AI-Driven Data Integration for Mergers and Acquisitions: Automating Entity Resolution and System Consolidation
DOI:
https://doi.org/10.15662/cq82ha45Keywords:
AI-Driven Data Integration, Mergers and Acquisitions (M&A), Entity Resolution, System Consolidation, Machine Learning, Data Matching and Deduplication, Data Integration Architecture, Schema Mapping Automation, Natural Language Processing (NLP ), Graph Data Models, Data Lakes, Enterprise Data Management, Intelligent Automation, Digital TransformationAbstract
Mergers and acquisitions (M&A) are critical strategic initiatives that enable organizations to expand market presence, achieve operational synergies, and enhance competitive advantage. However, one of the most complex and time-consuming challenges in M&A execution is the integration of heterogeneous data systems across merging entities. Traditional data integration approaches often rely on manual processes, rule-based matching, and rigid transformation pipelines, which are insufficient to handle the scale, diversity, and ambiguity of modern enterprise data landscapes
This article explores the role of Artificial Intelligence (AI) in transforming data integration during M&A, with a specific focus on automating entity resolution and system consolidation. AI-driven techniques such as machine learning-based record linkage, natural language processing (NLP), and graph-based data modeling enable organizations to accurately identify, match, and unify entities across disparate systems. These approaches significantly reduce data duplication, improve data quality, and accelerate integration timelines
Furthermore, the paper examines architectural strategies for implementing AI-driven data integration, including the use of data lakes, integration platforms, and metadata-driven pipelines. It highlights how intelligent automation can streamline schema mapping, data transformation, and system harmonization across legacy and modern platforms. Real-world scenarios and conceptual models are discussed to demonstrate how enterprises can achieve scalable, resilient, and efficient integration outcomes
The study concludes that AI-driven data integration is not merely an enhancement but a necessity for successful M&A execution in the digital era. Organizations that adopt intelligent integration frameworks can significantly reduce risk, lower operational costs, and unlock faster time-to-value from their mergers and acquisitions
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