Examining Heterogeneous Patterns of AI Capabilities.

This study explores the heterogeneous patterns of companies in terms of their AI capabilities by analyzing various combinations of AI-specific resources. Drawing on the resource-based theory of the firm, we develop an analytical framework comprising two key dimensions: AI infrastructure and AI competencies, and employ two scores to quantify these dimensions. We apply this approach to a dataset of 215 companies and categorize them into four distinct groups: beginners, followers with strong AI-infrastructure, followers with strong AI-specific human resource, and leaders in terms of AI capabilities. Our analysis provides insights into the companies’ sectoral affiliation, size classes, fields of usage of AI, and make or buy decisions regarding the uptake of AI solutions. Our findings suggest that the manufacturing and construction industry had the highest proportion of beginner companies with low AI capabilities, while the services and IT industry had the largest share of leader companies with strong AI capabilities. The study also shows that companies with different levels of AI capabilities have distinct motives for adopting AI technologies, and leading companies are more likely to use AI for product innovation purposes. Overall, the study provides a comprehensive analysis of the various AI-specific resources that contribute to a company’s AI capabilities and sheds more light on configurations of AI-specific resources. Our analytical framework can help organizations better understand their AI capabilities and identify areas for improvement.