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  "name": "How do I tell whether my AI customer support automation is actually helping?",
  "description": "Track accuracy, first-response time, resolution rate, repeat contacts, escalations, customer feedback, and review time—not just automated-message volume. Compar",
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        "text": "Measure support quality and business impact together: resolution accuracy, escalation quality, first-response time, time saved, repeat contacts, and customer feedback. Avoid judging success only by the number of automated replies, because a high automation rate can hide poor answers or more follow-up work. Context: Run My AI Biz frames AI applications as opportunities to test and improve rather than guaranteed outcomes. A useful workflow should make a defined process clearer or less repetitive. Key factors: - baseline performance before automation - error and rework rate - unresolved-ticket rate - owner review time. Practical guidance: Record a baseline from recent tickets, run a limited pilot, and compare like-for-like categories. Review failed cases weekly and widen the scope only when quality remains acceptable. This gives a solopreneur evidence for deciding whether to keep, change, or stop the workflow.",
        "description": "Track accuracy, first-response time, resolution rate, repeat contacts, escalations, customer feedback, and review time—not just automated-message volume. Compare results with a pre-automation baseline, inspect failed cases, and expand only when quality and workload improve together."
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