  Despite advancements in causal inference and prescriptive AI, its adoption in
enterprise settings remains hindered primarily due to its technical complexity.
Many users lack the necessary knowledge and appropriate tools to effectively
leverage these technologies. This work at the MIT-IBM Watson AI Lab focuses on
developing the proof-of-concept agent, PrecAIse, a domain-adaptable
conversational agent equipped with a suite of causal and prescriptive tools to
help enterprise users make better business decisions. The objective is to make
advanced, novel causal inference and prescriptive tools widely accessible
through natural language interactions. The presented Natural Language User
Interface (NLUI) enables users with limited expertise in machine learning and
data science to harness prescriptive analytics in their decision-making
processes without requiring intensive computing resources. We present an agent
capable of function calling, maintaining faithful, interactive, and dynamic
conversations, and supporting new domains.
