Background: The rapid infusion of artificial intelligence (AI) into corporate finance and transactional workflows is reshaping fundamental activities in mergers and acquisitions (M&A). This paper examines how AI alters pre-deal screening, due diligence, valuation, integration planning, and post-merger performance measurement, and it situates these changes within the broader M&A literature on efficiency, behavioral dynamics, and integration challenges. Methods: Drawing strictly on the provided scholarly and practitioner sources, the study constructs a conceptual framework that synthesizes empirical findings, theoretical models, and practitioner narratives to identify mechanisms by which AI affects M&A outcomes. The methodological approach is deductive and integrative: it dissects each stage of the M&A lifecycle, maps AI capabilities to stage-specific tasks, and evaluates anticipated benefits and risks across financial, organizational, and regulatory dimensions. Results: AI-driven tools reduce information asymmetries through automated document analysis, pattern detection, and structured prediction (Emmi, 2025; Fang et al., 2025; Fedyk et al., 2022). AI also shifts the locus of subjectivity in valuation towards algorithmic consistency while creating new sources of model risk and bias (Geertsema et al., 2025; Freire-González, 2025). For post-merger integration, AI enables more rapid cultural and operational mapping but does not eliminate human-leadership and governance challenges, and may create novel misalignment between automated recommendations and managerial incentives (Graebner, 2004; Graebner et al., 2017; Ellis et al., 2011). Discussion: AI's promise in M&A is substantial, but realization depends on governance, data quality, model validation, and integration of AI outputs into decision rights. The analysis reveals tensions between efficiency gains and risks of model opacity, regulatory scrutiny, and workforce displacement. The paper identifies a research agenda to empirically measure AI's causal impact on deal success and outlines policy-relevant safeguards that preserve managerial accountability and market stability. Conclusion: AI is not merely an efficiency tool in M&A; it is a structural technology that reframes valuation, due diligence, and integration processes. Successful adoption requires deliberate orchestration of technical, human, and governance systems to harness advantages while managing persistent and emergent risks.

artificial intelligence, mergers and acquisitions, due diligence, valuation

Emmi, P. A. (2025). The Impact of Artificial Intelligence on M&A Deals—Part I. https://www.reedsmith.com/en/perspectives/2025/03/impact-of-artificial-intelligence-ma-deals-part-i

Fang, Y., He, Y., & Zhang, Z. (2025). Artificial Intelligence and Mergers and Acquisitions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5121385

Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? SSRN. https://ssrn.com/abstract=4345969

Freire-González, J. (2025). The AI Imperative: Reshaping Global Investment Banking in the Decade Ahead. https://doi.org/10.2139/ssrn.5272841

Shounik, S. (2025). Redefining Entry-Level Analyst Roles in M&A: Essential Skillsets in the Age of AI-Powered Diligence. The American Journal of Applied Sciences, 7(07), 101–110. https://doi.org/10.37547/tajas/Volume07Issue07-11

Geertsema, P., Lu, H., & Stouthuysen, K. (2025). AI Can Make the Relative-Valuation Process Less Subjective. Harvard Business Review. https://hbr.org/2025/04/ai-can-make-the-relative-valuation-process-less-subjective

Eckbo, B. E. (2018). Corporate takeovers and economic efficiency. Annual Review of Financial Economics, 10, 31–74.

Ellis, K. M., Reus, T. H., & Lamont, B. T. (2011). Coping with misaligned structures and systems in M&As. Advances in Mergers and Acquisitions, 10, 159–182.

Erel, I., Liao, R. C., & Weisbach, M. S. (2012). Determinants of Cross-Border Mergers and Acquisitions. The Journal of Finance, 67(3), 1045–1082.

Ferreira, M. P., Reis, N. R., & Dos Santos, J. C. (2015). Mergers & acquisitions research: A bibliometric study of top strategy and international business journals, 1980–2010. Journal of Business Research, 68, 185–190.

Ferreira, M. P., Reis, N. R., & Santos, J. C. (2023). Mergers and acquisitions research: A review of the field over the past 30 years. Journal of Business Research, 137, 13–29.

Gomes, E., et al. (2013). Critical success factors through the mergers and acquisitions process: revealing pre-and post-M&A connections for improved performance. Thunderbird International Business Review, 55(1), 13–35.

Graebner, M. E. (2004). Momentum and serendipity: How acquired leaders create value in the integration of technology firms. Strategic Management Journal, 25(8-9), 751–777.

Graebner, M. E., et al. (2017). The process of postmerger integration: A review and agenda for future research. Academy of Management Annals, 11(1), 1–32.

Haleblian, J., et al. (2008). Taking stock of what we know about mergers and acquisitions: A review and research agenda. Journal of Management, 34, 469–502.