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AI-Powered Support Triage

TL;DR

• AI can categorize tickets, suggest routing, and draft responses. It will misroute, miscategorize, and miss context.
• Use AI for first pass. Human review for edge cases and high-value customers. Tune based on feedback.
• Triage is the front door. Get it wrong and you multiply work. Get it right and you scale.

Support volumes are up. AI can read tickets, assign categories, route to the right queue, and suggest solutions. That's real. So are the failures: a ticket about "login broken" routed to billing, a VIP sent to the wrong team, a nuanced issue flattened into a wrong category. Your job: use AI to handle volume, keep humans where it matters.

What AI Can Do

Categorization:

• "This looks like a billing issue" or "This is a technical bug." AI can classify from subject and body. Often right for obvious cases.
• Tune on your categories. Your taxonomy might be different from training data. Retrain or prompt with examples.

Routing:

• "Send to Tier 2" or "Escalate to engineering." AI can suggest. You configure rules. You override when wrong.
• High-value or ambiguous tickets: human routing. Don't let AI send a Fortune 500 account to the wrong queue.

First-response drafting:

• "Here's a suggested reply based on similar past tickets." AI can draft. You edit, personalize, and approve. Saves time for repetitive cases.
• For complex or emotional tickets: skip the draft. Respond personally.

Similar-ticket search:

• "Find tickets like this one." AI can surface. You use them to inform your response. Don't copy-paste blindly. Context differs.

What AI Misses

Context and history:

• "I contacted you last week." AI might not connect the dots. You have the full history. You see the pattern.
• VIP, churn risk, or escalations: human should handle. Flag them. Don't auto-route.

Tone and urgency:

• "This is the third time" or "We're losing money." AI might categorize by keywords. You read the emotion. You prioritize accordingly.
• Urgency isn't always in the words. It's in the history, the account, the silence.

Edge cases and new issues:

• AI trains on past tickets. New bugs, new products, new edge cases — it might misroute. Human review for "unknown" or low-confidence picks.
• When in doubt, human. Better to over-review than to lose a customer.

The Workflow

1. AI triages — Category, route, suggested response. For high-volume, low-stakes: let it run. For high-stakes: human in the loop.
2. Human reviews — Sample. Edge cases. Low-confidence. Fix misroutes. Tune the model or prompts over time.
3. Measure — Misroute rate, resolution time, CSAT. If AI helps, you'll see it. If it hurts, you'll see that too. Adjust.

Your Role

• Tuner. AI isn't set-and-forget. You provide feedback. You fix misroutes. You improve the taxonomy. That's ongoing work.
• Human fallback. When AI is wrong, you fix it. When the customer is frustrated, you step in. That's the job.
• Voice of the customer. You see the patterns. You know what's broken. You feed that back to product and eng. AI helps with volume; you own the insight.

Do This Next

1. Run a week of AI triage — Compare to baseline. How many misroutes? How many tickets needed human override? Tune.
2. Define "human required" — VIP, churn risk, escalation history, low-confidence. Document. Implement. Don't auto-route these.
3. Sample 20 AI-drafted responses — Before they went out. What would you have changed? Use that to improve prompts or add guardrails.