When deploying artificial skills, decision-makers often assume that layering human oversight is a safe harbor that mitigates 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 highly contingent on situational and design factors. To investigate this gap, we develop an in-silico exploratory framework for policy analysis based on Monte Carlo simulations to quantify the economic impact of skill policies in the execution of tasks presenting varying levels of complexity across diverse setups. Our results show that in complex scenarios, a human-machine strategy can yield the highest economic utility, 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 sufficiently compensated by performance gains. This finding points to a key implication for decision-makers: 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 when surprise is the norm, even when machines become more effective than humans in handling uncertainty.

翻译：在部署人工智能技能时，决策者通常认为叠加人类监督是一种安全港，能够缓解高复杂度任务中完全自动化的风险。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底线经济效用高度依赖于情境与设计因素。为探究这一差距，我们基于蒙特卡洛模拟开发了一个用于策略分析的硅基探索框架，以量化在不同设置下执行具有不同复杂度水平的任务时技能策略的经济影响。我们的研究结果表明，在复杂场景中，人机策略能够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策略或纯人工策略更差，在成本压力下主动破坏价值，而性能提升不足以补偿这些成本。这一发现对决策者提出了一个关键启示：当情境复杂且关键时，仅仅将人类与机器技能分配给任务可能是不够的，远非一种万全之策或低风险的折中方案。相反，这是一个提升竞争力的关键机遇，需要组织对实现增强做出强有力的承诺。此外，我们的研究还表明，随着时间的推移提高机器技能的成本效益虽然有用，但并不能替代在意外成为常态时聚焦于实现增强的根本需求，即使机器在处理不确定性方面变得比人类更有效。