The emergence of autonomous systems reshapes risk assessment drastically as industry and surrounding key actors approach R&D and technology investment in new ways. Autonomy's focus on system behavior—rather than on the performance of individual subsystems—necessitates a fundamental shift in how industries define their output. Quantifiable subsystem performance metrics is no longer at the heart of R&D. Systems that rely on complex, adaptive behaviors, instead stem from a specification of desired system interactions, often in dynamic, emergent and unpredictable environments.

This shift has significant implications for policy. Systems will be built to ensure agents that independently adapt to various scenarios, identify risks, and make decisions. This evolving landscape demands cross-functional collaboration, as engineering teams work closely with behavioral specialists to capture these nuanced requirements. Additionally, risk assessment must move beyond technical reliability to incorporate situational adaptability and ethical decision-making under uncertainty.

In terms of predicting which technologies to invest in, there is value in new tools of forecasting capabilities that assess both technological maturity and potential integration complexity. In short: industry leaders need to track advancements related to autonomy. Such areas should drive research agendas and investment priorities in a large variety of sciences. Since industry often funds academic research, this requires new methods of engagement with universities and research institutions, shifting from prescriptive studies of individual components to research that explores emergent system behaviors and decision-making under complex conditions.