Accuracy was not the thing that broke our system.
At least not in the way we expected.
We were working on a real-time AI system that had to react to human movement during live sessions. The early versions looked great on paper. Accuracy metrics were strong. Benchmarks improved steadily.
From a model perspective, everything was “working.”
But once the system was used outside controlled conditions, problems started to appear.
Lighting changed across sessions.
Users moved slightly differently each time.
Hardware behaved inconsistently over time.
None of these were bugs. They were normal.
The system, however, treated them like errors.
Small variations triggered corrections.
Feedback jittered.
Outputs changed from moment to moment.
Nothing was technically incorrect, but the behavior felt unreliable.
That’s when we realized the real issue wasn’t accuracy.
It was sensitivity.
The system was reacting too quickly to noise. It was optimized to be precise frame-by-frame, but not stable across real usage.
So we made a decision that felt wrong at first.
We reduced model sensitivity.
We smoothed signals over time.
We raised confidence thresholds.
We allowed small variations to pass without reaction.
On paper, accuracy metrics dropped.
In production, usability improved almost immediately.
The system stopped overcorrecting.
Outputs became predictable.
Users trusted it more.
What surprised us most was how quickly behavior changed once trust returned. Coaches used the system consistently. Sessions lasted longer. Adoption improved without changing anything else.
This experience changed how we think about production AI.
Accuracy metrics answer an important question:
“How often is the model correct under expected conditions?”
Production systems need to answer a different one:
“How does the system behave when conditions are wrong?”
If a system behaves inconsistently when inputs are noisy, users will stop trusting it — even if the model is technically accurate.
This pattern shows up far beyond sports.
In healthcare, overly sensitive systems create alert fatigue.
In operations platforms, false positives cause teams to ignore signals.
In real-time tools, unstable behavior breaks workflows.
In all of these cases, consistency matters more than precision.
The takeaway for anyone building production AI is simple:
Accuracy matters.
But predictable behavior matters more.
If a system cannot tolerate noise, misuse, and imperfect conditions, it won’t survive contact with real users.
Benchmarks don’t reveal that.
Production does.
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