Micro-Foundations of Dynamic Capabilities for Generative AI Integration
  • Author(s): Wekesa, Evans Malava; Mwende, Victoria Stephen
  • Paper ID: 1714608
  • Page: 1785-1790
  • Published Date: 27-02-2026
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 9 Issue 8 February-2026
Abstract

This study provides a rare, detailed empirical account of the specific routines, roles, and processes that constitute the micro-foundations of dynamic capabilities for Generative AI in an African legacy firm. Through an immersive 14-month ethnography of a Kenyan financial services company, we go beyond theoretical concepts to document the actual organizational structure of technological adaptation. Our findings identify and define three key organizational routines that form the core of the firm's dynamic capabilities: (1) the "AI Opportunity Radar," a structured sensing system for systematic environmental scanning; (2) the "Proof-of-Concept Sprint," a disciplined process for rapidly validating opportunities; and (3) the "AI Integration Squad," a transformative approach for continuous capability embedding. We contribute to Africa-focused management research by offering unprecedented empirical detail on how African firms organize internally to seize technological opportunities, providing both a theoretical advancement in understanding the micro-foundations of dynamic capabilities and a practical guide for legacy firms navigating digital transformation on the continent.

Keywords

Dynamic Capabilities, Micro-Foundations, Generative AI, Organizational Routines, Legacy Firms, Africa, Kenya, Digital Transformation

Citations

IRE Journals:
Wekesa, Evans Malava, Mwende, Victoria Stephen "Micro-Foundations of Dynamic Capabilities for Generative AI Integration" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 1785-1790 https://doi.org/10.64388/IREV9I8-1714608

IEEE:
Wekesa, Evans Malava, Mwende, Victoria Stephen "Micro-Foundations of Dynamic Capabilities for Generative AI Integration" Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714608