Most AI projects fail not because the model is bad, but because the system around it isn’t built to last. After deploying computer vision systems in production logistics environments, here’s what I’ve learned about building AI that actually works.
The model is the easy part
Getting a model to 95% accuracy on a test set is straightforward. Getting it to stay at 95% when the lighting changes, the camera gets bumped, or someone parks a truck in an unexpected spot — that’s the real engineering challenge.
The difference between a demo and a production system is everything that surrounds the model: data pipelines, monitoring, fallback logic, and graceful degradation.
Design for failure
Every AI system will produce wrong outputs. The question is what happens next. Good systems are built with this assumption baked in:
- Confidence thresholds that route low-certainty predictions to human review
- Monitoring that detects distribution shift before accuracy drops
- Fallback modes that keep operations running when the model is unavailable
Observability over accuracy
A system you can debug is more valuable than a system that’s marginally more accurate. Invest in logging, tracing, and visualization early. When something goes wrong at 2 AM, you need to understand why the system made the decision it made.
Ship the simplest thing that works
Start with rules. Add ML when rules break down. Add deep learning when classical ML hits a ceiling. Every layer of complexity should be justified by a measurable improvement that matters to the people using the system.