The AI Governance Go-Live Checklist

Don't let the checkboxes fool you — this isn't a to-do list. It's a decision tool. Every item below maps to a governance component that confident AI adopters validate before go-live. Most production failures trace back to one of these being missed. Run through the checklist with your team to pressure-test launch readiness and surface gaps before customers do.

Your score

0 / 19

1

Accuracy thresholds

Section 1

AI accuracy varies by ticket type, complexity, and customer context. Strong governance segments the ticket landscape, sets a defensible baseline per segment, and defines where the model answers alone versus where a human stays in the loop.

• Ticket types segmented and baseline accuracy established for each
• Confidence thresholds defined per segment and tested against a representative sample
• Threshold review cadence set for post-launch (monthly minimum recommended)

2

Guardrails

Section 2

Guardrails are what separate AI as a productivity tool from AI as a brand liability. The five items below cover where AI is allowed to operate, how it's allowed to sound, what it's not allowed to touch, what it's not allowed to promise, and how long it can run unattended. All five need to be configured before launch — not after the first incident.

• Topic and scope restrictions documented and configured
• Tone and brand controls set and tested
• PII and sensitive data handling rules configured and verified
• Commitment prevention guardrails in place and tested
• Multi-turn conversation limits defined

3

Human-in-the-loop escalation

Section 3

The most predictable AI failure mode is staying in a conversation it should have handed off. Strong escalation needs three components: a trigger system, a named owner, and a tested handoff experience. The customer should never feel the seam between AI and human.

• Escalation triggers defined across confidence, sentiment, topic, turn count, and account tier
• Escalation rules assigned to a named owner
• Handoff experience tested end-to-end from the customer's perspective

4

Knowledge base governance

Section 4

AI is only as accurate as the knowledge base it's built on. A KB with outdated articles, conflicting answers, or unowned content will produce outdated, conflicting, and unowned responses at scale and speed. KB cleanup happens before launch, not as recovery work after.

• Full KB audit completed pre-go-live
• Content owners assigned by product area
• Refresh cadence tied to product release cycle

5

Audit & incident response

Section 5

Every AI-assisted interaction is a data point. Logging them, sampling them, and having a process to act on the failures you find is what separates AI deployments that improve over time from ones that drift quietly. Most teams underinvest here because the incidents haven't happened yet.

• Logging enabled for all AI-assisted interactions
• QA sampling process defined (frequency, volume, weighting by ticket type)
• Failure classification rubric created
• Incident response process documented and shared with the full support team
• All stakeholders signed off on their governance component

Calculate your score

• 17–19: Go-live ready

You've done the work most teams skip. Launch with confidence.

• 13–16: Critical gaps

You're close, but the unchecked items are typically the ones that cause production failures. Triage them before you launch.

• <13: Slow down

You're not ready to put this in front of customers. Use this as the framework for your governance work and come back when the gaps close.

How Mosaic supports each governance component

Every item on this checklist maps to a Mosaic capability — out of the box, no implementation sprint required.

Accuracy thresholds

Mosaic Intelligence surfaces real-time confidence scores and per-segment accuracy dashboards — so thresholds aren't a one-time setup, they're a live signal.

Guardrails

Topic restrictions, PII filtering, tone controls, and commitment guardrails are all configurable in Mosaic Admin — no engineering required to change them post-launch.

Escalation

Mosaic Assist supports configurable escalation triggers across confidence, sentiment, topic, turn count, and account tier — with native handoff to your ticketing system so the seam is invisible to the customer.

KB governance

Mosaic Knowledge tracks content health, flags stale articles, assigns ownership by product area, and prompts refresh based on your release cycle — so the KB stays accurate without a manual audit every quarter.

Audit & incident response

Full interaction logging, QA sampling queues, and failure tagging are built into Mosaic Intelligence from day one — not bolted on after an incident.