It can fail because the physical system cannot reliably execute what the model decided. A recent article from the Association for Advancing Automation highlights an important point about reliable robot manipulation: better models are only part of the equation. Physical AI also needs: → Adaptability to real-world variation → Reliable execution of AI-generated actions → Physical feedback beyond vision and simulation → Flexible tooling that expands what the system can actually d

Accuracy is a lab metric. Production needs better. I've shipped enough AI systems to know this truth: a model can be 98% accurate in testing and still fail spectacularly in production. Why? Because accuracy doesn't capture reliability, explainability, or business impact. 📊 The metric I actually trust? Time to resolution for errors. How fast can the system detect when it's wrong, route to human oversight, and learn from the correction? That tells me everything about operation

Your model is fine. Your data is not. I've lost count of how many times I've seen teams obsess over model accuracy while ignoring the garbage going into their pipelines. Here's what 24+ years in tech has taught me: the best model in the world can't fix bad data. 📊 When I led a finance automation initiative, we reduced manual effort by 30%. The secret wasn't fancy algorithms—it was ruthlessly fixing data quality at the source. We built annotation pipelines, implemented valida