Case interview: "Our customer support costs are too high. How can AI help?" Walk through your full answer.
1. Clarify (ask): contacts per month by channel, top contact reasons, average handle time, cost per contact, CSAT, languages, current tools, constraints.
2. Structure the problem (support cost = contacts × cost per contact):
| Lever | AI application | Typical impact area |
|---|---|---|
| Reduce contacts | Proactive notifications; better self-service help content | Contact volume |
| Deflect | AI assistant resolving FAQs and status queries with system lookups | Contacts reaching agents |
| Faster handling | Agent assist: summaries, suggested replies, KB answers | Handle time |
| Better routing | Intent classification, priority, language routing | Transfers, resolution time |
| Quality and coaching | Auto QA on 100% of conversations instead of a 2% sample | Quality, training |
| Root causes | Analytics of contact drivers fed back to product and ops | Long-term volume |
3. Estimate (example): 200K contacts/month at ₹80 each = ₹1.6 Cr/month. If 25% are deflected and handle time drops 20% on the rest: 50K deflected (₹40L saved) + 150K × ₹80 × 20% (₹24L) ≈ ₹64L/month gross savings before costs, to be validated in a pilot.
4. Prioritise: start with agent assist + classification (low risk, quick to deploy), then deflection on the top 5 intents with order lookups, then auto-QA and analytics.
5. Risks: wrong answers to customers, so use grounding, confidence thresholds and easy handoff to humans; integration effort; agent adoption; data privacy.
6. Next steps: export 3 months of transcripts, a top-intent analysis in week 1, an eval set and baseline metrics, and a pilot in one channel.
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