El rol transformador de la inteligencia artificial en la gerencia sostenible: Optimizando las cadenas de valor circulares
DOI:
https://doi.org/10.71068/amwjw653Palabras clave:
Inteligencia artificial, gestión sostenible, economía circular, cadena de valor, sostenibilidadResumen
Este estudio presenta una revisión sistemática de la literatura que explora la convergencia entre inteligencia artificial (IA) y gestión sostenible, destacando el papel de la IA en la optimización de la cadena de valor circular y su impacto en la sostenibilidad empresarial (ODS 12). Partiendo del análisis temático de un total de treinta y siete (37) artículos académicos y diversas fuentes especializadas, se identificaron las principales prácticas en las que la IA contribuye a lo largo del ciclo de vida del producto, desde las fases de diseño y producción hasta la logística inversa y la gestión de residuos. Los resultados muestran que la IA se posiciona como un catalizador fiable para la transición hacia una economía circular (EC), impulsando nuevos modelos de negocio como el producto como servicio (PaaS) y la remanufactura. Sin embargo, también surgen ciertos desafíos importantes que deben abordarse con un sentido crítico, humano y profundamente reflexivo, incluyendo el aumento del consumo energético, la huella de carbono asociada y los riesgos éticos asociados a los sesgos presentes en los algoritmos. La investigación concluye que una implementación exitosa de la inteligencia artificial parte de exigir un enfoque de gestión integral capaz de anticipar riesgos, fortalecer la colaboración entre los actores involucrados y cultivar un liderazgo adaptativo. Se propone un marco estratégico que orienta la acción gerencial abriendo el camino a nuevas líneas de investigación, contribuyendo además, al uso de la inteligencia artificial de un modo más ético, sostenible y transformador en el ámbito empresarial bajo el ODS 9.
Referencias
Acerbi, F., Forterre, D. A., & Taisch, M. (2021). Role of Artificial Intelligence in Circular Manufacturing: A Systematic Literature Review. IFAC-PapersOnLine, 54(1), 367-372. https://doi.org/10.1016/j.ifacol.2021.08.040
Agrawal, R., Wankhede, V. A., Kumar, A., Luthra, S., & Huisingh, D. (2022a). Progress and trends in integrating Industry 4.0 within Circular Economy: A comprehensive literature review and future research propositions. Business Strategy and the Environment, 31(1), 559-579. https://doi.org/10.1002/bse. 2910
Agrawal, R., Wankhede, V. A., Kumar, A., Luthra, S., Majumdar, A., & Kazancoglu, Y. (2022b). An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling. Operations Management Research, 15(3-4), 609-626. https://doi.org/10.1007/s12063-021-00212-0
Alonso, S. L. N., Forradellas, R. F. R., Morell, O. P., & Jorge-Vazquez, J. (2021). Digitalization, circular economy and environmental sustainability: The application of artificial intelligence in the efficient self-management of waste. Sustainability (Switzerland), 13(4), 1-20. https://doi.org/10.3390/su13042092
Amatucci, C. (2022). Sustainable growth and the role of artificial intelligence in improving the circular economy. Law & Digital Technologies, 2(1), 7. https://doi.org/10.18254/S278229070019620-8
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting and Social Change, 163, 120420. https://doi.org/10.1016/j.techfore.2020.120420
Belenguer L. (2022). AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry. AI and ethics, 2(4), 771–787. https://doi.org/10.1007/ s43681-022-00138-8
Bernat, K. (2023). Post-Consumer Plastic Waste Management: From Collection and Sortation to Mechanical Recycling. Energies, 16(8), 3504. https://doi.org/10. 3390/en16083504
Branch-Bedoya, J. W., Villa-Garzón, F., & Villa-Garzón, A. (7 de Abril de 2025). Generar una imagen de IA requiere más agua de la que usted debe tomar al día. Periódico UNAL. https://periodico.unal.edu.co/articulos/generar-una-imagen-de-ia-requiere -mas-agua-de-la-que-usted-debe-tomar-al-dia#:~:text=Investigacion es%20 de%20la%20%%2020%20Universidad%20de,imagen%5B3%2C%208%5D
Çetin, S., De Wolf, C., & Bocken, N. (2021). Circular Digital Built Environment: An Emerging Framework. Sustainability, 13(11), 6348. https://doi.org/10.3390/su13116348
Chauhan, C., Parida, V., & Dhir, A. (2022). Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises. Technological Forecasting and Social Change, 177. https://doi.org/10.1016/j.techfore.2022.121508
Chen, M., Liu, Q., Huang, S., & Dang, C. (2020). Environmental cost control system of manufacturing enterprises using artificial intelligence based on value chain of circular economy. Enterprise Information Systems, 16(8-9), 1856422. https://doi.org/10.1080/17517575.2020.1856422
Cherrafi, A., Chiarini, A., Belhadi, A., El Baz, J., & Chaouni Benabdellah, A. (2022). Digital technologies and circular economy practices: Vital enablers to support sustainable and resilient supply chain management in the post-COVID-19 era. The TQM Journal, 34(7), 179-202. https://doi.org/10.1108/TQM-12-2021-0374
Ding, Z., Chen, Z., Liu, J., Evrendilek, F., He, Y., & Xie, W. (2022). Co-combustion, life-cycle circularity, and artificial intelligence-based multi-objective optimization of two plastics and textile dyeing sludge. Journal of Hazardous Materials, 426, 128069. https://doi.org/10.1016/j.jhazmat.2021.128069
Fallahi, S., Mellquist, A., Mogren, O., Listo Zec, E., Algurén, P., & Hallquist, L. (2023). Financing solutions for circular business models: Exploring the role of business ecosystems and artificial intelligence. Business Strategy and the Environment, 32(6), 3233-3248. https://doi.org/10.1002/bse.3297
Ferrara, E. (2024). Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci, 6(1), 3. https://doi.org/10.3390/sci6010003
Fraga-Lamas, P., Lopes, S. I., & Fernández-Caramés, T. M. (2021). Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case. Sensors, 21(17), 5745. https://doi.org/10.3390/s21175745
Han, Y., Shevchenko, T., Yannou, B., Ranjbari, M., Shams Esfandabadi, Z., Saidani, M., Bouillass, G., Bliumska-Danko, K., & Li, G. (2023). Exploring How Digital Technologies Enable a Circular Economy of Products. Sustainability, 15(3), 2067. https://doi.org/10.3390/su15032067
Pradeep, R.K.., & Amit, Palaniappan. (2025). Product as a service (PaaS) for traditional product companies: an automotive lease practice evaluation. Journal of Indian Business Research. 15 (1). 40–54. https://doi.org/10.1108/JIBR-04-2022-0107
Jose, R., Panigrahi, S. K., Patil, R. A., Fernando, Y., & Ramakrishna, S. (2020). Artificial Intelligence-Driven Circular Economy as a Key Enabler for Sustainable Energy Management. Materials Circular Economy, 2(1), 8. https://doi.org/10.1007/s42824-020-00009-9
Kurniawan, T. A., Othman, M. H. D., Liang, X., Goh, H. H., Gikas, P., Kusworo, T. D., Anouzla, A., & Chew, K. W. (2023). Decarbonization in waste recycling industry using digitalization to promote net-zero emissions and its implications on sustainability. Journal of Environmental Management, 338. https://doi.org/10.1016/j.jenvman.2023.117765
Langley, H. (25 de julio de 2023). Google tiene sed: el consumo de agua de la tecnológica se dispara y parece que la IA solo va a empeorar las cosas. Business Insider. https://www.businessinsider.es/tecnologia/google-tiene-sed-parece-ia-solo-va-empeorar-cosas-1280416
Laskurain-Iturbe, I., Arana-Landín, G., Landeta-Manzano, B., & Uriarte-Gallastegi, N. (2021). Exploring the influence of industry 4.0 technologies on the circular economy. Journal of Cleaner Production, 321, 128944. https://doi.org/10.1016/j.jclepro.2021.128944
Liu, Z., Han, S., Yao, M., Gupta, S., & Laguir, I. (2023). Exploring drivers of eco-innovation in manufacturing firms’ circular economy transition: An awareness, motivation, capability perspective. Annals of Operations Research, 1-36. https://doi.org/10.1007/s10479-023-05473-5
Matheri, A. N., Mohamed, B., Ntuli, F., Nabadda, E., & Ngila, J. C. (2022). Sustainable circularity and intelligent data-driven operations and control of the wastewater treatment plant. Physics and Chemistry of the Earth, Parts A/B/C, 126, 103152. https://doi.org/10.1016/j.pce.2022.103152
Mboli, J., Thakker, D., & Mishra, J. (2023). Artificial Intelligence-Powered Decisions Support System for Circular Economy Business Models: Proceedings of the 25th International Conference on Enterprise Information Systems, 656-666. https://doi.org/10.5220/0011997100003467
Narong, DK, y Hallinger, P. (2023). Análisis de coocurrencia de palabras clave en la investigación sobre aprendizaje-servicio: Enfoques conceptuales y tendencias de investigación emergentes. Ciencias de la Educación , 13 (4), 339. https://doi.org/10.3390/educsci13040339
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed.), 372, n71. https://doi.org/10.1136/bmj.n71
Ramírez Chávez , M. A., & Litardo Caicedo , C. E. (2025). Agua e Inteligencia Artificial: El Lado Oculto del Progreso Tecnológico. Estudios Y Perspectivas Revista Científica Y Académica , 5(2), 47–65. https://doi.org/10.61384/r.c.a.v5i2.1096
Satav, A.G., Kubade, S., Amrutkar, C., Arya, G., & Pawar, A. (2023). A state-of-the-art review on robotics in waste sorting: scope and challenges. International Journal on Interactive Design and Manufacturing. 17, 2789–2806. https://doi.org/10.1007/s12008-023-01320-w
Schöggl, J.-P., Rusch, M., Stumpf, L., & Baumgartner, R. J. (2023). Implementation of digital technologies for a circular economy and sustainability management in the manufacturing sector. Sustainable Production and Consumption, 35, 401-420. https://doi.org/10.1016/j.spc.2022.11.012
Shaji, G., Hovan, g., & Gabrio, M. (2023). The Environmental Impact of AI: A Case Study of Water Consumption by Chat GPT. Partners Universal International Innovation Journal (PUIIJ). 1 (2). 93-104. http://dx.doi.org/10.5281/zenodo.7855594
Trivedi, A., & Hait, S. (2023). Metal bioleaching from printed circuit boards by bio-Fenton process: Optimization and prediction by response surface methodology and artificial intelligence models. Journal of Environmental Management, 326. https://doi.org/10.1016/j.jenvman.2022.116797
Wamba, S. F., Fotso, M., Mosconi, E., & Chai, J. (2023). Assessing the potential of plastic waste management in the circular economy: A longitudinal case study in an emerging economy. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05386-3
Wilts, H., Garcia, B. R., Garlito, R. G., Gómez, L. S., & Prieto, E. G. (2021). Artificial intelligence in the sorting of municipal waste as an enabler of the circular economy. Resources, 10(4). https://doi.org/10.3390/resources10040028
Wong, Y. A. (2025). A Study on the Effectiveness of Patagonia’s Sustainability-Driven Brand Strategy. Advances in Economics, Management and Political Sciences,180,302-306. https://doi.org/10.54254/2754-1169/2025.23726
Zota, R. D., Cîmpeanu, I. A., & Dragomir, D. D. (2023). Use and Design of Chatbots for the Circular Economy. Sensors, 23(11). https://doi.org/10.3390/s23114990
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2025 Carluys Suescum Coelho, Car Emyr Suescum Coelho, Carlysmar Suescum Coelho, Carelys Suescum Coelho (Autor/a)

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Los artículos publicados en la revista se distribuyen bajo la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0). Esta licencia permite a terceros descargar, copiar, distribuir, adaptar y reutilizar una obra, incluso con fines comerciales, siempre que se otorgue el crédito adecuado al autor original.
