Building the Data Foundation for Enterprise AI
A large Australian enterprise wanted to move beyond manual, offshore-dependent reporting and give business teams greater control of their data.
Skillfield designed a dynamic solution to bring data from a legacy warehouse to Microsoft Fabric, enabling self-service reporting and automation while laying the semantic foundations needed for future AI agents.
What began as a targeted pilot has now become the foundation for a broader enterprise rollout.
The Problem
Business teams relied on a legacy data warehouse and offshore resources to manually transform data in spreadsheets before it could be used for reporting. This slowed decision-making and created ongoing dependence on specialist teams.
At the same time, the organisation recognised that meaningful AI adoption required more than simply connecting AI tools to raw enterprise data. Its data needed greater structure, consistency and business context before future AI agents could deliver reliable outcomes. The challenge was to create those foundations without the high cost, complexity and disruption of a time-consuming wholesale migration.
The Solution
Skillfield initially partnered with one business team to pilot Microsoft Fabric, creating dynamic, metadata-driven pipelines that brought data from the existing warehouse into Microsoft Fabric without hard-coding logic for individual tables.
For very large, frequently refreshed datasets where standard copying was impractical, Skillfield re-engineered the required logic directly in Fabric using robust data modelling rather than a temporary workaround.
The result was a trusted environment where business users could build their own reports and automations. Building on the successful pilot, Skillfield is helping design the organisation’s enterprise-wide rollout, addressing the governance, security and integration requirements needed to scale safely, while positioning the data foundation to support future AI capabilities.
The Outcome
The pilot demonstrated that an offshore-dependent, manually intensive reporting model could be replaced with a scalable self-service approach. Business users gained greater control over reporting and automation, reducing their reliance on a centralised reporting team.
Its success led to the approach being selected for expansion across additional areas of the enterprise.
Just as importantly, the pipelines and data models established during the pilot give enterprise data greater consistency, structure and business context.
Rather than approaching AI as a standalone technology initiative, the organisation now has a scalable data foundation designed to support broader automation and future AI agents.
