Building Regional AI Ecosystem Capability: Introducing the Dynamic Rural Artificial Intelligence Ecosystem Framework (D-RAIEF)

Building Regional AI Ecosystem Capability: Introducing the Dynamic Rural Artificial Intelligence Ecosystem Framework (D-RAIEF)

Markus A. Launer, CoSiM Research Institute by Institut für gemeinsame Dienstleistungen gGmbH und Ostfalia University for Applied Sciences, https://orcid.org/0000-0001-9384-0807,  https://www.researchgate.net/profile/Markus-Launer

This article is simultaneously published in the CoSiM Journal Vol. 6, ISSN 2943-9019, 143 pages, language English, and archived in Zenodo for open access, long-term preservation, and scholarly discoverability. © 2026 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Published July 14, 2026.

https://institutfuerdienstleistungen.com/en/cosim-journal-no-6/ 

DOI: 10.5281/zenodo.21442041

Abstract

Artificial intelligence (AI) is increasingly recognised as a fundamental driver of regional innovation and economic transformation. Existing research, however, predominantly examines AI adoption at the organisational level or explains regional development through largely disconnected perspectives such as regional innovation systems, innovation ecosystems, AI readiness, digital transformation, dynamic capabilities and AI governance. Consequently, little is known about how regions collectively develop, coordinate, govern and continuously renew AI capabilities under rapidly evolving technological, institutional and regulatory conditions. This limitation is particularly pronounced in rural regions, where fragmented organisational structures, resource constraints and dependence on external AI infrastructures require ecosystem-level rather than organisation-level explanations.

To address this gap, this paper develops Regional AI Ecosystem Capability as a new ecosystem-level theoretical construct and conceptualises it through the Dynamic Rural Artificial Intelligence Ecosystem Framework (D-RAIEF). Building on an integrative review of research on regional innovation systems, innovation ecosystems, AI readiness, digital transformation, dynamic capabilities, collaborative governance and AI governance, the framework conceptualises regional AI development as a multi-level socio-technical dynamic capability emerging from the coordinated interaction of organisations, shared infrastructures, governance arrangements, external ecosystem integration and continuous learning and adaptation.

The framework consists of six interdependent dimensions: Regional Ecosystem Analysis, Organisational AI Transformation, Shared AI Infrastructure and Data Architecture, External Ecosystem Integration, Regional AI Governance, and Dynamic AI Ecosystem Management. Together, these dimensions explain how Regional AI Ecosystem Capability emerges, develops and continuously adapts over time rather than representing a static level of AI maturity.

This study makes three theoretical contributions. First, it introduces Regional AI Ecosystem Capability as a distinct ecosystem-level construct that extends existing research on organisational AI capability and AI readiness. Second, it integrates previously fragmented research on regional innovation, digital transformation, AI governance and ecosystem theory into a coherent conceptual explanation of regional AI ecosystem development. Third, it reconceptualises AI transformation as a continuous, multi-level process of socio-technical ecosystem adaptation rather than organisational technology adoption. The paper further develops propositions, antecedents, boundary conditions and consequences to establish a foundation for cumulative empirical research on regional AI ecosystems.

By shifting the analytical focus from organisational AI adoption to Regional AI Ecosystem Capability, this study provides a new theoretical lens for analysing, designing, governing and continuously adapting rural AI ecosystems under conditions of accelerating technological and institutional change.

Keywords: Artificial Intelligence; Regional AI Ecosystem Capability; Rural AI Ecosystems; Innovation Ecosystems; Dynamic Capabilities; AI Governance; Regional Innovation Systems; D-RAIEF.