Purpose: As artificial intelligence (AI) fundamentally reshapes the competitive landscape, traditional strategic frameworks—specifically the Resource-Based View (RBV) and standard Dynamic Capabilities—require recalibration. This paper investigates the emergence of "Algorithmic Dynamic Capabilities," defined as the firm's capacity to utilize machine learning algorithms to autonomously sense environmental changes, seize market opportunities, and reconfigure resources.

Design/Methodology/Approach: Through an integrative theoretical review of strategic management literature (spanning 1984–2021), this study synthesizes concepts from ecosystem strategy, dynamic managerial capabilities, and AI governance. We analyze the intersection of human managerial cognition and algorithmic processing to propose a new microfoundational framework.

Findings: The analysis suggests that sustainable competitive advantage in the digital age is no longer solely dependent on possessing rare, valuable resources (VRIN), but on the architecture of the firm's data ecosystems. We identify a shift from "deliberate strategy" to "emergent algorithmic strategy," where AI enables real-time adaptation that exceeds human cognitive limits. However, we also find that the "Automation-Augmentation Paradox" necessitates a continued, elevated role for human strategic oversight in handling ambiguity and ethical governance.

Originality/Value: This research contributes to the strategic management field by extending the Dynamic Capabilities framework into the domain of AI. It provides a novel typology for understanding how firms can orchestrate complex ecosystems without centralized hierarchical control, offering a roadmap for leaders navigating the post-pandemic digital economy.