AI Transformation from a Network-Centric perspective: A Conceptual Research

Abstract.
The prevailing paradigm guiding artificial intelligence (AI)
transformation in firms remains fundamentally data-centric, positioning data as the primary strategic asset and emphasizing its collection, quality, and
governance. While this orientation has enabled improvements in model
performance and analytics capability, it does not adequately explain the dynamic, relational, and adaptive character of intelligent systems embedded within organizations. This conceptual work proposes a theoretical reorientation: AI transformation should be understood through a network-centric lens. Rather than treating firms and AI systems as discrete entities managing data assets, we conceptualize them as interconnected nodes within a recursive sociotechnical ecosystem. The proposed framework integrates five foundational dimensions: the human architect as originating intelligence; the operational human–AI interaction loop; the environment as co-participant through cyber-physical integration and unknown users; the material infrastructure grounding cognition; and cybersecurity as systemic connective tissue. These dimensions operate through nested learning loops unfolding across immediate, short-term, and long-term timescales. The contribution is theoretical: we reposition AI transformation from resource optimization to relational system design. Firms that succeed will be those that cultivate intelligence as an evolving relationship rather than accumulate it as an asset.

Keywords:
AI transformation, network-centric, sociotechnical systems, hybrid
intelligence, recursive learning, conceptual framework.