Intelligent Enterprises and Strategic Management: A Conceptual Framework for Artificial Intelligence, Automation, and Data-Driven Organisational Decision-Making

Abstract

Purpose: This paper proposes the Intelligent Enterprise Strategic Management (IESM) framework — a conceptual model that addresses the absence of an integrated, theoretically grounded account of how artificial intelligence (AI), robotic process automation, and advanced analytics jointly transform organisational decision-making architectures and competitive advantage dynamics. Methodology/Approach: A structured conceptual research design is employed, synthesising dynamic capability theory (DCT), upper echelon theory (UET), and information-processing theory (IPT) through systematic literature identification and deductive proposition construction, consistent with MacInnis (2011) and Jaakkola (2020). Key Findings: The IESM framework delineates four interacting constructs — AI-Enabled Sensing Capabilities (AESC), Algorithmic Decision Augmentation (ADA), Automation-Induced Organisational Reconfiguration (AIOR), and Adaptive Governance Mechanisms (AGM) — and derives seven theoretically grounded, falsifiable propositions mapping the relationships among them. The framework extends DCT to AI-intensive environments, advances UET to accommodate algorithmic co-decision, and positions governance as an endogenous strategic capability rather than a compliance cost. Practical Implications: Organisational leaders can use the IESM framework as a diagnostic instrument to assess AI-integration maturity across four dimensions and to guide proportionate investment. Policymakers are directed toward governance-incentive frameworks that complement technical compliance standards with capability-building requirements.