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Traditional business process automation primarily concentrates on digitizing processes to achieve specific outcomes. However, it grapples with challenges stemming from manual configurations and inflexible workflows, restricting adaptability and creativity. This paper delves into the transformative potential of Generative Artificial Intelligence (AI) in revolutionizing process automation, addressing the limitations inherent in traditional approaches. In contrast to traditional automation, Generative AI adopts a groundbreaking approach by harnessing data from past automation solutions to identify more effective methods. This goes beyond conventional orchestration, dynamically integrating software components, automatically generating rules from historical automation solutions data, and seamlessly integrating external systems. Generative AI unlocks its potential through the analysis of past workflows, gaining insights into components, decisions, and sequences. This knowledge serves as the foundation for creating new, efficient processes based on user-described natural language, resulting in robust workflows and rules that assimilate lessons from past best practices and adapt as needed. This innovative approach signifies a notable departure from traditional constraints, paving the way for a future where business processes evolve dynamically, aligning with the efficiency demands of the ever-changing digital landscape.
Paulose et al. (Thu,) studied this question.