THE INTELLIGENT CANVAS: INTEGRATING AI ACROSS THE HR, MARKETING, LEGAL, AND FINANCIAL PILLARS OF MODERN VISUAL ARTS ORGANIZATIONS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7413Keywords:
Artificial Intelligence, Visual Arts Organizations, Digital Transformation, Human Resource Management, AI Marketing, Legal GovernanceAbstract [English]
The fast development of artificial intelligence (AI) is shifting the environment in which the visual arts organizations are organized and operate, as more and more of them implement the digital technologies in order to become more creative, efficient, and appealing to their audience. Nevertheless, even with the increased topicality of AI, its implementation in the arts industry is still sporadic and mostly limited to separate functional areas. This paper fills this gap by introducing the Intelligent Canvas framework, a three-dimensional conceptual framework integrating AI into four main pillars of an organization, namely human resource management, marketing, legal governance, and financial management. Based on the established theoretical lenses, such as the Resource-Based View, the Dynamic Capabilities Theory and the models of technology adoption, the paper builds a comprehensive picture of how AI can be used as an enabling and integrative process. The framework also focuses on the use of AI to improve talent management, customized marketing, the protection of intellectual property as well as in making financial decisions alongside the significance of cross-functional integration and ethical considerations. The paper adds to the literature by applying AI studies to the under-researched domain of visual arts organizations and providing a multi-dimensional solution to the digital revolution. The suggested model offers important implications to the researchers and practitioners who would like to use AI to achieve sustainable expansion, innovation and organizational results in the creative industry.
References
Acemoglu, D., and Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716 DOI: https://doi.org/10.1086/705716
Agrawal, A. K., Gans, J. S., and Goldfarb, A. (2018). Exploring the Impact of Artificial Intelligence: Prediction Versus Judgment (NBER Working Paper No. 24626). National Bureau of Economic Research. https://doi.org/10.3386/w24626 DOI: https://doi.org/10.3386/w24626
Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T DOI: https://doi.org/10.1016/0749-5978(91)90020-T
Aletras, N., Tsarapatsanis, D., Preoțiuc-Pietro, D., and Lampos, V. (2016). Predicting Judicial Decisions of the European Court of Human Rights: A Natural Language Processing Perspective. PeerJ Computer Science, 2, e93. https://doi.org/10.7717/peerj-cs.93 DOI: https://doi.org/10.7717/peerj-cs.93
Ashley, K. D. (2017). Artificial Intelligence and Legal Analytics. Cambridge University Press. https://doi.org/10.1017/9781316761380 DOI: https://doi.org/10.1017/9781316761380
Autor, D. H. (2015). Why are There Still so Many Jobs? The History and Future of Workplace Automation. Journal of Economic Perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3 DOI: https://doi.org/10.1257/jep.29.3.3
Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 DOI: https://doi.org/10.1177/014920639101700108
Benbya, H., Davenport, T. H., and Pachidi, S. (2020). Artificial Intelligence in Organizations: Current State and Future Opportunities. MIS Quarterly Executive, 19(1), 9–21. https://doi.org/10.2139/ssrn.3741983 DOI: https://doi.org/10.2139/ssrn.3741983
Bessen, J. E. (2017). AI and Jobs: The Role of Demand. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3078715 DOI: https://doi.org/10.2139/ssrn.3078715
Binns, R. (2018). Fairness in Machine Learning: Lessons from Political Philosophy. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research, Vol. 81,149–159).
Brynjolfsson, E., and McAfee, A. (2017, July 18). The Business of Artificial Intelligence. Harvard Business Review.
Brynjolfsson, E., Rock, D., and Syverson, C. (2021). The Productivity J-Curve: How Intangibles Complement General Purpose Technologies. American Economic Journal: Macroeconomics, 13(1), 333–372. https://doi.org/10.1257/mac.20180386 DOI: https://doi.org/10.1257/mac.20180386
Chen, H., Chiang, R. H. L., and Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503 DOI: https://doi.org/10.2307/41703503
Cockburn, I. M., Henderson, R., and Stern, S. (2019). The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis. In A. Agrawal, J. Gans, and A. Goldfarb (Eds.), The Economics of Artificial Intelligence: An Agenda (115–146). University of Chicago Press. https://doi.org/10.7208/chicago/9780226613475.003.0004 DOI: https://doi.org/10.7208/chicago/9780226613475.003.0004
Davenport, T. H., Guha, A., Grewal, D., and Bressgott, T. (2020). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0 DOI: https://doi.org/10.1007/s11747-019-00696-0
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008 DOI: https://doi.org/10.2307/249008
Dima, J., Gilbert, M.-H., Dextras-Gauthier, J., and Giraud, L. (2024). The Effects of Artificial Intelligence on Human Resource Activities and the Roles of the Human Resource Triad: Opportunities and Challenges. Frontiers in Psychology, 15, 1360401. https://doi.org/10.3389/fpsyg.2024.1360401 DOI: https://doi.org/10.3389/fpsyg.2024.1360401
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., Le Meunier-FitzHugh, K., Le Meunier-FitzHugh, L. C., Misra, S., Mogaji, E., Sharma, S. K., Singh, J. B., Raghavan, V., Raman, R., Rana, N. P., Samothrakis, S., Spencer, J., Tamilmani, K., Tubadji, A., Walton, P., and Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Elgammal, A., Liu, B., Elhoseiny, M., and Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms. arXiv. https://doi.org/10.48550/arXiv.1706.07068
Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., Pentland, A., and Russakovsky, O. (2023). Art and the Science of Generative AI: A Deeper Dive. arXiv. https://doi.org/10.48550/arXiv.2306.04141 DOI: https://doi.org/10.1126/science.adh4451
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., and Vayena, E. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 DOI: https://doi.org/10.1007/s11023-018-9482-5
Gadde, S. (2025). Artificial Intelligence Integration in HR and Finance: A Framework for Strategic Transformation. International Journal of Scientific Research in Computer Science. https://doi.org/10.32628/CSEIT25112781 DOI: https://doi.org/10.32628/CSEIT25112781
Ghahramani, Z. (2015). Probabilistic Machine Learning and Artificial Intelligence. Nature, 521(7553), 452–459. https://doi.org/10.1038/nature14541 DOI: https://doi.org/10.1038/nature14541
Gu, S., Kelly, B., and Xiu, D. (2020). Empirical Asset Pricing Via Machine Learning. Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009 DOI: https://doi.org/10.1093/rfs/hhaa009
Heaton, J. B., Polson, N. G., and Witte, J. H. (2016). Deep Learning in Finance. arXiv. https://doi.org/10.48550/arXiv.1602.06561
Huang, M.-H., and Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459 DOI: https://doi.org/10.1177/1094670517752459
Huang, M.-H., and Rust, R. T. (2021). A Strategic Framework for Artificial Intelligence in Marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9 DOI: https://doi.org/10.1007/s11747-020-00749-9
Jarrahi, M. H. (2018). Artificial Intelligence and the Future of Work: Human–AI Symbiosis in Organizational Decision Making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 DOI: https://doi.org/10.1016/j.bushor.2018.03.007
Jatobá, M. N., Ferreira, J. J., Fernandes, P. O., and Teixeira, J. P. (2023). Intelligent Human Resources for AI Adoption: A Systematic Literature Review. Journal of Organizational Change Management. https://doi.org/10.1108/JOCM-03-2022-0075 DOI: https://doi.org/10.1108/JOCM-03-2022-0075
Jordan, M. I., and Mitchell, T. M. (2015). Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415 DOI: https://doi.org/10.1126/science.aaa8415
Kaplan, A., and Haenlein, M. (2019). Siri, Siri, in my Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 DOI: https://doi.org/10.1016/j.bushor.2018.08.004
Minbaeva, D. (2021). Disrupted HR? Human Resource Management Review, 31(4), 100820. https://doi.org/10.1016/j.hrmr.2020.100820 DOI: https://doi.org/10.1016/j.hrmr.2020.100820
Murugesan, U., Subramanian, P., Srivastava, S., and Dwivedi, A. (2023). A Study of Artificial Intelligence Impacts on Human Resource Digitalization in Industry 4.0. Decision Analytics Journal, 7, 100249. https://doi.org/10.1016/j.dajour.2023.100249 DOI: https://doi.org/10.1016/j.dajour.2023.100249
Mwita, K. M., and Kitole, F. A. (2025). Potential Benefits and Challenges of Artificial Intelligence in Human Resource Management in Public Institutions. Discover Global Society, 3, 35. https://doi.org/10.1007/s44282-025-00175-8 DOI: https://doi.org/10.1007/s44282-025-00175-8
Palos-Sánchez, P. R., Baena-Luna, P., Bădică, A., and Infante-Moro, J. C. (2022). Artificial Intelligence and Human Resources Management: A Bibliometric Analysis. Applied Artificial Intelligence, 36(1), e2145631. https://doi.org/10.1080/08839514.2022.2145631 DOI: https://doi.org/10.1080/08839514.2022.2145631
Raisch, S., and Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072 DOI: https://doi.org/10.5465/amr.2018.0072
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., and Johnson, R. (2020). Artificial Intelligence in Human Resource Management: Challenges and a Path Forward. Human Resource Management Review, 30(1), 100679. https://doi.org/10.1016/j.hrmr.2019.100679
Teece, D. J. (2018). Business Models and Dynamic Capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007 DOI: https://doi.org/10.1016/j.lrp.2017.06.007
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 DOI: https://doi.org/10.2307/30036540
Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., and Haenlein, M. (2021). Digital Transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, 889–901. https://doi.org/10.1016/j.jbusres.2019.09.022 DOI: https://doi.org/10.1016/j.jbusres.2019.09.022
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., and Childe, S. J. (2017). Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/j.jbusres.2016.08.009 DOI: https://doi.org/10.1016/j.jbusres.2016.08.009
Wilson, H. J., and Daugherty, P. R. (2018). Collaborative Intelligence: Humans and AI are Joining Forces. Harvard Business Review. https://doi.org/10.2139/ssrn.3178371 DOI: https://doi.org/10.2139/ssrn.3178371
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Copyright (c) 2026 Dr. Kiruthiga V, Dr. Umamaheswari S, Dr. Joyce R, Dr. A. Geetha, Dr. Rudhra T S, Dr. Veena Christy

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