To migrate to a new ATS, export your candidate, job and pipeline data, map the fields to the new system, and import into a test environment first. Reconnect job boards, HRIS and calendar integrations, recreate active requisitions, and run a short parallel period before retiring the old tool. Train the team and validate data and compliance after cutover.
Prioritize active candidates, open requisitions, and recent pipeline history — the data your team needs day one. Include candidate profiles, resumes, application status, interview notes and key communications. Older archives can be migrated later or kept read-only in the old system. Mapping fields carefully matters: a clean migration of the data you actually use beats a messy dump of everything, which clutters the new system and slows adoption.
Import into a test environment first and validate record counts, field mapping and attachments before going live. Keep the previous ATS accessible read-only until you've confirmed the new system is complete. Run both in parallel for active roles for a short window so in-flight candidates don't fall through. Assign one owner to spot-check high-priority pipelines after cutover. The losses people fear come from rushed, unvalidated cutovers, not from migration done in stages.
For active data and a handful of integrations, migration is often part of a few-days-to-few-weeks implementation. Volume, data quality and the number of integrations drive the timeline. Clean source data and a defined field map are the biggest accelerators; messy historical data with inconsistent fields is what stretches projects. Migrating only what you need first keeps go-live fast, with the archive handled afterward.
For a short window, yes. Running the old and new ATS in parallel for active requisitions gives you a fallback while the team learns the new tool and you validate data and integrations. Set a firm end date so the overlap doesn't drag on, freeze new activity in the old system once cutover is confirmed, and then archive it read-only for compliance and reference.
A migration is a chance to move forward cleanly, not to drag every old record into a new system. Decide deliberately what data genuinely needs to migrate: active candidates and open roles clearly do; recent, relevant candidate history and your talent pool usually do; ancient, stale, or low-value records often do not. Migrating everything indiscriminately imports clutter and can carry over data-quality problems, while migrating too little loses valuable history. The right approach is to identify the data with ongoing value — active pipelines, useful candidate relationships, records you are obligated to keep — and clean and map that, leaving genuine dead weight behind. This selective, planned migration is what makes the new system start organized rather than inheriting the old one's mess.
The biggest risk in migration is dropping candidates who are mid-process while systems change. Protect against it by keeping active hiring visible and continuous through the transition. A common approach is to avoid migrating in-flight candidates abruptly: either close out active roles in the old system while starting new roles in the new one, or overlap the systems briefly so nothing is lost in the gap. Communicate with candidates so they experience no silence during the switch. Verify after migration that active pipelines transferred correctly before relying solely on the new system. The principle is that no candidate should fall into a crack between the old and new tools, which means sequencing the cutover around live hiring rather than through it.
Migration timelines vary with data volume and complexity, integrations, and organization size, much like implementation. Moving a small, clean candidate set into a simple tool can be quick; migrating a large, messy dataset with many integrations for a big team takes considerably longer. The data work — exporting, cleaning, mapping fields between the old and new systems, and verifying it imported correctly — is usually the main driver, and rushing it is where migrations go wrong. Configuring the new tool to your process and testing integrations add time. Planning a realistic timeline based on your actual data volume and complexity, and resourcing the data work properly, prevents the compressed, error-prone migration that causes lost or garbled records.
Running the old and new systems in parallel for a period is a common risk-reducing tactic, though it has trade-offs. The benefit is safety: you keep the old system available as a reference and fallback while the new one beds in, so a migration error does not immediately disrupt hiring, and you can verify the new system before fully committing. The cost is the overhead and potential confusion of maintaining two systems at once, so parallel running should be time-boxed, not open-ended. A practical pattern is to start new roles in the new system while closing out in-flight roles in the old, effectively a controlled parallel period that phases out the old tool. Keep the overlap deliberate and short to capture the safety without the ongoing burden.
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