Artificial Intelligence—Optical Character Recognition: Resource-Based View of Two Indonesian Companies

Advancements in Artificial Intelligence (AI), including Optical Character Recognition (OCR), have significantly transformed various industries. In developing countries like Indonesia, the relevance of AI-OCR remains debated due to low labor costs. This study aims to understand the motivations, challenges, and outcomes of AI-OCR adoption in the Procure-to-Pay (P2P) process of two Indonesian companies. Utilizing a qualitative approach grounded in Resource-Based View (RBV) theory, data were collected from 11 respondents, including management accountants, Chief Financial Officers (CFOs), IT personnel, and an AI expert provider. A semi-structured interview was conducted to gather deeper insights. Data analysis was conducted using thematic analysis to identify and interpret patterns and themes related to the adoption of AI-OCR in the P2P process. This study reveals that both companies are motivated by the overall efficiency enhancement of the P2P process despite low labor costs in Indonesia. However, the family-owned company is also driven by sustainability initiatives, aligning with RBV's emphasis on leveraging unique resources. Challenges faced by the companies differ due to business complexity, resource constraints, and resistance to change, which highlight differences in their resource capabilities. The multinational company experienced a smoother implementation of AI-OCR due to strategic alignment and active CFO involvement, illustrating RBV's principles of effective resource management. This study provides insights into motivations like sustainability and challenges like resource constraints. It addresses a gap in the literature by comparing AI adoption in resource-constrained environments, highlighting the role of strategic alignment and management support in successful technology implementation.