Human-Artificial Intelligence Collaboration in Strategic Decision-Making: A Systematic Literature Review

Abstract

Artificial intelligence is increasingly involved in strategic decision-making, yet its contribution depends on how decision authority is distributed between humans and machines. This systematic literature review critically examines AI delegation, where analytical or decision tasks are transferred to AI, and AI augmentation, where AI informs but does not replace human judgment. Guided by PRISMA 2020, the review screened 4,087 records and retained 25 studies for thematic synthesis. The evidence was interpreted through bounded rationality, algorithm aversion and appreciation, organization design, and the automation-augmentation paradox. Five themes emerged: the conditional value of delegation, the complementarity of augmentation, trust calibration and expertise, task structure and uncertainty, and organizational governance. The synthesis shows that delegation can improve speed, consistency, and analytical scale when problems are structured and feedback is reliable, but can reduce contestability and magnify model error when strategic ambiguity is high. Augmentation is more robust for uncertain, irreversible, politically sensitive, and novel strategic problems because human actors can contextualize predictions, challenge outputs, and integrate tacit knowledge. However, augmentation is not automatically superior because poorly calibrated trust, anchoring, automation bias, and weak AI literacy can make human oversight nominal rather than substantive. The review therefore rejects a universal choice between automation and augmentation and proposes a contingent collaboration framework in which decision quality depends on matching AI engagement mode to task characteristics, human expertise, and governance capacity. The study contributes a strategic-management-specific synthesis and identifies priorities for longitudinal, field-experimental, and generative-AI research.