ODSC East 2025
May 13-15, 2025

Organizations have been investing heavily in traditional machine learning (ML), deep learning, and now generative AI (GenAI). However, truly impactful decision intelligence solves the most complex business problems using more than accurate predictions or multimodal content generation—it demands fast, scalable, and explainable decision-making. By integrating mathematical optimization into their Composite AI frameworks, businesses can unlock provably unmatched efficiency, with the ability to explain decisions and scalability required for smarter, more reliable outcomes. ​​

AI + Mathematical Optimization: Build Smarter Decision-Making Models​

In the world of data science, teams are often deeply familiar with statistical and machine learning techniques. With the rapid rise of AI—especially generative AI—the scope of solvable problems has expanded dramatically. However, one powerful yet often overlooked tool remains crucial for tackling complex decision-making challenges: mathematical optimization.​

 While AI-driven techniques like deep learning and reinforcement learning have dominated recent advancements, mathematical optimization offers a transparent, robust and highly efficient approach to solving constrained decision problems. Unlike traditional machine learning, which focuses on predictive analytics, mathematical optimization is prescriptive— it enables organizations to make optimal decisions while adhering to business constraints and operational rules. Mathematical optimization has been successfully applied across industries for decades, offering robust solutions where AI and machine learning alone may fall short, yet remains underutilized in modern AI-driven workflows.​