Domain Adaptable Prescriptive AI Agent for Enterprise
This addresses the challenge for enterprise users who lack machine learning knowledge, though it appears incremental as it builds on existing causal and prescriptive tools with a new interface.
The paper tackles the problem of low adoption of causal inference and prescriptive AI in enterprises due to technical complexity by developing PrecAIse, a domain-adaptable conversational agent that enables users with limited expertise to access these tools via natural language, resulting in a proof-of-concept system for making better business decisions.
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.