The framework identifies three key ways in which AI impacts open innovation: by enhancing existing practices through greater efficiency and scalability, by enabling new forms of collaboration and business models, and by replacing or reshaping traditional open innovation methods with autonomous, AI-driven processes.  

AI enhances existing practices: 

The study, published in a special issue celebrating 20 years of open innovation, was authored by Marcus Holgersson (Chalmers University of Technology), Linus Dahlander (ESMT Berlin), Henry Chesbrough (University of California, Berkeley), and Marcel Bogers (Eindhoven University of Technology). It reveals that AI significantly enhances open innovation by optimizing traditional methods. AI-driven tools such as natural language processing and predictive analytics can elevate practices like external knowledge searches, idea evaluation, and partner identification. These technologies can also streamline critical processes, including idea generation and feasibility assessments, making them faster, more accurate, and highly scalable. 

AI enables new models: 

AI is fostering the emergence of innovative business models and markets, paving the way for unprecedented levels of collaboration and decentralization. A notable example is federated learning, which facilitates collaborative innovation across organizations while ensuring data privacy. By enabling entities to work together without sharing sensitive data, federated learning exemplifies how AI is transforming traditional boundaries of cooperation and driving new opportunities for secure, collective advancement. 

AI reshapes or even replaces traditional methods:

AI is reshaping—and in some cases entirely replacing—traditional open innovation practices. Automated ideation and synthetic data generation minimize reliance on collaborative human inputs, enabling efficient and highly scalable innovation processes. However, these advancements also prompt critical questions about the evolving role of human creativity and collaboration in an AI-driven innovation landscape. 

“AI offers significant opportunities to advance open innovation, but it also introduces complex challenges,” said Linus Dahlander. “To maximize its potential, we must strike a balance between AI-driven efficiency and human creativity, while addressing critical issues such as ethical concerns, intellectual property disputes, and the possible erosion of traditional collaborative practices.”