The study focuses on analyzing the impact of corporate social responsibility (CSR) on economic growth and reducing inequality, highlighting the importance of CSR in achieving sustainable development and social justice. The main aim is to analyze how different CSR initiatives contribute to economic development, social prosperity, and the reduction in inequality by reviewing the methods used to assess their impact. The research methodology includes a detailed literature review, bibliometric analysis and scientific mapping, surveys of various business organizations, and a gap analysis regarding the identification of gaps between the current state of CSR activities and the expected outcomes. The research shows that companies perceive CSR as a key tool for improving corporate image, responding to stakeholder expectations, and investing in social justice. Despite positive intentions, challenges include the lack of clearly defined methodologies for measuring the impact on economic inequality, as well as difficulties in assessing the long-term effects of CSR initiatives. Key conclusions highlight the need for more structured approaches to assessing the social and economic effects of CSR, recommending that companies improve their transparency and accountability and implement clear indicators of success to achieve sustainable economic and social outcomes.

The research focuses on the attitudes and readiness of organizations to integrate Artificial Intelligence (AI) technology into their internal business processes. The present study aims to determine how organizations perceive technological innovations related to AI. Specific goals include measuring the degree of readiness and acceptance of technological innovations by organizations, as well as identifying factors influencing the success or failure of this process. The main object is AI technology and its potential for enhancing the efficiency of internal business process management. The significance of this analysis is threefold, providing valuable information on current trends and challenges in internal business processes and their transformation under the influence of AI. In the course of the study shall be justified the thesis that AI technology holds significant potential for optimizing internal business processes, that is not yet fully realized and utilized due to various obstacles. Overcoming these obstacles is possible through individualized strategies, the establishment of ethical standards, active training, and other measures that contribute to the successful integration of artificial intelligence into organizational dynamics. The methodology includes a comprehensive literature review combined with the use of questionnaire surveys, Gap analysis and SWOT analysis. The main conclusions are related to the diversity in motivations among surveyed companies, necessitating differentiated strategies. Improving operational efficiency and customer service, and enhancing competitiveness, transpire as driving power for AI implementation. Evaluating attitudes reveals differences in readiness among business organizations, resp. some of them actively taking steps to implement AI, while others are still exploring possibilities or are uncertain about the overall approach to adopt. The recommendations for organizations are multifaceted. Constantly exploring new technologies and updating approaches are necessary for a sustainable transition to more intelligent business process management.

ERP systems significantly optimize many business processes and activities, but often their implementation and use in companies is a risky endeavor. They are the subject of various scientific studies and analyses in the fields of business, accounting and finance. The main focus in them falls on the process of implementing these systems, while the subsequent stages, risk analysis and long-term strategy are less affected. On this basis, this research paper proposes a methodology for quantitative assessment of identified ERP risks. It is based on a five-level matrix measuring three risk factors—influence, impact and vulnerability. The methodology has been empirically tested in three companies, different in size and operating in different economic sectors. The results show that the level of risk depends not only on the scale and complexity of the business, but also on the degree of integration of ERP solutions. Periodic application of the risk assessment methodology helps identify problem areas and facilitates management decision-making.

This study develops and applies a three-factor matrix model for assessing counterparty risk within Anti-Money Laundering (AML) measures. The research problem arises from the need to transform qualitative features related to ownership, effective control, economic logic of operations, geographical links, documents and review behavior into a comparable and documented risk score. The model uses three factors: counterparty exposure, transaction impact and control vulnerability. Each factor is assessed on a five-point ordered scale, and the overall risk score is obtained by multiplying the three values. The methodology includes scoring rules, rating interpretation, an application algorithm and a link between the profile, due diligence, ongoing monitoring and updating. The model was applied in 2026 to 25 anonymized counterparty profiles selected for methodological demonstration across different risk configurations. Identifying data, personal information and confidential business information were removed, while the relevant risk characteristics were preserved. The results demonstrate how the model distinguishes between low, moderate, increased, high and critical risk and how it creates a traceable link between established facts, numerical assessment and follow-up control actions. The study does not claim predictive accuracy, detection of money laundering or empirical validation against external AML outcomes. Its contribution is methodological and applied: it provides a transparent and explainable framework for structured, documented and proportionate management of counterparty AML risk, including potential use in AI-assisted monitoring under human oversight.