Year
2025
Abstract
This study investigates how Artificial Intelligence (AI) serves as a catalyst for managerial strategy transformation, focusing on two critical areas: operational efficiency and business model innovation. Drawing on multiple case studies, the paper analyzes how companies such as Unilever, Wayfair, Amazon, and Alibaba integrate AI technologies into their strategic processes. Results show that AI implementation has enabled cost reductions of up to 15-20%, improved throughput in logistics operations, and enhanced personalization strategies that significantly impact revenue. For instance, Alibaba attributes approximately 35% of its sales volume to AI-powered recommendation systems. These outcomes demonstrate that AI not only improves operational workflows but also redefines how firms deliver value, develop capabilities, and respond to dynamic market needs. Nevertheless, challenges persist, including misalignment between AI initiatives and strategic objectives, talent gaps, and fragmented data infrastructures. This paper concludes that organizations must reframe AI adoption as a long-term strategic investment rather than a short-term technological upgrade. It offers practical insights for decision-makers seeking to align AI capabilities with corporate goals while navigating the structural and cultural shifts required for successful implementation.
Keywords:
Artificial Intelligence; Managerial Strategy; Operational Efficiency; Business Model Innovation; Strategic Transformation.
Recommended Citation
Hidayah, Nazilatul, "AI as a Catalyst for Managerial Strategy Transformation: Operational Efficiency and Business Model Innovation" (2026). International Conference on Business and Management Research (ICBMR). 27.
https://scholarhub.ui.ac.id/icbmr/2025/1/27
AI as a Catalyst for Managerial Strategy Transformation: Operational Efficiency and Business Model Innovation
This study investigates how Artificial Intelligence (AI) serves as a catalyst for managerial strategy transformation, focusing on two critical areas: operational efficiency and business model innovation. Drawing on multiple case studies, the paper analyzes how companies such as Unilever, Wayfair, Amazon, and Alibaba integrate AI technologies into their strategic processes. Results show that AI implementation has enabled cost reductions of up to 15-20%, improved throughput in logistics operations, and enhanced personalization strategies that significantly impact revenue. For instance, Alibaba attributes approximately 35% of its sales volume to AI-powered recommendation systems. These outcomes demonstrate that AI not only improves operational workflows but also redefines how firms deliver value, develop capabilities, and respond to dynamic market needs. Nevertheless, challenges persist, including misalignment between AI initiatives and strategic objectives, talent gaps, and fragmented data infrastructures. This paper concludes that organizations must reframe AI adoption as a long-term strategic investment rather than a short-term technological upgrade. It offers practical insights for decision-makers seeking to align AI capabilities with corporate goals while navigating the structural and cultural shifts required for successful implementation.