Key Takeaways:

• AI-Enabled Data Transformation: RubinBrown conducted a comprehensive AI Data Readiness Assessment to establish the foundation for digital modernization—linking data quality, ERP optimization, and intelligent automation.
• ERP Modernization Roadmap: Delivered a strategic roadmap that aligned data strategy, process redesign, and technology modernization to prepare the client for a future ERP implementation.
• Improved Operational Visibility: Enabled leadership to make real-time, data-driven decisions across production, engineering, finance, and supply chain functions through unified and reliable data.
• Actionable Data Governance: Developed a data and AI governance model that ensures quality, accountability, and sustainable adoption of new technologies across global operations.
• AI Readiness at Scale: Identified and prioritized AI use cases such as predictive maintenance, intelligent quality inspection, and performance analytics—creating measurable value through automation and advanced insights.
• Validated Framework: The client’s success led to subsequent engagements within its subsidiaries, reinforcing the value and scalability of RubinBrown’s AI-Enabled Transformation Framework.

Client:

A leading electronics manufacturer engaged RubinBrown to conduct an AI Data Readiness Assessment and develop a strategic roadmap to guide its enterprise transformation. The company sought to modernize its fragmented systems, enhance data quality, and leverage AI-driven insights to improve decision-making, operational efficiency, and global scalability.

Challenge:

Through a period of rapid growth and acquisitions, the client’s business operations had become disconnected across multiple systems and divisions. Redundant processes, manual workarounds, and siloed data limited the company’s ability to analyze performance, manage costs, and standardize reporting across facilities.

Leadership recognized that sustainable growth required a data-first strategy supported by AI, analytics, and ERP modernization. The organization needed a clear path to align people, processes, and technology under a single intelligent data framework.

Key challenges included:

• Fragmented data models and inconsistent reporting
• Limited real-time visibility into production, cost, and supply chain performance
• Manual interventions and reliance on tribal knowledge
• Disconnected systems slowing decision-making and scalability
• Lack of readiness for AI, automation, and predictive analytics

The company required a scalable, intelligent infrastructure to drive measurable business improvement that could enable better cost control, process standardization, and data-driven decision support.