A Human-AI Teaming Approach to Closing the Gap in Critical Infrastructure

Abstract

Many critical infrastructure sectors are facing significant talent gaps among their workforce. The Industrial Internet of Things revolution has introduced new technologies and requirements for workers to understand while continuing to perform the duties for which they were hired, and the introduction of these data-driven technologies has concurrently created the need for new team roles with their own sets of capabilities. One possible solution for overcoming these talent gaps is the integration of artificially intelligent teammates. Research suggests human-AI teaming could potentially offload tedious, repetitive, or dangerous human work and accomplish tasks that, while difficult for a human to complete, cater well to what computers do best. This paper proposes a simple 3-steps guiding framework for teams in critical infrastructure organizations to determine a) the gaps on their team, by distinguishing between gaps caused by insufficient personnel (capacity) and those driven by new technological demands (capability), b) which roles are well-suited for an AI teammate, based on the match between task demands and AI capabilities, and c) the human-centered design considerations, including presence, explainability, autonomy management, and ethical alignment, that are essential to its integration as an effective teammate.