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.

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.

Tax administrations have rapidly expanded their use of artificial intelligence (AI), particularly for risk assessment and the detection of tax evasion and fraud. This technological transformation has generated expectations that AI-supported tax administration should strengthen tax-crime enforcement and increase the identification of complex cases requiring forensic-accounting expertise. However, existing research remains focused predominantly on technology adoption, algorithmic performance, and individual administrative applications, while systematic cross-country evidence connecting AI implementation with observable forensic-investigative activity remains limited. This study examines whether AI implementation in national tax administrations is associated with greater forensic accounting investigative activity across European Union (EU) Member States. Variable analyzed are revenue-fraud investigations per 100,000 inhabitants, revenue-fraud cases as a share of all active EPPO investigations, and revenue-fraud damage relative to VAT and customs revenue, based on an exploratory cross-sectional design combining descriptive statistics, percentile rankings, Welch tests, Mann-Whitney tests, and regression analysis. AI-implementing Member States exhibit higher values across all three indicators analyzed. The most consistent evidence concerns the share of revenue-fraud cases within the EPPO caseload, although the non-parametric evidence is significant only at the 10% level. Overall, the findings are consistent with a complementary relationship in which AI supports risk identification and case prioritization, while forensic accounting expertise remains necessary to reconstruct transactions, quantify losses, and develop evidentially sustainable cases.