Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 08 (2025): Volume 05 Issue 08
Articles

Artificial Intelligence‑Enabled Mergers & Acquisitions: A Paradigm Shift in Deal Origination, Valuation, and Integration

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  • Dilnoza Zubayd qizi Ismoilova
Dilnoza Zubayd qizi Ismoilova
Assistant of the Department of Medical and Biological Chemistry, Bukhara State Medical Institute, Uzbekistan

Published 2025-08-31

Keywords

  • Artificial Intelligence,
  • Mergers and Acquisitions,
  • Deal Valuation,
  • Due Diligence

How to Cite

Dilnoza Zubayd qizi Ismoilova. (2025). Artificial Intelligence‑Enabled Mergers & Acquisitions: A Paradigm Shift in Deal Origination, Valuation, and Integration. Stanford Database Library of American Journal of Applied Science and Technology, 5(08), 90–95. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/35

Copyright (c) 2025 Dilnoza Zubayd qizi Ismoilova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The advent of artificial intelligence (AI) in corporate finance and mergers & acquisitions (M&A) represents a transformative shift in the way deals are sourced, evaluated, structured, and integrated. This study synthesizes insights from recent industry analyses, academic research, and sector reports to examine how AI is reshaping the M&A lifecycle—target identification, due diligence, business valuation, deal structuring, and post-merger integration. The article explores not only the operational efficiencies and improved decision‑making capabilities unlocked by AI, but also the evolving human capital requirements, governance challenges, and risks of overreliance on algorithmic outputs. We highlight key mechanisms by which AI adds value, evaluate empirical and anecdotal evidence for improved deal performance, and identify critical challenges including data privacy, bias, transparency, workforce reskilling, and regulatory compliance. The analysis concludes with a roadmap for future research and practice, arguing that successful AI-driven M&A demands a hybrid model in which human expertise, ethical judgment, and AI-powered insight are tightly integrated.

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