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Purpose This paper specifically aims to examine how (via which activities, methods and capabilities) organizations’ management deploy Artificial Intelligence (AI) systems to address underperformance. Five mitigation strategies/recommendations are introduced to manage the challenges and facilitate greater efficacies in changing organizations. Design/methodology/approach This paper conceptually synthesizes 47 articles, thematically reports and critically analyzes the AI–HRM–managerial decision-making relationship in changing organizations and discusses the impacts. Findings The results highlight three significant challenges and opportunities for changing organizations: (1) job performance challenges, (2) organizational performance challenges and HR and (3) collaborative intelligence opportunities. Originality/value The paper’s originality lies in addressing the current lack of a theoretical framework guiding HRM and AI experts on the managerial and strategic capabilities needed to address underperformance and their impacts in facilitating collective efficacies in human–AI collaboration in changing organizations. By further capturing an innovative HR Framework’s (1) human, (2) AI, (3) employees’ well-being, (4) jobs and (5) organizational performance, and its five key managerial recommendations/strategies, this paper develops two concepts: “technological servitization” and “re-ontological in-securitization” to advance theory in Managerial Psychology regarding the unintended/paradoxical consequences of managements’ AI-driven organizational performance interventions, including meaninglessness in organizations.
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Stored only when both papers are in the corpus — 35 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.