Is your Jira Service Management migration AI-ready?

[Kvitka Martsynyshyn]

Many teams migrate to Jira Service Management to utilize Atlassian Intelligence, Rovo, and automation. But AI is only as good as the data behind it.

If you're migrating from Zendesk to Jira Service Management, resist the temptation to move your entire archive at once. An AI-first migration focuses on clean, relevant data before historical records. 

Years of duplicate tickets, outdated knowledge base articles, inconsistent tags, and unresolved requests can reduce AI accuracy from day one. Instead, consider a two-phase migration strategy:

Phase 1: Build an AI-ready foundation

• Recent resolved tickets
• Current knowledge base articles
• Well-structured tags
• Associated customers and organizations

Validate AI performance before enabling it for production.

Phase 2: Complete the historical migration

Import the remaining tickets and records for compliance, reporting, and institutional knowledge without disrupting your AI baseline.

Migrating your knowledge base before ticket history is especially important, since AI relies on it to generate accurate responses.

I'm curious how other Jira Service Management teams are approaching AI-ready migrations.

• Do you filter historical tickets before migrating?
• How do you validate AI performance after go-live?
• Have you found any best practices that improved AI accuracy?

I'd love to hear what's worked for your team.