MERGERS AND ACQUISITIONS
It is demonstrated that M&A activities in the 21st century must leverage the capabilities of intelligent automation to reduce failure rates, ensure value creation, and build resilient cross-functional integration mechanisms.
Abstract
Mergers and Acquisitions (M&A) represent strategic corporate actions that aim to enhance competitive advantage, drive innovation, optimize operational synergies, and access new markets. In today's data-driven economy, the success or failure of M&A activities depends not just on financial metrics and managerial acumen but increasingly on the capability to harness digital tools for decision support. This study provides an exhaustive analysis of the M&A domain with a special focus on the infusion of AI technologies. It integrates traditional financial and strategic evaluation with the application of machine learning (ML), deep learning (DL), and natural language processing (NLP) to predict M&A success, identify undervalued targets, and automate due diligence. The research explores how AI-enabled models can decode vast datasets—from stock prices and balance sheets to press releases and CEO communications—to provide deeper insights into acquisition suitability and potential synergy creation. In this interdisciplinary framework, real-world M&A cases from India and abroad, such as Tata Motors–Jaguar, Amazon–Whole Foods, and Facebook–Instagram, are examined for pre-merger alignment and post-merger success rates. A software simulation dashboard developed using Python and Streamlit allows financial analysts and entrepreneurs to input company data and predict postacquisition outcomes. This report demonstrates that M&A activities in the 21st century must leverage the capabilities of intelligent automation to reduce failure rates,ensure value creation, and build resilient cross-functional integration mechanisms