From Prediction to Generation: A Literature Review on the Role of Generative AI in Enhancing Organizational Problem-Solving Dynamics
DOI:
https://doi.org/10.70142/ijbmel.v2i1.445Keywords:
Generative Artificial Intelligence, Organizational Problem-Solving, Hybrid Intelligence, Strategic Decision-Making, Cognitive AugmentationAbstract
This qualitative literature review explores the transformative role of generative artificial intelligence (GenAI) in reshaping organizational problem-solving. Moving beyond prediction, GenAI supports ideation, design, and decision-making by enhancing exploration, reducing cognitive constraints, and enabling hybrid human-machine intelligence. Drawing on recent studies in strategic management, organizational learning, and AI innovation, this review synthesizes evidence of GenAI’s capacity to augment creativity, frame redefinition, and solution diversity. The findings highlight both opportunities—such as improved search efficiency and strategic adaptability—and challenges, including algorithmic opacity, trust issues, and socio-technical complexity. Ultimately, GenAI represents a generative shift in how organizations define problems and pursue innovation, requiring thoughtful integration to maximize its cognitive and strategic value
References
Afuah, A., & Tucci, C. L. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37(3), 355–375.
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston, MA: Harvard Business School Press.
Allen, R., & Choudhury, P. (2022). Algorithm-augmented work and domain expertise: The countervailing forces of ability and aversion. Organization Science, 33(1), 149–169.
Amabile, T. (2020). Creativity, artificial intelligence, and a world of surprises. Academy of Management Discoveries, 6(3), 351–354.
Anantrasirichai, N., & Bull, D. (2021). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55, 1–68.
Anthony, C. (2021). When knowledge work and analytical technologies collide. Administrative Science Quarterly, 66(4), 1173–1212.
Arthur, R. (2018). Artificial Intelligence empowers designers in IBM, Tommy Hilfiger, and FIT collaboration. Forbes.
Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26(5), 655–672.
Balasubramanian, N., Ye, Y., & Xu, M. (2022). Substituting human decision-making with machine learning: Implications for organizational learning. Academy of Management Review, 47(3), 448–465.
Baumann, O., Schmidt, J., & Stieglitz, N. (2019). Effective search in rugged performance landscapes: A review and outlook. Journal of Management, 45(1), 285–318.
Billinger, S., Benincasa, S., Baumann, O., Kretschmer, T., & Schumacher, T. R. (2023). Learning to search collaboratively: How dyads overcome complexity and misaligned incentives in imperfect modular decompositions. Industrial and Corporate Change, 32(1), 208–233.
Burrell, J. (2016). How the machine “thinks”: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.
Chow, A. R. (2020). There’s a wide-open horizon of possibility: Musicians are using AI to create otherwise impossible new songs. Time Magazine.
Cortex. (2022). Visit Utah discovers what content skiers love and boosts engagements by 23%. Retrieved from https://www.meetcortex.com/visit-utah-case-study
Csaszar, F. A., & Levinthal, D. A. (2016). Mental representation and the discovery of new strategies. Strategic Management Journal, 37(10), 2031–2049.
Csaszar, F. A., & Ostler, J. (2020). A contingency theory of representational complexity. Organization Science, 31(5), 1198–1219.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126.
Eka Wahyu Kasih, Ngadi Permana, & Mohammad Chaidir. (2024). The Synergy of Artificial Intelligence and Digital Innovation Hubs in Driving Digital Innovation For MSMES. Indonesian Economic Review, 4(1), 14-28.
https://doi.org/10.53787/iconev.v4i1.37
Elgammal, A. (2019). AI is blurring the definition of artist. American Scientist.
Ethiraj, S. K., & Levinthal, D. (2009). Hoping for A to Z while rewarding only A: Complex organizations and multiple goals. Organization Science, 20(1), 4–21.
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.
Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.
Gavetti, G., Greve, H. R., Levinthal, D. A., & Ocasio, W. (2012). The behavioral theory of the firm: Assessment and prospects. Academy of Management Annals, 6(1), 1–40.
Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago, IL: Aldine.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press.
Grace Yulianti, Benardi Benardi, & Seger Santoso. (2024). Tantangan dan Peluang Integrasi Kecerdasan Buatan Generatif dalam Praktik Manajemen SDM. Jurnal Visi Manajemen, 10(1), 28–41. https://doi.org/10.56910/jvm.v10i1.522
Hale, C. (2021a). Insilico Medicine begins first human trial of its AI-designed drug for pulmonary fibrosis. Fierce Biotech.
Hao, K. (2023). What is ChatGPT? What to know about the AI chatbot. Wall Street Journal.
Jeppesen, L. B., & Lakhani, K. R. (2010). Marginality and problem-solving effectiveness in broadcast search. Organization Science, 21(5), 1016–1033.
Jumper, J. et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
Kaplan, S. (2011). Research in cognition and strategy: Reflections on two decades of progress. Journal of Management Studies, 48(3), 665–695.
Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(5), 1425–1452.
Langley, A. (1999). Strategies for theorizing from process data. Academy of Management Review, 24(4), 691–710.
Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022). To engage or not to engage with AI for critical judgments: How professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33(1), 126–148.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), 1–45.
Lindebaum, D., Vesa, M., & den Hond, F. (2020). Rational assumptions in algorithmic decision making and implications for organizations. Academy of Management Review, 45(2), 247–263.
Metcalf, L., Askay, D. A., & Rosenberg, L. B. (2019). Keeping humans in the loop: Pooling knowledge through artificial swarm intelligence to improve business decision making. California Management Review, 61(4), 84–109.
Murray, A., Rhymer, J., & Sirmon, D. G. (2021). Humans and technology: Forms of conjoint agency in organizations. Academy of Management Review, 46(3), 552–571.
Ngadi Permana, Ruslaini Ruslaini, & Muhammad Rizal. (2024). Transformasi Organisasi untuk Meningkatkan Kreativitas Produk dan Inovasi Layanan. Jurnal Visi Manajemen, 10(3), 213–230. https://doi.org/10.56910/jvm.v10i3.525
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13.
Oh, S., Jung, Y., Kim, S., Lee, I., & Kang, N. (2019). Deep generative design: Integration of topology optimization and generative models. Journal of Mechanical Design, 141(11), 1–13.
Piezunka, H., & Dahlander, L. (2015). Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Academy of Management Journal, 58(3), 856–880.
Posen, H. E., Keil, T., Kim, S., & Meissner, F. D. (2018). Renewing research on problemistic search—A review and research agenda. Academy of Management Annals, 12(1), 208–251.
Puranam, P. (2021). Human-AI collaborative decision-making as an organization design problem. Journal of Organization Design, 10(1), 75–80.
Puranam, P., Stieglitz, N., Osman, M., & Pillutla, M. M. (2015). Modelling bounded rationality in organizations. Academy of Management Annals, 9(1), 337–392.
Raisch, S., & Fomina, K. (2024). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2). https://doi.org/10.5465/amr.2021.0421
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
Ruslaini Ruslaini, & Ekawahyu Kasih. (2024). Integrasi IQ, EQ, Penguasaan Teknologi dan Ketelitian pada Kualitas Keputusan Organisasi. Journal of Business, Finance, and Economics (JBFE), 5(1), 310–318. https://doi.org/10.32585/jbfe.v5i1.5617
Savage, N. (2023). Drug discovery companies are customizing ChatGPT. Nature Biotechnology, 41(5), 585–586.
Schilling, M. A., & Green, E. (2011). Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy, 40(10), 1321–1331.
Senoner, J., Netland, T., & Feuerriegel, S. (2022). Using explainable artificial intelligence to improve process quality: Evidence from semiconductor manufacturing. Management Science, 68(8), 5704–5723.
Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83.
Shrestha, Y. R., Krishna, V., & Von Krogh, G. (2021). Augmenting organizational decision-making with deep learning. Journal of Business Research, 123, 588–603.
Simone, S. (2021). Pencil offers new AI-driven ad creation service to help small businesses. KM World.
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356–367.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Vinoski, J. (2019). Generative design opens new paths to a sustainable future. Forbes.
Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii–xxiii.
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review and reconceptualization. Academy of Management Review, 27(2), 185–203.
