Challenges of Industry Portfolio Management with Artificial Intelligence

Abstract

Artificial intelligence has evolved from early concepts like Turing's machine to today's advanced vision, machine learning and neural networks. AI revolutionizes various industries: manufacturing processes, financial services, healthcare and energy management. These applications highlight AI's role in augmenting human capabilities and driving industry innovation and efficiency. The paper aims to explore the intricacies and hurdles associated with integrating AI  into the realm of industry portfolio management. The primary goal of this study is to critically assess how AI can optimize portfolio management in various industries. Methodologically, the paper adopts a multi-dimensional approach, analysing case studies across different sectors, and employing a comparative use of AI-driven and traditional portfolio management strategies. The conclusion emphasizes that while AI can significantly improve predictive accuracy and operational efficiency, its effectiveness is largely contingent on the quality of data and the adaptability of algorithms to dynamic market conditions. Secondly, the paper addresses the critical need for balancing technological innovation with ethical considerations and regulatory compliance, especially in data-sensitive industries. Finally, it suggests that the successful integration of AI in portfolio management requires a synergistic approach, combining technological prowess with human expertise to mitigate risks and capitalize on opportunities presented by AI advancements.