Artificial Intelligence in Mergers and Acquisitions: Enhancing Decision-Making, Due Diligence, and Strategic Integration in the US Capital Market

Abstract

Background: The application of artificial intelligence (AI) to mergers and acquisitions (M&A) is transforming classical workflows—from target identification and valuation to due diligence, integration planning, and post-transaction monitoring. A growing corpus of practitioner reports and academic research highlights both opportunities and risks when AI is embedded into M&A decision chains. This article synthesizes contemporary findings and provides an extended theoretical, methodological, and practical analysis of AI-enabled M&A practice within the US capital market context.

Objectives: The primary objective is to articulate how AI techniques—natural language processing, unsupervised clustering, predictive modeling, and knowledge representation—can materially enhance decision-making and efficiency across M&A stages; to examine the interplay of AI with adjacent technologies such as blockchain; to enumerate attendant risks and governance requirements; and to propose a descriptive, reproducible case-study oriented methodology for evaluating AI’s contributions to M&A outcomes.

Methods: Using a structured, case-study-oriented methodology informed by recent practitioner reports and foundational technical literature, the article constructs an analytical framework that links algorithmic functions to specific M&A tasks. The methods narrative integrates algorithmic primitives (e.g., transformer-based language models, clustering algorithms, information-theoretic measures) with qualitative process mapping and risk assessment techniques. The approach draws on previous empirical observations and practitioner accounts to model typical data flows and decision points where AI produces measurable benefits.

Results: Descriptive findings show that AI markedly reduces time and cognitive load for target screening, enhances evidence aggregation and anomaly detection during diligence, supports scenario modeling in valuation, and improves post-merger integration (PMI) monitoring through automated event detection. However, the gains are moderated by data quality constraints, interpretability deficits, regulatory uncertainty, and integration frictions between technical and domain experts.

Conclusions: AI is neither a panacea nor a mere automation tool; it is a structural change agent that requires organizational redesign, investment in data governance, and new skill profiles for analysts. To capture value, firms must codify AI’s role in decision governance, invest in explainability, and actively manage legal and ethical risk. Future research should empirically quantify effect sizes across M&A outcomes and test governance interventions in field experiments.