Generative Artificial Intelligence in Innovation Management: A Preview of Future Research

Generative Artificial Intelligence (GenAI) is quickly becoming a transformative force in the world of innovation management. In their 2024 paper, "Generative Artificial Intelligence in Innovation Management: A Preview of Future Research Developments," Mariani and Dwivedi explore the impact of GenAI on innovation processes and its potential to shape future research and practice. Through a Delphi study involving leading scholars, they identified ten major themes that are expected to guide future research in this rapidly evolving field.

GenAI and Types of Innovation

One of the foundational insights of the paper is that GenAI will support all existing types of innovation—product, process, organizational, and marketing—rather than creating entirely new categories. Mariani and Dwivedi introduce terms like GenAI-enabled Product Innovation (GenAIProI) and GenAI-enabled Process Innovation (GenAIProcI) to describe the way GenAI can enhance these areas. They stress that GenAI's most prominent impact has been in product innovation, particularly in the content it generates for end-users.

“GenAI has mainly been associated with product innovation: namely, the new content that it can generate for end-users (consumers and managers).”

Dominant Designs and Technology Evolution

A dominant design for GenAI has not yet emerged, leaving space for both uncertainty and opportunity. The authors suggest that existing frameworks on technology evolution can be applied to understand how GenAI might develop over time. Data availability and control will play a critical role in determining which organizations gain a competitive edge.

“Users may flock to the technology if it shifts the power to innovate away from highly skilled technicians to less skilled individuals.”

Scientific and Artistic Creativity

GenAI offers the potential to enhance both scientific and artistic creativity. The paper highlights the need for further research into how GenAI creativity differs from human creativity, and what the implications of this difference might be for innovation. The authors also underscore the importance of how audiences receive GenAI-enabled innovations.

“GenAI might expand the set of suitable inquiries and alter how scientific and technical communities shape their research questions.”

Intellectual Property (IP)

GenAI poses significant challenges to traditional notions of intellectual property (IP), particularly around the ownership and copyright of generated content. As companies leverage proprietary datasets for innovation, new legal frameworks will be essential to address IP issues related to GenAI training data and outputs.

“GenAI will undermine our current conceptualizations of intellectual property (IP) in innovation management.”

New Product Development (NPD)

GenAI has the potential to transform New Product Development (NPD) by speeding up development cycles, improving product-market fit, and reducing costs. The integration of GenAI with existing NPD tools and the role of human-machine hybrid teams in this process will be a key area for future exploration.

“GenAI has the potential to talk to a broader array of customers, and potentially distinguish between more advanced and novice users.”

Multimodal vs. Unimodal GenAI

Multimodal GenAI systems, which process various data types simultaneously, are expected to drive greater innovation outcomes than unimodal systems. Future research should focus on the specific benefits of multimodal systems, particularly in terms of personalization and enhanced user experience.

Agency and Innovation Ecosystems

As GenAI becomes more integrated into innovation processes, new innovation ecosystems involving distributed agency among humans and machines will emerge. Theoretical constructs are needed to better understand the dynamics of these complex ecosystems and how they foster innovation.

Regulation and Policy

Policymakers face a critical challenge in regulating GenAI-enabled innovation, particularly regarding IP, bias, and ethical concerns. The EU’s AI Act provides a potential framework, but further regulation is necessary to address the growing influence of companies with proprietary GenAI capabilities.

“Policymakers and lawmakers will need novel frameworks to regulate GenAI-enabled innovation.”

Misuse and Ethical Considerations

The potential for GenAI misuse, such as the creation of deepfakes or biased outputs, raises significant ethical challenges. Researchers need to explore ways to mitigate these risks and promote the responsible development of GenAI.

“Apart from deepfakes, the content generated by GenAI is often of debatable quality.”

Organizational Design and Boundaries

GenAI is likely to reshape organizational design, affecting job roles, skills, and workflows. Organizations will need to adapt to these changes, and future research should investigate how they can leverage GenAI to gain a competitive advantage.

Conclusion

The integration of GenAI into innovation management presents both tremendous opportunities and complex challenges. Mariani and Dwivedi’s study outlines critical areas where research is needed to better understand GenAI’s potential, from enhancing creativity to redefining intellectual property. As GenAI continues to evolve, it will be crucial to balance its innovative capabilities with the ethical and regulatory frameworks required to ensure responsible use.

By exploring these ten key themes, this article provides a roadmap for future research, offering insight into how GenAI could transform innovation management and redefine the boundaries of creativity, collaboration, and competitiveness in the coming years.