Capture AI suggestions where your team already works
Start by capturing every AI suggestion in an app your team already uses: CRM tasks (HubSpot or Salesforce), a shared spreadsheet, or a simple Kanban/list in the same tool you use today (Trello, Notion, Marketo programmes or Pardot lists). Keep the capture step manual or via your current automation (a Zap/Make action or native workflow) so nothing new has to be learned.
Keep the captured record minimal and consistent: record id (link to the customer or case), the prompt or trigger text, the AI output summary, model name, confidence score, who/what generated it and a timestamp. That small set gives reviewers what they need without extra fields or new systems.
Triage, queue rules and reviewer limits
- Prioritisation: assign Risk = High / Medium / Low. Rules: automatic High if action changes payment, contract, refund or legal fields; Medium if it updates billing/contact details; Low for copy, suggested tags or subject-line edits.
- Confidence thresholds: treat outputs <0.60 as High risk (manual review), 0.60–0.85 as Medium (review queue), >0.85 as Low (eligible for gated automation and sampling audit).
- Visible queue options: use CRM task queue or a shared sheet with Status (New / In Review / Accepted / Rejected / Escalated) and a lightweight Kanban for visibility. Include a link back to the record and the minimal metadata above in each item.
- Reviewer load limits: cap open assignments at 8–10 items per reviewer; auto-assign overflow to a fallback reviewer or the team lead. Use a simple filter in your spreadsheet or CRM to show only items within each reviewer’s limit.
- SLAs and escalation: set SLAs by risk—High: 2 business hours, Medium: 24 hours, Low: 48–72 hours. If SLA breached, auto-change Status to Escalated and notify the fallback owner.
- Rollback and snapshot: before any automated change, save a ‘before’ snapshot (previous field values) in the sheet or as an internal CRM note so you can revert quickly. If an action is automated, log the action id, timestamp and who approved it for rollback.
Phased rollout, sampling and metrics to reduce reviewer overhead
Run in three short phases. Phase 1 (afternoon): capture-only and shadow mode — AI suggestions appear in the queue but no actions run. Phase 2 (1–2 weeks): enable gated automation only for Low-risk + confidence >0.85 items; reviewers still approve Medium and High. Phase 3 (weeks 3–6): expand automation coverage gradually while reducing review sampling as error rates fall.
Use sampling and simple metrics to keep review work small: sample 10% of automated Low-risk actions or at least 1 per day, whichever is greater. Track queue age, reviewer throughput (items closed per day), acceptance rate of AI suggestions and automation error rate. Aim for an error rate under 2% before widening automation; if it drifts up, return to shadow mode for the affected action type.
Small teams can set this up without buying tools: use existing CRM queues (HubSpot tasks / Salesforce queues), a shared sheet or a simple ticket board in the app you already own, and enforce the rules above. If you’d like a short checklist or one‑page rollout plan to hand to the team, Optira can help put it together as a practical next step.