Enabling Complexity: A Systematic Literature Review on Task Allocation, Communication, Interaction, and Augmentation in Human-AI Teams

As artificial intelligence (AI) systems increasingly act as collaborators rather than tools, Human-AI Teams (HATs) are emerging in domains that demand complex, adaptive decision-making. This study investigates how four key enablers—task allocation, communication, interaction, and cognitive augmentation—support Human-AI collaboration in complexity-rich environments. A systematic literature review (SLR) of 50 peer-reviewed studies was conducted, with 39 rated as high-quality, supported by a custom Human-AI review environment combining natural language processing (NLP) and large language models (LLMs). The findings reveal recurring patterns, interdependencies, and research gaps across domains such as healthcare, logistics, and strategy. While each enabler has been examined independently, their combined role remains underexplored. Results highlight that structured task delegation, explainable and contextual communication, and alignment between AI augmentation and human intent significantly enhance team performance and trust. Nonetheless, issues such as user overreliance, cognitive misalignment, and transparency—particularly in the context of emerging Generative AI (GenAI) tools—remain key challenges for effective collaboration. This review contributes a synthesized understanding of effective hybrid teaming and demonstrates how Human-AI collaboration can improve not only task performance but also the research process itself. The study advances the field of Human-Centered AI and supports the vision of Industry 5.0 through actionable insights into hybrid team design.