From AI proof-of-concept to production: the industrialization gap
The POC trap
Every enterprise has successful AI proofs-of-concept. Notebooks that demonstrate value. Demos that impress leadership. Models that work on clean data.
Yet most never reach production. The industry estimates that 80-90% of AI projects stall between prototype and deployment.
Why the gap exists
The gap between POC and production is not about model quality. It is about everything the model needs to function in a real system:
- Data pipelines that deliver clean, timely data
- Infrastructure that scales and stays available
- Monitoring that catches drift before it causes damage
- Integration with existing workflows and systems
- Governance that ensures compliance and auditability
The missing discipline: AI delivery
AI delivery is the discipline of moving from validated concept to governed production capability. It requires:
- Architecture that defines how AI components connect to the enterprise
- MLOps pipelines that automate training, deployment, and monitoring
- Governance frameworks that track model lineage and performance
- Delivery plans that sequence work into deployable increments
Structure enables scale
A single model in production is a project. Ten models governed, monitored, and integrated is a capability. The difference is structure.
The CMX approach
We treat AI delivery as an architecture problem, not just an engineering one. We design the structural conditions that allow AI to move from concept to production reliably and repeatedly.