Prescriptive AI represents a transformative shift in decision-making, offering causal insights and actionable recommendations. Despite its huge potential, enterprise adoption often faces several challenges. The first challenge is caused by the limitations of observational data for accurate causal inference which is typically a prerequisite for good decision-making. The second pertains to the interpretability of recommendations, which is crucial for enterprise decision-making settings. The third challenge is the silos between data scientists and business users, hindering effective collaboration. This paper outlines an initiative from IBM Research, aiming to address some of these challenges by offering a suite of prescriptive AI solutions. Leveraging insights from various research papers, the solution suite includes scalable causal inference methods, interpretable decision-making approaches, and the integration of large language models (LLMs) to bridge communication gaps via a conversation agent. A proof-of-concept, PresAIse, demonstrates the solutions' potential by enabling non-ML experts to interact with prescriptive AI models via a natural language interface, democratizing advanced analytics for strategic decision-making.

IBM Research提出了一套可解决企业采用Prescriptive AI所面临挑战的解决方案，其中包括可扩展的因果推断方法、可解释的决策方法以及通过对话代理来弥合该领域中数据科学家与业务用户之间的沟通鸿沟，该解决方案通过一个名为PresAIse的概念验证展示了其潜力，使非机器学习专家能够通过自然语言界面与Prescriptive AI模型进行交互，从而为战略决策民主化先进分析。

企业处方式人工智能解决方案PresAIse