ENHANCING DECISION-MAKING IN BUSINESS PROCESS MANAGEMENT WITH PREDICTIVE ANALYTICS BASED ON ARTIFICIAL INTELLIGENCE

This thesis examines the role of artificial intelligence (AI), specifically AI-based predictive analytics, in enhancing decision-making within the framework of Business Process Management (BPM). As organizations strive for increased efficiency and adaptability in their processes, predictive analytics has emerged as a key tool that empowers businesses to make more informed decisions. By leveraging AI models such as ChatGPT, Gemini AI, and others, companies can analyze vast amounts of historical and real-time data to forecast trends, optimize resource allocation, and mitigate risks in their operations. Predictive analytics, driven by AI, is revolutionizing how BPM is approached. The ability to anticipate potential future events based on data analysis allows businesses to proactively adjust workflows, schedules, and resource use. This shift leads to higher productivity, reduced operational costs, and more agile responses to market dynamics. AI models are particularly effective in analyzing large datasets that would be too complex or time-consuming for human analysts, thus enhancing the speed and accuracy of decision-making. This thesis also delves into the underlying algorithms and machine learning techniques used by AI models to generate predictive insights, including regression analysis, neural networks, and decision trees. It explores the integration of AI-based predictive analytics into existing BPM systems and examines its implications for both operational and strategic decision-making. Furthermore, the work addresses challenges such as data quality, integration complexity, and the need for continuous model training to maintain high prediction accuracy. The research underscores how predictive analytics powered by AI can transform business operations, especially in areas like supply chain management, customer relationship management, and financial forecasting. Additionally, the thesis considers the future potential of AI in BPM, particularly how predictive models might evolve to become more autonomous and adaptive over time, ultimately leading to smarter, self-optimizing business processes.