Purpose : This study aimed to identify and prioritize the benefits of AI integration through such technologies as machine learning, natural language processing, and robotic process automation for improving environmental, social, and governance (ESG) performance in the high-tech sector.

Methodology : The items (benefits) were generated through a comprehensive literature review and qualitative interviews with industry practitioners involved in AI integration for ESG. Qualitative thematic analysis categorized the items into relevant themes. Subsequently, survey data were collected from 170 respondents, including business heads, managers, AI/ESG consultants, and specialists. Exploratory factor analysis (EFA) was employed to identify the factors representing AI’s benefits for ESG. Furthermore, Pareto analysis identified the factors that significantly impacted the ESG parameters due to AI integration.

Findings : EFA resulted in a six-factor structure representing the benefits of AI: proactive governance (PG), environmental preservation (EP), risk management (RM), data management (DM), operational optimization (OO), and stakeholder engagement (SE). Pareto analysis indicated that PG, EP, and RM represented the most impactful areas.

Practical Implications : The study offered empirical evidence for improving the high-tech sector’s ESG performance. It guided industry practitioners to leverage AI for better positioning in ever-evolving competitive markets strategically. The validated framework will enable decision-makers to focus their investments in areas with the highest impact.

Originality : This research empirically validated the benefits of linking artificial intelligence to sustainability performance in the high-tech sector, contributing to academic literature and practical implementation.

1. Alkaraan, F., Albitar, K., Hussainey, K., & Venkatesh, V. G. (2022). Corporate transformation toward Industry 4.0 and financial performance: The influence of environmental, social, and governance (ESG). Technological Forecasting and Social Change, 175, Article ID 121423. https://doi.org/10.1016/j.techfore.2021.121423
2. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. https://doi.org/10.2307/3150783
3. Arora, A. K., & Rathi, P. (2019). Determinants for the adoption of digitalization by manufacturing SMEs in India: An empirical study. Prabandhan: Indian Journal of Management, 12(8), 7–21. https://doi.org/10.17010/pijom/2019/v12i8/146410
4. Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117. https://doi.org/10.1177/0049124187016001004
5. Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017). Artificial intelligence the next digital frontier? McKinsey & Company.
6. Burnaev, E., Mironov, E., Shpilman, A., Mironenko, M., & Katalevsky, D. (2023). Practical AI cases for solving ESG challenges. Sustainability, 15(17), 12731. https://doi.org/10.3390/su151712731
7. Chen, L., Chen, Z., Zhang, Y., Liu, Y., Osman, A. I., Farghali, M., Hua, J., Al-Fatesh, A., Ihara, I., Rooney, D. W., & Yap, P.-S. (2023). Artificial intelligence-based solutions for climate change: A review. Environmental Chemistry Letters, 21(5), 2525–2557. https://doi.org/10.1007/s10311-023-01617-y
8. Chen, P., & Hao, Y. (2022). Digital transformation and corporate environmental performance: The moderating role of board characteristics. Corporate Social Responsibility and Environmental Management, 29(5), 1757–1767. https://doi.org/10.1002/csr.2324
9. Chen, R., & Zhang, T. (2025). Artificial intelligence applications implication for ESG performance: Can digital transformation of enterprises promote sustainable development? Chinese Management Studies, 19(3), 676–701. https://doi.org/10.1108/CMS-11-2023-0653
10. Chen, W., Huang, X., Liu, Y., Luan, X., & Song, Y. (2019). The impact of high-tech industry agglomeration on green economy efficiency—Evidence from the Yangtze River economic belt. Sustainability, 11(19), 5189. https://doi.org/10.3390/su11195189
11. Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10(1), 7. https://doi.org/10.7275/jyj1-4868
12. de Villiers, C., Dimes, R., & Molinari, M. (2024). How will AI text generation and processing impact sustainability reporting? Critical analysis, a conceptual framework, and avenues for future research. Sustainability Accounting, Management and Policy Journal, 15(1), 96–118. https://doi.org/10.1108/SAMPJ-02-2023-0097
13. Dixit, A., Jha, R., Baber, R., & Baber, P. (2024). The impact of artificial intelligence on digital employee engagement. Prabandhan: Indian Journal of Management, 17(9), 24–43. https://doi.org/10.17010/pijom/2024/v17i9/173940
14. Dogru, A. K., & Keskin, B. B. (2020). AI in operations management: Applications, challenges and opportunities. Journal of Data, Information and Management, 2(2), 67–74. https://doi.org/10.1007/s42488-020-00023-1
15. Dwibedi, P., Pahi, D., & Mishra, A. P. (2025). Corporate environmental, social, and governance disclosure and performance dynamics: An empirical analysis of BRICS nations. Indian Journal of Finance, 19(2), 27–44. https://doi.org/10.17010/ijf/2025/v19i2/174769
16. Ernst & Young. (2023). Artificial intelligence ESG stakes. https://assets.ey.com/content/dam/ey-sites/ey-com/en_ca/topics/ai/ey-artificial-intelligence-esg-stakes-discussion-paper.pdf
17. Evalueserve. (2023). Building the future: AI's foundational role in ESG data production and assessment. https://www.evalueserve.com/wp-content/uploads/2023/09/Building_the_Future_AIs_Foundational_Role_in_ESG_Data_Production_and_Assessment_.pdf
18. Fang, M., Nie, H., & Shen, X. (2023). Can enterprise digitization improve ESG performance? Economic Modelling, 118, Article ID 106101. https://doi.org/10.1016/j.econmod.2022.106101
19. Galaz, V., Centeno, M. A., Callahan, P. W., Causevic, A., Patterson, T., Brass, I., Farber, D., Fischer, J., Garcia, D., McPhearson, T., Jimenez, D., King, B., Larcey, P., & Levy, K. (2021). Artificial intelligence, systemic risks, and sustainability. Technology in Society, 67, Article ID 101741. https://doi.org/10.1016/j.techsoc.2021.101741
20. Gupta, K., Raman, T. V., Deol, O. S., & Gupta, K. (2022). Environment, social, and governance performance and firm risk: A study of the Indian consumer goods sector. Indian Journal of Finance, 16(8), 29–45. https://doi.org/10.17010/ijf/2022/v16i8/171373
21. Hao, X., & Demir, E. (2024). Artificial intelligence in supply chain decision-making: An environmental, social, and governance triggering and technological inhibiting protocol. Journal of Modelling in Management, 19(2), 605–629. https://doi.org/10.1108/JM2-01-2023-0009
22. Henisz, W., Koller, T., & Nuttall, R. (2019). Five ways that ESG creates value: Getting your environmental, social, and governance (ESG) proposition right links to higher value creation. Here's why. McKinsey & Company. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/five-ways-that-esg-creates-value
23. Jain, K. (2023). Taking AI adoption in high tech to the mainstream. Infosys. https://www.infosys.com/insights/ai-automation/high-tech-mainstream.html
24. Johanson, G. A., & Brooks, G. P. (2009). Initial scale development: Sample size for pilot studies. Educational and Psychological Measurement, 70(3), 394–400. https://doi.org/10.1177/0013164409355692
25. Lee, Y.-C., Dervishi, I., Mousa, S., Safiullin, K. I., Ruban-Lazareva, N. V., Kosov, M. E., Ponkratov, A. S., Mikhina, E. V., & Elyakova, I. D. (2023). Sustainable development adoption in the high-tech sector: A focus on ecosystem players and their influence. Sustainability, 15(18), 13674. https://doi.org/10.3390/su151813674
26. Li, S., Younas, M. W., Maqsood, U. S., & Zahid, R. M. (2024). Impact of AI adoption on ESG performance: Evidence from Chinese firms. Energy & Environment. https://doi.org/10.1177/0958305X241269041
27. Lin, B., & Zhu, Y. (2025). Does AI elevate corporate ESG performance? A supply chain perspective. Business Strategy and the Environment, 34(1), 586–597. https://doi.org/10.1002/bse.3999
28. Loureiro, S. M., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. https://doi.org/10.1016/j.jbusres.2020.11.001
29. Lu, Y., Xu, C., Zhu, B., & Sun, Y. (2024). Digitalization transformation and ESG performance: Evidence from China. Business Strategy and the Environment, 33(2), 352–368. https://doi.org/10.1002/bse.3494
30. Meng, S., Su, H., & Yu, J. (2022). Digital transformation and corporate social performance: How do board independence and institutional ownership matter? Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.915583
31. Minimol, M. C., Akhila, K. V., & Kavya, N. P. (2025). Firm performance in India: Does sustainability reporting matter? Indian Journal of Finance, 19(3), 8–25. https://doi.org/10.17010/ijf/2025/v19i3/174847
32. Mohapatra, A. K., Matta, R., Soni, R., & Hiremath, N. V. (2024). Evaluating the role of artificial intelligence on ESG reporting: Evidence from India. Prabandhan: Indian Journal of Management, 17(11), 8–22. https://doi.org/10.17010/pijom/2024/v17i11/174020
33. Nadeem, F., & Singh, A. (2024). ESG investing from the perspective of 4Ps of marketing. Prabandhan: Indian Journal of Management, 17(6), 63–77. https://doi.org/10.17010/pijom/2024/v17i6/173561
34. Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, Article ID 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104
35. Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: Paving the way towards digital transformation and sustainable societies. Information Systems and E-Business Management, 16(3), 479–491. https://doi.org/10.1007/s10257-018-0377-z
36. Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147
37. Ren, S., & Mohd Isa, S. (2023). The impact of digital transformation on corporate ESG performance: Empirical evidence from Chinese listed companies. Journal of International Business, Economics and Entrepreneurship, 8(1), 1–10. https://doi.org/10.24191/jibe.v8i1.23147
38. Sætra, H. S. (2023). The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities. Sustainable Development, 31(2), 1027–1037. https://doi.org/10.1002/sd.2438
39. Sault, S. (2021). SDIS21: Tech for good - What are the challenges in making technology more sustainable? World Economic Forum. https://www.weforum.org/agenda/2021/09/tech-for-good-what-are-the-challenges-in-making-technology-more-sustainable-sdgs-4g-5g-cybersecurity-data-privacy-e-waste/
40. Singh, S., Dutta, S., & Sharma, D. (2024). Mapping the landscape of sustainability reporting: A bibliometric analysis. Prabandhan: Indian Journal of Management, 17(11), 61–77. https://doi.org/10.17010/pijom/2024/v17i11/174023
41. Sipola, J., Saunila, M., & Ukko, J. (2023). Adopting artificial intelligence in sustainable business. Journal of Cleaner Production, 426, Article ID 139197. https://doi.org/10.1016/j.jclepro.2023.139197
42. Taherdoost, H. (2016). Validity and reliability of the research instrument; How to test the validation of a questionnaire/survey in a research. SSRN. https://doi.org/10.2139/ssrn.3205040
43. Trahan, R. T., & Jantz, B. (2023). What is ESG? Rethinking the “E” pillar. Business Strategy and the Environment, 32(7), 4382–4391. https://doi.org/10.1002/bse.3371
44. Truant, E., Borlatto, E., Crocco, E., & Bhatia, M. (2023). ESG performance and technological change: Current state-of-the-art, development and future directions. Journal of Cleaner Production, 429, Article ID 139493. https://doi.org/10.1016/j.jclepro.2023.139493
45. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), Article no. 233. https://doi.org/10.1038/s41467-019-14108-y
46. Wei, J., & Zheng, Q. (2024). Environmental, social and governance performance: Dynamic capabilities through digital transformation. Management Decision, 62(12), 4021–4049. https://doi.org/10.1108/MD-10-2023-2028
47. World Economic Forum. (2022). Defining the 'G' in ESG governance factors at the heart of sustainable business. https://www3.weforum.org/docs/WEF_Defining_the_G_in_ESG_2022.pdf
48. Yang, P., Hao, X., Wang, L., Zhang, S., & Yang, L. (2024). Moving toward sustainable development: The influence of digital transformation on corporate ESG performance. Kybernetes, 53(2), 669–687. https://doi.org/10.1108/K-03-2023-0521
49. Zhang, C., & Yang, J. (2024). Artificial intelligence and corporate ESG performance. International Review of Economics & Finance, 96(Part C), Article ID 103713. https://doi.org/10.1016/j.iref.2024.103713