<p>West (2026) concludes that Generative Artificial Intelligence (GenAI) is capable of undertaking modelling activities within Soft Systems Methodology (SSM), suggesting that practitioners may ‘no longer need the skills to produce such models’. This commentary argues that this conclusion is not supported by the evidence presented. West’s article conflates the automated generation of textual modelling artefacts with the conduct of SSM as a structured process of systemic inquiry and action research as envisaged by Checkland. Drawing on foundational SSM literature and recent work on participant engagement and analytics in operational research, the commentary demonstrates that West’s example illustrates the production of linguistically plausible artefacts rather than the automation of systemic practice. When considered against the participatory foundations of SSM and the methodological requirements of recoverability in action research, West’s claims appear substantially overstated.</p>

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Producing Models Without Systemic Inquiry?: A Response to West (2026)

  • Katharina Burger

摘要

West (2026) concludes that Generative Artificial Intelligence (GenAI) is capable of undertaking modelling activities within Soft Systems Methodology (SSM), suggesting that practitioners may ‘no longer need the skills to produce such models’. This commentary argues that this conclusion is not supported by the evidence presented. West’s article conflates the automated generation of textual modelling artefacts with the conduct of SSM as a structured process of systemic inquiry and action research as envisaged by Checkland. Drawing on foundational SSM literature and recent work on participant engagement and analytics in operational research, the commentary demonstrates that West’s example illustrates the production of linguistically plausible artefacts rather than the automation of systemic practice. When considered against the participatory foundations of SSM and the methodological requirements of recoverability in action research, West’s claims appear substantially overstated.