This position paper argues that adopting AI in organizational practice does not guarantee productivity gains, because human and environmental factors critically moderate the relationship between AI deployment and realized productivity improvements. Following the advent of high-performance generative models, AI use has been rapidly encouraged in some sectors while being restricted in others. Most practitioners assume that AI brings productivity boosts owing to enhanced technical capabilities, but regardless of apparent performance advances in AI technology, human and environmental factors of the organization may substantially attenuate -- or even negate -- the effective productivity benefits. We identify five key moderating factors: human resource composition, baseline capability of individuals, learning curve of practitioners, incentives for fair use, and flexibility of objectives. Drawing on the partial equilibrium model of Gries and Naudé (2022), we argue that existing economic frameworks may inadvertently overlook these factors. We revise the existing framework to redefine effective organizational determinants and shed light on practical implications including industry and education, responding to alternative views and calling for action of stakeholders.

翻译：摘要：本文立场论文认为，在组织实践中采用人工智能并不能保证生产力提升，因为人因与环境因素对人工智能部署与实际生产力改进之间的关系起着关键的调节作用。随着高性能生成模型的出现，人工智能在某些领域被迅速鼓励使用，而在其他领域则受到限制。多数从业者假定人工智能凭借增强的技术能力能带来生产力提升，但无论人工智能技术在表面性能上取得多大进步，组织中的人因与环境因素都可能大幅削弱——甚至抵消——实际的生产力效益。我们识别出五个关键的调节因素：人力资源构成、个体基线能力、从业者学习曲线、公平使用激励，以及目标灵活性。基于Gries和Naudé（2022）的局部均衡模型，我们论证现有经济框架可能无意中忽视这些因素。我们修正现有框架以重新界定有效的组织决定因素，并阐明包括工业与教育在内的实际启示，回应不同观点，呼吁利益相关方采取行动。