complete absence of research on ethical and algorithmic risks in mission-driven HRM contexts; and (3) the lack of engagement with established human-machine collaboration frameworks in third-sector management research. These findings establish a documented evidence baseline for a nascent research field, grounding the broader theoretical paper on human-machine partnerships.