The Human-AI Interface: Socio-Technical Challenges in Implementing Generative AI for Project Documentation and Governance

Journal of Perspectives in Management

Rethinking the future

The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.

Governing the adaptive organism: A framework for human-AI symbiosis in anticipatory project management

Purpose: The integration of artificial intelligence (AI) into project management is accelerating, yet governance frameworks have failed to keep pace with the transition from tool-based automation to genuine human-AI symbiosis. This article synthesizes literature across four fragmented domains to propose a unified governance model for anticipatory project management. Design/methodology/approach: A systematic, integrative review was conducted covering: AI in project management, Human-AI collaboration, Anticipatory governance, and Complexity theory. The synthesis draws on peer-reviewed literature from 2023-2026, integrating seminal work on human-AI co-agency and adaptive systems. Findings: The review reveals a fundamental tension: technological capability outpaces organisational absorption. To address this, the article introduces the Adaptive Organism Governance (AOG) Framework. This model reconceptualises the project organisation as a complex adaptive system comprising four interdependent dimensions: Cognitive Augmentation, Collaborative Intelligence, Anticipatory Sensing, and Complexity Absorption. The framework is operationalized through six governance principles: subsidiarity, transparency, adaptability, resilience, human-centricity, and continuous learning. Originality/value: This article provides the first comprehensive synthesis linking AI technical capabilities with anticipatory governance theory. The AOG Framework offers a theoretically grounded, actionable pathway for practitioners navigating the digital transformation of project delivery, shifting the focus from controlling machines to cultivating symbiotic ecosystems.

A GOVERNANCE-ORIENTED PROMPT ENGINEERING FRAMEWORK FOR AI-ASSISTED INTERNAL AUDITING

The diffusion of generative Artificial Intelligence (AI) across organizational environments is reshaping both the objects of internal audit and the tools auditors use during assurance work. Although prior research has discussed AI adoption in auditing and the risks associated with large language models (LLMs), less attention has been paid to how auditors should structure, document, and govern their own interaction with these systems. This study adopts a conceptual synthesis design and integrates three literature domains: internal audit standards and governance guidance, LLM risk and security frameworks, and prompt engineering / human–AI interaction research. Based on this synthesis, the study develops a governance-oriented framework that links 13 audit-specific prompt types to the main phases of the internal audit lifecycle: planning, fieldwork, reporting, and follow-up. The proposed framework treats prompts and AI-assisted outputs not as informal productivity aids, but as reviewable working-paper artifacts subject to evidentiary discipline, human validation, and documentation controls. The analysis indicates that structured prompting can improve repeatability, transparency, and professional skepticism by making assumptions explicit, constraining unsupported inference, and strengthening the linkage between AI-generated text and auditor-supplied evidence. The article contributes to the literature by reframing prompt engineering as a governable audit competency and by providing a lifecycle-based prompt taxonomy for practical implementation. It also clarifies that LLM output should be used as intermediate analytical support rather than as audit evidence or authoritative judgment.

From Digital Administration to AI-Supported Governance: The Transformation Mechanism of Management Information Systems and Artificial Intelligence at UIN Kiai Ageng Muhammad Besari Ponorogo

This study addresses a governance problem often obscured by technology inventories: how an integrated Management Information System (MIS) serves as an institutional foundation for the emerging use of Artificial Intelligence (AI) in Islamic higher education governance. The literature has extensively examined MIS and AI as separate technologies, but provides less empirical explanation of what their integration changes in governance practice. This study therefore aims to examine (1) how MIS and AI are implemented and integrated at UIN Kiai Ageng Muhammad Besari Ponorogo, (2) how the integration contributes to institutional governance, and (3) what organizational and technological conditions enable or constrain the transition toward AI-supported governance. A qualitative case study was conducted using an in-depth interview with the Head of Information Technology and Database (TIPD), observation of digital services, and relevant institutional documentation. Data were analyzed using the interactive model of Miles, Huberman, and Saldaña through data condensation, data display, and conclusion drawing, supported by source and technique triangulation and member checking. The findings show that MIS integration through SIAKAD, SIMPEG, SIMKEU, LMS, SISTER, E-Office, and Single Sign-On has created a more connected institutional information infrastructure. AI, however, remains in an emerging stage: an NLP-based chatbot is under development, while predictive analytics, academic success and dropout prediction, and the smart-campus concept are identified as developing or prospective applications rather than fully operational systems. The central empirical contribution is that AI’s governance value depends on the maturity of the underlying MIS infrastructure: integration enables information to move from fragmented administrative records toward a shared data environment in which AI can potentially support anticipatory analysis and decision-making. Thus, the transformation mechanism is better understood as infrastructure integration → data consolidation → analytical capability → governance decision support, rather than simple technology adoption. The study implies that Islamic higher education institutions should prioritize interoperable information infrastructure, responsible AI governance, and institutional capacity before scaling AI-supported decision-making.

A research framework for the evaluation of operational impacts of AI solutions in logistics use cases

A research framework for analysing the added value of generative AI in logistics organizations, with a focus on “difficult to automate” tasks and processes is presented.