Zastosowanie sztucznej inteligencji na poszczególnych etapach cyklu życia produktu. Analiza z perspektywy współczesnego zarządzania biznesem
Use of artificial intelligence at the various stages of the product life cycle. An analysis from the perspective of contemporary business management

Summary/Abstract: RESEARCH OBJECTIVE: The aim of this article is to analyse the use of artificial intelligence at different stages of PLC and to present selected machine learning tools that help companies manage their products on the market as a whole. THE RESEARCH PROBLEM AND METHODS: The research problem concerns the effectiveness of implementing AI to optimise business processes and its role in improving market decisions. The analysis is based on a critical review of the literature, case studies, and an assessment of selected AI tools for product management. THE PROCESS OF ARGUMENTATION: The article describes the theoretical foundations of AI and its practical applications in four phases of the product life cycle: introduction, growth, maturity, and decline. The authors present examples of tools that help analyse markets, forecast trends, optimise costs, and manage sales in each phase of product development. RESEARCH RESULTS: The research confirmed the accuracy of the main assumption. A wide range of machine learning solutions allows for faster prototyping and testing of new ideas, more accurate demand forecasting, and better resource management in the product introduction phase. During the growth stage, AI helps businesses optimise sales processes and monitor customer feedback, maintaining steady development and strengthening customer relations. In the maturity phase, AI plays an important part in marketing campaigns and improving product offers. In the decline stage, it provides tools for forecasting market changes, identifying new trends, and making timely decisions about product restructuring. CONCLUSIONS, INNOVATIONS, AND RECOMMENDATIONS: The successful implementation of AI depends on its integration with existing IT systems and the development of data analysis skills within the team. It is recommended to gradually adapt AI tools to industry specifics and test their scalability across different areas of the digital economy.

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