AI in strategic alliance formation: a framework for human–AI collaboration

Purpose The purpose of this study is to clarify how artificial intelligence (AI) can enhance strategic alliance formation by (1) explaining AI’s roles in the two core stages of understanding alliance needs and assessing partner fit and (2) matching three human–AI collaboration modes (augmentation with specialization, augmentation with ensembling and human-centric) to three alliance decision contexts (stationary, structured but shifting and unstructured/novel). Design/methodology/approach The authors offer a conceptual evaluation of how AI influences alliance formation by laying the theoretical foundations of how different alliance decision contexts warrant different human–AI collaboration modes. Findings AI can expand alliance managers’ bounded rationality by widening the search, processing unstructured data and surfacing patterns. The authors show that this influence is context-specific: in stationary contexts, augmentation with specialization (i.e. AI scales screening/scoring while humans make contextual judgments) creates the best outcomes. In structured but shifting contexts, augmentation with ensembling (i.e. combining independent human and AI assessments to handle uncertainty and the degradation of AI model performance over time) would be the most effective approach. Finally, in unstructured or novel contexts, human-centric decisions (i.e. theory-led exploration with AI in a limited support role) are the best approach. Research limitations/implications This work underscores that the value of AI in strategic alliances lies not only in its analytical power but also in how organizations learn to integrate it with human foresight, paving the way for more adaptive and resilient approaches to alliance building. Practical implications This research provides executives with a decision guide for when to deploy specialization versus ensembling versus human-centric approaches in alliance formation decisions. In addition, it details implementation practices to improve AI use in alliance formation. Originality/value To the best of the authors’ knowledge, this work is among the first to use a bounded rationality lens to articulate a context-specific framework linking alliance decision contexts to specific human–AI collaboration modes.