Implementing agentic artificial intelligence to enable autonomous decision-making in enterprise operations

Intelligent Decision-Making (IDM) is a hot topic in the research frontier of artificial intelligence, as it holds the potential for substantial transformation of productivity and efficiency in different markets. As the efficacy of deep learning methods have improved dramatically, many academics believe that AI may help with high-dimensional data concerns too. As a result, many real-world decision-making issues may be reframed as prediction tasks and automated substantially. Though a significant number of methodologies and models have been developed for different applications of IDM, several weaknesses still exist. Broadly speaking, the literature on IDM addresses three key questions. How to formalize a decision-making model optimally, including states, actions, and decisions? How to properly implement a cognition model to address uncertainties? And how to train the model without heavily relying on human intervention?