Background: The accelerating integration of artificial intelligence (AI) and adjacent data technologies into financial services and corporate strategy has created a transformative moment for mergers and acquisitions (M&A). Existing literature documents discrete advances—digital transformation in banking and finance, AI-enhanced financial reporting, and big data approaches to enterprise value assessment—but a comprehensive, practice-focused synthesis tailored to M&A due diligence and deal structuring remains underdeveloped (Alam, 2025; Antwi et al., 2024; Rodríguez-Mazahua et al., 2016).

Objectives: This article develops an original, publication-grade theoretical and applied framework explaining how generative and analytic AI reshape each stage of the M&A lifecycle—sourcing, valuation, due diligence, negotiation, integration—and how organizational capabilities and human capital must evolve to capture value. The study aims to bridge technical, managerial, and strategic perspectives to guide practitioners, private equity actors, and policy-oriented scholars (Ellencweig et al., 2024; Emmi, 2025).

Methods: Drawing on cross-disciplinary theory from digital transformation, finance, and organizational learning, this work synthesizes prior empirical and conceptual research to construct a narrative model of AI-enabled M&A. The methodology is text-based and integrative: comparative theoretical analysis, critical synthesis of domain literature, and scenario-driven mapping of AI tools to M&A tasks (Corea, 2017; Farboodi & Veldkamp, 2020).

Results: The framework identifies five transformative vectors: (1) Data-driven deal sourcing and screening; (2) Automated and semi-automated financial and operational due diligence; (3) AI-assisted valuation models that augment rather than replace human judgement; (4) Contract and legal automation to accelerate negotiation and risk identification; and (5) Post-merger integration (PMI) intelligence systems that operationalize value capture. Each vector presents unique capability requirements, governance demands, and biases/risks which the framework disaggregates and remediates with proposed organizational and technical controls (Betts & Jaep, 2017; Antwi et al., 2024; Shounik, 2025).

Conclusions: AI fundamentally recalibrates resource allocation, timing, and expertise in M&A. Successful adoption requires firms to invest concurrently in data architecture, continuous human learning, specialized AI governance, and hybrid teams that combine domain and data-science skills. The article concludes with a practical roadmap for private equity firms and corporate acquirers and outlines future research avenues for empirical validation and regulatory design (Baskin, 2023; Brown et al., 2019; Chowdhury et al., 2024).