The audit-only pattern in three simple steps
- Capture intent, don’t write: the AI produces an "intended change" record (what to change, where, and why). Store that record in a log or review queue instead of updating the live system.
- Human review gate: surface intended changes to a lightweight queue for a human to approve, edit or reject. Include a confidence score so reviewers see how confident the model was.
- Gradual enablement: run metrics on the logged intents, fix issues, then allow a short staged rollout (canary → small percentage → full) where only approved intents write to the live system.
What to record and how to measure success
Keep the capture simple and consistent. Record an id, the target record id, a short intended_change description (old → new), a confidence_score (0–1), model_version, a concise rationale sentence, a snapshot of the input that produced the change, timestamp and the actor_id that triggered the AI. Also record review_status, reviewer_id and review_timestamp once a human sees it, plus a small rollback_note if the change is applied.
Measure a few operational metrics each week: proposed_changes_per_day, approval_rate, average_time_to_review, post-apply_error_rate (issues per 100 writes) and false_positive_rate (approved but later reverted). Track these per model_version and per target-field so you can spot regressions quickly.
Fast, practical options for small UK teams and a safe rollout
Start small and platform-friendly. In HubSpot create a few custom properties for intended_change, confidence_score, model_version and review_status and use a workflow to create tasks for reviewers. In Salesforce add equivalent custom fields and surface review items via a queue or a simple Lightning list; Flow can be used to snapshot the record. If you use Marketo or Pardot, write the intent to a custom lead/contact field or to a shared sheet and triage from there. If you prefer no code, route intents to a Google Sheet or Airtable and use that as your review board.
Staged rollout guidance: begin with canary records you know well (10–20 rows), run the audit-only pipeline for 1–2 weeks, fix issues, then allow auto-write only for high-confidence cases (example thresholds: auto-apply ≥ 0.90, review required 0.70–0.90, block < 0.70). Add a processing_state flag on records so changes are idempotent and reversible, snapshot before applying writes, and keep a manual kill switch handy. If you want a short, practical build and run plan to get started, Optira can help set this up across HubSpot, Salesforce or marketing automation platforms.