AI ENABLED BUSINESS TRANSFORMATION IN INCUMBENT FIRMS: FROM OPERATIONAL ADOPTION TO STRATEGIC BUSINESS MODEL RENEWAL

Arjun Chaturvedi

doi:10.67809/ijetrm.1789742441

Abstract

Artificial intelligence (AI) is increasingly deployed by incumbent firms to automate processes, optimize
operations, and enhance decision-making. Yet, evidence suggests that operational AI adoption rarely translates
on its own into lasting competitive advantage. This article examines how established businesses move beyond
narrow, efficiency-oriented AI use toward strategic business model renewal. Drawing on dynamic capabilities
theory and business model innovation theory, the article develops a conceptual framework linking AI adoption,
organizational capabilities, and business model transformation to new mechanisms of value creation and value
capture. The review synthesizes recent literature on AI-enabled business model innovation in industrial
ecosystems, highlighting the organizational, technological, and strategic conditions under which incumbents
convert AI investment into renewal rather than incremental efficiency gains. The discussion identifies persistent
barriers organizational inertia, legacy infrastructure, capability gaps, and strategic misalignment that constrain
transformation, and it offers managerial implications for firms seeking to align AI initiatives with long-term
strategic objectives. The article contributes a synthesis that connects operational and strategic perspectives on AI
in incumbent firms and proposes directions for future empirical research.

Keywords

Artificial intelligence, Business model innovation, Dynamic capabilities, Digital transformation, Incumbent firms,

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