Habilidades en analítica digital como impulsoras de la investigación innovadora en IA: implicaciones para el desarrollo avanzado de talento

Juan David Corrales Liévano; Juan Carlos Reyes Rojas

doi:10.12804/revistas.urosario.edu.co/empresa/a.14464

##plugins.block.makeSubmission.linkLabel##

Habilidades de análise digital como impulsionadoras de pesquisas inovadoras em ia: implicações para o desenvolvimento avançado de talentos

DOI: https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.14464

PDF (Español (España)) HTML (Español (España)) XML (Español (España))

Publicado: feb 7, 2025

Edição: v. 27 n. 48 (2025)

Seção Artículo de investigación científica y tecnológica

Palavras-chave: habilidades em análise digital inteligência artificial talentos em ciência de dados produtividade em pesquisa desenvolvimento de talentos transformação digital

Autores

Juan David Corrales Liévano
Universidad Militar Nueva Granada

Juan Carlos Reyes Rojas
Universidad Militar Nueva Granada

Resumo

Objetivo: neste estudo, exploramos a relação entre o talento em ciência de dados e a produtividade da pesquisa em inteligência artificial (IA), usando dados do Global AI Index 2023 para analisar como as habilidades de análise de dados impulsionam a inovação em ia globalmente. Metodologia: por meio de análise comparativa em 44 países, identificamos padrões significativos que ressaltam a importância das habilidades avançadas em ciência de dados para o desenvolvimento e inovação da IA. Principais resultados: os resultados destacam correlações claras entre a disponibilidade de talento em ciência de dados e a qualidade e a quantidade de resultados de pesquisa em IA, o que sugere que o fortalecimento da educação e do treinamento em ciência de dados é fundamental para o avanço do progresso tecnológico na área. Conclusões: este trabalho não só fornece evidências empíricas sobre o impacto do talento da ciência de dados na inovação da ia, mas também oferece recomendações de políticas e práticas que podem promover um ecossistema de ia mais dinâmico e produtivo.

Biografia do Autor

Juan Carlos Reyes Rojas, Universidad Militar Nueva Granada

Administrador de Empresas Universidad Externado de Colombia-Bogotá, Especialista en Docencia Universitaria Universidad Cooperativa de Colombia-Bogotá, Maestría en Educación Universidad Externado de Colombia-Bogotá, Maestría en Mercadotecnia Tecnológico de Monterrey Atizapán-México, Dr. en Gerencia Pública y Política Social Universidad de Baja California Tepic-México. Universidad Militar Nueva Granada Facultad de Ciencias Económicas. Profesor de Planta. Bogotá Colombia. Email: juan.reyes@unimilitar.edu.co ORCID: https://orcid.org/0009-0009-3929-4601

Como Citar

Corrales Liévano, J. D., & Reyes Rojas, J. C. (2025). Habilidades de análise digital como impulsionadoras de pesquisas inovadoras em ia: implicações para o desenvolvimento avançado de talentos. Revista Universidad Y Empresa, 27(48), 1–31. https://doi.org/10.12804/revistas.urosario.edu.co/empresa/a.14464

Referências

Abdullah, A. A. H., & Almaqtari, F. A. (2024). The impact of artificial intelligence and Industry 4.0 on transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity, 10(1). https://doi.org/10.1016/j.joitmc.2024.100218

Appio, F. P., Frattini, F., Messeni, A., Politecnico, P., Bari, D., Neirotti, P., & Di Torino, P. (2018). Digital transformation and innovation management: Opening up the black box motivation for the special issue. Journal of Product Innovation Management, 38(1). https://doi.org/10.1111/jpim.12562

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

Berger, T., & Frey, C. B. (2016). Digitalization, jobs, and convergence in Europe: Strategies for closing the skills gap. Oxford Martin School and University of Oxford. https://oms-www.files.svdcdn.com/production/downloads/reports/SCALE_Digitalisation_Final.pdf

Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of Manufacturing Systems, 49, 93-106. https://doi.org/10.1016/j.jmsy.2018.09.005}

Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia - Social and Behavioral Sciences, 195. https://doi.org/10.1016/j.sbspro.2015.06.134

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … 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. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Elsaadani, A., Purdy, M., & Hakutangwi, E. (2018). How artificial intelligence can drive diversification in the Middle East. https://www.readkong.com/page/how-artificial-intelligence-can-drive-diversification-in-5156266

Fredström, A., Wincent, J., Sjödin, D., Oghazi, P., & Parida, V. (2021). Tracking innovation diffusion: ai analysis of large-scale patent data towards an agenda for further research. Technological Forecasting and Social Change, 165. https://doi.org/10.1016/j.techfore.2020.120524

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162. https://doi.org/10.1016/j.techfore.2020.120392

Heart, T., Ben-Assuli, O., & Shabtai, I. (2017). A review of phr, emr and ehr integration: A more personalized healthcare and public health policy. Health Policy and Technology, 6(1). https://doi.org/10.1016/j.hlpt.2016.08.002

Hidayat, M., Defitri, S. Y., & Hilman, H. (2024). The impact of artificial intelligence (ai) on financial management. Management Studies and Business Journal (Productivity), 1(1). https://doi.org/10.62207/s298rx18

Kergroach, S. (2017). Industry 4.0: New challenges and opportunities for the labour market. Foresight and sti Governance, 11(4). https://doi.org/10.17323/2500-2597.2017.4.6.8

Lei, Y., Liang, Z., & Ruan, P. (2023). Evaluation on the impact of digital transformation on the economic resilience of the energy industry in the context of artificial intelligence. Energy Reports, 9. https://doi.org/10.1016/j.egyr.2022.12.019

Leone, D., Schiavone, F., Appio, F. P., & Chiao, B. (2021). How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research, 129. https://doi.org/10.1016/j.jbusres.2020.11.008

Li, G., Yuan, C., Kamarthi, S., Moghaddam, M., & Jin, X. (2021). Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis. Journal of Manufacturing Systems, 60. https://doi.org/10.1016/j.jmsy.2021.07.007

Mahmud, M. M., & Wong, S. F. (2022). Digital age: The importance of 21st century skills among the undergraduates. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.950553

McGunagle, D., & Zizka, L. (2020). Employability skills for 21st-century stem students: The employers’ perspective. Higher Education, Skills and Work-Based Learning, 10(3). https://doi.org/10.1108/HESWBL-10-2019-0148

Mićić, L. (2017). Digital transformation and its influence on gdp. Economics - Innovative and Economics Research Journal, 5(2). https://doi.org/10.1515/eoik-2017-0028

Moldovan, L. (2019). State-of-the-art analysis on the knowledge and skills gaps on the topic of industry 4.0 and the requirements for work-based learning. Procedia Manufacturing, 32. https://doi.org/10.1016/j.promfg.2019.02.217

North, C., Hills, D., Maher, P., Farkić, J., Zeilmann, V., Waite, S., Takano, T., Prince, H., Pedersen Gurholt, K., Muthomi, N., Njenga, D., Karaka-Clarke, T. H., Houge Mackenzie, S., & French, G. (2024). The impact of artificial intelligence on adventure education and outdoor learning: International perspectives. Journal of Adventure Education and Outdoor Learning, 24(1). https://doi.org/10.1080/14729679.2023.2248302

Nuseir, M. T., & Refae, G. E. (2022). The role of artificial intelligence, marketing strategies, and organizational capabilities in organizational performance: The moderating role of organizational behavior. Uncertain Supply Chain Management, 10(4), 1457-1466. https://doi.org/10.5267/j.uscm.2022.6.010

Omrani, N., Rivieccio, G., Fiore, U., Schiavone, F., & Agreda, S. G. (2022). To trust or not to trust? An assessment of trust in ai-based systems: Concerns, ethics and contexts. Technological Forecasting and Social Change, 181. https://doi.org/10.1016/j.techfore.2022.121763

Pandya, B., & Al Janahi, M. M. (2021). The intervention of artificial intelligence in the recruitment function in uae’s hospitality industry. Transnational Marketing Journal, 9(1). https://doi.org/10.33182/tmj.v9i1.1033

Pandya, B., Ruhi, U., & Patterson, L. (2023). Preparing the future workforce for 2030: The role of higher education institutions. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1295249

Park, H. J., & Choi, S. O. (2019). Digital innovation adoption and its economic impact focused on path analysis at national level. Journal of Open Innovation: Technology, Market, and Complexity, 5(3). https://doi.org/10.3390/joitmc5030056

Pirvu, R., Melania Mihai, D., Ecobici Nicolae, Antoniu, M.-E., Chirtoc, I. E., & Busan, G. (2024). How does artificial intelligence influence the socio-economic performance of companies in the European Union? Amfiteatru Economic, 26(66), 458-474.

Pricewater Coopers. (2018). us $320 billion by 2030? The potential impact of ai in the Middle East. https://www.pwc.com/m1/en/publications/documents/economic-potential-ai-middle-east.pdf

Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54. https://doi.org/10.1016/j.jmsy.2019.11.004

Sharma, R., & Sakpal, P. (2019). Study of Smart hr hybrid competency in industry 4.0. The Management Quest, 2(1).

Shen, Y., & Zhang, X. (2024). The impact of artificial intelligence on employment: The role of virtual agglomeration. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-02647-9

Suen, H.-Y., & Yang, J.-M. (2013). hr professionalism in the computing environment: Predicting job performance within different hr roles. International Management Review, 9(1).

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48. https://doi.org/10.1016/j.jmsy.2018.01.006

Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8). https://doi.org/10.1016/j.respol.2017.01.015

Thakur, R., Borkar, P., Rane, D., Raut, R., & Chatterjee, P. (2022). Challenges in Industry 4.0 for machine vision: A conceptual framework, a review and numerous case studies. En Machine vision for industry 4.0. CRC Press.

Tortoise Media. (2023). The Global ai Index. https://www.tortoisemedia.com

Truong, Y., & Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Technological Forecasting and Social Change, 183. https://doi.org/10.1016/j.techfore.2022.121852

Vista, A. (2020). Data-driven identification of skills for the future: 21st-century skills for the 21st-century workforce. Sage Open, 10(2). https://doi.org/10.1177/2158244020915904

Xu, J., Hong, N., Xu, Z., Zhao, Z., Wu, C., Kuang, K., Wang, J., Zhu, M., Zhou, J., Ren, K., Yang, X., Lu, C., Pei, J., & Shum, H. (2023). Data-driven learning for data rights, data pricing, and privacy computing. Engineering, 25. https://doi.org/10.1016/j.eng.2022.12.008

Yan, R. (2024). The impact of artificial intelligence on the labor market. International Journal of Global Economics and Management, 2(1). https://doi.org/10.62051/ijgem.v2n1.29

Zeba, G., Dabić, M., Čičak, M., Daim, T., & Yalcin, H. (2021). Technology mining: Artificial intelligence

in manufacturing. Technological Forecasting and Social Change, 171. https://doi.org/10.1016/j.techfore.2021.120971

Estatísticas de consulta