enterprise ai

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.

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.

Production-grade autonomous AI workflows face significant engineering challenges in reliability, observability, maintainability, and security governance. Method: We propose a structured, full-lifecycle methodology comprising a multi-agent architecture with collaborative reasoning, tool augmentation, and dynamic orchestration—integrated with the Model Context Protocol (MCP), deterministic orchestration, pure function invocation, containerized deployment, and modular tool integration. We further define nine core engineering practices, including tool-first design, single-responsibility agents, externalized prompt management, and model-federation-driven responsible AI design. Contribution/Results: This work establishes the first systematic engineering paradigm for Agentic AI productionization, markedly improving system simplicity, observability, and governability. Empirical validation via a multimodal news analysis–media generation use case demonstrates robustness and scalability. The methodology provides a reusable framework and practical benchmark for industrial-scale autonomous AI systems.

This study addresses the prevailing gap in AI education, which emphasizes model development while neglecting system engineering practices, leaving students ill-equipped to handle real-world challenges such as architectural design, deployment, and monitoring. To bridge this gap, the authors implemented a master’s-level course in which students built a movie recommendation system under realistic constraints, with a focus on integrating AI components into robust software systems, adopting data-driven machine learning practices, and cultivating systems-level thinking. Using a mixed-methods approach—combining analysis of student project artifacts with survey data—the research evaluates learners’ performance in architectural decision-making, integration of heterogeneous models, and adaptation to evolving requirements. Findings reveal common difficulties students encounter in AI system engineering and demonstrate the course’s effectiveness in addressing critical deficiencies in AI engineering education and enhancing systems-aware competencies.

Contemporary executive teams struggle to manage environmental, structural, and strategic tensions arising from the rise of the AI economy, organizational transformation, and shifting competitive paradigms. This study develops the first theoretical framework for establishing a Chief Artificial Intelligence Officer (CAIO), integrating insights from strategic management, organizational design, and AI governance. It identifies critical antecedents for CAIO appointment and delineates the role’s core responsibilities: AI strategy formulation, cross-functional governance coordination, and technology–business alignment. Innovatively, the paper proposes a novel CAIO role model and a phased implementation roadmap, elevating AI leadership from operational execution to enterprise-level strategic governance. The findings provide both theoretical foundations and actionable guidance for global organizations seeking to institutionalize AI leadership at the C-suite level, thereby enabling deep integration of AI into corporate strategy and decision-making architecture.

This study addresses the semantic, behavioral, and interoperability challenges inherent in cross-organizational SysML model integration. By surveying Model-Based Systems Engineering (MBSE) stakeholders through online questionnaires employing Likert scales, we evaluate the supportive utility of large language models (LLMs). Our findings reveal multidimensional alignment difficulties and identify high-value scenarios where AI excels in semantic analysis and inconsistency detection. Consequently, this work proposes an AI-assisted paradigm that augments rather than replaces engineering accountability. The core contribution lies in establishing a human-in-the-loop collaborative verification mechanism, providing both empirical evidence and a practical framework for AI-empowered complex systems engineering.

This study addresses the limitations of current AI auditing practices, which predominantly focus on individual models while overlooking integration risks arising from interactions among system components and between systems and their environments. Through a scoping review and reflexive thematic analysis of 58 studies, the work systematically codes existing literature to delineate, for the first time, three distinct domains of AI integration auditing: inter-component, system–environment, and multi-system. It further introduces domain-specific evaluation dimensions—compatibility, completeness, and oversight—that capture unique aspects of integrated AI systems. The findings reveal that current auditing practices remain fragmented and nascent, underscoring the critical role of accessible information and resource support in effective audit design. The paper calls for novel auditing frameworks capable of spanning components, environments, and systems to enable systematic exploration, identification, coordination, and standardization of integration-related risks.

本文提出AI-GRACE框架，通过连接组织治理与技术实施来解决智能体AI部署中的验证、控制和观察问题，以确保达成预期目标并履行义务。

This study addresses the transformative challenges that increasing AI agent autonomy poses to team structures, competencies, and strategies within embedded software organizations. Drawing on a case study of a large-scale embedded systems enterprise, the research employs semi-structured workshops and a mixed-methods approach to conduct an empirical analysis. The work proposes a federated AI team-building model alongside a human–machine collaboration practice framework. Furthermore, it elucidates the profound impact of AI integration on organizational roles and capabilities, ultimately delivering a sustainable transformation roadmap for organizations transitioning toward an AI-first paradigm.

针对多域决策环境的复杂性，提出一种自主代理AI架构，以支持巴西武装部队的决策，涵盖数据访问、执行工具及安全保障。