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 digital transformation in the United States is at a critical crossroads, influenced by the integration of artificial intelligence (AI), business analytics (BA), and the quest for IT project excellence. In contrast to previous digitization efforts that emphasized efficiency, the current transformation is strategic, systemic, and value-oriented, addressing national priorities including economic competitiveness, cybersecurity, sustainability, and equity. This study contends that AI and BA function as complementary facilitators of this transformation: AI automates processes, improves decision-making, and stimulates creativity, whilst BA supplies the analytical foundation for evidence-based strategies, workforce optimization, and stakeholder alignment. They redefine IT project excellence as a multifaceted concept that encompasses efficiency, adaptability, security, and social responsibility.

Both the adoption and AI integration of education means that there are significant possibilities for education through scalable systems of AI education. The following research focuses on the utilization of IT management strategies in dealing with the issue, and enhancing scalability of institutions in America, namely schools and universities. This cross-sectional survey study employs a quantitative research approach and a sample of about 400 participants to test the impact of institutional readiness, educator technical proficiency, and data privacy on scalability, mediated by government funding and policy. Outcomes confirm that data protection and governmental grants are the key factors for trust enhancement, resources’ equalization, and extensive changes. Moreover, the findings show that the measures of institutional readiness and external support are not independent variables; instead, they should be discussed as related, yet relatively separate constructs that should be combined and optimized properly. This research advances the understanding of multifaceted practice-based frameworks for the fair and effective implementation of AI solutions so that varied needs are fulfilled, learning and teaching are enhanced, and accessibility is achieved.

Autism Spectrum Disorder (ASD) impacts millions worldwide, posing distinct obstacles in schooling, communication, and access to therapy. Conventional educational and therapeutic approaches, while sometimes helpful, frequently lack the adaptability and customization necessary to meet the varied needs of kids with ASD. This research tackles existing gaps by integrating advanced Artificial Intelligence (AI) techniques and data-driven personalization in digital health systems. This study presents a novel, AI-driven adaptive learning and treatment system specifically developed for kids with autism spectrum disorder (ASD). This research assesses the efficacy of AI-driven personalization in improving learning outcomes, communication skills, and therapeutic accessibility through the utilization of simulated experimental data. Adaptive machine learning algorithms, natural language processing, and reinforcement learning techniques were incorporated into digital platforms, resulting in tailored intervention models that dynamically adjust to the cognitive and communicative profiles of each learner. Results from the simulated experiments demonstrate substantial enhancements in tailored adaptive learning pathways, quantifiable progress in communication skills, and heightened therapeutic accessibility and engagement compared to conventional methods. The performance assessment of AI models reveals strong accuracy, responsiveness, and efficiency in customizing instructional and therapeutic content to meet individual learner requirements. This research enhances previous work by providing empirical insights and practical consequences, demonstrating how AI-driven devices can substantially improve educational experiences and treatment outcomes for kids with ASD. Future directions encompass empirical testing, ongoing enhancement of AI models, and additional investigation into scalable application options within educational and healthcare contexts.

This paper looks at how digital tools with AI help increase learning, support interaction among learners and ensure that resources are accessible to learners on the spectrum. Because they struggle with social interaction, language and understanding senses, learners with ASD typically have a hard time in regular classrooms. It is creating digital media that makes it possible to support and include more students in the classroom. Artificial intelligence enables these environments to offer learning that adapts to what every child with ASD needs, likes and understands best. In this study the results collected by AI from trials of students using the learning platform for ASD. The visual aids and responses to emotions, students became more interested and stressed out less. The report states that when programming an AI component for a digital education system. It is important to design it with the individual visual and cognitive needs of people with ASD in mind. More research needs to focus on putting these programs into wider practice and finding out the long-term effects on students and society.

This research assesses the way AI-driven analytics help organizations operate more flexibly, make decisions quickly, and fully align their IT with business priorities. In the current digital era, companies depend on business analytics powered by AI to modify their IT strategies and improve their ability to decide. AI with business intelligence, organizations process huge amounts of data quickly to get useful information that supports efficient work and gives them an edge over their competitors. The focus is on how using smart automation and integrated data allows IT governance to become proactive and enterprise planning to adapt. A combination of qualitative and quantitative methods is used in this study. Through surveys given to 300 IT experts and leaders from many industries. The experience with AI-driven analytics tools and how they see AI helping with strategic planning was recorded. Deep insight into these factors was gathered through interviews with 20 IT executives. The author used SPSS software for statistics on numbers and thematic coding on words to look for similar themes and links between AI adoption and IT plan results. AI technology in business analytics is shown to help companies react more quickly, support data-driven choices and ensure alignment between the business and IT. With intelligent automation, there is less complexity in operating the business and to real-time analytics, reactions to market changes are faster. Some integration obstacles and data quality issues did not stop organizations from achieving better innovation and standing out in the market. The research makes clear that AI is key to having successful data-based and flexible IT systems in the future.