Key Takeaways:

• AI-Driven Efficiency: RubinBrown conducted an enterprise-wide AI readiness and data maturity assessment to identify opportunities for automation, predictive analytics, and improved operational control.
• Data-First Transformation: Delivered a strategic roadmap aligning AI strategy, ERP modernization, and governance to enable scalable, data-driven decision-making.
• Operational Impact: Improved visibility, quality, and safety performance while establishing digital frameworks for continuous improvement.
• Strategic Confidence: Empowered leadership to align board-level investment decisions with AI innovation and long-term growth objectives.

Client:

A leading engineering and construction firm with north of a billion in revenue engaged RubinBrown to conduct an AI Readiness and Data Maturity Assessment. The goal was to define how AI and advanced analytics could enhance operational efficiency, improve quality and safety outcomes, and create a foundation for data-driven decision-making and ERP modernization.

The client sought to transition from traditional project management models to a digitally enabled, insight-driven enterprise, where data fuels smarter, faster, and more accurate business decisions across engineering, construction, and corporate functions.

Challenge:

Despite its scale and strong market reputation, the firm’s leadership recognized that its data and technology ecosystem could not yet support modern, predictive, or automated operations. Legacy platforms, manual workflows, and decentralized information limited the ability to deliver real-time reporting, AI-driven analytics, and cross-functional visibility, particularly in tracking backlog status, percentage of completion, and revenue recognition accuracy.

The organization needed to:

• Modernize legacy ERP and project management systems
• Improve integration between field operations, finance, and engineering
• Standardize data for quality, safety, and compliance reporting
• Define a roadmap to scale AI and automation capabilities across the enterprise

Two high-impact AI use cases were prioritized early:

1. Automating invoicing and document processing to reduce cycle times and improve data accuracy.
2. Enhancing remote usability and anomaly detection in time entry and resource tracking for predictive and preventive action.