AI readiness assessment for MSPs
Six questions about your ticket data, documentation, volume and oversight preferences — scored instantly to show how ready your MSP is for AI-assisted service delivery.
How this assessment works
- Each answer is weighted: clean data, current documentation, higher volume, defined triage, approval-based oversight and a consolidated stack all increase readiness.
- Your score is the percentage of the maximum possible readiness.
- Bands: 67%+ ready, 34–66% ready with groundwork, below 34% build the foundation first.
- Indicative only — real readiness depends on your specific environment and goals.
Why data quality decides AI outcomes
AI service tools learn from your ticket history. If boards, types and notes are consistent, triage and resolution suggestions are useful within days. If every technician works differently, the AI learns inconsistency.
The single highest-value preparation is standardizing how tickets are categorized and documented — it improves reporting and billing accuracy at the same time.
Start with approvals, not autonomy
The MSPs that succeed with AI start in assisted mode: the AI drafts, triages and recommends, and a human approves. As accuracy proves out on your own tickets, you expand what it can do alone.
Corei supports four escalation modes — manual, assisted, approval-based and autonomous — so you can set the level per workflow and change it as trust builds.
Frequently asked questions
Does AI replace technicians?
No. It handles intake, triage, drafting and routine resolutions so technicians spend time on work that needs judgment. You decide what it may do autonomously.
How much history does AI need?
More clean, consistent tickets beat more tickets. A few weeks of standardized data is enough to start with suggestions.
Is this assessment a commitment?
No. It's an indicative self-assessment. Book a demo to see the AI on workflows like yours.
Want to talk through your result?
Walk through your situation in a 45–60 minute demo, or join the 3–6 month pilot program.