Resolving AI Value Paradox: Unlocking AI's Value Through Strategic Knowledge Integration

Abstract

Artificial Intelligence (AI) has transformed industries, yet firms often struggle to extract value from it. To address this AI value paradox, we examine how cross-boundary AI knowledge-AI expertise applied within specific industries-influences a firm value. Using a longitudinal dataset of patents, academic papers, and firm data, we find that how firms integrate cross-boundary AI knowledge with complementary assets significantly impacts their value. Specifically, firms leveraging cross-boundary AI knowledge in exploration strategies are more likely to see higher market returns, while those focusing on exploitation face greater challenges. This study extends the literature on knowledge transfer and exploration-exploitation strategies in the AI era, offering insights into AI's application in the industry and providing practical strategies for firms to maximize AI's value in innovation efforts. Managerial Summary: Artificial Intelligence (AI) is transforming various industries, yet many firms struggle to create economic value from their AI investments. This research explores how companies can better integrate AI knowledge into their innovation processes to resolve this "AI value paradox." Our findings show that firms combining cross-boundary AI knowledge with external knowledge bases achieve higher market returns. Conversely, firms that rely on integrating AI knowledge with familiar internal knowledge face more challenges in deriving value from AI. For managers, this indicates that success with AI depends on how it is leveraged alongside complementary assets. This research provides practical strategies to help firms maximize AI's value and remain competitive in rapidly evolving industries.

Keywords: AI Usage, Firm Performance, Innovation Generation, Exploration and Exploitation, Value Creation, knowledge management, AI innovation, Difference-in-Difference