Here is our perspective on why AI projects stall: in most cases, the model was never the problem. The data engineering layer underneath it was. This article diagnoses the four data engineering mistakes that most consistently break AI programs with clear explanations, real-world examples, and actionable fixes for each.
Horizon Education Centers (HEC) has been shaping the lives of children aged 6 weeks to 14 years old for over 48 years. As a non-profit early childhood education provider operating across multiple centers and funding streams, HEC made a deliberate decision to build a data foundation that matched the scale and ambition of their mission. Partnering with Paragon Shift, HEC connected its systems, automated its data flows, and gave leadership a unified view across all centers for the first time. Attendance reconciliation time dropped by 70% for center administrative staff, total weekly manual reporting hours fell from 100 to 29, and the team that had been spending evenings on reconciliation got their time back.
Here is our perspective on where AI is delivering in manufacturing today: not in broad platform deployments or enterprise transformation programs, but in three specific, proven use cases where the data is already being generated, and the operational case is clear. Demand forecasting, predictive maintenance, and operational optimization each have a defined path to measurable return when the right conditions are in place.
Here is our perspective on the data management layer: Azure/Fabric and Databricks are not competing platforms for the same job. They are complementary layers of a unified data architecture. Databricks for engineering and AI, Fabric for governance and consumption, connected by a zero-copy integration that became generally available in 2025 and changes the economics of running both. This post explains how that architecture works and what it means for financial services firms and manufacturers building on it today.