<\/span><\/h2>\nCase study 1: The \u201cdeflection hero\u201d that increased repeat contacts.<\/strong>
\nA mid-market SaaS team celebrated a 20% jump in containment. However, repeat contacts also spiked. Their scorecard showed why. The agent gave plausible steps, but it didn\u2019t confirm success. Resolution quality averaged 2\/4, while tone scored high. Fix: add verification steps and \u201cstop conditions\u201d that trigger escalation after two failed attempts.<\/p>\nCase study 2: The cautious agent that escalated too early.<\/strong>
\nAn ecommerce brand rolled out stricter policy guardrails. As a result, escalations jumped and handle time rose. The scorecard highlighted a pattern. Accuracy was fine, but escalation decision averaged 1\/4 because the agent escalated on mild ambiguity. Fix: add a short clarification question before escalation. Then improve KB coverage for top intents.<\/p>\nNotice what happened in both examples. The problem wasn\u2019t \u201cAI is bad.\u201d It was misaligned incentives and missing diagnostics.<\/p>\n
<\/span>Common mistakes (and how to avoid them)<\/span><\/h2>\n\n- Mistake: Scoring only outcomes.<\/strong> If you only track \u201cresolved vs escalated,\u201d you miss risky behavior. Add criteria for grounding and policy adherence.<\/li>\n
- Mistake: Treating escalation as failure.<\/strong> Smart escalation is success when it prevents harm. Reward good handoffs.<\/li>\n
- Mistake: One scorecard for every intent.<\/strong> High-stakes flows need different thresholds. Create \u201cadd-on\u201d rules for refunds, payments, or safety topics.<\/li>\n
- Mistake: No calibration.<\/strong> Two reviewers, two different grades. Run weekly calibration with 5 shared conversations.<\/li>\n
- Mistake: Not tying findings to fixes.<\/strong> If insights don\u2019t become backlog items, people stop scoring. Create a simple \u201cscorecard to sprint\u201d loop.<\/li>\n<\/ul>\n