# "It Was a Magical Box": Understanding Practitioner Workflows and Needs in Optimization

arXiv: https://arxiv.org/abs/2509.16402 · HTML: https://mlsanity.com/paper/2509.16402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [LLMs Can Design Near-Optimal OR Algorithms](https://mlsanity.com/paper/2608.27296/) (2026)

## Abstract (arXiv)

Optimization underpins decision-making in domains from healthcare to logistics, yet for many practitioners it remains a "magical box": powerful but opaque, difficult to use, and reliant on specialized expertise. While prior work has extensively studied machine learning workflows, the everyday practices of optimization model developers (OMDs) have received little attention. We conducted semi-structured interviews with 15 OMDs across diverse domains to examine how optimization is done in practice. Our findings reveal a highly iterative workflow spanning six stages: problem elicitation, data processing, model development, implementation, validation, and deployment. Importantly, we find that optimization practice is not only about algorithms that deliver better decisions, but is equally shaped by data and dialogue - the ongoing communication with stakeholders that enables problem framing, trust, and adoption. We discuss opportunities for future tooling that foregrounds data and dialogue alongside decision-making, opening new directions for human-centered optimization.