AI in Mergers and Acquisitions Due Diligence: A Study of Perceived Artificial Intelligence Capability and Outcomes Among Finance Professionals

Author

Chalasani Rushi Ram Chowdary and Dr. Ajay Kumar Varshney

Abstract

 Due diligence is among the most information-intensive stages of a merger or acquisition, and artificial intelligence is increasingly proposed as a means of accelerating it and improving its quality. Evidence on how practitioners connect AI capability with specific due-diligence outcomes remains limited. This study investigates how finance and M&A professionals relate perceived AI capability to perceived efficiency, accuracy and risk identification in due diligence. Drawing on task–technology fit theory, the study uses an exploratory, cross-sectional survey of investment banking and M&A professionals. A structured five-point Likert questionnaire was analysed with reliability analysis, Pearson and Spearman correlations and sensitivity analyses, and the results were contextualised against published industry evidence. The study distinguishes perceived effectiveness from objectively measured performance and examines how consistently perceived capability is associated with each outcome. Based on these insights and the wider literature, the paper proposes a five-stage framework for governed, human-verified AI-assisted due diligence, covering task-fit screening, data readiness, AI first-pass analysis, human verification, and governance and performance measurement. The paper contributes to research on AI in M&A and technology fit, and offers investment banks, advisers and technology providers a practical basis for responsible AI adoption and a foundation for future empirical testing.

Keywords

artificial intelligence; mergers and acquisitions; due diligence; task–technology fit; practitioner perceptions; human–AI collaboration; AI governance

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References

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