Customer Support & Success

inbound ticket → triage classification → draft response → sent resolution → CSAT/close record

• Automate
  Intake & triage — AI classifies intent, product area, sentiment, and urgency, and routes to the right queue or bot flow · high volume, verifiable against routing outcomes, trivially reversible by re-routing
• Automate
  Deflection — an AI agent fully resolves password resets, order status, plan changes, and other Tier-0/1 issues end-to-end · rules-based, low stakes, and the resolution is checkable against system state
• Automate
  Context assembly — AI pulls account history, past tickets, and relevant KB articles into one briefing for the human agent · pure retrieval, wrong pulls are visible and cheap
• Draft
  Response drafting — AI writes the reply for Tier-2 issues; the agent edits and sends under their own name · answers are verifiable against docs, but tone and edge-case judgment still need an owner
• Assist
  Exception handling — refunds above threshold, legal threats, security reports, and furious VIPs go to humans, with AI only summarizing the thread · high stakes, low reversibility, relationship-heavy
• Automate
  Close & tag — AI writes the resolution summary and applies taxonomy tags for downstream analytics · repetitive, and tag errors wash out at aggregate scale

ticket-cluster gap report → article draft → reviewed/published article → freshness audit

• Automate
  Gap detection — AI clusters recent tickets and flags topics with high volume but no matching article · statistical pattern-finding over high volume; a bad flag costs one glance
• Draft
  Article drafting — AI drafts the article from resolved-ticket transcripts where agents already gave the right answer · source material is verified-correct resolutions, but the KB is load-bearing for every bot answer, so a human must own publication
• Assist
  SME review & publish — product owner verifies technical accuracy and approves · this is the verification step itself; automating it removes the only quality gate the deflection bot depends on
• Automate
  Freshness audit — AI diffs articles against release notes and flags stale screenshots, prices, and procedures · mechanical comparison, high repetition, flags are reversible
• Draft
  Retrieval tuning — AI rewrites titles and adds question-phrased variants so both search and the support bot find articles · improvements are A/B-testable, but a human should watch for meaning drift

signed deal notes → kickoff plan → configuration checklist → training materials → go-live sign-off

• Automate
  Handoff digestion — AI turns sales call recordings and CRM notes into an onboarding brief: goals, stakeholders, promised outcomes, red flags · summarization of existing context, and the CSM reviews it in the kickoff prep anyway
• Draft
  Kickoff plan drafting — AI adapts the standard onboarding template to this customer's use case and timeline · templated and verifiable, but the plan is a commitment the CSM must own
• Assist
  Configuration & data migration — AI assists with field mapping and flags anomalies in imported data · errors here are hard to reverse post-go-live and context lives in the customer's head, not the tools
• Draft
  Training content — AI generates role-specific quickstart guides and walkthrough videos from the master docs · derivative content, checkable against the product, low blast radius
• Draft
  Progress nudges — AI monitors adoption telemetry and drafts check-in emails when milestones slip · the signal is automatic, but a templated nudge to a stalling customer can read as spam; CSM should personalize
• Avoid
  Go-live sign-off — human-led review that success criteria are met · this is the relationship moment and the contractual checkpoint; delegating it signals the customer doesn't matter

interaction transcripts → scored QA evaluations → coaching notes → agent scorecard trends

• Automate
  Interaction scoring — AI scores 100% of tickets and calls against the QA rubric instead of the traditional 2% sample · rubric-based, high volume, and disputed scores can be re-reviewed by a human
• Assist
  Calibration — QA leads spot-check AI scores weekly and adjust the rubric where AI and human graders diverge · this is the human-in-the-loop that keeps the automated scoring honest
• Draft
  Coaching note drafting — AI compiles each agent's misses into specific, example-linked coaching notes · evidence is verifiable, but feedback delivered to a person needs a person's judgment on framing
• Avoid
  Coaching conversation — the team lead delivers feedback 1:1 · pure relationship work; AI-delivered criticism reliably damages trust and gets gamed
• Automate
  Trend reporting — AI rolls scores up into team-level trend reports with drill-down examples · aggregation over verified data, weekly repetition

raw feedback (tickets, surveys, reviews, call notes) → tagged theme dataset → insight report → product/CX action items

• Automate
  Aggregation & tagging — AI ingests tickets, NPS verbatims, app reviews, and sales-call mentions into one taxonomized dataset · high volume, individual tag errors are immaterial at aggregate level
• Automate
  Theme detection — AI surfaces emerging complaint clusters and quantifies trend deltas month-over-month · statistical work humans do badly and slowly; outputs are checkable against the raw data
• Draft
  Insight narrative — AI drafts the "what changed and why it matters" report with representative quotes · quotes are verifiable, but the causal story and prioritization need an owner who knows the product context
• Assist
  Roadmap recommendation — the CX lead argues for specific fixes in the product forum · stakes are a quarter of engineering time, and the persuasion is political, not textual
• Draft
  Loop-closing — AI drafts "you asked, we shipped" customer comms once fixes land · templated, verifiable against release notes, low risk with a quick review

usage + support + billing signals → health score → risk alert → save-play brief → renewal outcome record

• Automate
  Signal assembly — AI joins product telemetry, ticket sentiment, invoice status, and champion-departure signals into one account timeline · mechanical integration over high volume; errors surface immediately in review
• Automate
  Risk scoring & alerting — AI flags accounts whose pattern matches past churners · a false alarm costs a CSM ten minutes; a model miss is no worse than today's status quo
• Draft
  Diagnosis — AI drafts the "why this account is at risk" narrative with supporting evidence · the evidence is checkable, but the model can't see the reorg or the champion's frustration, so the CSM must correct it
• Assist
  Save-play selection & outreach — the CSM chooses the intervention and makes the call · high stakes, irreversible if botched, and entirely relationship-dependent; AI preps the brief and talking points
• Draft
  Renewal forecast rollup — AI aggregates account-level risk into the revenue forecast with confidence bands · leadership consumes it, so the CS ops lead must own the number even though the math is automatic