HR data reconciliation across four systems is exactly the kind of tedious work AI handles well.

The logic is structurally identical every run: extract, deduplicate by email/ID, flag field mismatches, output CSV plus report. This is a strong candidate for a scheduled recurring pipeline, not a one-off judgment call.

Success criteria are crisp: one row per employee, mismatches flagged, provenance tracked per field. The only ambiguity is conflict resolution rules (which system wins when fields disagree), which should be specified upfront but is a solvable configuration problem.

ADP, BambooHR, and Okta all have APIs, but credential provisioning, rate limits, and the legacy spreadsheet's format and location must be handled before the agent can run. This is the most likely point of failure in practice.

A bad master list could propagate incorrect payroll, access, or HR data downstream, which is a real but recoverable problem — the output is a CSV for human review, not an automated write-back to production systems. Errors are visible and correctable before they cause harm.

Deduplication by email and employee ID is deterministic. Mismatch flagging requires no taste or ethics — just comparison logic. A human should review flagged conflicts, but the agent can surface them without needing to resolve them.