This paper reviews recent developments in artificial intelligence (AI), with a focus on technological trends, associated risks, and regulatory frameworks. The analysis considers AI across short, medium, and long-term horizons, identifying key patterns in its development and deployment. The findings underscore the rapid expansion of generative AI and large language models. AI has been widely adopted in business functions, and more generalized systems are expected to significantly impact society and the economy over time. The study examines a comprehensive set of challenges related to AI implementation, including algorithmic bias and discrimination, limited transparency and explainability, data protection concerns, model reliability and robustness, cybersecurity risks, scalability constraints, and environmental impacts. Organizational and labor-related issues, such as workforce skill gaps, system integration complexity, and resistance to adoption, are also addressed. These challenges are interrelated and necessitate coordinated technical, organizational, and governance responses, particularly in high-impact and regulated sectors. The paper also evaluates the European Union’s regulatory approach, with particular attention to the AI Act’s risk-based framework and its relationship with supporting instruments such as the Digital Operational Resilience Act (DORA) and the Network and Information Security Directive (NIS2). The analysis concludes that AI development is influenced by the need to balance innovation and regulation, and that progress depends on aligning system performance, risk management, and legal accountability within a coherent governance structure.
Honfi et al. (Wed,) studied this question.