ear Ege Academic Review 1303-099X Ege University 10.21121/eab.20260210 Business Administration İşletme USE OF ARTIFICIAL INTELLIGENCE SUPPORTED PROMPT TOOLS IN OFFICE APPLICATIONS https://orcid.org/0000-0002-4027-3789 Çelik Cemal BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ 04 01 2026 26 2 303 312 08 18 2024 12 25 2025 Copyright © 2000, Ege Academic Review 2000 Ege Academic Review

Artificial Intelligence (AI)-enabled prompting technologies have begun to produce more functional solutions for improving personal productivity and creativity by providing office software users with simpler information search and prompting commands. Prompt technologies that facilitate ways of accessing data and information sources from different systems aim to strengthen the effects of individual contributions to the improvement of business processes by contributing to the creation of more creative documents, presentations, e-mails and spreadsheets. Copilot is an AI-enabled prompting technology developed for this purpose. It is an assistant that enables users to reveal their more productive and creative sides with the features of gathering and transferring information and documents created in Microsoft 365 and other office applications. In this article, the basic components and functionality of the AI-supported Copilot prompt technology are introduced, and its individual and corporate effectiveness regarding the company processes is tried to be explained with an application. In the application process, it is aimed to support the decision processes of a national company operating in the e-commerce market on pet foods by processing the data of a national company operating in the e-commerce market with Copilot AI assistant integrated into Excel application.

Yapay Zeka (AI) destekli istem teknolojileri, ofis yazılımları kullanıcılarına daha basit bilgi arama ve istem komutları sunarak kişisel üretkenliği ve yaratıcılığı artırmaya yönelik daha işlevsel çözümler üretmeye başladı. Farklı sistemlerden veri ve bilgi kaynaklarına erişim yollarını kolaylaştıran istem teknolojileri, daha yaratıcı belgeler, sunumlar, e-postalar ve elektronik tablolar oluşturulmasına katkı sağlayarak iş süreçlerinin iyileştirilmesinde bireysel katkıların etkilerini güçlendirmeyi hedefliyor. Copilot bu amaçla geliştirilmiş yapay zeka destekli bir yönlendirme teknolojisidir. Microsoft 365 ve diğer ofis uygulamalarında oluşturulan bilgi ve belgeleri bir araya getirme ve aktarma özellikleri ile kullanıcıların daha üretken ve yaratıcı yönlerini ortaya çıkarmalarını sağlayan bir asistandır. Bu makalede yapay zeka destekli Copilot istem teknolojisinin temel bileşenleri ve işlevselliği tanıtılmış, şirket süreçlerine ilişkin bireysel ve kurumsal etkinliği bir uygulama ile anlatılmaya çalışılmıştır. Uygulama sürecinde, e-ticaret pazarında faaliyet gösteren ulusal bir firmanın evcil hayvan mamaları konusundaki verilerinin Excel uygulamasına entegre edilen Copilot yapay zeka asistanı ile işlenerek karar süreçlerinin desteklenmesi amaçlanmıştır.

 Prompt Prompt Engineer Data Analysisi Prompt Tools İstem ; İstem Mühendisi Veri Analizi İstem Araçları Agrawal, A., Gans, J. S. & Goldfarb, A. (2019). Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives, 33(2), 31-50. https://doi.org/10.1257/jep.33.2.31 Ahuja, M. (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 Knowledge Management, 71, 1-63. https://doi.org/10.1016/j.ijinfomgt.2023.102642 Berente, N., Gu, B., Recker, J. & Santhanam, R. (2021). Managing Artifıcial Intelligence. MIS Quarterly, 45(3), 1433-1448. https://doi.org/10.25300/MISQ/2021/16274 Bird, C., Ford, D. & Zimmermann, T. (2022). Taking Flight with Copilot. Communications of the ACM, 66(6), 56-62. https://doi.org/10.1145/3589996 Cusumano, M. A. (2023). Generative AI as a New Innovation Platform. Communications of the ACM , 66(10), 18-21. https://doi.org/10.1145/3615859 Çapar, M. C. & Ceylan, M. (2022). Durum Çalışması ve Olgubilim Desenlerinin Karşılaştırılması. Anadolu Üniversitesi SBD, 22(2), 295-313. https://doi.org/10.18037/ausbd.1227359 Darvishi, A.,Khosravi, H., Sadiq, S., Gasevic, D. & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 1-18. https://doi.org/10.1016/j.compedu.2023.104967 Devlin, J., Lee, Chang, M., Lee, Kenton. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Paper presented at the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.(pp.4171-4186). https://aclanthology.org/N19-1423/ Fiannaca, A. J., Kulkarni, C. & Carrie J Cai, M. T. (2023). Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming. Paper presented at the CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. (pp.1-7) https://doi.org/10.1145/3544549.3585737 France, S. L. (2024). Navigating software development in the ChatGPT and GitHub Copilot era. Business Horizons, 67(5), 1-13. https://doi.org/10.1016/j.bushor.2024.05.009 Giray, L. (2023). Prompt Engineering with ChatGPT: A Guide for Academic Writers. Biomedical Engineering Society, 51, 2629–2633. https://doi.org/10.1007/s10439-023-03272-4 Goloujeh, A. M., Sullivan, A. & Magerko, B. (2024). Is It AI or Is It Me? Understanding Users Prompt Journey with Text-to-Image Generative AI Tools, Paper presented at the CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems.(pp.1-13) https://dl.acm.org/doi/proceedings/10.1145/3613904 Goktas, P. & Grzybowski, A. (2024). Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review. Journal of Clinical Medicine,13(19), 5909. https://doi.org/10.3390/jcm13195909 Goktas, P. & Grzybowski, A. (2025). Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. Journal of Clinical Medicine, 14(5), 1605. https://doi.org/10.3390/jcm14051605 Goktas, P., Kucukkaya, A. & Karacay, P. (2023). Utilizing GPT 4.0 with prompt learning in nursing education: A case study approach based on Benner's theory. Teaching and Learning in Nursing, 19(2), 358-368. https://doi.org/10.1016/j.teln.2023.12.014 Goktas, P., Kucukkaya, A. & Karacay, P. (2023). Leveraging the efficiency and transparency of artificial intelligence-driven visual Chatbot through smart prompt learning concept. Skin Research and Technology, 1-2. https://doi.org/10.1111/srt.13417 Gu, X., Yoo, X. M. & Lee, S.-W. (2021). Response Generation with Context-Aware Prompt Learning. arXiv, 1-10. https://doi.org/10.48550/arXiv.2111.02643 Gupta, R., Nair, K., Mishra, M., Ibrahim, B. & Bharwaj, S. (2024). Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. International Journal of Information Management Data Insights, 4(1), 100232. https://doi.org/10.1016/j.jjimei.2024.100232 Haghighat, P., Nguyen, T. & Valizadeh, M. (2023). Effects of an intelligent virtual assistant on office task performance and workload in a noisy environment. Applied Ergonomics, 109, 1-10. https://doi.org/10.1016/j.apergo.2023.103969 Jeff, C. & Stephen, J. (2024). The rise of AI copilots: How LLMs turn data into actions, advance the business intelligence industry and make data accessible company-wide. Applied Marketing Analytics, 9(3), 207-214. https://doi.org/10.69554/SJEI6374 Kar, A. K., Varsha, P. S. & Rajan, S. (2023). Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature. Global Journal of Flexible Systems Management, 24(5), 659-689. https://doi.org/10.1007/s40171-023-00356-x Knoth, N., Tolzin, A., Janson, A. & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies, Computers and Education Artificial Intelligence, 6, 1-14. https://doi.org/10.1016/j.caeai.2024.100225 Kyto, M. (2024). Copilot for Microsoft 365: A Comprehensive End-user Training Plan for Organizations. (Yüksek Lisans Tezi), Haaga-Helia University of Applied Sciences Li, Y., Sha, L., Yan, L. & Lin, J. (2023). Can large language models write reflectively. Computers and Education: Artificial Intelligence, 4, 1-11. https://doi.org/10.1016/j.caeai.2023.100140 Lim, W. M., Gunasekara, A. & Pallant, J. L. (2023). Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 1-13. https://doi.org/10.1016/j.ijme.2023.100790 Liu, P., Yuan, W., Fu, J. & Jiang, Z. (2021). Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys, 55(9), 1-35, https://doi.org/10.1145/3560815 Luitse, D. & Denkena, W. (2021). The great Transformer: Examining the role of large language models in the political economy of AI. Big Data & Society, 1-14. https://doi.org/10.1177/20539517211047734 Mahdi, M. & Yekta, J. (2024). The general intelligence of GPT–4, its knowledge diffusive and societal influences, and its governance. Meta-Radiology, 2(2), 1-17. https://doi.org/10.1016/j.metrad.2024.100078 Microsoft. (2024). Copilot Studio/ Fundamentals. https://learn.microsoft.com/tr-tr/microsoft-copilot-studio/fundamentals-what-is-copilot-studio Moorhouse, B. L. & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 1-10. https://doi.org/10.1016/j.system.2024.103290 Morgan, D. L. (2023). Exploring the Use of Artificial Intelligence for Qualitative Data Analysis: The Case of ChatGPT. International Journal of Qualitative Methods, 22 , 1-10. https://doi.org/10.1177/16094069231211248 Nah, F. H., Zheng, R., Cai, J., Siau, K. & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal Of Information Technology Case And Application Research, 25(3), 277-297. https://doi.org/10.1080/15228053.2023.2233814 Nazari, M. & Saadi, G. (2023). Developing efective prompts to improve communication with ChatGPT: a formula for higher education stakeholders. Discover Education, 3(45),1-17. https://doi.org/10.1007/s44217-024-00122-w Peres, R., Schreier, M., Schweidel, D. & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(1), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001 Shin, T., Razeghi, Y., Wallace, E. & Singh, S. (2020). Autoprompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. Paper presented at the Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, (pp.4222–4235). https://doi.org/10.48550/arXiv.2010.15980 Tepe, M. & Emekli, E. (2024). Decoding medical jargon: The use of AI language models (ChatGPT-4, BARD, microsoft copilot) in radiology reports. Patient Education and Counseling, 126,1-5. https://doi.org/10.1016/j.pec.2024.108307 White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Henry, G., S.,S., Jesse. & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. Paper Presented at the PLoP '23: Proceedings of the 30th Conference on Pattern Languages of Programs.(pp. 1-31.https://dl.acm.org/doi/10.5555/3721041.3721046 Yoşumaz, İ. (2024). Prompt Engineering Awareness: A Study on Google Trends Data. International Journal Of Social And Economic Studies,5(2), 248-268. https://doi.org/10.62001/gsijses.1532474 Zamfirescu-Pereira, J., Wong, R. Y., Hartmann, B. & Yang, Q. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts, Paper Presented at the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.(pp. 1-21). https://doi.org/10.1145/3544548.3581388 Zhang, B., & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. Oxford: Oxford Press. http://dx.doi.org/10.2139/ssrn.3312874