Causal Predictive Optimization and Generation for Business AI

TL;DR

This paper presents a comprehensive causal predictive optimization framework for business AI, integrating causal ML, constraint optimization, and generative AI, demonstrated through deployment at LinkedIn to improve sales processes.

Contribution

It introduces a novel three-layer system combining causal prediction, optimization, and generative AI for sales process enhancement, with practical deployment insights.

Findings

Significant improvements over legacy systems at LinkedIn

Effective integration of causal ML with optimization and generative AI

Broadly applicable insights for business AI systems

Abstract

The sales process involves sales functions converting leads or opportunities to customers and selling more products to existing customers. The optimization of the sales process thus is key to success of any B2B business. In this work, we introduce a principled approach to sales optimization and business AI, namely the Causal Predictive Optimization and Generation, which includes three layers: 1) prediction layer with causal ML 2) optimization layer with constraint optimization and contextual bandit 3) serving layer with Generative AI and feedback-loop for system enhancement. We detail the implementation and deployment of the system in LinkedIn, showcasing significant wins over legacy systems and sharing learning and insight broadly applicable to this field.