Understanding how and where AI can enhance the effectiveness of PMM-mediated organisational change: a conceptual framework

Helen Mackenzie

International Journal of Productivity and Performance Management, 2026, 1-21

Abstract: PurposeTo develop a social complexity-based understanding of how and where artificial intelligence (AI) and generative artificial intelligence (GAI) can enhance the effectiveness of performance measurement and management (PMM) mediated organisational change.Design/methodology/approachThis article draws on a social complexity interpretation of PMM, conviction narrative theory and the AI and GAI literature, to present a conceptualisation of AI-augmented PMM-mediated organisational change that describes how and where machine-based and human-based decision-making processes can augment performance measurement systems and performance management practices.FindingsAI's and GAI's inability to replicate human reflexivity and judgement in equivocal and indeterminate situations limits what these technologies can do in PMM-mediated organisational change. AI and GAI can augment decision-making processes associated with performance measurement and the execution of performance management-initiated interventions; however, creating these interventions relies on human reflexivity and judgement.Originality/valueThis conceptual investigation highlights the limitations of AI and GAI technologies in PMM-mediated organisational change and explains why human-based decision-making continues to play a distinctive role. It contributes to our understanding of how AI and GAI can influence organisational change in general, and performance management in particular, with implications for PMM researchers and practitioners.

Keywords: Artificial intelligence; Generative artificial intelligence; Social complexity; Human reflexivity; Performance measurement and management; Organisational change (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijppmp:ijppm-03-2026-0316

DOI: 10.1108/IJPPM-03-2026-0316

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