PE-backed multi-unit operator — data infrastructure and AI implementation

The sequencing mattered. AI implementation without a trusted data foundation is theater. The work started with the data warehouse — building the governance framework, defining the metrics that would become the operating standard, and making the numbers reliable before making them faster.

Analytics migrated from the legacy stack to Azure and Tableau. The goal was not just a better tool but a different relationship between the data and the people who needed to use it. Operators in the field should be able to answer their own questions. Finance should not be the bottleneck between a question and an answer.

AI deployment came after the foundation held. Practical tools across Finance, Accounting, and field support — not pilots, not proofs-of-concept, but tools embedded in the actual workflow with governance built in from day one. Every automation was designed to be owned and maintained by the team, not dependent on the person who built it.

Training ran alongside deployment. The automation playbook that emerged from the engagement is repeatable — the team knows how to extend it as the business changes. That is the difference between an AI initiative and an AI capability.