AI deployments aren’t like clicking “Get started” on a spreadsheet app—you need tight feedback loops to train the models and tweak behavior. Here’s what I recommend for the first six weeks:

Week 1: Kick-off & limited rollout
• 30-minute sync to verify basic setup, scope of data sources, and success criteria.
• Agree on tooling for flagging bad answers.

Week 2: Ingest & iterate
• 30-minute check-in on Week 1 issues: what questions fell through the cracks?
• Adjust data feeds, add missing knowledge bases, refine tone settings.

Week 3: Quality deep-dive
• 30-minute review of 100–200 customer questions.
• Use an automated evaluation to tag good vs. bad answers, then triage the “bad” batch together.

Week 4: Behavior tuning
• 30-minute stand-up to tackle edge cases: deprecated info, new product lines, weird queries.
• Update filters on what the AI should never answer, and add new sources as needed.

Week 5: Confidence check
• 30-minute sync to measure “no-answer” rate (aiming for ≤ 10%).
• Celebrate early wins and finalize any lingering tweaks.

Week 6: Handoff prep
• 30-minute wrap-up: confirm you’re at ~90% or better answer accuracy.
• Define ongoing cadence (bi-weekly or monthly), ownership of long-tail cases, and SLAs.

Why weekly, 30 minutes?
• Keeps momentum without overwhelming your team.
• Focuses purely on the “delta” of questions that missed the mark—your highest ROI improvements.
• By Week 6 you’ll have a tightly-trained AI that your support org can’t live without.

If you’d rather front-load more touchpoints in Week 1–2, you could do two 20-minute sessions instead of one—but don’t stretch it beyond weekly, or the model drifts. After six weeks, transition to a rhythm that matches your support volume—every two weeks, then monthly.

Good luck with your AI deployment — it’s a sprint up front that pays dividends in speed and scale. 🚀

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