Prospects for the usage of neural networks in decision-making processes

Introduction. Neural networks are transforming business decision-making by enabling organizations to process vast amounts of data, identify patterns, and automate strategic processes. Their applications span various industries, including market analysis, financial forecasting, risk management, production optimization, and personalized recommendations. Despite their advantages, challenges such as data quality requirements, model interpretability, cybersecurity risks, and ethical concerns limit their widespread adoption. This purpose of the paper is to analyze the role of neural networks in decision-making, assess their impact on business efficiency, and identify key challenges and future prospects. The research explores how AI-driven solutions optimize operations, mitigate risks, and enhance strategic planning, focusing on their integration with corporate systems and hybrid modeling approaches. Results. Findings indicate that neural networks significantly improve decision-making across various domains by increasing forecasting accuracy, automating processes, and enhancing risk management. AI-powered models contribute to demand prediction, fraud detection, supply chain optimization, and real-time financial analytics. However, issues related to data dependency, explainability, and security remain critical barriers. The study highlights the growing role of cloud computing and hybrid AI models in overcoming these limitations. The prospects for the further development of neural networks in decision-making processes have been substantiated, highlighting their strategic importance in enhancing management efficiency, minimizing risks, and creating competitive advantages. Conclusions. While challenges persist, neural networks continue to evolve, driven by advancements in AI, big data analytics, and cloud-based solutions. Their integration into enterprise management systems enhances automation and predictive capabilities, offering businesses a competitive edge. Future developments in hybrid AI approaches and explainable AI will further refine decision-making accuracy and reliability, ensuring more ethical and transparent applications in business environments.