CLOUD-BASED MANAGEMENT OF ART INSTITUTIONS USING AI
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6667Keywords:
Visitor Engagement, Data Analytics, Digital Transformation, Art Institution Management, Cloud Computing, Artificial IntelligenceAbstract [English]
In the digital era, the cultural and creative industries have seen the emergence of new opportunities that have opened the door to improving the efficiency and sustainability of the art institutions. The paper considers the creation and implementation of cloud-based management systems in conjunction with artificial intelligence (AI) for the purposes of enhancing operational, curatorial, and administrative performance in the art institutions, including museums, galleries, and cultural centers. With cloud computing, businesses would be able to store their data in one place, enable scientists and other collaborators to collaborate outside of the institution, with archives and exhibitions as well as visitor analytics accessible in real time and securely. The fusion of AI results in intelligent automation, which manifests itself, for example, in the cataloguing of the artwork, predictive maintenance of the collections, visitor engagement through recommendation systems, and data-driven resource distribution decisions. It also discusses how AI-powered analytics can help curators and managers to better understand the behavior of the audience and predict their visit patterns, as well as to personalize digital experiences. It will be based on the effectiveness evaluation of the system through a mixed-method methodology: case studies, system design and user feedback analysis. It is expected that the findings will demonstrate that the combination of cloud technology with artificial intelligence can contribute to simplify institutional management as well as their capacity to be more welcoming, sustainable, and inclusive with regard to the arts sector. Finally, the paper contributes to the debate on the digital transformation of cultural management and proposes a comprehensive framework of AI-based cloud-based management of art institutions which can be commoditized.
References
Ajani, S. N., Shivadekar, S., Pareek, V., Joshi, I., Nalawade, D. B., and Kokane, C. D. (2024). Intelligent Automation of Security Policy Decisions using AI: Analysis of ML and DL Approach. In Lecture Notes in Networks and Systems (Vol. 1147, pp. 403–412). Springer. https://doi.org/10.1007/978-981-97-7880-5_34 DOI: https://doi.org/10.1007/978-981-97-7880-5_34
Ambalavan, A. S., and Chauhan, A. (2024). AI Implementation in Art Museums through Sensory Design. KnE Social Sciences, 9(32), 289–309. https://doi.org/10.18502/kss.v9i32.17443 DOI: https://doi.org/10.18502/kss.v9i32.17443
Avlonitou, C., Papadaki, E., and Apostolakis, A. (2025). A Human–Ai Compass for Sustainable Art Museums: Navigating Opportunities and Challenges in Operations, Collections Management, and Visitor Engagement. Heritage, 8, Article 422. https://doi.org/10.3390/heritage8100422 DOI: https://doi.org/10.3390/heritage8100422
Bossi, S., Portaluppi, S., Cazzulani, V., and Achille, C. (2024). Fruizione Digitale di Ambienti e Oggetti Digitali: Una Buona Pratica tra museo e università. Viglevanum, XXXIV, 80–84.
Chen, A., Jesus, R., and Vilarigues, M. (2025). Synergy of Art, Science, and Technology: A Case Study of Augmented Reality and Artificial Intelligence in Enhancing Cultural Heritage Engagement. Journal of Imaging, 11, Article 89. https://doi.org/10.3390/jimaging11030089 DOI: https://doi.org/10.3390/jimaging11030089
Devkar, R. D., Patle, V. L., Kolhatkar, R. O., Chafle, K. R., and Shivankar, I. B. (2025). The Neural Revolution: A Review of Brain–Machine Interfaces and Neuralink’s Contribution. International Journal of Electrical Engineering and Computer Science (IJEECS), 14(1), 126–130.
Iervolino, S., and Milne, A. (2025). Curating AI-Driven Art: Actors, Institutional Strategies and Organisational Change. Museum Management And Curatorship, 1–20. https://doi.org/10.1080/09647775.2025.2562854 DOI: https://doi.org/10.1080/09647775.2025.2562854
Liu, Y., and Zhu, C. (2025). The Use of Deep Learning and Artificial Intelligence-Based Digital Technologies in Art Education. Scientific Reports, 15, Article 15859. https://doi.org/10.1038/s41598-025-00892-9 DOI: https://doi.org/10.1038/s41598-025-00892-9
Mao, J., Chen, B., and Liu, J. C. (2024). Generative Artificial Intelligence in Education and its Implications for Assessment. TechTrends, 68(1), 58–66. https://doi.org/10.1007/s11528-023-00911-4 DOI: https://doi.org/10.1007/s11528-023-00911-4
Perfetti, L., Spettu, F., Achille, C., Fassi, F., Navillod, C., and Cerutti, C. (2023). A Multi-Sensor Approach to Survey Complex Architectures Supported by Multi-Camera Photogrammetry. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-M-2-2023, 1209–1216. https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1209-2023 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1209-2023
Spettu, F., Achille, C., and Fassi, F. (2024). State-of-the-Art Web Platforms for the Management and Sharing of Data: Applications, Uses, and Potentialities. Heritage, 7, 6008–6035. https://doi.org/10.3390/heritage7110282 DOI: https://doi.org/10.3390/heritage7110282
Spettu, F., Achille, C., Fassi, F., and Della Giovampaola, I. (2023). Web Platforms for Cultural Heritage Management: The Parco Archeologico del Colosseo Case Study. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-M-2-2023, 1493–1500. https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1493-2023 DOI: https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1493-2023
Tennent, P., Martindale, S., Benford, S., Darzentas, D., Brundell, P., and Collishaw, M. (2020). Thresholds: Embedding Virtual Reality in the Museum. ACM Journal on Computing and Cultural Heritage, 13, 1–35. https://doi.org/10.1145/3369394 DOI: https://doi.org/10.1145/3369394
Umar, S., Veeramachineni, V. R., Ginjupalli, S., Thummala, R., and Safare, D. (2025). Security Vulnerability Management and Automated Patching Systems. International Journal of Research in Applied Engineering and Technology (IJRAET), 14(2), 29–41. https://doi.org/10.65521/ijacect.v14i1.729 DOI: https://doi.org/10.65521/ijacect.v14i1.729
Zhao, J., Guo, L., and Li, Y. (2022). Application of Digital Twin Combined with Artificial Intelligence and 5G Technology in the Art Design of Digital Museums. Wireless Communications and Mobile Computing, 2022, Article 8214514. https://doi.org/10.1155/2022/8214514 DOI: https://doi.org/10.1155/2022/8214514
Zhou, C. (2023). Integration of Modern Technologies in Higher Education on the Example of Artificial Intelligence Use. Education and Information Technologies, 28(4), 3893–3910. https://doi.org/10.1007/s10639-022-11309-9 DOI: https://doi.org/10.1007/s10639-022-11309-9
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. Ramya G Franklin, Wamika Goyal, Dr. Prabhat Kumar Sahu, Dr. Sontakke Dnyandev Manik, Nitish Vashisht, Dr.Prakash Divakaran

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.























