MANAGEMENT STRATEGIES FOR AI-INTEGRATED CRAFT INDUSTRIES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i1s.2026.7086Keywords:
Artificial Intelligence, Craft Industries, Strategic Management, Human–AI Collaboration, Cultural Heritage Preservation, Supply Chain ManagementAbstract [English]
Traditional craft industry is crucial in preserving culture, rural livelihoods, and creative economies but continues to encounter the same issues with fragmentation of value chains, lack of market confidence, loss of skills and the inability to be sustainable. Artificial intelligence (AI) brings fresh possibilities to overcome these issues, but there is a need to use it in craft ecosystems mindfully, so as to prevent a degradation of culture and marginalization of artisans. This paper looks at AI implementation in craft industries and how to manage these industries, presenting the argument that the main challenge of implementing AI in craft industry is management and governance-related, and not a technological issue. The paper suggests a conceptual model that places strategic management in the mediating role between the traditional craft foundations and the AI capabilities. In the systematic discussion on the strategic alignment, human and AI work, operational integration, and ethical governance, the study proves that AI could be used to improve coordination, quality assurance, market responsiveness, and sustainability without compromising cultural authenticity. The operational mappings and pictorial performance analyses also indicate that the balanced improvement of supply-chain functions through the use of phased and collaborative AI adoption models is still possible without losing handcrafted variability. The results reported add up to strategic management and creative industry publications by reshaping AI as a supplementary technology that enhances the power of artisans instead of eliminating them. The article also brings out the significance of governance systems in respect to intellectual property, ownership of data and representation of cultures. Further studies must apply the suggested frameworks to different craft settings empirically and research involved design strategies of participatory AI development to create inclusive innovation.
References
Aboada, A., Daneshpajouh, N., Toledo, and De Vass, T. (2023). Artificial Intelligence-Enabled Project Management: A Systematic Literature Review. Applied Sciences, 13(9), 5014. https://doi.org/10.3390/app13095014 DOI: https://doi.org/10.3390/app13085014
Auth, J., Jöhnk, J., and Wiecha, D. A. (2021, September). A Conceptual Framework for Applying Artificial Intelligence in Project Management. In Proceedings of the IEEE 23rd Conference on Business Informatics (CBI) (161–170). IEEE. https://doi.org/10.1109/CBI52690.2021.00027 DOI: https://doi.org/10.1109/CBI52690.2021.00027
Du, D., Si, M., Ahmad, M., and Gu, X. (2024). Advancing Environmental Sustainability: A Study on Energy and Resource Efficiency Through Technological Innovation in China. International Journal of Environmental Research, 18, 92. https://doi.org/10.1007/s41742-024-00645-y DOI: https://doi.org/10.1007/s41742-024-00645-y
Gil, J., Torres, M., and González-Crespo, R. (2021). The Application of Artificial Intelligence in Project Management Research: A Review. International Journal of Interactive Multimedia and Artificial Intelligence, 6(1), 54–66. https://doi.org/10.9781/ijimai.2020.12.003 DOI: https://doi.org/10.9781/ijimai.2020.12.003
Gaikwad, R. R., and Damodaran, D. (2024). The Rise of Predictive Analytics in Management Accounting: From Descriptive to Prescriptive. ShodhAI: Journal of Artificial Intelligence, 1 (1), 159–167. https://doi.org/10.29121/shodhai.v1.i1.2024.54 DOI: https://doi.org/10.29121/shodhai.v1.i1.2024.54
Hariyani, D., and Mishra, S. (2023). A Descriptive Statistical Analysis of Enablers for Integrated Sustainable Green-Lean-Six Sigma-Agile Manufacturing Systems in Indian Manufacturing Industries. Benchmarking: An International Journal, 31(3), 824–865. https://doi.org/10.1108/BIJ-06-2022-0344 DOI: https://doi.org/10.1108/BIJ-06-2022-0344
Karadimas, G., Pagone, E., and Salonitis, K. (2025). Comparative Life Cycle Assessment of Swarf Cleaning Methods for Sustainable Manufacturing. Procedia CIRP, 134, 1005–1010. https://doi.org/10.1016/j.procir.2025.02.240 DOI: https://doi.org/10.1016/j.procir.2025.02.240
Kombaya Touckia, N., Hamani, N., and Kermad, L. (2022). Digital Twin Framework for Reconfigurable Manufacturing Systems (RMSs): Design and Simulation. International Journal of Advanced Manufacturing Technology, 120, 5431–5450. https://doi.org/10.1007/s00170-022-09118-y DOI: https://doi.org/10.1007/s00170-022-09118-y
Misra, N. N., et al. (2022). IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry. IEEE Internet of Things Journal, 9(9), 6305–6324. https://doi.org/10.1109/JIOT.2020.2998584 DOI: https://doi.org/10.1109/JIOT.2020.2998584
Napoleone, A., Bruzzone, A., Andersen, A. L., and Brunoe, T. D. (2022). Fostering the Reuse of Manufacturing Resources for Resilient and Sustainable Supply Chains. Sustainability, 14(9), 5890. https://doi.org/10.3390/su14105890 DOI: https://doi.org/10.3390/su14105890
Pansare, R., Yadav, G., Garza-Reyes, J. A., and Nagare, M. R. (2023). Assessment of Sustainable Development Goals Through Industry 4.0 and Reconfigurable Manufacturing System Practices. Journal of Manufacturing Technology Management, 34(2), 383–413. https://doi.org/10.1108/JMTM-05-2022-0206 DOI: https://doi.org/10.1108/JMTM-05-2022-0206
Polonevych, O. V., et al. (2020). Artificial Intelligence Applications for Project Management. Connectivity, 146, 054855. https://doi.org/10.31673/2412-9070.2020.054855 DOI: https://doi.org/10.31673/2412-9070.2020.054855
Prifti, V. (2022). Optimizing Project Management Using Artificial Intelligence. European Journal of Formal Sciences and Engineering, 5(1), 30–38. https://doi.org/10.26417/667hri67 DOI: https://doi.org/10.26417/667hri67
Ullah, S., Kukreti, M., Sami, A., and Shaukat, M. R. (2025). Leveraging Technological Readiness and Green Dynamic Capability to Enhance Sustainability Performance in Manufacturing Firms. Journal of Manufacturing Technology Management, 36(4), 714–730. https://doi.org/10.1108/JMTM-05-2024-0268 DOI: https://doi.org/10.1108/JMTM-05-2024-0268
Ullah, S., Mehmood, T., and Ahmad, T. (2023). Green Intellectual Capital and Green HRM Enabling Organizations Go Green: Mediating Role of Green Innovation. International Journal of Innovation Science, 15(2), 245–259. https://doi.org/10.1108/IJIS-12-2021-0222 DOI: https://doi.org/10.1108/IJIS-12-2021-0222
Vavrík, V., Fusko, M., Bučková, M., Gašo, M., Furmannová, B., and Štaffenová, K. (2022). Designing of Machine Backups in Reconfigurable Manufacturing Systems. Applied Sciences, 12(5), 2338. https://doi.org/10.3390/app12052338 DOI: https://doi.org/10.3390/app12052338
Yazdani, M. A., Khezri, A., and Benyoucef, L. (2022). Process and Production Planning for Sustainable Reconfigurable Manufacturing Systems (SRMSs): Multi-Objective Exact and Heuristic-Based Approaches. International Journal of Advanced Manufacturing Technology, 119, 4519–4540. https://doi.org/10.1007/s00170-021-08409-0 DOI: https://doi.org/10.1007/s00170-021-08409-0
Zheng, T., Ardolino, A. B. M., and Perona, M. (2021). The Applications of Industry 4.0 Technologies in Manufacturing Context: A Systematic Literature Review. International Journal of Production Research, 59(6), 1922–1954. https://doi.org/10.1080/00207543.2020.1824085 DOI: https://doi.org/10.1080/00207543.2020.1824085
Zidi, S., Kermad, L., Hamani, N., and Zidi, H. (2023). Reconfigurable Supply Chain Selection: Literature Review, Research Roadmap, and New Trends. Applied Sciences, 13(7), 4561. https://doi.org/10.3390/app13074561 DOI: https://doi.org/10.3390/app13074561
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Copyright (c) 2026 Dr. Chaitrali Dilip Kale, Gurpreet Kaur, Bipin Sule, Rajendra Subhash Jarad, Dr. Prabha D, Dr. Daljeet Singh Bawa, Priyadharshini K

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