Optimizing the mergers and acquisitions process through artificial intelligence: from target screening to post-merger integration

Mergers and acquisitions (M&A) are key strategies for corporate growth, technological progress, and market repositioning, but suffer high failure rates due to flaws in traditional methods. This thesis examines artificial intelligence (AI) as a solution, focusing on the question: Could an AI pipeline, using news sentiment analysis and machine learning, replicate Stellantis' acquisition of aiMotive by screening pre-deal data? Chapter 1 details M&A drivers, such as economies of scale, capability acquisition, synergies, and defensive positioning, and the lifecycle, noting issues like information gaps and integration challenges. Chapter 2 develops a framework for AI's role, from traditional to generative models, with vertical (domain-specific) and horizontal (cross-process) applications. It covers target screening via NLP-driven discovery, due diligence in financial, legal, operational, cultural, tax, and ESG areas, improved valuation (e.g., enhanced DCF and relative methods), negotiation aids like counterparty modeling, and post-merger integration through sentiment analysis and optimization. Chapter 3 validates empirically with an AI pipeline in the autonomous driving sector, sourcing data from Media Cloud and Orbis, using BERTopic filtering, spaCy extraction, financial enhancements, and CatBoost inference. Results confirm the pipeline prioritized aiMotive, leveraging sentiment to highlight technological synergies missed in financial evaluations. Chapter 4 synthesizes findings, emphasizing AI's potential for faster, unbiased M&A in innovative sectors, theoretical AI-M&A integration, and practical benefits. Despite limitations like data biases, future avenues include multi-domain extensions, automated tools, and hybrid systems for better strategic results.