Accelerating decision making with AI and ML

Abstract

This chapter discusses the potential impact of artificial intelligence (AI) and machine learning (ML) on real-time decision making. It highlights the role of various industries in operational cost, operational efficiency, and reliability. The focus is on the use of advanced AI and ML techniques in predictive maintenance which are more efficient in manufacturing systems and plants. Real-world applications of such technologies will be more effective if integrated into decision making processes. However, several technical problems persist such as latency in the decision-making process, handling heterogeneous statistics, preprocessing strategies, and many more. This chapter also explores the need for preprocessing strategies and ethical concerns, which include algorithmic bias, transparency, and data privacy, requiring careful evaluation. This discussion also covers the significance of equity and duty in automated systems, especially in high-stakes environments like health care and criminal justice. Regarding practical applications, the chapter discusses key challenges and solutions, such as scalability of AI systems and organizational resistance to adopting advanced technologies. Predictive models play an essential role in maintaining efficiency and reliability in dynamic environments. Real-time systems can operate more effectively and maintain efficiency when integrated with AI– and ML–based models, ultimately reducing the risk of mistakes in real-time decision making. The integrations can also save time, machinery, manpower, and resources and provide a new version of more advanced systems, with efficiency. Collaborative models in real-time environments, particularly through multi-agent systems, are highlighted as promising avenues for future exploration in AI and ML domain usage in real-time systems and decision making.

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