This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management.

The integration of artificial intelligence (AI) into Agile IT project management has emerged as a transformative strategy for enhancing risk assessment and decision-making processes. Despite the increasing interest, a fragmented understanding persists regarding the application, effectiveness, and limitations of AI-driven tools in Agile environments. This thematic review systematically analyses 20 peer-reviewed studies published between 2010 and 2025 to synthesise current knowledge and identify research gaps. The literature reveals that machine learning, predictive analytics, natural language processing, and reinforcement learning offer substantial benefits, including improved sprint planning, early risk detection, optimised resource allocation, and greater responsiveness to dynamic project demands. The study categorises findings into four core themes: AI integration in Agile workflows, AI-driven risk assessment methodologies, AI-enhanced decision support systems, and challenges to adoption. Barriers such as data quality issues, model interpretability, integration complexities, organisational resistance, and high implementation costs are critically examined. A conceptual framework, the Dynamic structuration framework (DSF), which leverages the adaptive structuration theory and Dynamic capability theory, is proposed to guide the structured integration of AI within Agile project lifecycles, emphasising the need for recursive co-evolution, iterative feedback loops, augmented capabilities as well as a governable human–AI collaboration. The review highlights the superiority of AI-driven approaches over traditional methods in terms of predictive accuracy and operational agility, while also calling for further empirical validation and development of explainable AI models. The findings offer valuable insights for researchers and practitioners seeking to advance AI-enabled Agile project management.