Vol. 2 No. 1 (2026): Applied Research Advances
Articles

Artificial Intelligence in Open Innovation and Project Management: A Systematic Literature Review of Technologies, Applications, and Future Research Directions

Sushil Kumar Sahoo
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India
Bibhuti Bhusan Choudhury
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India
Prasant Ranjan Dhal
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India
Supriya Sahu
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India
Sudhakar Majhi
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India
Ipsita Dhar
1) Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India: 2) Biju Patnaik University of Technology, Rourkela, Odisha, India

Published 2026-07-14

Keywords

  • Artificial Intelligence,
  • Open Innovation,
  • Project Management,
  • Generative AI,
  • Machine Learning,
  • Bibliometric Analysis,
  • Systematic Literature Review,
  • Innovation Management
  • ...More
    Less

How to Cite

Sahoo, S. K., Choudhury, B. B., Dhal, P. R., Sahu, S., Majhi, S., & Dhar, I. (2026). Artificial Intelligence in Open Innovation and Project Management: A Systematic Literature Review of Technologies, Applications, and Future Research Directions. Applied Research Advances, 2(1), 199-224. https://doi.org/10.65069/ara21202629

Abstract

Artificial Intelligence (AI) is reshaping open innovation and project management by enhancing knowledge discovery, collaborative innovation, intelligent decision-making, and project execution. Despite the rapid growth of research in these domains, existing studies remain fragmented and lack a comprehensive synthesis of AI technologies, applications, and emerging research directions. This study systematically reviews the evolution of AI-driven open innovation and project management research through a systematic literature review and bibliometric analysis of publications from 2000 to 2026. Relevant studies were identified following the PRISMA guidelines and analyzed using bibliometric and thematic analysis techniques. The findings reveal a significant increase in research activity, particularly since 2018, driven by advances in machine learning, natural language processing, predictive analytics, and generative AI. AI applications are primarily concentrated in idea generation, knowledge management, crowdsourcing, project planning, risk assessment, resource optimization, and decision support. Furthermore, the review identifies major research themes, emerging trends, and critical gaps related to explainable AI, human–AI collaboration, governance, and sustainability. Finally, a conceptual framework and a future research agenda are proposed to facilitate the development of intelligent, resilient, and sustainable innovation and project management ecosystems.

Downloads

Download data is not yet available.

References

  1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: a modern approach (4th US ed.). https://aima.cs.berkeley.edu/
  2. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.
  3. Huizingh, E. K. R. E. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2–9. https://doi.org/10.1016/j.technovation.2010.10.002
  4. Gassmann, O., Enkel, E., & Chesbrough, H. (2010). The future of open innovation. R&D Management, 40(3), 213–221. https://doi.org/10.1111/j.1467-9310.2010.00605.x
  5. Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. https://doi.org/10.1016/j.ijpe.2019.01.004
  6. Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2), 5–16. https://doi.org/10.1177/0008125617745086
  7. Bhavana, S., Navadeep, J., & Rao, T. V. N. (2026). AI-Enabled Transformation in Modern Project Management. In The Role of AI in Modern Project Management (pp. 39–72). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-7605-9.ch002
  8. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  9. Prasetyo, M. L., Peranginangin, R. A., Martinovic, N., Ichsan, M., & Wicaksono, H. (2025). Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100445. https://doi.org/10.1016/j.joitmc.2024.100445
  10. Uriarte, S., Baier-Fuentes, H., Espinoza-Benavides, J., & Inzunza-Mendoza, W. (2026). Artificial intelligence technologies and entrepreneurship: a hybrid literature review. Review of Managerial Science, 20(1), 251–299. https://doi.org/10.1007/s11846-025-00839-4
  11. Liu, Y., Shen, F., Guo, J., Hu, G., & Song, Y. (2025). Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China. Energy Economics, 143, 108280. https://doi.org/10.1016/j.eneco.2025.108280
  12. Nepal, R., Zhao, X., Dong, K., Wang, J., & Sharif, A. (2025). Can artificial intelligence technology innovation boost energy resilience? The role of green finance. Energy Economics, 142, 108159. https://doi.org/10.1016/j.eneco.2024.108159
  13. Hirsch-Kreinsen, H. (2024). Artificial intelligence: A "promising technology". AI & Society, 39(4), 1641–1652. https://doi.org/10.1007/s00146-023-01629-w
  14. Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Symagulov, A., & Yelis, M. (2022). Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges. Mathematics, 10(15), 2552. https://doi.org/10.3390/math10152552
  15. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Harvard Business Press.
  16. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open innovation: Researching a new paradigm. Oxford University Press.
  17. Huizingh, E. K. R. E. (2011). Open innovation: State of the art and future perspectives. Technovation, 31(1), 2–9. https://doi.org/10.1016/j.technovation.2010.10.002
  18. Dahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39(6), 699–709. https://doi.org/10.1016/j.respol.2010.01.013
  19. Barile, D., Secundo, G., & Del Vecchio, P. (2026). An artificial intelligence-based innovation ecosystem enabling open innovation and sustainable growth: evidence from a case study. Innovation, 28(1), 14–36. https://doi.org/10.1080/14479338.2025.2514468
  20. Duong, C. D. (2025). Artificial intelligence innovation, innovation niches and entrepreneurial performance: the curvilinear role of open innovation. Business Process Management Journal, 1–28. https://doi.org/10.1108/BPMJ-04-2025-0500
  21. Johri, A., Singh, R. K., Kushwaha, B. P., Alhumoudi, H., Alakkas, A., & Khoja, M. (2025). Leveraging open innovation for e-commerce success: The contingent role of accounting information systems and artificial intelligence. Journal of Innovation & Knowledge, 10(4), 100737. https://doi.org/10.1016/j.jik.2025.100737
  22. Holgersson, M., Dahlander, L., Chesbrough, H., & Bogers, M. L. (2024). Open Innovation in the Age of AI. California Management Review, 67(1), 5–20. https://doi.org/10.1177/00081256241279323
  23. Sahoo, S., Kumar, S., Donthu, N., & Singh, A. K. (2024). Artificial intelligence capabilities, open innovation, and business performance–Empirical insights from multinational B2B companies. Industrial Marketing Management, 117, 28–41. https://doi.org/10.1016/j.indmarman.2023.12.008
  24. Kuzior, A., Sira, M., & Brożek, P. (2023). Use of artificial intelligence in terms of open innovation process and management. Sustainability, 15(9), 7205. https://doi.org/10.3390/su15097205
  25. Rose, K. H. (2013). A guide to the project management body of knowledge (PMBOK guide). Project Management Journal, 44(3), e1. https://doi.org/10.1002/pmj.21345
  26. Plotnikov, A., Demiryurek, K., Plotnikova, A., Andreeva, O., & Suzdaleva, G. (2024). Agile methodology catalyzing digital transformation: Implementation objectives and evaluation criteria in organizational settings. In The Future of Industry: Human-Centric Approaches in Digital Transformation (pp. 141–161). Springer. https://doi.org/10.1007/978-3-031-66801-2_10
  27. Vergara, D., del Bosque, A., Lampropoulos, G., & Fernández-Arias, P. (2025). Trends and applications of artificial intelligence in project management. Electronics, 14(4), 800. https://doi.org/10.3390/electronics14040800
  28. Salimimoghadam, S., Ghanbaripour, A. N., Tumpa, R. J., Kamel Rahimi, A., Golmoradi, M., Rashidian, S., & Skitmore, M. (2025). The rise of artificial intelligence in project management: A systematic literature review of current opportunities, enablers, and barriers. Buildings, 15(7), 1130. https://doi.org/10.3390/buildings15071130
  29. Shamim, M. M. I., Hamid, A. B. B. A., Nyamasvisva, T. E., & Rafi, N. S. B. (2025). Advancement of artificial intelligence in cost estimation for project management success: A systematic review of machine learning, deep learning, regression, and hybrid models. Modelling, 6(2), 35. https://doi.org/10.3390/modelling6020035
  30. Mannava, M. K., Gupta, H., Mishra, M. V., & Banerjee, S. (2025). Optimizing Financial Processes Through AI-Enhanced Project Management, Big Data Engineering, and Sustainability. In AI-Enabled Sustainable Innovations in Education and Business (pp. 203–224). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-3952-8.ch009
  31. Almalki, S. S. (2025). AI-driven decision support systems in agile software project management: Enhancing risk mitigation and resource allocation. Systems, 13(3), 208. https://doi.org/10.3390/systems13030208
  32. Sahoo, S. K., Choudhury, B. B., Dhal, P. R., Majhi, S., Dhar, I., & Sahu, S. (2026). Three Decades of Multiple Criteria Decision-Making (MCDM) Methods (1996–2026): A Comprehensive Review of Advancements, Applications, and Future Directions. Journal of Contemporary Decision Science, 2(1), 400–439. https://doi.org/10.67334/cds21202631
  33. Sahoo, S. K., Choudhury, B. B., Dhal, P. R., Sahu, S., Majhi, S., & Dhar, I. (2026). Large Language Models in Multi Criteria Decision Making: A Systematic Review, Taxonomy, and Future Research Agenda. Applied Research Advances, 2(1), 116–136. https://doi.org/10.65069/ara21202614
  34. Wang, X., Chen, X., Yan, M., & Kong, L. (2025). A scientometric analysis of machine learning in schizophrenia neuroimaging: Trends and insights (2012–2024). Journal of Affective Disorders, 387, 119485. https://doi.org/10.1016/j.jad.2025.119485
  35. Choudhury, D., Nayak, S., & Sahoo, S. K. (2025, December). Emerging Trends in Nanorobotics for Environmental Remediation: A Bibliometric Review. In 2025 1st International Conference on Data Science and Intelligent Network Computing (ICDSINC) (pp. 830–834). IEEE. https://doi.org/10.1109/ICDSINC66221.2025.11448191
  36. Sahu, S., Sahoo, S. K., Choudhury, B. B., Dhal, P. R., Dhar, I., & Imreh-Tóth, M. (2026). Artificial Intelligence Driven Sustainable Consumption Decisions of Generation Z: A Systematic Literature Review. Decision Making Advances, 1–21. https://doi.org/10.31181/dma0002026171
  37. Sahoo, S. K., Fischer, S., Sahu, S., Choudhury, B. B., Dhal, P. R., & Dhar, I. (2026). Mapping Tools, Techniques, and Applications for Research Excellence: A Bibliometric Study. Spectrum of Engineering and Management Sciences, 4(1), 120–145. https://doi.org/10.31181/sems41202672s
  38. Sahoo, S. K., & Choudhury, B. B. (2024). Autonomous navigation and obstacle avoidance in smart robotic wheelchairs. Journal of Decision Analytics and Intelligent Computing, 4(1), 47–66. https://doi.org/10.31181/jdaic10019022024s
  39. Bouraima, M. B. (2026). Unveiling the Challenges of Artificial Intelligence Use in Auditing: A Holistic Multi-Criteria Decision-Making Approach. Applied Research Advances, 2(1), 137–145. https://doi.org/10.65069/ara21202613
  40. Becerra-Fernandez, I. (2000). The role of artificial intelligence technologies in the implementation of people-finder knowledge management systems. Knowledge-Based Systems, 13(5), 315–320. https://doi.org/10.1016/S0950-7051(00)00091-5
  41. Weber, R., Aha, D. W., & Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Expert Systems with Applications, 20(1), 17–34. https://doi.org/10.1016/S0957-4174(00)00046-4
  42. Okudan, O., Budayan, C., & Dikmen, I. (2021). A knowledge-based risk management tool for construction projects using case-based reasoning. Expert Systems with Applications, 173, 114776. https://doi.org/10.1016/j.eswa.2021.114776
  43. Sumbal, M. S., Amber, Q., Tariq, A., Raziq, M. M., & Tsui, E. (2024). Wind of change: how ChatGPT and big data can reshape the knowledge management paradigm? Industrial Management & Data Systems, 124(9), 2736–2757. https://doi.org/10.1108/IMDS-06-2023-0360
  44. Gupta, A., Basu, D., Ghantasala, R., Qiu, S., & Gadiraju, U. (2022, April). To trust or not to trust: How a conversational interface affects trust in a decision support system. In Proceedings of the ACM Web Conference 2022 (pp. 3531–3540). https://doi.org/10.1145/3485447.3512244
  45. Dai, P., Hu, K., Wu, X., Xing, H., & Yu, Z. (2021, May). Asynchronous deep reinforcement learning for data-driven task offloading in MEC-empowered vehicular networks. In IEEE INFOCOM 2021-IEEE Conference on Computer Communications (pp. 1–10). IEEE. https://doi.org/10.1109/INFOCOM42981.2021.9488886
  46. Hadjiiski, L., Cha, K., Chan, H. P., Drukker, K., Morra, L., Näppi, J. J., & Armato III, S. G. (2023). AAPM task group report 273: recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging. Medical Physics, 50(2), e1–e24. https://doi.org/10.1002/mp.16188
  47. Pokhrel, S. R. (2021). Blockchain brings trust to collaborative drones and LEO satellites: An intelligent decentralized learning in the space. IEEE Sensors Journal, 21(22), 25331–25339. https://doi.org/10.1109/JSEN.2021.3060185
  48. Liang, T., Chen, L., Huang, M., Deng, X., Zhang, S., Xiong, N. N., & Liu, A. (2023). RLTD: A reinforcement learning-based truth data discovery scheme for decision support systems under sustainable environments. Applied Soft Computing, 143, 110369. https://doi.org/10.1016/j.asoc.2023.110369
  49. Li, L., Yang, L., Zhao, M., Liao, M., & Cao, Y. (2022). Exploring the success determinants of crowdfunding for cultural and creative projects: An empirical study based on signal theory. Technology in Society, 70, 102036. https://doi.org/10.1016/j.techsoc.2022.102036
  50. Patil, R., & Gudivada, V. (2024). A review of current trends, techniques, and challenges in large language models (LLMs). Applied Sciences, 14(5), 2074. https://doi.org/10.3390/app14052074
  51. Mohy, A. A., Bassioni, H. A., Elgendi, E. O., & Hassan, T. M. (2026). Innovations in safety management for construction sites: the role of deep learning and computer vision techniques. Construction Innovation, 26(2), 551–578. https://doi.org/10.1108/CI-04-2023-0062
  52. Aktürk, B., & Irlayıcı Çakmak, P. (2025). Digital twins for enhanced construction project management. Smart and Sustainable Built Environment, 14(7), 2176–2200. https://doi.org/10.1108/SASBE-03-2024-0082
  53. Tun, H. M., Rahman, H. A., Naing, L., & Malik, O. A. (2025). Trust in artificial intelligence–based clinical decision support systems among health care workers: systematic review. Journal of Medical Internet Research, 27, e69678. https://doi.org/10.2196/69678
  54. Blohm, I., Riedl, C., Füller, J., & Leimeister, J. M. (2016). Rate or trade? Identifying winning ideas in open idea sourcing. Information Systems Research, 27(1), 27–48. https://doi.org/10.1287/isre.2015.0605
  55. Olatokun, W. M., & Oladokun, B. D. (2026). Hallucination in Generative AI: Challenges to data integrity, ethical concerns and implications for knowledge management. Information Services and Use, 18758789261463098. https://doi.org/10.1177/18758789261463098
  56. Malik, A., Nguyen, T. M., & Budhwar, P. (2022). Towards a conceptual model of AI-mediated knowledge sharing exchange of HRM practices: antecedents and consequences. IEEE Transactions on Engineering Management, 71, 13083–13095. https://doi.org/10.1109/TEM.2022.3163117
  57. Saen, R. F., Yousefi, F., & Azadi, M. (2024). Artificial intelligence powered predictions: enhancing supply chain sustainability. Annals of Operations Research, 1–44. https://doi.org/10.1007/s10479-024-06088-0
  58. Kumar, R., & Pamucar, D. (2025). A comprehensive and systematic review of multi-criteria decision-making (MCDM) methods to solve decision-making problems: two decades from 2004 to 2024. Spectrum of Decision Making and Applications, 2(1), 177–196. https://doi.org/10.31181/sdmap21202524
  59. Koh, S. L., Genovese, A., Acquaye, A. A., Barratt, P., Rana, N., Kuylenstierna, J., & Gibbs, D. (2013). Decarbonising product supply chains: design and development of an integrated evidence-based decision support system–the supply chain environmental analysis tool (SCEnAT). International Journal of Production Research, 51(7), 2092–2109. https://doi.org/10.1080/00207543.2012.705042
  60. Xu, Y., Zhou, B., Jin, S., Xie, X., Chen, Z., Hu, S., & He, N. (2022). A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method. Computers, Environment and Urban Systems, 95, 101807. https://doi.org/10.1016/j.compenvurbsys.2022.101807
  61. Amith, M., He, Z., Bian, J., Lossio-Ventura, J. A., & Tao, C. (2018). Assessing the practice of biomedical ontology evaluation: Gaps and opportunities. Journal of Biomedical Informatics, 80, 1–13. https://doi.org/10.1016/j.jbi.2018.02.010
  62. Balayn, A., Lofi, C., & Houben, G. J. (2021). Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems. The VLDB Journal, 30(5), 739–768. https://doi.org/10.1007/s00778-021-00671-8
  63. O'Leary, D. E. (2014). Embedding AI and crowdsourcing in the big data lake. IEEE Intelligent Systems, 29(5), 70–73. https://doi.org/10.1109/MIS.2014.82