Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 30 references
TL;DR
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.
Abstract
Despite the strategic priority of digital transformation and Artificial Intelligence (AI), many organizational initiatives fail to achieve sustainable outcomes due to insufficient institutional readiness and fragmented governance. To address this gap, this paper introduces the Abuhaimed Digital & AI Excellence Model (ADAIEM), a comprehensive conceptual framework designed to foster institutional readiness and guide enduring transformation. The framework integrates three interdependent pillars: Institutional Foundation: Governance, strategy, organizational structure, processes, knowledge management, and talent development, Digital Enablement: Core digital systems, data infrastructure, automation, analytics, and platforms, and AI Enablement: AI governance, intelligent agents, decision-support mechanisms, and enterprise-wide adoption. Central to the framework is the ADAIEM Conditional Transformation Logic (ACTL), which utilizes capability gates to enforce progression only when prerequisite maturity levels are met. Unlike traditional static maturity models, ACTL actively facilitates continuous capability development, mitigates execution risks, and reinforces operational sustainability. Grounded in Business Engineering and organizational capability theory, ADAIEM advances the literature on digital transformation and AI governance by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations. Building on the concepts of Business Engineering and based on a variety of organizational and transformation theories, ADAIEM brings together governance, organizational design, knowledge management, talent development, digital capabilities and AI enablement under a single transformation architecture. The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation.
It is argued that AI adoption should be understood not merely as automation but as a technology-transfer problem, and the AITTF is introduced, a seven-stage governance model designed to help organisations transfer workflows, expertise and decision-making into AI-enabled systems while maintaining accountability, operational integrity and organisational memory.
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
Artificial Intelligence (AI) is transforming the global business landscape by enabling organizations to improve efficiency, innovation, and sustainability performance. In recent years, businesses have increasingly integrated AI technologies into their operational and strategic activities to strengthen Environmental, Social, and Governance (ESG) practices and contribute toward achieving the United Nations Sustainable Development Goals (SDGs). This paper examines the role of AI in enabling sustainable business transformation and enhancing ESG performance.The study is conceptual in nature and is based on secondary data collected from journal articles, sustainability reports, industry publications, and global case examples. The research highlights how AI-driven technologies such as machine learning, predictive analytics, intelligent automation, and data analytics support organizations in resource optimization, carbon emission reduction, supply chain resilience, ethical governance, and stakeholder engagement. The findings indicate that AI can significantly improve sustainability performance by enabling data-driven decision-making, operational transparency, and efficient resource management. AI applications in smart energy systems, waste reduction, workforce management, and ESG reporting are helping organizations align business goals with sustainability objectives. Companies such as Microsoft, Tesla, and Unilever demonstrate how AI can be strategically integrated into sustainable business models to create long-term economic, environmental, and social value.However, the study also identifies key challenges including data privacy concerns, algorithmic bias, ethical risks, implementation costs, and workforce displacement. Therefore, organizations must adopt responsible AI governance frameworks and ensure transparency, accountability, and inclusiveness in AI adoption.
S. R, Neetha Veronica A, Arpita Sastri et al.· International journal of com...· 0 citations
A process-centric reference architecture designed for technical implementability, traceability, auditability, and human-supervised enterprise-scale GAI adoption is contributed.
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
The study contributes to information systems research by reframing AI readiness from a static resource inventory to an evolving organisational capability and offers managers a diagnostic logic for sequencing AI investments and avoiding premature scaling.
K. Jonak, Andrzej Wodecki· Discover Artificial Intellig...· 0 citations
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