Challenges in Adoption of AI-Driven Collaborative Innovation

Abstract

The implementation of artificial intelligence (AI) within collaborative innovation processes drives fundamental transformations in how organizations develop, generate, and implement new ideas. AI technologies enable the automation of complex tasks, real-time analysis of vast datasets, and uncover new insights that elevate innovation speed and gain market advantages across different industries. However, while the potential of AI-based collaborative innovation is immense, its adoption presents significant challenges that organizations must strategically address. In doing so, this study investigates these challenges associated with the adoption of AI-based collaborative innovation by applying the Analytical Hierarchy Process (AHP), a multi-criteria decision-making (MCDM) method, based on the opinions of 29 experts from Asia, Europe, and North America. The findings reveal that ethical concerns (EC) should be the top priority, followed by Organizational Change (OC) as the second priority, Technical Complexity (TC) as the third priority, and Legal & Regulatory (LR) issues as the fourth priority. These areas represent the primary obstacles to the successful implementation of AI in collaborative innovation. This research study provides insights for future research work by examining AI adoption throughout multiple organizational settings to develop better theoretical understandings related to technological innovation, organizational change, and strategic management in the digital age.