Task success rate

• Measuring end-to-end effectiveness of AI features.
• Choosing between quality improvements and latency/cost tradeoffs.
• Reporting to leadership on business impact of AI investments.

TSR is the north star for many AI features. PMs define what counts as success, how to log it, and acceptable thresholds per segment. Raising TSR usually lifts NPS and lowers support cost, but may require more compute or guardrails that affect speed.

Instrument tasks with clear start/finish events and outcomes (success, fail, assisted). Segment by intent and customer tier. In 2026, pair TSR with cost and latency per task to see ROI, and add lightweight human validation samples weekly to confirm logging accuracy.

After adding a critique step, TSR for drafting release notes climbs from 68% to 81% while cost per task rises 7% and latency stays under 1.6 s—an acceptable trade for the PM.

• Counting partial completions as success, inflating numbers.
• Not segmenting by intent, masking poor performance on long-tail tasks.
• Ignoring human follow-up work, underestimating true cost.