When a Fortune 500 manufacturing company approached us in mid-2025, they had a familiar problem: dozens of isolated AI experiments across business units, none of which had made it to production. The gap between proof-of-concept and enterprise deployment was costing them millions in unrealized value.
The Challenge
Each of the 12 business units had their own data silos, their own vendor relationships, and their own definition of what "AI-ready" meant. There was no shared infrastructure, no model governance, and no way to measure ROI consistently across the organization.
The CTO needed a unified platform that could serve models to production while maintaining the flexibility each unit required. More importantly, they needed it operational within six months to hit their fiscal year targets.
Our Approach
We designed a three-layer architecture: a shared data mesh at the foundation, a centralized MLOps platform in the middle, and domain-specific application layers on top. This gave each business unit autonomy over their use cases while standardizing the deployment pipeline.
- Built a centralized feature store serving all 12 business units
- Implemented automated model monitoring with drift detection
- Created a self-service deployment pipeline reducing time-to-production from weeks to hours
- Established model governance and audit trails for regulatory compliance
Results
Within six months, the client had 23 models in production across supply chain optimization, demand forecasting, quality control, and customer analytics. Operational costs dropped by 34%, and the platform now processes over 2 million predictions daily.
The key lesson: scaling AI is not a technology problem — it is an organizational design problem. The companies that succeed are the ones that invest in shared infrastructure and governance before they invest in individual use cases.