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AI for Customer-Facing Work

5 min read
Support EngSolutions EngDevrel

Support Eng

AI drafts fast. You verify accuracy, add empathy, and catch the 'this customer is frustrated' cues.

Solutions Eng

Use AI for proposal outlines and demo scripts. Discovery and relationship-building stay 100% human.

AI for Customer-Facing Work

TL;DR

  • AI can draft support responses, solution docs, and demo talking points.
  • Customers can tell when it's robotic. You add the empathy and accuracy.
  • Never send AI output unedited. One wrong answer erodes trust.

Customer-facing work is trust work. AI accelerates the drafting. You ensure it's right and that it feels human.

Support Responses

Good use cases:

  • "Draft a response to this ticket. Tone: helpful, concise. We don't have a workaround yet."
  • "Summarize this thread and suggest next steps"
  • "Turn this KB article into a personalized response"

Critical rules:

  • Verify every technical claim. Wrong fix = angry customer + escalated ticket.
  • Add empathy. "I understand this is frustrating" — AI can say it, but you know when it's needed.
  • Don't over-promise. AI might suggest "we'll fix this in the next release" when you have no idea.
  • Know when to escalate. AI won't. You read the subtext: angry customer, legal risk, executive involvement.

Workflow: AI draft → you verify accuracy → you add tone and judgment → send.

Solution Proposals and Demos

Good use cases:

  • "Draft a solution outline for [customer scenario]. Include: architecture, timeline, risks"
  • "Create demo talking points for features X, Y, Z"
  • "Write a follow-up email after this discovery call"

What AI can't do:

  • Read the room. Did they care about X or Y?
  • Know your pricing, packaging, or what you're allowed to promise
  • Build the relationship. That's you.

Use AI for structure. You fill in the "we listened" and "we get your problem" parts.

Discovery and Scoping

Good use cases:

  • "Suggest discovery questions for a customer in [industry] with [stated needs]"
  • "Draft a scoping document template from this conversation"

Cautions:

  • AI doesn't know what was said, implied, or off the record. You do.
  • Proposals based on AI scope can be wrong. Verify assumptions.

The "Robotic" Trap

Customers notice when a response feels generic. "We value your feedback" and "I'd be happy to assist" are red flags. Edit. Add specifics. Reference their situation. One personalized sentence beats three paragraphs of AI fluff.

Customer ticket: 'Your API keeps returning 500 when I pass a large payload.' You paste into AI. AI drafts: 'We value your feedback. Our team is looking into this.' Customer replies: 'That's not helpful. I've been down for 3 days.' Escalation.

Click "Verify accuracy + add specifics + empathy" to see the difference →

Quick Check

You're drafting a support response with AI. The customer sounds frustrated. What's the critical step before sending?

Do This Next

  1. Draft one support response with AI. Edit it for accuracy and tone. Send. Compare to your usual time.
  2. Create a "support response" prompt template — include your tone, common disclaimers, and escalation triggers. Reuse it.