Retiring a Costly Legacy HR System With Unattended RPA

Overview

After migrating to a new Human Resources Information System (HRIS), a company was left with a common but expensive problem: a legacy platform they no longer wanted, but could not fully shut down.

A complete one-to-one data migration from the old HR system to the new one was technically possible, but the mapping effort was complex, time-consuming, and disproportionately costly relative to the business value of the full dataset. At the same time, regulatory and tax record retention obligations meant the organization could not simply discard the historical information.

Instead of paying ongoing fees “just in case,” the organization used unattended robotic process automation (RPA) to extract and migrate only the legally required subset of records, validate accuracy with audit-ready controls, and fully retire the legacy system.

The Challenge

A migration that did not fully “finish”

The company successfully moved off a legacy HR management system to a newer platform. However, the remaining historical dataset in the legacy system presented two issues:

• Complex data mapping: The legacy platform stored records in a way that did not translate neatly into the new system’s data model.

• High migration cost: Building a full one-to-one mapping (and the associated transformation logic) would have been expensive and slow.

Compliance still required access

Even though the business no longer needed the legacy system for day-to-day HR operations, it still had to retain certain records for tax and regulatory reasons. The result was a frustrating and costly compromise:

• Ongoing fees for limited “read-only” access

• Operational risk if the legacy vendor changed terms, access methods, or support

• Compliance risk if records could not be produced quickly during audits or disputes

The organization was effectively paying a premium every month to keep a system alive that the business had already outgrown.

The Insight That Changed the Plan

A full historical migration felt like the “proper” solution, but it was not the necessary solution.

Working with finance and legal subject matter experts (SMEs), we reframed the goal:

The legal obligation was not to preserve every historical field in the legacy system.
It was to retain a critical subset of records that satisfied retention requirements.

Once that subset was clearly defined, an important reality emerged:

• The required records had an obvious, consistent mapping from the old system into the new system (or into a compliant archive structure supported by the new environment).

That turned an unclear integration problem into a well-scoped automation problem.

The Solution

1) Define the compliance retention scope

We started by converting “we have to keep everything” into a precise, auditable retention definition.

Activities included:

• Workshops with finance, payroll, and legal stakeholders

• Agreement on:

  • Which record types were required (for example: person records, tax records, pay stubs, expenses)

  • Required fields and retention windows

  • Format and accessibility expectations for audit purposes

• A documented mapping spec approved by SMEs

This step reduced risk and prevented unnecessary work.

2) Build an unattended RPA migration worker

Because the legacy system’s data structure and interfaces made bulk export and transformation difficult, we used RPA to replicate the actions a person would perform, only faster and continuously.

The bot was designed to:

• Iterate through a master list of records

• Navigate the legacy UI reliably (including handling popups, timeouts, and inconsistent page loads)

• Extract required data points

• Transpose the data into the new system’s target fields

• Log every step for traceability

The automation ran unattended, meaning it could work overnight and on weekends without tying up staff.

3) Add audit trails and quality assurance controls

Because these were compliance-relevant records, the project included controls from day one, not as an afterthought.

Controls included:

• Detailed run logs (record IDs processed, timestamps, outcomes)

• Exception handling queues (records that failed validation or required review)

• QA spot checks by accountants to confirm accuracy

• Progress reporting and reconciliation summaries

This made the process defensible and reviewable by both internal teams and external auditors.

4) Reconcile and retire the legacy system

Once migration was complete:

• A final comparison report validated that the retained subset in the new environment matched the legacy source

• The organization formally decommissioned the legacy system and eliminated the recurring cost

Why We Did Not Run Bots in Parallel

Yes, the work could have finished faster by running multiple bots at once.

The company deliberately chose not to optimize for maximum throughput.

There was no urgent deadline, and the organization was risk-averse. Running a single bot at a controlled pace had real advantages:

• Clear visibility into what was being moved and when

• Simpler issue investigation if an anomaly appeared

• Lower operational risk to the target system (avoiding spikes in writes or user-like activity)

• More comfortable governance for finance and legal teams monitoring the migration

In other words, the organization traded speed for confidence, and that was the right call for this scenario.

Results

Business outcomes

• Retired an expensive legacy HR platform that was only being kept for compliance access

• Avoided a costly full data mapping project that delivered little incremental value

• Reduced ongoing vendor risk related to access and support for an aging system

• Improved audit readiness with structured, accessible records and clear evidence trails

Operational outcomes

• Eliminated the manual effort of transposing records

• Reduced human error risk through consistent automation steps

• Established a repeatable approach the organization can reuse for other legacy retirements

What This Project Demonstrates

This case study is a good example of how automation creates value when it is paired with good governance:

• Start with the obligation, not the dataset.
  “What must we retain?” is a better question than “How do we migrate everything?”

• Use RPA when integration is not practical.
  If the system has no reliable export path or the mapping is disproportionately complex, RPA can bridge the gap safely.

• Treat compliance migrations like controlled operations.
  Logging, exception handling, QA sampling, and reconciliation are not optional. They are the product.

Where AI Fit (and Where It Didn’t)

This project succeeded primarily through automation and control design, not by forcing AI into the mix.

That said, similar legacy retirement projects often benefit from AI in targeted ways, such as:

• Detecting anomalies in migrated values (outlier spotting)

• Classifying unstructured legacy attachments

• Assisting with mapping discovery when field naming is inconsistent

The principle is simple: use AI where it reduces risk or effort, and use deterministic automation where accuracy and traceability must be exact.

Looking to Retire a Legacy System Without the Usual Pain?

If you are paying ongoing costs for a system you no longer want, you are not alone. Many organizations get stuck in “read-only forever” mode after a platform migration.

A short process audit can usually uncover:

• What you actually need to retain

• The safest path to migrate or archive it

• The controls required to do it with confidence

Book a Process Audit today