JETIM Cover Page

Journal of Emerging Technologies and Innovation Management

eISSN : 3107-8001 | Frequency: Half-yearly

Editor in Chief : Dr. Vikram Kumar Sharma

About : The Journal of Emerging Technologies and Innovation Management is a bi-annual, peer-reviewed, national, e-journal, committed to advancing the understanding and integration of cutting-edge technological advancements into modern management practices. As an interdisciplinary, peer-reviewed journal, it Read more

Article Metrics
Views : 68 | Downloads: 100
Crossref Citations: Loading...

View in Google Scholar

Dimensions
Altmetric Attention
PlumX:

Innovation Management in the Age of Generative AI: Strategic Challenges and Opportunities for Business Leaders

  • Rhythm Mittal Rhythm Mittal Amity Business School, Amity University, Noida, UP-201303, India India Rhythm Mittal ORCID Id ,  
  • Kunal Saxena* Kunal Saxena Corresponding author Amity Business School, Amity University, Noida, UP-201303, India India Kunal Saxena ORCID Id
Received: November 20, 2025
Accepted: December 09, 2025
Published: December 10, 2025
Volume: 1 (2) | Page: 50-56

Abstract

Generative artificial intelligence (GenAI) is a transformative shift in how organizations can navigate innovation processes and stay competitive in dynamic markets. This paper explores the synergy between innovation management theory and the capabilities of generative AI, offering a holistic perspective of its strategic challenges and opportunities to business leaders. Based on the concepts of dynamic capabilities theory, open innovation and recent empirical studies on the adoption of GenAI, we propose an integrated theoretical framework that conceptualizes GenAI as an enabler and disruptor of the existing innovation processes. Three specific areas of strategic challenges emerge from our analysis: organizational and cultural barriers, such as adaptation and resistance to change by the workforce; technical and governance issues, such as data quality and risk of AI hallucination; and ethical and regulatory issues, such as IP and algorithmic bias. At the same time, we pose three key strategic questions: how can innovation cycles be accelerated with automated ideation and prototyping? How can innovation be made accessible for everyone, by democratizing the process? And how can new business models emerge with content generated by AI? Finally, the paper offers practical managerial suggestions and a research agenda for scholars. The present work is a theoretically informed, but practically relevant, study at the intersection of artificial intelligence and strategic management, providing invaluable guidance for the innovation landscape in the era of GenAI.

Keywords: sap (socio-academic practitioner) programs on generative artificial intelligence, innovation management, dynamic capabilities, strategic leadership, digital transformation

References

  1. Autor, D. (2022). The labor market impacts of technological change. Journal of Economic Perspectives, 36(3), 121-148.
  2. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
  3. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots. Proceedings of FAccT, 610-623.
  4. Bommasani, R., et al. (2022). On the opportunities and risks of foundation models. arXiv:2108.07258.
  5. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84-92.
  6. Brynjolfsson, E., & McAfee, A. (2014). The second machine age. W. W. Norton.
  7. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work. NBER Working Paper No. 31161.
  8. Chesbrough, H. W. (2003). Open innovation. Harvard Business School Press.
  9. Chesbrough, H. (2023). Understanding generative AI's strategic implications. Rotman Management Magazine, Winter, 32-37.
  10. Christensen, C. M. (1997). The innovator's dilemma. Harvard Business School Press.
  11. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity. Administrative Science Quarterly, 35(1), 128-152.
  12. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  13. Davenport, T. H., et al. (2020). How AI will change marketing. J. of the Academy of Marketing Science, 48(1), 24-42.
  14. Dell'Acqua, F., et al. (2023). Navigating the jagged technological frontier. HBS Working Paper No. 24-013.
  15. Fui-Hoon Nah, F., et al. (2023). Generative AI and ChatGPT applications and challenges. JITCAR, 25(3), 277-304.
  16. Girotra, K., et al. (2023). Ideas are dime a dozen: LLMs for idea generation. SSRN.
  17. Goldman Sachs. (2023). Generative AI could raise global GDP by 7%. GS Economics Research.
  18. Henderson, P., et al. (2023). Foundation models and fair use. arXiv:2303.15715.
  19. Ji, Z., et al. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38.
  20. Kotter, J. P. (1996). Leading change. Harvard Business School Press.
  21. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607
  22. Mollick, E., & Mollick, L. (2022). New modes of learning enabled by AI chatbots: Three methods and assignments. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4300783
  23. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
  24. Rigby, D. K., Sutherland, J., & Takeuchi, H. (2016). Embracing agile. Harvard Business Review, 94(5), 40–50.
  25. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
  26. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. Harper & Brothers.
  27. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
  28. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640
  29. Teece, D. J. (2023). Dynamic capabilities and strategic management: Organizing for innovation and growth (2nd ed.). Oxford University Press.
  30. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
  31. Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
  32. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30, pp. 5998–6008).
  33. Veale, M., & Zuiderveen Borgesius, F. J. (2021). Demystifying the draft EU Artificial Intelligence Act. Computer Law Review International, 22(4), 97–112. https://doi.org/10.9785/cri-2021-220402
  34. von Hippel, E. (2005). Democratizing innovation. MIT Press.
Recently Cited By
Loading citations...
Citation copied to clipboard
×
Cite this Article