This study examines the impact of artificial intelligence (AI) on workforce skill requirements as AI becomes increasingly integrated into business operations. Using foresight analysis and scenario-based methods, we anticipate the necessary skills for future AI-integrated workplaces. A SWOT analysis evaluates three potential paths for AI adoption—gradual, aggressive, and selective—to project the evolving skills needed for employee success in changing business environments. The findings emphasize the critical need for both enhanced technical proficiency and soft skills, such as creative problem-solving and interpersonal abilities, across all AI adoption scenarios. The study highlights the importance of strategic reskilling and continuous learning to align employee skills with the new business paradigms shaped by AI. It provides a roadmap for businesses, educators, and policymakers to collaboratively develop a resilient and adaptable workforce for an AI-enhanced future.

To achieve sustainable development, companies are aware that they need to move from the ‘take-make-consume-throw away’ pattern toward an economic model based on sharing, re-use, repair, refurbishing, and recycling. Implementing the circular economy concept into a company’s business model is not an easy process, given that some eco-innovations have higher costs with direct effects on financial performance. Hence, this study aims to determine the relationship between corporate financial performance and the performance measures of the circular economy through multivariate econometric estimations. The observations considered in our sample reflect the financial and circular economy performance for 411 companies with headquarters in the European G7 members disclosed in the period 2014-2020. The EU was chosen to carry out the study because the member states subscribe to a general reporting framework and the European Green Deal agreement, making it the most active in implementing the circular economy model. The findings are based on the econometric models related to the nature and amplitude of the association relation between firms’ financial performance and circular economy in connection with firms’ performance. The results show that the performance of the circular economy has a relatively small influence on the firms’ financial performance.

Climate change, increased government efforts towards sustainable economic growth, and all types of resource constraint have raised concern among academics and practitioners about the path to follow for keeping firms’ competitive advantage in such a dynamic environment. The purpose of the paper is to explore how firms operating in the agricultural sector have understood the need to identify and follow the best practitioners in the market, performing a benchmarking analysis aimed at providing insights on firms’ financial sustainability. As the literature has not sufficiently addressed the problem of firms’ financial sustainability operating in the Romanian Agriculture sector, we provide some empirical evidence related to the before and post-pandemic periods. The benchmarking analysis is developed using the Data Envelopment Analysis (DEA) method, which measures firms’ efficiency in terms of financial performance. Overall, the results suggest there is still much work to be done, and firms operating in the agricultural sector show high variation in terms of productivity from the perspective of financial indicators. The results are even more relevant in the context of the current COVID-19 pandemic, showing that only a part of the firms analyzed have developed their own dynamic capabilities that help them effectively find solutions to adjusting to the volatility of the market in a short timeframe.

This paper investigates the effectiveness of machine learning algorithms in enhancing the accuracy and reliability of predicting financial distress. The dataset includes Altman Z-Scores and Corporate Governance Compliance (CGC) indicators calculated for manufacturing firms listed on the Bucharest Stock Exchange (BSE) from 2016 to 2022. Leveraging Signaling Theory, the study analyzes financial and governance data for 60 non-financial firms, comprising 420 firm-year observations. Financial distress is classified into three categories: no distress, moderate distress, and severe distress. The study employs a Random Forest classification model, leveraging artificial intelligence techniques to identify critical predictive variables and evaluate their combined effectiveness in signaling financial distress. The findings reveal that machine learning algorithms significantly improve the predictive accuracy and reliability of financial distress classifications, effectively distinguishing between different distress levels by integrating financial ratios and corporate governance variables. These results emphasize the advantages of involving artificial intelligence and advanced analytics in financial distress prediction models, enhancing transparency and strengthening investor confidence. The research contributes to the literature on digital transformation in financial analysis and corporate governance, offering practical implications for investors, managers, creditors, and policymakers in emerging market environments.