Organizational strategies for ethical and sustainable AI implementation

Bence Pistrui; Petra Jenei

doi:10.14267/VEZTUD.2026.11.04

Authors

Bence Pistrui Corvinus University of Budapest https://orcid.org/0000-0002-9984-3352
Petra Jenei Corvinus University of Budapest

DOI:

https://doi.org/10.14267/VEZTUD.2026.11.04

Keywords:

AI governance, Triple Bottom Line (TBL), Technology-Organization-Environment (TOE) framework, digital transformation

Abstract

The rapid diffusion of artificial intelligence (AI) has intensified debate about its dual role as a driver of efficiency and a source of environmental, social, and ethical challenges. These challenges include the growing energy and water demands of data centers, hardware-related emissions and e-waste, data governance risks, workforce concerns, and increasing regulatory pressure. While prior research highlights AI’s sustainability potential, less is known about how organizations translate ethical and sustainability principles into concrete implementation strategies. This study examines responsible AI adoption under sustainability constraints, using the Technology-Organization-Environment framework extended with ESG and ethics considerations, and the Triple Bottom Line perspective to interpret outcomes. Empirical insights are based on five semi-structured expert interviews with senior executives, AI specialists, and sustainability professionals across industries and national contexts. The findings show that AI supports efficiency, predictive analytics, and sustainability monitoring, but responsible deployment depends on readiness, data quality, workforce capabilities, and governance structures.

Downloads

Download data is not yet available.

Author Biographies

Bence Pistrui, Corvinus University of Budapest

assistant professor
Petra Jenei, Corvinus University of Budapest

PhD student

References

Alharahsheh, H.H., & Pius, A. (2020). A review of key paradigms: positivism VS interpretivism. Global Academic Journal of Humanities and Social Sciences, 2, 39-43. https://doi.org/10.36348/gajhss.2020.v02i03.001

Alzoubi, Y.I., & Mishra, A. (2024). Green artificial intelligence initiatives: Potentials and challenges. Journal of Cleaner Production, 468, 143090. https://doi.org/10.1016/j.jclepro.2024.143090

Baker, J. (2012). The technology-organization-environment framework. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information systems theory: Explaining and predicting our digital society (Vol. 1, pp. 231-245). Springer. https://doi.org/10.1007/978-1-4419-6108-2_12

Bauer, A., & Agárdi, I. (2020). Strategic implications of digitalization and the changing logic of value creation. Vezetéstudomány, 51(4), 23-35.

Baumgartner, M., Kopp, T., & Niever, M. (2025). Twin transition — A literature analysis of the relationship between two megatrends and the role of artificial intelligence. International Journal of Innovation Management, 29(05n06). https://doi.org/10.1142/s1363919625400110

Bokolo, A., Jr. (2024). Decentralized AIoT-based intelligence for sustainable energy prosumption in local energy communities: A citizen-centric prosumer approach. Cities, 152, 105198. https://doi.org/10.1016/j.cities.2024.105198

Chotisarna, N., & Phuthong, T. (2025). A bibliometric analysis insights into the intellectual dynamics of artificial intelligence for the micro, small, and medium enterprises. Cogent Business & Management, 12(1), 2491684. https://doi.org/10.1080/23311975.2025.2491684

Di Chiacchio, L., Al-Khateeb, H., Butt, U., & Yussof, S. (2026). AI legitimacy in energy: A model to improve corporate narratives on sustainability and responsibility. Technological Forecasting & Social Change, 222, 124378. https://doi.org/10.1016/j.techfore.2025.124378

Dwivedi, Y.K., Hughes, D.L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Eccles, R.G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835-2857. https://doi.org/10.1287/mnsc.2014.1984

El Saadani Gazar, O.A.E.K., Auda, H., & Youssif, A.A.A. (2024). The role of applying artificial intelligence in improving supply chain management and sustainability: Evidence from Egypt ports. International Journal of Mathematical, Engineering and Management Sciences, 9(5), 1145-1162. https://doi.org/10.33889/IJMEMS.2024.9.5.060

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … 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

Fu, T., Qiu, Z., Yang, X., & Li, Z. (2024). Charting the digital route to net-zero: A framework for sustainable industry practices. Technological Forecasting & Social Change, 209, 123821. https://doi.org/10.1016/j.techfore.2024.123821

Gandía, J.a.G., Gavrila, S.G., De Lucas Ancillo, A., & Del Val Núñez, M.T. (2024). Towards sustainable business in the automation era: Exploring its transformative impact from top management and employee perspective. Technological Forecasting and Social Change, 210, 123908. https://doi.org/10.1016/j.techfore.2024.123908

Gillan, S.L., Koch, A., & Starks, L.T. (2021). Firms and social responsibility: A review of ESG and CSR research. Journal of Corporate Finance, 66, 101889. https://doi.org/10.1016/j.jcorpfin.2021.101889

Hordieiev, M., Nalyvaiko, N., Parkhomenko-Kutsevil, O., Molyboha, R., & Chabanov, A. (2025). The metamorphosis of global trade routes: Immediate challenges and advanced logistics techniques. Management (Montevideo), 3, 257. https://doi.org/10.62486/agma2025257

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI Ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2

Jorzik, P., Antonio, J. L., Kanbach, D. K., Kallmuenzer, A., & Kraus, S. (2024). Sowing the seeds for sustainability: A business model innovation perspective on artificial intelligence in green technology startups. Technological Forecasting & Social Change, 208, 123653. https://doi.org/10.1016/j.techfore.2024.123653

Khan, A.N., Mehmood, K., & Ali, A. (2024). CSR impact: Leveraging artificial intelligence and process optimization for sustainability performance management. Corporate Social Responsibility and Environmental Management, 31(5), 4849-4861. https://doi.org/10.1002/csr.2832

Kibiswa, N. (2019). Directed Qualitative Content Analysis (DQLCA): a tool for conflict analysis. The Qualitative Report, 24(8), 2059-2079. https://doi.org/10.46743/2160-3715/2019.3778

Kittipanyangam, P., Tan, K.H., & Cavite, H.J. (2025). Future- ready AI: A framework for ethical and sustainable adoption. Technology in Society, 83, 102993. https://doi.org/10.1016/j.techsoc.2025.102993

Kupi, M. (2025). The impact of an artificial intelligence- based forecasting model on the development of sustainable tourism. Geojournal of Tourism and Geosites, 61(3), 1758-1766. https://doi.org/10.30892/gtg.61334-1544

Kusmendar, A.M.S.A., & Normasari, N.M.E. (2025). Exploring sustainable pathways: A systematic literature review of three pillars of sustainability applications. Sustainable Futures, 10, 100970. https://doi.org/10.1016/j.sftr.2025.100970

Kvale, S., & Brinkmann, S. (2015). InterViews: Learning the craft of qualitative research interviewing (3rd ed.). Sage Publications.

Litvinets, V. (2024). AI and Sustainability: Opportunities, Challenges, and Impact. Ernst & Young. https://www.ey.com/en_nl/insights/climate-change-sustainability-services/ai-and-sustainability-opportunities-challenges-and-impact

Luo, J., & Zhang, X. (2025). Corporate sustainability: An advanced driver of artificial intelligence. International Journal of Enterprise Information Systems, 20(1). https://doi.org/10.4018/IJEIS.387397

Monroy-Osorio, J.C. (2024). The impact of artificial intelligence on green technology cycles in China. Cleaner Logistics and Supply Chain, 13, 100191. https://doi.org/10.1016/j.clscn.2024.100191

Monsalve-Castro, C., Molina, R.I.R., Ortiz, E.J.F., & Polo, G.P.S. (2025). Artificial intelligence as a driver in adopting green innovation in the agricultural sector: evidence from an emerging economy. Cogent Business & Management, 12(1), 2575261. https://doi.org/10.1080/23311975.2025.2575261

Oliveira, T., & Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Information & Management, 48(4-5), 110-121. https://doi.org/10.1016/j.im.2011.03.002

Pietrzak, P. (2025). How artificial intelligence can support the sustainable development of organizations? Findings from a literature review. Issues in Information Systems, 26(1), 296-307. https://doi.org/10.48009/1_iis_122

Podrecca, M., Culot, G., Tavassoli, S., & Orzes, G. (2024). Artificial intelligence for climate change: A patent analysis in the manufacturing sector. In IEEE Transactions on Engineering Management (vol. 71, pp. 15005- 15024). IEEE. https://doi.org/10.1109/TEM.2024.3469370

Porkodi, S., Tantravahi, V.V.P.K., & Tabash, B.K.H. (2025). Transforming sustainable and green supply chains with artificial intelligence: A strategic review and future research opportunities. International Journal of Production Management and Engineering, 13(2). https://doi.org/10.4995/ijpme.2025.22632

Rashid, A., Rasheed, R., Ngah, A.H., & Amirah, N.A. (2024). Unleashing the power of cloud adoption and artificial intelligence in optimizing resilience and sustainable manufacturing supply chain in the USA. Journal of Manufacturing Technology Management, 35(7), 1329-1353. https://doi.org/10.1108/JMTM-02-2024-0080

Rastogi, S., & Pandita, D. (2025). Driving entrepreneurial success: Navigating AI-driven transformation through workforce agility and sustainability. Journal of Innovation and Entrepreneurship, 14, 75. https://doi.org/10.1186/s13731-025-00554-0

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education.

Senna, P.P., Barros, A.C., Roca, J.B., & Azevedo, A. (2023). Development of a digital maturity model for Industry 4.0 based on the technology-organization-environment framework. Computers & Industrial Engineering, 185, 109645. https://doi.org/10.1016/j.cie.2023.109645

Stilgoe, J., Owen, R., & Macnaghten, P. (2013). Developing a framework for responsible innovation. Research Policy, 42(9), 1568-1580. https://doi.org/10.1016/j.respol.2013.05.008

Szabó, Z.R., & Csontos, R. (2021). Sustainability and digitalization in corporate strategy. Vezetéstudomány, 52(3), 2-13.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

Varga, J., & Csesznák, A. (2019). Digital transformation and corporate strategy in Hungary. Vezetéstudomány, 50(5), 2-13. https://www.epa.oszk.hu/05300/05345/00276/pdf/

Varriale, V., Cammarano, A., Michelino, F., & Caputo, M. (2026). Artificial intelligence in technology networks: A catalyst for achieving the SDGs. Technovation, 151, 103398. https://doi.org/10.1016/j.technovation.2025.103398

von Schomberg, R. (2013). A vision of responsible research and innovation. In R. Owen, J. Bessant, & M. Heintz (Eds.), Responsible innovation (pp. 51-74). Wiley. https://doi.org/10.1002/9781118551424.ch3

Xu, Z., Ni, G., & Hu, R. (2025). AI technology innovation, knowledge management and corporate environmental sustainability: Evidence from Chinese patent data. Technology in Society, 83, 102984. https://doi.org/10.1016/j.techsoc.2025.102984

Zhang, J. (2025). Artificial intelligence and green innovation: Investigating the effects of executive pay and firm age. Research in International Business and Finance, 77(PartB), 102953. https://doi.org/10.1016/j.ribaf.2025.102953