1. Start small: pick message types and add redaction + provenance
Decide which message types are allowed for AI drafting and keep the list deliberately short. Good first candidates: short FAQ answers, appointment confirmations, simple delivery updates — not dispute responses, refunds, legal or medical advice. Record this as a one‑line policy per queue (e.g. "AI drafts: FAQ & appointment confirmations only").
Before anything touches a model, add a minimal redaction step and three provenance fields on the record: `ai_input_snapshot`, `ai_draft_source`, `ai_review_decision`. Redaction should remove direct identifiers (PII) and sensitive tokens while keeping operational context (product, date, non‑PII issue text).
Minimal redaction regex examples you can use in a spreadsheet or Zap/Make step: `\b[\w.%+-]+@[\w.-]+\.[A-Za-z]{2,6}\b` (emails), `\b0?7\d{9}\b` (UK mobile), `\b[A-Z]{1,2}\d{1,2}\s?\d[A-Z]{2}\b` (UK postcode). Keep this list short — redact rather than attempt perfect parsing — and flag any record with multiple redaction hits for manual review.
2. Prompt templates, confidence rules and a human‑review queue
Create 2–3 templated prompts that enforce tone, length and policy. Example templates:
- FAQ reply: "Customer says: {{redacted_text}}. Reply as a friendly Hampshire small‑business support agent in ≤80 words, confirm next steps, do not include contact info, do not guess personal details. Output: SUBJECT and BODY."
- Appointment confirmation: "Confirm appointment for {{service}} on {{date}} at {{time}}. Keep it short, polite, include cancellation instructions and no PII."
- Escalation note (internal): "Summarise the issue in 2 sentences and list 3 suggested next steps for the agent."
Add a simple confidence threshold — for example, a model score or heuristic: if the model returns a low confidence or uses redacted tokens markers like `[REDACTED]` inconsistently, route to manual review. Queue all drafts into a lightweight inbox: a CRM task list, a dedicated list view, or a shared Google/Excel sheet with columns for `ai_draft`, `reviewer`, `action (send/edit/do-not-send)`. Include one‑click Accept/Edit/Send actions where possible.
3. Hour‑by‑hour pilot, audit trail, cost controls and platform notes
Hour‑by‑hour afternoon pilot checklist (6 hours):
- Hour 0–1: Define scope, allowed message types and the "do not send" rule for sensitive records. Pick a 1–2 person pilot team in Fareham or your local office.
- Hour 1–2: Add redaction step and provenance fields in your CRM or sheet; implement the three regex rules above.
- Hour 2–3: Write 2 prompt templates and set a confidence threshold (example: auto‑send if human edit rate expected ≤10%).
- Hour 3–4: Create the human‑review queue (CRM list or sheet) and one‑click actions; seed with 20 historical messages for blind testing.
- Hour 4–5: Run 50 sampled drafts in "audit only" mode (do not send). Record `ai_input_snapshot`, `ai_model_output`, `reviewer_decision` per item.
- Hour 5–6: Review metrics, tune prompts, set daily caps and token limits, decide go/no‑go for live sends.
Audit trail and flags: capture `ai_input_snapshot` (redacted), `ai_model_output`, `ai_reviewer`, `ai_review_decision` (Accept/Edit/Do‑Not‑Send), and timestamp each action. Add a per‑record `do_not_send` flag that immediately blocks any automated send. Keep these fields simple so non‑technical staff can read them during CRM optimisation or CRM data cleanup.
Cost and safety controls: sample volume (start at 5–10% of relevant messages), per‑message token limit, daily cap (e.g. 50 drafts/day), and stop condition rules such as human edit rate >30% or >1 near‑miss incident (sensitive data included) per 500 drafts. Monitor two headline metrics weekly: Human Edit Rate (percentage of drafts edited before send) and Near‑Miss Incidents (sensitive data or policy breaks caught in review). If Human Edit Rate stays below your threshold for 2 weeks and Near‑Miss is zero, consider widening scope.
Platform notes (short):
- HubSpot: use a custom property and a List/View for the review queue, plus Tasks or a simple workflow to create review tasks; prevent downstream workflows from firing by gating on `do_not_send` or `ai_review_decision` properties.
- Salesforce: create a small custom object (AI_Draft__c) or use Tasks; link to Contact/Case and store provenance fields on the draft record; use Process Builder/Flows to gate sends.
These are operational principles — the same redaction, templating, queueing and auditing work with Marketo, Pardot or other CRMs. Keep the pattern low‑friction so a small team on the South Coast can run it without an engineering project.
If you want a quick walk‑through or help turning this into a 3‑hour pilot for Fareham teams, marketing-automation-support-hampshire.html can help set the first queue and metrics — and Optira is available for a short, practical assist if you prefer.