The single biggest factor in how smoothly an AI automation project goes isn’t the AI — it’s whether your ticket data is actually usable when the project starts. Here’s how to get ready before you talk to anyone.
Know where your data actually lives
Most PEOs have ticket history split across a few systems: a helpdesk or ticketing platform (common ones include Zendesk and Freshdesk), an HRIS or payroll platform (ADP, Paycor, or similar), and sometimes a separate email inbox or phone log that never made it into either system. Before anything else, get a clear picture of which of these actually holds usable historical data, and for how far back.
Four things worth checking before you start
Category tagging. If your helpdesk already tags tickets by type (benefits, PTO, paycheck, enrollment), you’re ahead of most companies. If it doesn’t, even a rough manual sample of a few hundred recent tickets, categorized by hand, is enough to start.
Time-to-resolution data. Most ticketing platforms track this natively. If yours doesn’t, timestamps on ticket creation and closure are usually enough to calculate it after the fact.
Volume by client. Aggregate totals hide a lot. Knowing which clients drive the most volume — and whether that’s proportional to their headcount or not — matters for scoping.
Export access. Confirm you (or whoever manages your platforms) can actually export raw ticket data, not just view dashboards. Some platforms restrict this by user role or require an admin to pull it.
What if your data isn’t clean yet?
This is more common than not, and it isn’t a blocker. A proper readiness assessment is built to work with imperfect data — categorizing a representative sample if full tagging doesn’t exist, and being explicit about where estimates are being made versus where the numbers are solid.
Our AI Helpdesk Readiness Assessment includes this data audit as its first step, so you don’t need perfect records going in — just access to what you already have.
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