PresAIse, A Prescriptive AI Solution for Enterprises
CoRR(2024)
摘要
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.
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