The AI Governance Go-Live Checklist

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 A KB with outdated articles, conflicting answers, or unowned content will produce outdated, conflicting, and unowned responses at scale and speed.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

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The AI Governance
Go-Live Checklist

Accuracy thresholds

Guardrails

Human-in-the-loop escalation

Knowledge base governance

Audit & incident response

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How Mosaic supports each governance component

See how Mosaic handles every check on this list