Artificial Intelligence and Automation in Administrative Practices: A Critical Examination of the Impact on Institutional Memory and Governance Frameworks
DOI:
https://doi.org/10.55578/hrdm.2507.003Keywords:
AI, Automation, Governance, Human Oversight, Institutional Memory, Minute-Taking, Organisational Processes, Socio-Technical SystemsAbstract
Introduction and Context: The integration of Artificial Intelligence (AI) into organisational processes — particularly the automation of administrative functions such as minute-taking has transformed traditional workplace practices. While tools like Otter, Zoom, and Microsoft Teams have enhanced operational efficiency, concerns remain regarding their capacity to accurately capture the contextual nuances and subtleties essential for high-quality, comprehensive minutes. The paper explored the implications for governance, institutional memory, and human control by drawing on Socio-Technical Systems Theory.
Objective: The study aimed to examine the impact of AI and automation on administrative tasks, including minute-taking, and its implications for governance, institutional memory, and human oversight. It investigated the balance between technical efficiency and human discretion in organisational situations.
Methods: A narrative literature review was conducted to synthesise research published between 2006 and 2025. Articles on emerging digital technologies, such as AI, minute-taking, and institutional memory, were identified through systematic searches in academic databases. Thematic analysis revealed core themes: the use of automation in minutes, governance deficit, the decline of human judgment and issues about data retention.
Findings and Results: Automated minute-taking effectively increased efficiency, but omitting important context was sometimes linked to governance issues and inaccuracies. Human judgment being treated as a smaller part of the equation meant that it risked missing small signals or indicators that an organisational norm or culture was developing against the interest of the quality of the records or the institution’s memory. The study overview highlights the importance of integrating human supervision with AI and the need to establish governance mechanisms and standardised metadata protocols to enhance performance.
Practical Implications: The study recommends integrating AI tools with human oversight to ensure the accuracy and reliability of institutional records. Additionally, it advocates for adopting governance frameworks that address data privacy and regulatory compliance alongside the implementation of standardised metadata practices and robust digital archiving systems to enhance record-keeping and preserve institutional knowledge over time.
References
1.Bughin, J., Seong, J., Manyika, J., et al. (2018, September). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy
2.Nawaz, N., Hemalatha, A., Pathi, B. K., et al. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
3.Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116
4.Duranti, L., Rogers, C., McLeod, J., et al. (2019, September). Trusting records and data in the cloud: The creation, management, and preservation of trustworthy digital content. Victoria Lemieux's Lab. https://doi.org/10.29085/9781783304042
5.Enemosah, A., & Ifeanyi, O. G. (2025). Cloud security frameworks for protecting IoT devices and SCADA systems in automated environments. World Journal of Advanced Research and Reviews, 22(3), 2232–2252. https://doi.org/10.30574/wjarr.2024.22.3.1485
6.Richards, N. M., & King, J. H. (2014). Big data ethics. Wake Forest Law Review, 49(2), 393–432.
7.Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
8.Sellen, A. J., & Whittaker, S. (2010). Beyond total capture: A constructive critique of lifelogging. Communications of the ACM, 53(5), 70–77. https://doi.org/10.1145/1735223.1735243
9.Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149–159). Organiser: Association for Computing Machinery (ACM). Location: New York, NY, USA. Country: United States.
10.Ali, O., Murray, P. A., Momin, M., et al. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076
11.Dwivedi, Y. K., Kshetri, N., Hughes, L., et al. (2023). Opinion paper: “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, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
12.Babashahi, L., Barbosa, C. E., Lima, et al. (2024). AI in the Workplace : A Systematic Review of Skill Transformation in the Industry. Administrative Sciences, 14(6), 127. https://doi.org/10.3390/admsci14060127
13.Susskind, R., & Susskind, D. (2020). The future of the professions: How technology will transform the work of human experts (Oxford, 2015; online edn, Oxford Academic, 12 Nov. 2020). https://doi.org/10.1093/oso/9780198713395.001.0001
14.Babu, N. S., Marda, K., Mishra, A., et al. (2024). The impact of artificial intelligence in the workplace and its effect on the digital wellbeing of employees. Journal for Studies in Management and Planning, 10(4), 1–32. https://doi.org/10.5281/zenodo.10936348
15.Abazi Chaushi, B., Nastovska, M., & Veseli-Kurtishi, T. (2024, September). MS Teams for virtual collaboration – An overview. 4th International Conference on Educational Technology and Online Learning, Anadolu University, Eskişehir, Türkiye.
16.Cordella, A., & Gualdi, F. (2024). Regulating generative AI: The limits of technology-neutral regulatory frameworks. Insights from Italy’s intervention on ChatGPT. Government Information Quarterly, 41(4), 101982. https://doi.org/10.1016/j.giq.2024.101982
17.Vatamanu, A. F., & Tofan, M. (2025). Integrating Artificial Intelligence into Public Administration: Challenges and Vulnerabilities. Administrative Sciences, 15(4), 149. https://doi.org/10.3390/admsci15040149
18.Modiba, M. T. (2021). Utilising artificial intelligence technology for the management of records at the Council for Scientific and Industrial Research in South Africa (Doctoral dissertation, University of South Africa). https://doi.org/10.13140/RG.2.2.11451.49443
19.Alugoju, N. R. (2024). The dark side of AI: A growing global threat in cybersecurity. International Journal of Computer Engineering & Technology, 9(2), 579–587. https://doi.org/10.5281/zenodo.14405057
20.Nyaboke, Y. (2024). Intellectual property rights in the era of artificial intelligence. Journal of Modern Law and Policy, 4(2), 57–72. https://doi.org/10.47941/jmlp.2162
21.Al-kfairy, M., Mustafa, D., Kshetri, N., et al. (2024). Ethical challenges and solutions of generative AI: An interdisciplinary perspective. Informatics, 11(3), 58. https://doi.org/10.3390/informatics11030058
22.Osasona, F., Amoo, O. O., Atadoga, A., et al. (2024). Reviewing the ethical implications of AI in decision-making processes. International Journal of Management & Entrepreneurship Research, 6(2), 322–335. https://doi.org/10.51594/ijmer.v6i2.773
23.Akinade, A. O., Ige, A. B., & Pub, A. (2024). Cloud security challenges and solutions: A review of current best practices. International Journal of Multidisciplinary Research and Growth Evaluation, 6(1), 26–35. https://doi.org/10.54660/IJMRGE.2025.6.1.26-35
24.Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7–18. https://doi.org/10.1007/s13174-010-0007-6
25.Parycek, P., Schmid, V., & Novak, A.-S. (2023). Artificial intelligence (AI) and automation in administrative procedures: Potentials, limitations, and framework conditions. Journal of the Knowledge Economy, 15, 8390–8415. https://doi.org/10.1007/s13132-023-00808-4
26.Herdiyanti, A. (2024). The use of automatic AI-based notes and transcription services in qualitative research: Ethical and methodological concerns. In Proceedings of the ALISE Annual Conference. Association for Library and Information Science Education (ALISE), Pittsburgh, Pennsylvania, United States. https://doi.org/10.21900/j.alise.2024.1717
27.Clegg, C. W. (2000). Socio-technical principles for system design. Applied Ergonomics, 31(5), 463–477. https://doi.org/10.1016/S0003-6870(00)00009-0
28.Mumford, E. (2006). The story of socio‐technical design: Reflections on its successes, failures and potential. Information Systems Journal, 16(4), 317–342. https://doi.org/10.1111/j.1365-2575.2006.00221.x
29.Buchal, R., & Songsore, E. (2019). Using Microsoft Teams to support collaborative knowledge building in the context of sustainability assessment. Proceedings of the Canadian Engineering Education Association (CEEA). https://doi.org/10.24908/pceea.vi0.13806
30.George, A. S. (2024). Consequences of enterprise cloud migration on institutional information technology knowledge. Partners Universal Innovative Research Publication (PUIRP), 2(2), 38–55. https://doi.org/10.5281/zenodo.10938874
31.Pillen, D., & Eckard, M. (2023). The impact of the shift to cloud computing on digital record-keeping practices at the University of Michigan Bentley Historical Library. Archival Science, 23(1), 65–80. https://doi.org/10.1007/s10502-022-09391-9
32.Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
33.Emery, F. E., & Trist, E. L. (1960). Socio-technical systems. In C. W. Churchman & M. Verhulst (Eds.), Management science models and techniques (Vol. 2, pp. 83–97). Pergamon.
34.Pasmore, W. A. (1988). Designing effective organisations: The socio-technical systems perspective. John Wiley & Sons.
35.Gregory, A. T., & Denniss, A. R. (2018). An introduction to writing narrative and systematic reviews—Tasks, tips and traps for aspiring authors. Heart, Lung and Circulation, 27(7), 893–898. https://doi.org/10.1016/j.hlc.2018.03.027
36.Sukhera, J. (2022). Narrative reviews: Flexible, rigorous, and practical. Journal of Graduate Medical Education, 14(4), 414. https://doi.org/10.4300/JGME-D-22-00480.1
37.Boma-Siaminabo, H. (2022). Office virtualisation, virtual meeting, office of today and the future. Journal of International Research in Accounting and Management, 10(3), 78–88.
38.Kurniati, E. Y., & Fithriani, R. (2022). Post-graduate students’ perceptions of QuillBot Utilization in English academic writing class. Journal of English Language Teaching and Linguistics, 7(3), 437–451. https://doi.org/10.21462/jeltl.v7i3.852
39.Xuyen, N. T. (2023). Using the online paraphrasing tool Quillbot to assist students in paraphrasing the source information: English-majored students’ perceptions. In T. T. Nghi (Ed.), Proceedings of the 5th Conference on Language Teaching and Learning (pp. 21–27). AIJR. Conference organised by AIJR Publisher, Vietnam, held in Hanoi. https://doi.org/10.21467/proceedings.150.3
40.Fitria, T. N. (2025). QuillBot grammar checker: The ability to evaluate students’ writing errors of English essay. International Journal of Computer and Information System (IJCIS), 6(1), 1–12. https://doi.org/10.29040/ijcis.v6i1.201
41.Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377
42.Molas, G., & Nowak, E. (2020). Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence. Applied Sciences, 11(23), 11254. https://doi.org/10.3390/app112311254
43.Canning, D., & Jaillant, L. (2025). AI to review government records: New work to unlock historically significant digital records. AI & Society. https://doi.org/10.1007/s00146-025-02221-0
44.Oyighan, D., Ukubeyinje, E. S., David-West, et al. (2024). The role of AI in transforming metadata management: Insights on challenges, opportunities, and emerging trends. Asian Journal of Information Science and Technology, 14(2), 20–26. https://doi.org/10.70112/ajist-2024.14.2.4277
45.Yeo, G. (2007). Concepts of record (1): Evidence, information, and persistent representations. The American Archivist, 70(2), 315–343. https://doi.org/10.17723/aarc.70.2.u327764v1036756q
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Data Availability Statement
The data used in this study were derived from secondary sources compiled through a comprehensive literature review. All data are publicly available and have been appropriately cited in the manuscript.
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