A New Technology Rises: Non-Human Knowledge Workers and Decision-Making in a System of Complex Systems

To accomplish their missions, organizations make decisions—a form of knowledge work—that consumes scarce resources. As advanced algorithms, like artificial intelligence (AI), become more ubiquitous and affordable, some organizations are turning to such technical systems to strengthen their decision-making and, by extension, system-level performance. However, the degree to which AI and autonomous systems impact system-level performance is suboptimal because existing approaches generally ignore two critical design factors—the extent to which algorithmic systems relieve humans of knowledge work and are structurally encoded in the system's task and communication structures. The purposeful design and incorporation of non-human knowledge workers (NHKWs) into organizations are central to system of systems engineering; NHKWs are likely to impact system complexity, the attainment of stakeholder goals, and other system performance measures. Against the backdrop of the ATLAS Experiment, this perspective paper explores the construct of NHKWs, distinguishes NHKWs from AI and similar algorithmic systems, and makes the case that intentionally designing NHKWs into an organization's technological framework is needed for more robust system-level performance and goal attainment.