Artificial Intelligence Strategic Lifecycle: A Literature Review-Based Framework

Abstract

Purpose.  This systematic literature review examines how firms utilise artificial intelligence (AI) as a strategic rather than purely operational resource and develops an integrative conceptual framework of the AI strategic lifecycle.  Design/methodology/approach.  A PRISMA‑guided search identified 147 peer‑reviewed articles published between 2020 and 2025 across major scholarly databases, including Elsevier (Scopus), Emerald, Springer, and Wiley. The evidence is synthesised through five dominant theoretical lenses: dynamic capabilities, resourcebased view (RBV), knowledgebased view (KBV), technology acceptance model (TAM), and disruptive innovation theory (DIT).  Findings.  Dynamic capabilities and RBV explain how organisations mobilise data, algorithms, and AI‑related human capital to build and sustain competitive advantage in sectors such as public administration, energy, human resource management (HRM), and researchintensive industries. KBV highlights the role of absorptive capacity and knowledgesharing routines in transforming AI outputs into innovation, particularly in user‑facing contexts such as healthcare and hospitality. In these sectors, TAM is central, emphasising trust, ease of use, and perceived usefulness as key drivers of adoption. In finance, DIT elucidates competitive disruption and incumbent response strategies triggered by AI‑enabled entrants.  Practical implications.  The review provides recommendations for practitioners, including investing in organisational learning and absorptive capacity and ensuring transparency of AI‑enabled interfaces to translate AI investments into sustainable performance gains.  Originality/value. By integrating five theoretical perspectives, the review develops a model of the AI strategic lifecycle, offering both a consolidated foundation for future research and a forwardlooking agenda for managers seeking to leverage AI as a strategic asset.