HCM Advisory: a Workday migration mapping accelerated by AI

CASE STUDY · HR SYSTEMS CONSULTING

AI proposes and accelerates, the expert decides and commits.

Data mapping is the longest and riskiest step of a large Workday migration. Here it was cut by 70%, on three times the volume, without a single critical error.

An Adservio team reviewing data mappings

Client

HCM Advisory

Expertise

AI-assisted migration · Automated mapping · Generated tests

Tech Stack

Workday · Human validation

CONTEXT

Project Context

HCM Advisory supports high-volume Workday migrations. In this kind of programme, one step concentrates most of the schedule and most of the risk: data mapping, the operation that matches every field of the source system to its equivalent in the target system.

That step has a reputation for being long, tedious and sensitive. It runs on HR and payroll data, where a single wrong match does not produce a bug to be fixed later, but a salary paid incorrectly or an entitlement lost.

The brief was therefore double-edged: cut the delay sharply, and keep the expert in the loop on every decision. Speed could not be bought with a margin of error.

Strategic Objectives

OBJECTIVES

(01)

The step that sets the schedule

Mapping is the longest phase of the migration. As long as it runs by hand, field by field, the whole programme waits, and the go-live date depends on it.

(02)

Data that forgives nothing

HR and payroll data are highly sensitive. The requirement was not a low error rate, but zero critical error, on the full scope of the migration.

(03)

Volume to absorb

A large migration means a volume of correspondences that grows faster than the team available to check them. The gain had to hold at scale, not on a sample.

(04)

The expert in the loop

Automating the proposals was acceptable; automating the decision was not. Every match had to remain attributable to an expert who validates it.

Solutions Delivered by Adservio

SOLUTIONS

AI-assisted mapping, built so that the machine handles the volume and the expert keeps the decision. The system reads the schemas, proposes the correspondences and generates the controls; the expert reviews and commits. Nothing goes to the target system on a proposal alone.

(01)

Automatic schema analysis

Source and target schemas are read and compared automatically. What used to be an inventory built by hand becomes the starting point of the work rather than its first weeks.

(02)

Correspondence proposals

The system proposes a match for each field, with the reasoning behind it. The expert no longer searches for the correspondence, they judge one that is already argued.

(03)

Generated controls and tests

Each correspondence comes with its controls and tests, generated at the same time. Verification is no longer a phase that comes afterwards, it is produced with the mapping.

(04)

Expert validation on every match

No correspondence reaches the target system without an expert validation. AI proposes and accelerates, the human decides and commits, on 100% of the scope.

Results

MEASURED FIGURES

−70%

Mapping time

The longest step of the migration cut by 70%, which shifts the whole programme schedule rather than one phase of it.

×3

Volume processed

Three times the volume of correspondences handled, at a review effort the team could still absorb.

Critical error

Not a single critical error on HR and payroll data, the requirement that framed the whole approach.

100%

Human validation

Every correspondence validated by an expert before reaching the target system. The acceleration never crossed into the decision.

Impact

WHAT IT CHANGES

A schedule that moves

Cutting the mapping by 70% does not shorten a phase, it moves the go-live. The step that used to set the pace of the migration stops being the one everything waits on.

Volume without a bigger team

Three times the volume processed, because the experts stop building correspondences and start judging them. Capacity grows without the review effort growing with it.

Risk kept where it belongs

Zero critical error on HR and payroll data. Controls and tests generated alongside each correspondence catch the discrepancy before it reaches the target system.

A decision that stays attributable

100% human validation means every match in production carries an expert's signature. On payroll data, that traceability is worth as much as the speed.