These are problems where the stakes are high, the options are near-infinite, and the wrong choice is expensive. They also share a common trait: the number of possible solutions is so vast that no human — and no simple rule — can reliably find the best one.

Leading organizations are increasingly turning to mathematical optimization, a specialized subfield of AI complementary to machine learning, to navigate that complexity and find answers that measurably outperform the status quo. Applying it well requires deep scientific expertise — and infrastructure that scales.

A team of specialized scientists with the AWS Generative AI Innovation Center does exactly this work — solving customers’ most challenging, high-impact problems through scientific innovation. Working backwards from customer needs, the team combines expertise in AI, mathematical modeling, optimization, quantum computing, and high-performance computing to deliver measurable business outcomes, all powered by AWS cloud services.

In this post, we introduce mathematical optimization, explain how it fits within the broader AI landscape, and showcase real-world success stories where the Innovation Center has partnered with customers to deliver concrete results.