Finding the Right Target in M&A - How AI-Based Decision Support Tools Are Used by Operational Acquirers in Innovation-Driven Acquisitions

Abstract

As Artificial Intelligence (AI) continues to reshape organizational decision-making, its application within Mergers and Acquisitions (M&A) has gained increasing attention. While existing research has primarily focused on post-acquisition outcomes and the technical capabilities of AI systems, limited attention has been paid to how AI-based tools are actually used in practice, particularly in the early stages of target identification. This study explores how AI-based decision-support tools are used by operational acquirers to identify acquisition targets in innovation-driven M&A processes. To address this, a qualitative research design was adopted, drawing on semi-structured interviews with practitioners actively engaged in M&A processes across a range of industries. The analysis is grounded in dynamic capabilities and absorptive capacity, which together provide a theoretical lens for examining how firms integrate and apply AI-driven insights within acquisition processes. The findings reveal that AI is primarily used to enhance the scale and efficiency of the initial screening process, enabling firms to conduct large-scale market screening, structured filtering, and comparative target evaluation in ways that traditional methods do not allow. At the same time, human judgment remains central throughout, as AI is consistently described as a complementary tool rather than a replacement for managerial expertise. The findings also highlight a set of contextual constraints that limit AI's effectiveness, including data availability, the inability to assess qualitative factors, and underlying trade-offs between speed, depth, and accuracy. The findings further indicate that AI is beginning to transform M&A practice more broadly, reshaping target monitoring and skill requirements within acquisition teams. Together, these findings suggest that effective target identification relies on a carefully balanced combination of AI and human judgment, and that the value of AI is not inherent but depends on how it is integrated into existing processes.