Ai-sewa: a framework for enhancing workforce development, ethical governance, and sustainability in the energy sector
DOI:
https://doi.org/10.25806/mevriz-477Keywords:
Artificial Intelligence O33, Energy Sector Q40, AI Implementation O32, Workforce Reskilling J24, Sustainability Q01Abstract
Integrating artificial intelligence (AI) in the energy sector holds significant potential to profoundly change the nature of operations, optimise resource management, and support global sustainability objectives. However, widespread adoption faces challenges, among these varying technological readiness levels, workforce skill gaps, and the need for ethical governance. This study explores these issues by analysing AI implementation across three leading energy companies – EDF, Lukoil, and Gazprom. Each company presents unique strategies and obstacles in their journey to embrace AI-driven innovations, offering valuable insights into the industry’s broader landscape. Building on these findings, the research introduces the AI-SEWA framework, a comprehensive model designed to address key barriers to AI adoption. The framework emphasises three interconnected areas: AI Capability Assessment, Workforce Development and Reskilling, and Ethical and Sustainability Integration. These components provide a structured roadmap for organisations aiming to harness AI’s potential while maintaining a strong focus on ethical and environmental alignment. This research contributes to the evolving conversation around AI’s transformative impact on the energy sector workforce by offering practical recommendations.
Информация о публикации
Финансирование: Исследование выполнено без привлечения внешнего финансирования, если иное не указано авторами.
Вклад авторов: Все авторы внесли существенный вклад в подготовку статьи, ознакомились с окончательной версией рукописи и одобрили ее к публикации.
Конфликт интересов: Авторы заявляют об отсутствии конфликта интересов, если иное не указано в публикации.
Правообладатель: ООО "Финпресс".
Условия использования. Полные тексты статей, опубликованных в журнале, распространяются по подписке. Все права защищены. Метаданные всех статей распространяются по открытой лицензии и могут свободно использоваться в информационных, научных и библиографических целях.
Машиночитаемый файл метаданных: JATS XML
Полная версия статьи доступна на сайте Научной электронной библиотеки eLIBRARY.RU: https://elibrary.ru/contents.asp?titleid=9628
References
2. Bidollahkhani M. & Kunkel J.M. (2024). Revolutionizing System reliability: The role of AI in predictive maintenance Strategies. arXiv (Cornell University). – 2024. doi.org/10.48550/arxiv.2404.13454
3. Tursunbayeva A. & Gal H.C. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders // Business Horizons. – 2024, 67 (4), 357–368. doi.org/10.1016/j.bushor.2024.04.006
4. Xie L., Zheng X., Sun Y., Huang T. & Bruton T. Massively digitised power grid: Opportunities and challenges of use-inspired AI. arXiv (Cornell University). – 2022. doi.org/10.48550/arxiv.2205.05180
5. Бутырин П.А. Электротехника и искусственный интеллект: общественные и правовые отношения, образование и коммуникации, угрозы // Электричество. – 2024. – №
11. – С. 4–8. doi.org/10.24160/0013-5380-2024-11-4-8
6. Elia G., Solazzo G., Lerro A., Pigni F. & Tucci C.L. The digital transformation canvas: A conceptual framework for leading the digital transformation process // Business Horizons. – 2024, 67 (4), 381–398. doi.org/10.1016/j.bushor.2024.03.007
7. Parasuraman A. & Colby C.L. Techno-Ready Marketing: How and Why Your Customers Adopt Technology. – 2015. – N.Y.: The Free Press.
8. Алфимов Г.Д. Развитие технологий искусственного интеллекта и машинного обучения в нефтегазовом комплексе // Экономика и бизнес: теория и практика. – 2024. – № 5-1 (111). doi.org/10.24412/2411-0450-2024-5-1-18-21
9. Ляндау Ю.В., Темирбулатов А.У. Обзор применения технологий искусственного интеллекта в электроэнергетической отрасли // Инновации и инвестиции. – 2023. – №
8. – С. 304–309.
10. Zechiel F., Blaurock M., Weber E., Büttgen M. & Coussement K. How tech companies advance sustainability through artificial intelligence: Developing and evaluating an AI x Sustainability strategy framework // Industrial Marketing Management. – 2024, 119, 75–89. doi.org/10.1016/j.indmarman.2024.03.010
11. Retkowsky J., Hafermalz E. & Huysman M. Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects // Business Horizons. – 2024, 67 (5), 511–523. doi.org/10.1016/j.bushor.2024.04.009
12. Schiuma G., Santarsiero F., Carlucci D. & Jarrar Y. Transformative leadership competencies for organisational digital transformation // Business Horizons. – 2024, 67 (4), 425–437. doi.org/10.1016/j.bushor.2024.04.004
13. Hashmi N. & Bal A.S. Generative AI in higher education and beyond // Business Horizons. – 2024, 67 (5), 607–614. doi.org/10.1016/j.bushor.2024.05.005
14. Khachatryan K. Entrepreneurship amid armed conflicts: Insights from Artsakh // Business Horizons. – 2024, 67 (6), 727–741. doi.org/10.1016/j.bushor.2024.07.004
15. Marcantonio R. Environmental violence and enterprise: The outsized role of business for environmental peacebuilding // Business Horizons. – 2024, 67 (6), 685–698. doi.org/10.1016/j.bushor.2024.07.003
16. Стрижакова Е.Н., Стрижаков Д.В. Искусственный интеллект в экономике: будущее чат-ботов // Менеджмент в России и за рубежом. – 2023. – №
5. – С. 32–39.
17. Berthon P., Yalcin T., Pehlivan E. & Rabinovich T. (2024). Trajectories of AI technologies: Insights for managers // Business Horizons. – 2024. doi.org/10.1016/j.bushor.2024.03.002
18. Badghish A. & Soomro B. AI adoption in SMEs: Insights from the Technology–Organization–Environment framework. Sustainability. – 2024, 16 (5), 18–64. doi.org/10.3390/su16051864
19. Hannigan T.R., McCarthy I.P. & Spicer A. (2024). Beware of botshit: How to manage the epistemic risks of generative chatbots // Business Horizons. – 2024. doi.org/10.1016/j.bushor.2024.03.001
20. Brewer J., Patel D., Kim D. & Murray A. Navigating the challenges of generative technologies: Proposing the integration of artificial intelligence and blockchain // Business Horizons. – 2024, 67 (5), 525–535. doi.org/10.1016/j.bushor.2024.04.011
21. Mancuso I., Petruzzelli A.M., Urbinati A. & Matzler K. Leadership in the metaverse: Building and integrating digital capabilities // Business Horizons. – 2024, 67 (4), 331–343. doi.org/10.1016/j.bushor.2024.04.005
22. Abdelhalim E., Anazodo K.S., Gali N. & Robson K. A framework of diversity, equity, and inclusion safeguards for chatbots // Business Horizons. – 2024, 67 (5), 487–498. doi.org/10.1016/j.bushor.2024.03.003
23. Williams C. Peacebuilding by MNE subsidiaries: The role of intangible capital and local initiative // Business Horizons. – 2024, 67 (6), 711–725. doi.org/10.1016/j.bushor.2024.08.005
24. Governing AI for humanity. – 2024. doi.org/10.18356/9789211067873
25. Yin R.K. Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications. – 2018.
26. Qu S.Q. & Dumay J. The Qualitative Research Interview. Qualitative Research in Accounting & Management. – 2011, 8 (3), 238–264. doi.org/10.1108/11766091111162070
27. Bryman A. Social Research Methods (5th ed.). Oxford University Press. – 2016.
28. Bowen G.A. Document Analysis as a Qualitative Research Method. Qualitative Research Journal. – 2009, 9 (2), 27–40. doi.org/10.3316/QRJ0902027
29. Braun V. & Clarke V. Using Thematic Analysis in Psychology. Qualitative Research in Psychology. – 2006, 3 (2), 77–101. doi.org/10.1191/1478088706qp063oa
30. Stake R.E. Multiple Case Study Analysis. Guilford Press. – 2006.
31. Wiles R. What Are Qualitative Research Ethics? Bloomsbury Publishing. – 2013.
32. Saunders B., Kitzinger J. & Kitzinger C. Anonymising Interview Data: Challenges and Compromise in Practice. Qualitative Research. – 2015, 15 (5), 616–632. doi.org/10.1177/1468794114550439
33. Busher H. & James N. Ethics of Qualitative Research: Realities, Reflections, and Implications. Sage Publications. – 2012.
34. Растопчина Ю.Л., Ковалева Е.И., Титова И.Н. Особенности применения искусственного интеллекта в корпоративном секторе // Региональная и отраслевая экономика. – 2025. – № 4 (апр.). doi.org/10.47576/2949-1916.2025.2.2.003
35. Weber M., Engert M., Schaffer N., Weking J. & Krcmar H. Organisational Capabilities for AI Implementation – Coping with Inscrutability and Data Dependency in AI // Information Systems Frontiers, 2023, 25, 1549–1569. doi.org/10.1007/s10796-022-10297-y
36. Landeta Echeberria A. AI Integration in the Digital Transformation Strategy. In Artificial Intelligence for Business, 2022 (pp. 115–140). Springer. doi.org/10.1007/978-3-030-88241-9_5
37. Park J.J. Unlocking training transfer in the age of artificial intelligence // Business Horizons. – 2024, 67 (3), 263–269. doi.org/10.1016/j.bushor.2024.02.002
38. Fountain T. & McCarthy B. Democratising artificial intelligence: Strategies for broad-based adoption // Harvard Business Review. – 2023, 101 (1), 78–85.
39. Пшеничная С.С. Формирование новых компетенций работников нефтегазовой отрасли в эпоху развития технологий искусственного интеллекта // Финансовые рынки и банки. – 2024.
40. Хабриева Т.Я. Правовые проблемы идентификации искусственного интеллекта // Вестник Российской академии наук. – 2024. – Т.
94. – №
7. – С. 609–622. doi.org/10.31857/s0869587324070015
41. Johnson D.A., Johnson C.M. & Dave P. Performance feedback in organizations: understanding the functions, forms, and important features // Journal of Organizational Behavior Management. – 2022, 43 (1), 64–89. doi.org/10.1080/01608061.2022.2089436
42. Valentini C., Munnukka J. & Zhao H. Stakeholder satisfaction with corporate conflict engagement actions: Exploring the effects of goodwill, trust, and value alignment // Business Horizons. – 2024, 67 (6), 797–813. doi.org/10.1016/j.bushor.2024.08.003
