Artificial intelligence (AI) is an evolving set of technologies used for solving a wide range of applied issues. The core of AI is machine learning (ML)—a complex of algorithms and methods that address the problems of classification, clustering, and forecasting. The practical application of AI&ML holds promising prospects. Therefore, the researches in this area are intensive. However, the industrial applications of AI and its more intensive use in society are not widespread at the present time. The challenges of widespread AI applications need to be considered from both the AI (internal problems) and the societal (external problems) perspective. This consideration will identify the priority steps for more intensive practical application of AI technologies, their introduction, and involvement in industry and society. The article presents the identification and discussion of the challenges of the employment of AI technologies in the economy and society of resource-based countries. The systematization of AI&ML technologies is implemented based on publications in these areas. This systematization allows for the specification of the organizational, personnel, social and technological limitations. This paper outlines the directions of studies in AI and ML, which will allow us to overcome some of the limitations and achieve expansion of the scope of AI&ML applications.

In the context of global digitalization, the creative economy is becoming a significant driver of economic diversification and labor market transformation. This study aims to develop a scenario-based forecasting model for the development of Kazakhstan’s creative economy, taking into account technological changes up to 2030. The empirical basis includes data on the current state of the sector, which accounts for approximately 3.5% of total employment (around 310 thousand people) and 2.7% of gross domestic product, as well as the results of a quantitative assessment of digitalization effects, indicating that a 1% increase in ICT service exports leads to an approximately 1.541% growth in the gross value added of creative industries. The research methodology integrates foresight approaches, scenario analysis, and econometric time series modeling. Three alternative development scenarios are proposed: “Innovative Breakthrough,” “Moderate Adaptation,” and “Technological Lag,” reflecting different trajectories of sectoral evolution. The results demonstrate a wide range of possible outcomes: under the innovative scenario, the share of creative employment may reach 5.8–6.2% by 2030, whereas the pessimistic trajectory suggests stagnation at 3.2–3.5%. The study identifies key success factors, including the development of digital infrastructure, reduction of regional disparities, modernization of the education system, and effective public policy support for creative industries. The practical significance lies in the formulation of implementation mechanisms and policy recommendations aimed at fostering the development of the creative economy and labor market.

Conventional risk assessment frameworks usually define risk as a function of vulnerabilities and threats, but they frequently lack a single quantitative model that incorporates the unique features of each element. In order to close this gap, this paper creates a flexible, open, and theoretically sound risk assessment formula that is still reliable even in the absence of complete vulnerability data. This is particularly important for financial institutions operating in emerging markets, where regulators rarely provide centralized vulnerability assessments and where Basel-type frameworks are only partially implemented. The contribution of the paper is a practically verified Bayesian network model that integrates threat likelihoods, vulnerability likelihoods, and their impacts within a probabilistic structure. Using 500 stratified Monte Carlo scenarios calibrated to real fintech and banking institutions operating under EU and national supervision, we demonstrate that excluding vulnerability impact from the model does not significantly reduce the predictive performance. These findings advance the theory of risk assessment, simplify practical implementation, and enhance the scalability of risk modeling for both traditional banks and fintech institutions in emerging economies.