A months-long operating process,
compressed to weeks.

A large hospital group had to reconcile thousands of individual paystubs and labor expenses. The original process meant manually reviewing PDF documents and comparing them to labor records, slow, tedious, error-prone, and hard to finish on deadline.

We redesigned the workflow and applied AI-assisted document understanding on the Microsoft Power Platform: we trained an AI model to read the PDF paystubs at 97%+ accuracy, built a Power Automate workflow to feed the files to the model and structure the output into a single dataset, then routed the validated output to the review team, who reconciled paystub data against labor expenses using standard formulas and queries.

A process that previously took a large team multiple months was completed in a few weeks, with fewer people required and reduced risk of human error. The client processed $500M in expenses on the new workflow.

This is what operational AI looks like when it's built around the work: a real bottleneck, a measurable result (time and headcount), and a controlled, repeatable process, not a demo. It's the kind of single high-value workflow an AI Transformation Sprint is designed to select, design, and prototype.

Anonymized; results as reported by WCG. The work predates the productized Sprint, shown as proof of operational AI delivery.