AI Lifecycle Audit Checklist

### What's inside the audit - Data validation matrix to confirm every attribute syncing into SFMC, Braze, or Iterable is production-ready. - Litmus-style QA plan for subject lines, AI-generated variants, and dynamic content blocks. - Suppression, failover, and monitoring questions to ask your Martech + Data teams. - Executive summary template to document gaps, effort, and owners. ### How to run it 1. **Inventory signals.** Capture every data feed, channel, and workflow that touches the customer experience. 2. **Score maturity.** Rank each section (Data, Journeys, AI Safeguards, Talent) from 1–5 using the rubric. 3. **Prioritize sprints.** Use the scoring sheet to auto-generate a backlog grouped by effort vs. impact. ### Why teams use it - It takes 90 minutes to run and becomes the backbone for your next-quarter roadmap. - Gives leadership clarity on risk, cost, and staffing needs without a 60-page deck. - Aligns AI experimentation with compliance so you can ship faster *and* safer.