Corporate Governance of AI-Driven Post-Merger Decision-Making: A Framework for Board Oversight and Executive Accountability

Abstract

Artificial intelligence (AI) is increasingly being integrated into organizational decision-making and is playing a growing role in mergers and acquisitions, particularly during post-merger integration activities. While AI can enhance decision quality through advanced analytics, predictive capabilities, and data-driven insights, its expanding influence in strategic decision processes raises critical governance concerns related to accountability, transparency, explainability, oversight, and responsibility. Existing research has examined AI governance, corporate governance, and post-merger integration largely as separate domains, resulting in limited understanding of how governance mechanisms should operate when AI influences post-merger decisions. This study addresses this gap by proposing the Dual-Governance Framework for AIDriven Post-Merger Decision-Making (DGF-PMD). The framework integrates Board Oversight and Executive Accountability within a unified governance architecture supported by transparency, explainability, auditability, human oversight, and ethical and ESG alignment. The proposed framework explains how AI-supported decision intelligence can be governed while preserving human responsibility and organizational control. Furthermore, the study develops governance maturity levels and theoretically derived propositions linking governance mechanisms to postmerger outcomes. The framework contributes to AI governance, corporate governance, and M&A literature by providing an integrated foundation for responsible AI-enabled post-merger decisionmaking and future empirical investigation.