This study examines the association between Corporate Digital Transformation (CDT) and information asymmetry (ASY) among firms listed on the Stock Exchange of Thailand during 2017–2022. Drawing on agency theory, institutional theory, and resource dependence theory, the study conceptualizes CDT as a portfolio of organization-wide digital initiatives and transformation projects embedded in firms' governance and information infrastructures rather than as isolated technological adoption. CDT is measured using textual analysis of firms' annual reports based on a multidimensional keyword dictionary capturing disclosed digital transformation activities, while information asymmetry is proxied by the effective bid–ask spread. Using firm-level panel data and fixed effects regression models, the analysis documents a negative and marginally significant association between CDT and information asymmetry, suggesting lower information frictions among firms with higher levels of reported digital transformation activity. A series of robustness tests employing alternative measures, estimation strategies, and subsample analyses yield qualitatively consistent results. The study contributes to the project management literature by providing empirical evidence on how sustained digital transformation initiatives, implemented through multiple projects over time, are associated with changes in organizational information environments in an emerging market context. While the findings should be interpreted as indicative rather than causal, they highlight the relevance of managing digital transformation as an integrated project portfolio with implications for transparency and stakeholder communication.

The rapid advancement of artificial intelligence (AI) has revolutionized project portfolio management (PPM), as it has in many other areas, by introducing data-driven methods that improve decision-making, risk assessment, and strategic alignment. Unlike traditional project management, which emphasizes individual project execution, PPM requires balancing multiple initiatives to optimize value creation and resource allocation. This paper presents a systematic review of scientific research on the integration of AI techniques into PPM, focusing on their applications, benefits, and challenges. The review synthesizes findings from 73 peer-reviewed studies covering a wide range of AI methodologies, such as machine learning, deep learning, neural networks, reinforcement learning, natural language processing, and hybrid optimization models. These approaches have been applied in diverse fields, including information technology, construction, healthcare, defense, energy, and telecommunications. Analysis shows that AI significantly improves project portfolio performance by predicting project outcomes, identifying interdependencies, optimizing resource allocation, and supporting adaptive strategies in dynamic environments. In addition, advanced AI tools provide project portfolio managers with predictive and prescriptive analytics, transforming PPM from reactive monitoring to proactive governance. Despite these advances, challenges remain regarding data quality, organizational readiness, and interpretability of AI-based models. Concerns about transparency, ethical implications, and integration with existing management frameworks also hinder wider adoption. However, recent developments indicate a growing trend toward hybrid systems that combine AI with traditional decision-making models, increasing both accuracy and practical applicability. This review contributes to theory and practice by synthesizing current knowledge, highlighting research gaps, and identifying emerging directions such as the use of large language models, ensemble methods, and sustainability-focused project portfolio optimization. The findings highlight the transformative potential of AI in advancing PPM and provide valuable insights for researchers and practitioners seeking to design smarter, more adaptive, and more sustainable project portfolio management strategies.