When deploying artificial skills, managers widely assume that combining them with the human factor is a safe harbor, mitigating the risks of full automation in high-complexity tasks. This paper formally challenges the economic validity of this widespread assumption, arguing that the true bottom-line economic utility of a human-machine skill policy is dangerously misunderstood and highly contingent on situational and design factors. To investigate this gap, we develop an in-silico framework based on Monte Carlo simulations grounded in empirical pragmatism to quantify the economic impact of human and machine skills in the execution of tasks presenting varying levels of complexity. Our results show that a human-machine strategy can yield the highest economic utility in complex scenarios, but only if genuine augmentation is achieved. In contrast, when failing to realize this synergy, the human-machine approach can perform worse than either the machine-exclusive or the human-exclusive policy, actively destroying value under the pressure of costs that are not compensated by sufficient performance gains. The takeaway for decision-makers is unambiguous: when the context is complex and critical, simply allocating human and machine skills to a task may be insufficient, and far from being a silver-bullet solution or a low-risk compromise. Rather, it is a critical opportunity to boost competitiveness that demands a strong organizational commitment to enabling augmentation. Also, our findings show that improving the cost-effectiveness of machine skills over time, while useful, does not replace the fundamental need to focus on achieving augmentation.

翻译：在部署人工智能技能时，管理者普遍认为将其与人力因素结合是一种安全策略，能够降低高复杂度任务中全自动化的风险。本文正式挑战了这一普遍假设的经济有效性，认为人机技能策略的真实经济效用被严重误解，且高度依赖于情境与设计因素。为探究这一差距，我们基于经验实用主义开发了一个基于蒙特卡洛模拟的计算机框架，以量化在复杂度不同的任务执行中人力与机器技能的经济影响。研究结果表明，人机策略在复杂场景下可能产生最高的经济效用，但前提是实现真正的增强。相反，若未能实现这种协同效应，人机方法的性能可能比纯机器或纯人力策略更差，在成本压力下因性能提升不足而主动破坏价值。对决策者的启示是明确的：当情境复杂且关键时，仅将人力与机器技能分配给任务可能不足，远非一劳永逸的解决方案或低风险折衷。相反，这是提升竞争力的关键机遇，需要组织对实现增强做出坚定承诺。此外，我们的研究还表明，随时间提高机器技能的成本效益虽有益处，但无法替代聚焦实现增强的根本需求。