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

Sammanfattning

As Artificial Intelligence (AI) continues to reshape organizational decision-making, itsapplication within Mergers and Acquisitions (M&A) has gained increasing attention. Whileexisting research has primarily focused on post-acquisition outcomes and the technicalcapabilities of AI systems, limited attention has been paid to how AI-based tools are actuallyused in practice, particularly in the early stages of target identification. This study explores howAI-based decision-support tools are used by operational acquirers to identify acquisition targetsin 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 processesacross a range of industries. The analysis is grounded in dynamic capabilities and absorptivecapacity, which together provide a theoretical lens for examining how firms integrate and applyAI-driven insights within acquisition processes. The findings reveal that AI is primarily used toenhance the scale and efficiency of the initial screening process, enabling firms to conductlarge-scale market screening, structured filtering, and comparative target evaluation in ways thattraditional 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 managerialexpertise. 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-offsbetween speed, depth, and accuracy. The findings further indicate that AI is beginning totransform M&A practice more broadly, reshaping target monitoring and skill requirementswithin acquisition teams. Together, these findings suggest that effective target identificationrelies on a carefully balanced combination of AI and human judgment, and that the value of AI isnot inherent but depends on how it is integrated into existing processes.