July 1, 2025

Optimizing business analytics and decision-making through the application of generative artificial intelligence in forecasting and simulation

Key Points

Generative AI improves forecasting accuracy, reducing uncertainty and enhancing decision quality.
AI-driven approaches can lower costs and mitigate risks, offering businesses a competitive advantage.
Qualitative synthesis examines academic literature and industry reports to highlight the effectiveness of AI in decision-making.
There are serious challenges such as algorithmic bias and data privacy concerns that require robust governance.

Abstract

This thesis investigates how generative artificial intelligence (AI) can optimize business analytics and decision-making in forecasting and simulation. Employing a qualitative synthesis of academic literature and industry reports, the study examines how AI models enhance forecasting accuracy and generate realistic simulations. Results indicate that AI-driven forecasting improves decision quality by reducing uncertainty, lowering costs, and mitigating risks, while also offering a competitive advantage. However, significant challenges remain, including algorithmic bias, data privacy concerns, high computational requirements, and regulatory hurdles. The findings underscore the need for robust governance and further research on broader applications of generative AI in strategic decision-making today.

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