Globally, artificial intelligence (AI) implementation is growing, holding the capability to fundamentally alter organisational processes and decision making. Simultaneously, this brings a multitude of emergent risks to organisations, exposing vulnerabilities in their extant risk management frameworks. This necessitates a greater understanding of how organisations can position themselves in response. This issue is particularly pertinent within the financial sector with relatively mature AI applications matched with severe societal repercussions of potential risk events. Despite this, academic risk management literature is trailing behind the speed of AI implementation. Adopting a management perspective, this study aims to contribute to the understanding of AI risk management in organisations through an exploratory empirical investigation into these practices. In-depth insights are gained through interviews with nine practitioners from different organisations within the UK financial sector. Through examining areas of organisational convergence and divergence, the findings of this study unearth levels of risk management framework readiness and prevailing approaches to risk management at both a processual and organisational level. Whilst enhancing the developing literature concerning AI risk management within organisations, the study simultaneously offers a practical contribution, providing key areas of guidance for practitioners in the operational development of AI risk management frameworks.

翻译：全球范围内，人工智能的部署正在加速，其具备从根本上改变组织流程与决策的能力。与此同时，这也为组织带来了大量涌现性风险，暴露了其现有风险管理框架中的脆弱性。这要求我们更深入地理解组织如何定位自身以做出应对。这一问题在金融领域尤为突出，该领域已具备相对成熟的人工智能应用，但潜在风险事件可能对社会造成严重冲击。尽管如此，学术界的风险管理文献仍滞后于人工智能的部署速度。本研究采用管理视角，通过对此类实践进行探索性实证调查，旨在增进对组织中人工智能风险管理的理解。通过对英国金融领域九个不同组织的实践者进行访谈，本研究获得了深入洞察。通过考察组织间的趋同与差异领域，研究发现揭示了在流程层面与组织层面的风险管理框架就绪程度及主流风险管理方法。本研究在丰富关于组织内人工智能风险管理的新型文献的同时，也为实践者在开发人工智能风险管理框架的运营过程中提供了关键指导方向，具有实践贡献价值。