The artificial intelligence (AI)-based architect's profile's selection (simply iSelection) uses a polymathic mathematical model and AI-subdomains' integration for enabling automated and optimized human resources (HR) processes and activities. HR-related processes and activities in the selection, support, problem-solving, and just-in-time evaluation of a transformation manager's or key team members' polymathic profile (TPProfile). Where a TPProfile can be a classical business manager, transformation manager, project manager, or an enterprise architect. iSelection-related selection processes use many types of artifacts, like critical success factors (CSF), AI-subdomain' integration environments, and an enterprise-wide decision-making system (DMS). iSelection focuses on TPProfiles for various kinds of transformation projects, like the case of the transformation of enterprises' HRs (EHR) processes, activities, and related fields, like enterprise resources planning (ERP) environments, financial systems, human factors (HF) evolution, and AI-subdomains. The iSelection tries to offer a well-defined (or specific) TPProfile, which includes HF's original-authentic capabilities, education, affinities, and possible polymathical characteristics. Such a profile can also be influenced by educational or training curriculum (ETC), which also takes into account transformation projects’ acquired experiences. Knowing that selected TPProfiles are supported by an internal (or external) transformation framework (TF), which can support standard transformation activities, and solving various types of iSelection’s problems. Enterprise transformation projects (simply projects) face extremely high failure rates (XHFR) of about 95%, which makes EHR selection processes very complex.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

The successful execution of large-scale infrastructure projects is essential for economic growth and societal development, but these projects are too often beset with financial risks. The main financial risks related to infrastructure projects, including cost overrun, funding uncertainty, currency fluctuation, and regulatory change are examined in this research. The study identifies and assesses the magnitude and frequency of these risks by combining surveys and analysis of financial reports. The findings show that current risk management strategies, including hedging, contingency funds, and public-private partnerships, are often unsuitable to respond to the specific needs of financial uncertainties. The research suggests the need for an all-encompassing financial risk management framework that relies on real-time data analysis and a cocktail of risk assessment tools. Additionally, the development of strategic tailored approaches to address financial risk recovery depends on proactive stakeholder engagement. This research complements the existing literature on risk management in infrastructure projects by highlighting the financial dimensions of risk management and suggesting future research on advanced financial tools and technologies. Ultimately, large-scale infrastructure project sustainability and success contribute to economic stability and societal well-being can only be achieved through effective financial risk management.