International business scholars examine how firms adapt to rapid global shifts. This paper explores the temporal dynamics of dynamic capability development in a legacy multinational enterprise (MNE) subsidiary in an emerging market responding to Generative AI—a disruption marked by unprecedented pace. Through an in-depth longitudinal case study of Shell Kenya (2022-2024), we examine how a historically stable, asset-intensive firm compresses its adaptation cycle to integrate a high-velocity, knowledge-based technology. We identify a process of “compressed adaptation,” in which the canonical stages of sensing, seizing, and transforming (Teece, 2007) are not sequential but highly iterative, concurrent, and mutually constitutive. Sensing evolves from periodic scanning to continuous, AI-augmented environmental monitoring. Seizing is parallelized through multiple, fast-moving “sprint teams” that prototype use cases in real time. Crucially, transforming—the reconfiguration of routines, structures, and skills—begins in medias res, before seizing is complete, to build the organizational capacity to absorb and scale AI initiatives. This compressed process challenges traditional, linear models of strategic renewal and highlights the acute temporal pressures on emerging-market subsidiaries of Western MNEs. We contribute to IB theory by (1) providing a temporal process model of dynamic capability development for high-velocity technologies, (2) explicating the microfoundations of pacing—the strategic orchestration of speed and sequence—in a legacy MNE context, and (3) theorizing the unique liability of legacy faced by established firms in emerging markets when confronting paradigm-shifting digital disruptions. The study reveals that competitive survival for such firms depends less on possession of cutting-edge AI assets and more on the ability to radically accelerate and re-sequence their internal learning and reconfiguration cycles to match the pace of the global technological frontier.
Wekesa et al. (Thu,) studied this question.