Most AI pilots fail to reach production. Here is the framework we use to help enterprise teams bridge the gap between promising proof-of-concept and reliable, scalable AI systems.
Most AI pilots fail to reach production. The gap between a successful proof-of-concept and a reliable production system is wider than most organisations expect. Based on our experience deploying AI across industries, we have identified the key failure modes and the practices that consistently produce working systems.
The three most common failure modes are: treating the pilot as the product, underestimating data engineering, and neglecting MLOps from day one.
The solution is to build for production from the start. This means designing your data pipeline before your model, establishing evaluation metrics before training, and putting monitoring in place before deployment.
A successful AI production deployment has five layers: data infrastructure, model development, serving infrastructure, monitoring, and governance. Most pilots only address model development and ignore the other four.
Start with the end in mind. Define success metrics before writing a line of code, build your evaluation framework before your model, and design your rollback strategy before go-live.