Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget. In the current study, the Balanced Scorecard (BSC) and Artificial Neural Networks (ANNs) are intertwined with forecasting the outcomes of a co-financed EU program by means of its impact on the non-financial measures of the government body that materialized it. The predictive accuracy of the present model advanced in this research study takes into account all the complexities of the business environment, within which the provided dataset is produced. The outcomes of the study showed that the measures taken to enhance customer satisfaction allows for further improvement. The utilization of the proposed model could facilitate the decision-making process and initiate changes to the administrational issues of the available funding programs.

The performance measurement of a great variety of enterprises is a highly complicated issue, especially taking into account that performance has a great many aspects and many variables which may, at times, be highly inconsistent with each other. The use of analytics and advanced machine learning promotes the decision-making process for each and every organizational structure. This paper combines the Balanced Scorecard and predictive analytics in order to assess the performance of a co-financed European Union program, which addressed 4071 Greek Small and Medium-sized Enterprises (SMEs) that requested funding. The application of predictive analytics tools and metrics in the available dataset of all addressed SMEs reveal the M5 Model Tree regressor to be an overall best prediction model for estimating the effect of the evaluation of companies’ funding proposals on their financial results after the finalization of the co-financed program.

This paper examines the organizational competitiveness and strategic transformation of an elite professional football entity in the Greek Super League during the period 2018–2020, using Panathinaikos as a case study within a comparative framework including Olympiacos, AEK, and PAOK. This period marked a phase of enforced reorientation for Panathinaikos due to UEFA sanctions for overdue debts and the club’s exclusion from European competitions, which resulted in extensive squad renewal and increased reliance on academy-developed players. The aim of the study is to identify the factors shaping Panathinaikos’ strategic position, diagnose the causes of its lagging performance, and suggest directions for strategic repositioning. To this end, a multi-criteria framework based on the Analytic Hierarchy Process (AHP) is employed, integrating qualitative assessments, expert judgements, and quantitative performance indicators through pairwise comparisons, weight calculations, and consistency checks. The analysis is based on a conceptually original model that defines the Football Organization as an integrated system composed of two interdependent subsystems: the Football Club and the Football Team (competitive subsystem). This approach highlights that league standings do not always reflect overall performance dynamics, as they are influenced by both organizational and on-field factors. The findings indicate that Panathinaikos is lagging behind in key areas and that a structural discontinuity between the Club and the Team limits strategic coherence and the ability to create a sustainable competitive advantage. The study concludes with proposals for restructuring and strategic repositioning, while the proposed model functions as a transferable decision-support tool for assessing organizational competitiveness, with broader applicability to complex organizational systems beyond professional football.