ai transformation

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.

In manufacturing, AI assets struggle to transition from prototypes to scalable deployment within cyber-physical production systems (CPPS), primarily due to high technical complexity, absence of domain-specific implementation standards, and fragmented organizational processes. Method: This paper proposes a full-lifecycle management process model for AI assets tailored to CPPS. Grounded in MLOps, the model integrates manufacturing systems engineering principles and industrial compliance requirements, customizing development collaboration, deployment integration, and operational governance dimensions for the manufacturing domain. Contribution/Results: It establishes the first industrial-grade AI asset management framework that jointly ensures technical feasibility, engineering operability, and regulatory compliance. The model significantly improves deployment efficiency, runtime robustness, and continuous iteration capability of AI models in dynamic production-line environments. By enabling systematic AI asset governance across the lifecycle, it provides a reusable methodological foundation for closing the AI value loop in smart manufacturing.

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.

Existing research lacks empirical grounding on how mission-driven organizations (MDOs)—particularly resource-constrained, values-oriented entities in the Global South and development sectors—prudently adopt AI. Method: Through 28 cross-regional, semi-structured interviews and thematic analysis, this study systematically examines AI’s practical deployment in content generation and data analytics, identifies adoption barriers, and explores integration aspirations. Contribution/Results: We propose a “Conditional AI Adoption” model, asserting that AI deployment must prioritize organizational sovereignty, mission integrity, and sustained human oversight—rejecting automation-first logic. Findings reveal that MDOs deliberately pause decision-making when efficiency gains conflict with core values, affirming AI’s role strictly as an augmentative tool for human-centered practice. This work addresses a critical empirical gap in AI governance literature across North–South contexts and offers both a theoretical framework and actionable pathways for responsible AI implementation in MDOs.

Small and medium-sized enterprises (SMEs) face significant challenges in operationalizing AI, primarily due to constrained resources, limited AI expertise, and the absence of lightweight, production-ready engineering and MLOps support. To address this gap, we propose the first lightweight AI engineering and MLOps blueprint framework specifically designed for SMEs. It integrates domain-customized reference architectures, automated toolchains, and iterative on-site validation mechanisms. Unlike generic enterprise-grade solutions, our blueprint prioritizes low entry barriers, high component reusability, and rapid deployment across the full AI lifecycle—encompassing model development, delivery, and operations. Empirical evaluation across multiple real-world business scenarios demonstrates an average 40% reduction in model delivery time and substantially improved development repeatability. Developer interviews confirm marked reductions in both technical adoption barriers and operational complexity. This work advances the scalable transfer of AI engineering practices from large enterprises to SMEs.

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.

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 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.

This study addresses the pervasive “capability–deployment validation gap” that impedes the real-world adoption of advanced AI agent systems in industry. Through in-depth interviews with 16 practitioners across 12 enterprises of varying scales and domains, and by applying a six-level AI maturity framework, the research systematically assesses current agent adoption practices. It reveals, for the first time, that this gap stems primarily from information asymmetry and a lack of organizational readiness. Key technical barriers include large language models’ context limitations, non-deterministic behavior, insufficient support for proprietary languages, and data confidentiality constraints. Findings indicate most organizations operate at Level 1 (AI assistant) or Level 2 (AI compensator), with only one reaching Level 3 (multi-agent orchestration); notably, four firms could not achieve production deployment due to the absence of output validation mechanisms.

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.