The human-machine paradox: how collaboration creates or destroys value, and why augmentation is key to resolving it

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.