project management

This study addresses the lack of a systematic review on the application of generative artificial intelligence (GenAI) in IT project management. Following the PRISMA methodology, it comprehensively synthesizes current research on GenAI’s technical approaches, application scenarios, adoption trends, and tool integration. The analysis reveals that existing studies remain largely exploratory, predominantly leveraging GPT-family models through prompt engineering. To advance the field, the paper proposes three innovative directions: process-group-oriented AI agents, role-based AI agents tailored to specific project functions, and hybrid collaborative networks that support human-guided coordination. These contributions offer a cohesive theoretical framework and integrative pathways for future research and practical implementation in AI-enhanced project management.

Current AI governance research lacks systematic integration of diverse frameworks and practices, with notable gaps in the operationalizability of key mechanisms and the implementation of inclusive, stakeholder-centered approaches. To address this, we conduct a rapid three-tier literature review, systematically synthesizing nine authoritative IEEE/ACM reviews published between 2020 and 2024. We introduce the novel “thematic semantic synthesis” analytical paradigm to identify high-frequency governance frameworks (e.g., the EU AI Act, NIST AI Risk Management Framework), core principles (e.g., transparency, accountability), and stakeholder role distributions. Our analysis reveals four critical knowledge gaps in AI governance scholarship and practice. Based on these findings, we propose a rigorously grounded, organizationally feasible governance roadmap—bridging theoretical advancement and real-world implementation. This work contributes both empirical evidence and methodological innovation to advance AI governance research and practice.

This study addresses the challenge of determining appropriate granularity and responsibility allocation for AI agents in human-AI collaborative software engineering. The authors propose a method that dynamically generates AI agent roles based on project-specific context by integrating object-centric process mining with both imperative and declarative process modeling. Leveraging event logs from software repositories, the approach automatically discovers agent structures aligned with the unique characteristics of a project’s development process and synthesizes corresponding specifications and implementations. Moving beyond predefined role templates, the method has been successfully deployed in prominent open-source projects. Empirical validation through functional testing and user studies demonstrates that the generated agents exhibit responsibility boundaries and collaboration efficiency closely matching developer expectations.

This study addresses the profound transformations in user roles, workflows, and collaboration patterns within enterprise software platforms driven by artificial intelligence, which existing role frameworks—such as the BTP user type matrix—struggle to accommodate. Through 20 expert interviews and a participatory design workshop involving 24 participants, the research employs qualitative methods to investigate structural shifts in developer roles on the SAP Business Technology Platform. Findings reveal three key trends: automation of operational tasks, expanded human-AI collaboration, and increased reliance on agent-based systems. In response, the study argues for a necessary reconfiguration of role taxonomies and governance mechanisms, offering both theoretical grounding and practical guidance for designing and governing AI-native enterprise software.

This study addresses the pervasive capability gaps and cultural barriers that traditional software teams encounter when integrating AI collaboration. It proposes an “AI champion” development framework—a systematic educational pathway that leverages internally cultivated talent to simultaneously drive organizational cultural transformation and technical architecture evolution. Implemented empirically within a Brazilian technology firm, the initiative combines principles of organizational learning, upskilling, and change management. Findings demonstrate the effectiveness of this paradigm in establishing effective human-AI collaborative teams and yield actionable insights into key enablers and recurring challenges. The work contributes both theoretical understanding and practical guidance for building organizational capacity in human-AI collaboration.

Ambiguity in attributing responsibility for AI system failures impedes accountability and redress. Method: This study establishes the first systematic conceptual framework for AI responsibility, formalizing responsibility as a ternary relation “Agent A is responsible for Outcome O.” It decomposes responsibility along three dimensions—responsible agent, responsibility type (e.g., causal, moral, legal), and outcome scope—revealing their inherent variability. The framework integrates a scalable responsibility ontology, a visualizable responsibility graph, and a scenario-driven analytical paradigm. Contribution/Results: Through conceptual modeling, semantic framework design, and empirical case analysis—including a hypothetical autonomous vessel collision—the study maps and analyzes the distributed, often conflicting responsibilities of developers, operators, regulators, and other stakeholders across multiple responsibility dimensions. It provides both theoretical foundations and practical tools to inform AI governance policy and responsible AI engineering practice.

This study addresses the prevalent conflation of human–AI interaction with genuine collaboration, noting that most current systems operate through consultation, instruction, or delegation rather than exhibiting core collaborative features such as symmetry, shared goals, and mutual regulation. Drawing on theories of collaborative learning, the work proposes a novel five-tier taxonomy of human–AI diagnostic collaboration—ranging from transactional to truly collaborative—and rigorously distinguishes pseudo-collaboration from authentic forms. It further identifies the critical functionalities and affordances necessary for achieving higher-order collaboration. Through process-sensitive empirical analysis of interaction data from educational writing and problem-solving tasks, the research demonstrates that mainstream AI systems predominantly remain at lower tiers, with only the highest level meeting established criteria for true collaboration. This framework offers a theoretical foundation, evaluative metric, and design guidance for responsible human–AI collaboration in education.

This study addresses coordination inefficiencies in human–AI collaboration within shared workspaces, where the absence of effective coordination mechanisms often incurs process losses and can even reduce team performance when new collaborators are introduced. To mitigate these issues, this work proposes a scaffolding mechanism that integrates shared group memory with human-in-the-loop (HITL) approval gating, employing structured coordination strategies to optimize responsibility allocation and expert knowledge scheduling. Evaluated across 1,482 experimental sessions using the Collaborative Gym environment and DiscoveryBench tasks, the approach significantly enhances joint decision-making performance in three-person teams, sharpens the clarity of responsibility signaling, and more effectively channels expert knowledge to guide collective action.

This study addresses the inadequacy of the current U.S. Department of Defense software acquisition pathways in effectively managing the unique challenges posed by artificial intelligence systems—particularly their data dynamism, model evolution, and governance requirements. Through scenario-based policy analysis, the authors embed a hypothetical AI-enabled project into critical junctures of the existing acquisition process to systematically evaluate how policies translate into practice. The analysis reveals that core guidance documents lack operational specificity, while AI-related controls are fragmented across supplementary materials, leading programs to rely on inconsistent local interpretations. To bridge this gap, the paper proposes a dedicated AI acquisition sub-pathway alongside targeted documentation enhancements, substantially aligning policy with practice in areas such as data provenance, lifecycle management, and human oversight.

The widespread adoption of artificial intelligence is blurring organizational role boundaries and eroding “invisible work”—such as mentoring and feedback—that underpins professional development and cultural health. Through semi-structured interviews with 24 product professionals in technology firms and subsequent thematic analysis, this study systematically uncovers AI’s dual impact: while enhancing peer-level collaboration, it simultaneously weakens traditional mechanisms of career support. To address these tensions, the research introduces a strategic framework that renders invisible work visible and offers actionable interventions for organizations, leaders, and individuals. These measures aim to preserve cultural sustainability without compromising operational efficiency in AI-integrated workplaces.

This study addresses the challenge of systematically integrating generative AI into the entire software development lifecycle to enhance productivity while ensuring quality and governance. The authors propose a progressive integration framework centered on an innovative “AI harness” that unifies management of project context, access control, validation, logging, and human approval workflows. This architecture enables seamless co-evolution of technical capabilities, organizational processes, and quality assurance mechanisms. The framework supports a transition from informal AI assistance toward controlled, agent-based development and is empirically validated through a case study in a mid-sized software enterprise, offering both a practical roadmap and evidence-based foundation for AI-driven transformation in software engineering.