Design and Implementation of an AI Agent-Based Workflow Automation System for Scheduling and Information Dissemination in Oil Palm Plantations
DOI:
https://doi.org/10.61255/decoding.v4i2.1370Keywords:
AI agent, Automation, Information dissemination, Meeting scheduling, Palm oil plantationsAbstract
Purpose – Oil palm plantation operations in Indonesia require coordination across multiple divisions; however, meeting scheduling and information dissemination are often managed through separate, manual processes. This may cause communication delays, scheduling conflicts, and inconsistent information deliveries. This study aims to design and implement an AI-based workflow automation system that integrates meeting scheduling and information dissemination into a centralized platform to support the automated coordination and information management across organizational units.
Methods – This study employed the Design Science Research (DSR) approach, covering problem identification, literature review, system design, implementation, testing and evaluation. The proposed system integrates Gemini AI, Natural Language Processing (NLP), Telegram Bot, Zoom API, Google Calendar API, and Google Sheets to automate meeting scheduling, information dissemination, and document management.
Findings – The implemented system successfully automated meeting scheduling, calendar synchronization, information dissemination, and documentation management within an integrated platform. Functional testing confirmed that the core features operated as intended in the scenarios evaluated. The system also supports information classification based on public and private access.
Research implications – The system was evaluated through functional testing in a simulated oil palm plantation context and depends on third-party API services, which may limit its generalizability. User acceptance, organizational effectiveness, and efficiency were not evaluated. Nevertheless, the proposed architecture can be adapted to other organizational settings that require automated coordination and centralized information management.
Originality – This study proposes an AI-based workflow automation architecture that integrates communication and productivity services to support the end-to-end automation of meeting scheduling and information dissemination in oil palm plantation operations.
Abstract views: 10
, 
PDF downloads: 6
Downloads
References
H. Tandra and A. I. Suroso, “The determinant, efficiency, and potential of Indonesian palm oil downstream export to the global market,” Cogent Economics & Finance, vol. 11, no. 1, Dec. 2023, doi: 10.1080/23322039.2023.2189671.
Sekretariat Jenderal - Kementerian Pertanian, “OUTLOOK KELAPA SAWIT 2024 Pusat Data dan Sistem Informasi Pertanian i OUTLOOK KELAPA SAWIT Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal-Kementerian Pertanian 2024,” Jakarta, 2024.
A. Nurhuda, A. Ganda Permana, R. Andrea, and A. Khoirunnita, “Design and Development of a Web-Based Information System for Palm Oil Plantations: A Case Study of PT Kaltim Utama Plantation I,” International Journal of Education and Management Engineering, vol. 16, no. 2, pp. 15–33, Apr. 2026, doi: 10.5815/ijeme.2026.02.02.
P. C. Verhoef et al., “Digital transformation: A multidisciplinary reflection and research agenda,” J. Bus. Res., vol. 122, pp. 889–901, Jan. 2021, doi: 10.1016/j.jbusres.2019.09.022.
J. Mendling, B. T. Pentland, and J. Recker, “Building a complementary agenda for business process management and digital innovation,” European Journal of Information Systems, vol. 29, no. 3, pp. 208–219, May 2020, doi: 10.1080/0960085X.2020.1755207.
Z. Zeng et al., “FlowMind: Automatic Workflow Generation with LLMs,” in 4th ACM International Conference on AI in Finance, New York, NY, USA: ACM, Nov. 2023, pp. 73–81. doi: 10.1145/3604237.3626908.
A. Rao et al., “Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study,” J. Med. Internet Res., vol. 25, 2023, doi: 10.2196/48659.
Y. K. Dwivedi et al., “Opinion Paper: ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” Int. J. Inf. Manage., vol. 71, p. 102642, Aug. 2023, doi: 10.1016/j.ijinfomgt.2023.102642.
M. Dumas et al., “AI-augmented Business Process Management Systems: A Research Manifesto,” ACM Trans. Manag. Inf. Syst., vol. 14, no. 1, pp. 1–19, Mar. 2023, doi: 10.1145/3576047.
N. Bienefeld, M. Kolbe, G. Camen, D. Huser, and P. K. Buehler, “Human-AI teaming: leveraging transactive memory and speaking up for enhanced team effectiveness,” Front. Psychol., vol. 14, Aug. 2023, doi: 10.3389/fpsyg.2023.1208019.
L. Wang et al., “A survey on large language model based autonomous agents,” Front. Comput. Sci., vol. 18, no. 6, p. 186345, Dec. 2024, doi: 10.1007/s11704-024-40231-1.
T. Tuunanen, R. Winter, and J. vom Brocke, “Dealing with Complexity in Design Science Research: A Methodology Using Design Echelons,” MIS Quarterly, vol. 48, no. 2, pp. 427–458, Jun. 2024, doi: 10.25300/MISQ/2023/16700.
M. Huseynli, U. Bub, and M. C. Ogbuachi, “Development of a Method for the Engineering of Digital Innovation Using Design Science Research,” Information, vol. 13, no. 12, p. 573, Dec. 2022, doi: 10.3390/info13120573.
C. K. Akello and J. Nabukenya, “Users involvement in the electronic health information systems development process in Uganda: what is missing in relation to requirements gathering and analysis,” Oxford Open Digital Health, vol. 2, Jan. 2024, doi: 10.1093/oodh/oqae020.
J. Hwang, T. Lee, H. Lee, and S. Byun, “A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study,” J. Med. Internet Res., vol. 24, no. 1, p. e28659, Jan. 2022, doi: 10.2196/28659.
A. Maspupah, “LITERATURE REVIEW: ADVANTAGES AND DISADVANTAGES OF BLACK BOX AND WHITE BOX TESTING METHODS,” Jurnal Techno Nusa Mandiri, vol. 21, no. 2, pp. 151–162, Sep. 2024, doi: 10.33480/techno.v21i2.5776.
H. Raihan and A. Voutama, “Pengujian Black Box Pada Aplikasi Database Perguruan Tinggi dengan Teknik Equivalence Partition,” Antivirus : Jurnal Ilmiah Teknik Informatika, vol. 17, no. 1, pp. 1–18, Jun. 2023, doi: 10.35457/antivirus.v17i1.2501.
S. Shah, H. Ghomeshi, E. Vakaj, E. Cooper, and S. Fouad, “A review of natural language processing in contact centre automation,” Pattern Analysis and Applications, vol. 26, no. 3, pp. 823–846, Aug. 2023, doi: 10.1007/s10044-023-01182-8.
C. Lin, Z. Peng, and W. Huang, “An AI Agent and Low-Code Platform Based Framework for Automated Meeting Scheduling,” Jan. 2026, doi: 10.3233/FAIA251689.
P. K. Ayuningtyas, D. Atmodjo WP, and P. Rachmadi, “Performance And Functional Testing With The Black Box Testing Method,” International Journal of Progressive Sciences and Technologies, vol. 39, no. 2, p. 212, Jul. 2023, doi: 10.52155/ijpsat.v39.2.5471.
P. A. D. A. Santi, R. Afwani, Moh. A. Albar, S. E. Anjarwani, and A. Z. Mardiansyah, “Black Box Testing with Equivalence Partitioning and Boundary Value Analysis Methods (Study Case: Academic Information System of Mataram University),” in Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science), Dordrecht: Atlantis Press International BV, 2022, pp. 207–219. doi: 10.2991/978-94-6463-084-8_19.
Sagar Mahableshwar Gadekar, “AI-Assisted Integration: Schema Matching, API Mapping, and Workflow Optimization,” International Journal of Computational and Experimental Science and Engineering, vol. 12, no. 2, May 2026, doi: 10.22399/ijcesen.5280.
C. Rutschi, N. Berente, and F. Nwanganga, “Data Sensitivity and Domain Specificity in Reuse of Machine Learning Applications,” Information Systems Frontiers, vol. 26, no. 2, pp. 633–640, Apr. 2024, doi: 10.1007/s10796-023-10388-4.
C. S. Bojer, B. Bygstad, and E. Øvrelid, “Speeding up Explorative BPM with Lightweight IT: the Case of Machine Learning,” Information Systems Frontiers, vol. 27, no. 2, pp. 823–840, Apr. 2025, doi: 10.1007/s10796-024-10474-1.
H. Harman and E. I. Sklar, “Multi-agent task allocation for harvest management,” Front. Robot. AI, vol. 9, Oct. 2022, doi: 10.3389/frobt.2022.864745.
J. Zhang et al., “AFlow: Automating Agentic Workflow Generation,” Apr. 2025, [Online]. Available: http://arxiv.org/abs/2410.10762
