Aug 2026· Electronics· Vol 15, pp. 3392· 0 citations· 35 references
TL;DR
A process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption is contributed.
Abstract
This study develops an integrated generative artificial intelligence (GAI) framework for improving business process performance in industrial enterprises. The framework treats GAI not as the isolated use of generative tools, but as a governable information systems capability embedded in recurring workflows, enterprise architectures, documented knowledge, and human decision roles. It integrates four functional subframeworks—manufacturing, marketing and sales, accounting and finance, and human resource management—with a shared orchestration and governance layer. This layer coordinates process architecture, approved data and knowledge sources, reusable GAI capabilities, human-in-the-loop validation, traceability, escalation, and performance measurement. A proof-of-concept maturity-readiness validation is conducted in an electronics company using maturity-readiness logic inspired by the Smart Industry Readiness Index (SIRI). The assessment shows an increase in the overall readiness score from 41.60 in the pre-GAI baseline to 79.08 in the post-GAI implementation scenario. Accordingly, the score increase is interpreted as expert-assessed maturity-readiness evidence rather than as a measured causal effect on operational performance. This study contributes a process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption.
The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations.
M. Abuhaimed· Journal of Intelligent Decis...· 0 citations
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
S. Rahman· International Journal of Art...· 0 citations
Small manufacturing enterprises remain economically important but continue to face recurring operational constraints in planning, scheduling, quality control, maintenance, and process-data use. Generative artificial intelligence offers increasingly accessible support for these bounded operational tasks, yet adoption remains uneven because many firms lack a coherent basis for linking digital opportunity to internal resources, organizational knowledge, and measurable operational improvement. This study develops a conceptual framework that integrates the resource-based view and the knowledge-based view to explain generative artificial intelligence adoption in small manufacturing enterprises. Using an evidence-grounded theory-development approach, the study builds a staged framework that separates foundational conditions, perceived operational AI opportunity, organizational translation mechanisms, and performance outcomes. The framework theorizes internal resources and knowledge assets as foundational antecedents, perceived generative artificial intelligence potential in operational functions as the adoption bridge, knowledge integration and dynamic capability as organizing mechanisms, and performance improvement as the downstream consequence. It further explains how conceptual clarity can support later empirical reduction without losing the richer logic needed for practical implementation. The study also clarifies how the framework can guide applied information-system design through data-readiness assessment, bounded decision-support use cases, human-in-the-loop verification, and operational KPI monitoring. The resulting architecture strengthens theoretical explanation and operational design logic for generative artificial intelligence adoption in constrained manufacturing environments, while preserving clear boundaries for subsequent validation and applied deployment.
Ikhwan Arief, Alizar Hasan, N. T. Putri et al.· Jurnal RESTI (Rekayasa Siste...· 0 citations
This paper systematically reviews the technological evolution and core capability characteristics of GAI and reveals the micro‑mechanisms by which GAI enables management innovation across three dimensions: knowledge recombination and abductive reasoning, simulation and counterfactual reasoning, and dynamic resource orchestration.
Zixuan Li· Business Management Perspect...· 0 citations
This study aims to develop a descriptive maturity model for evaluating the integration of IIoT technologies within enterprise environments. Although IIoT enables data-driven and automated operations, integration remains complex because of technological and organisational interdependencies. Existing models lack conceptual consistency and provide limited insight into operational integration. The study therefore proposes a theory-informed maturity model, specifically focused on IIoT integration into business operations.
The study uses a maturity model development methodology guided by design science research. It draws on literature-based insights to develop the initial model components and applies a theoretical foundation to interpret and structure the model dimensions. The model was then evaluated and refined through two design cycles involving expert interviews.
The maturity model comprises four dimensions: physical object, data, process and business service, organised and interpreted using the Semiotic Ladder and five maturity levels: pre-pilot, pilot, operational, organisation-scale and ecosystem-scale. It also identifies the key capabilities required to effectively integrate IIoT into business operations.
This study develops a theory-informed maturity model based on the semiotic ladder of organisational semiotics, offering a novel semiotics-based perspective for conceptualising and assessing IIoT maturity. It explicitly focuses on the integration of IIoT into business operations, emphasising the key dimensions required to support this integration.
Himashi Sandamini Widana Kankanamge, Alireza Nili, K. Desouza et al.· Journal of Enterprise Inform...· 0 citations
Cloud ERP platforms have passed through three distinct automation eras. Scripted batch jobs gave way to robotic process automation, and RPA is now giving way to autonomous agentic AI — systems that reason over enterprise data, select tools dynamically, and execute multi-step business workflows without human direction at every step. The shift is not merely a capability upgrade. Agentic systems behave non-deterministically, invoke tools whose scope may exceed what static governance models anticipate, and can produce cascading process consequences in live financial environments. Governance frameworks built for predictive models and rule-based bots were not designed for this. This paper proposes the Agentic ERP Governance Framework (AEGF), a five-dimension instrument designed to guide the responsible deployment of autonomous AI agents in cloud-based industrial management systems. Drawing on Sociotechnical Systems Theory, the Technology-Organisation-Environment framework, and the NIST AI Risk Management Framework, the AEGF addresses Process Suitability, Autonomy Tiering, Governance and Auditability, Organisational Readiness, and Risk and Continuity Management as an integrated governance architecture. An application to accounts payable automation on Oracle ERP Cloud illustrates how the framework operates in a representative industrial management context.
Venkata Ramachandra Karthik Chundi· International journal of com...· 0 citations
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