While artificial intelligence (AI) has become central to digital transformation strategies, organizations frequently struggle to move beyond pilot projects and symbolic adoption. We argue that this challenge stems from an overemphasis on implementation and an underappreciation of how AI is internalized by organizational members. Drawing on research on organizational practices, leadership, and ethical AI, this paper conceptualizes AI adoption as the synergistic interaction of implementation and internalization. We introduce a conceptual framework that identifies four patterns of AI adoption and highlights internalization as the primary mechanism through which AI creates value. Specifically, we develop a 2×2 implementation–internalization matrix that distinguishes four qualitatively different patterns of organizational AI adoption, ranging from embedded AI organizations to ceremonial AI adoption, grassroots AI emergence, and AI inertia. We further theorize internalization as a multi-pathway process driven by knowledge, experience, and aspiration, offering insight into how leaders shape trust, reliance, and ethical use of AI. The framework provides a practice-based lens for understanding AI success and failure and offers actionable guidance for leaders seeking to embed AI into organizational decision-making and culture.

Trust is a fundamental mechanism enabling participation in digital transformation environments characterised by uncertainty, technological mediation, and perceived risk. As organisations increasingly operate through platform-based digital ecosystems, trust becomes critical for facilitating interactions between customers, digital technologies, and service providers. Despite extensive research across e-commerce, artificial intelligence, fintech, and digital platforms, the literature remains fragmented across theoretical perspectives, constructs, and operationalisations. This study conducts a systematic literature review of 337 Q1 journal articles published between 2020 and 2025 to synthesise how customer and consumer trust has been conceptualised and examined in digital transformation ecosystems. The review identifies dominant theoretical models, consolidates recurring trust antecedents, and develops a layered socio-technical framework explaining trust formation across digital platforms and ecosystems. Findings reveal that trust emerges through the interaction of three interrelated layers: interface-level cognitive cues, experiential interaction mechanisms, and institutional security safeguards. The proposed framework contributes to digital transformation and technology and innovation management research by providing an integrated perspective on trust formation in platform-based ecosystems and offering a conceptual foundation for future empirical validation.

Digital transformation is continuously reshaping software-intensive systems, requiring architectures capable of operating under increasing complexity and constant change. Creating and ensuring that software architectures remain robust under complex conditions require systematic methods that connect abstract architectural intentions with concrete implementation choices. This paper introduces a structured technique for deriving technological software architectures from logical models within digitally transforming environments. This technique was used in a real project that addressed a scalable and interoperable data solution framework. The approach begins with a logical architecture model, a comprehensive set of non-functional requirements, and a catalog of well-established architectural patterns. From these inputs, the technical architecture is derived in an iterative and incremental manner, processing each non-functional requirement as an independent concern to be analysed and resolved. The technique applies a quantitative assessment of non-functional requirements risk, guiding architectural decisions. By applying this technique, the resulting architecture achieves the overall quality attributes required to satisfy distinct stakeholder needs while remaining adaptable to future changes by using FIWARE and Smart Data Models.

Enterprise Resource Planning (ERP) systems are widely adopted to streamline business processes, integrate organizational data, and enhance operational efficiency and performance. While existing research confirms ERP’s significant impact on project management, this study quantitatively examines this relationship across key performance metrics, including project performance, cost control, decision-making effectiveness, and productivity.
To achieve this, a comprehensive survey was conducted among ERP project stakeholders including managers, employees, and vendors. Regression analysis using Python was employed to evaluate the association between ERP implementation success and these metrics. The findings demonstrate a statistically significant positive correlation between successful ERP implementation and improvements across all examined metrics. This study provides valuable, data-driven insights that can support managers in optimizing ERP implementation strategies, thereby increasing overall project success.