When companies build their first AI solutions, they pour resources into making models more sophisticated without addressing the choke point that caps performance. Product teams often make this same error by missing where the constraint lies, which might be in their data pipeline struggling to keep up with GPU cycles, or in preprocessing that takes longer than inference, rather than in model architecture. Sometimes their annotation workflow creates significant lag before new examples reach training. Their entire pipeline typically can only perform as well as its weakest link allows.
When constraints in AI systems change during development, static optimization strategies become less effective. A bottleneck-focused approach would apply the theory of constraints by identifying where delay accumulates, which resources sit idle, and when throughput drops, then redirecting effort to that specific constraint. Drawing from data governance work, the constraint might be data quality that affects model training, system integration issues that slow data flow, or infrastructure limitations that cap throughput.