Lawson-to-Workday AI data conversion

what we've seen

From engagements we can speak to, with program details anonymized:

• Up to 90% better data accuracy than legacy ETL, measured at parallel-payroll reconciliation and first-pass Workday load acceptance.
• 73% reduction in new-module development and defect-fixing time, measured against the SI partner's prior baseline on comparable Lawson-to-Workday conversions.
• De-risked deployments: defects surface in validation instead of at parallel payroll, so cutover cycles close on schedule.
• DGW counts, EIB outputs, and Advanced Loads coming out of a single conversion path. Reconciliation evidence generates alongside the load, not as a separate cycle afterward.
• Defect closure rates improve over the life of the engagement. The AI layer's context compounds as the project's tracker fills in, so early-cycle triage drafts get sharper.

These ranges are hedged because the spread is real. They aren't the headline number off one best-case program.

the situation we're built for

You have a Workday go-live date. Your team is already running today's payroll, managing today's benefits, and prioritizing audit cycles. Now they're also expected to extract, scrub, validate, and reconcile two decades of Lawson data into an object model they're learning as they go. Conversion gets squeezed last, and it breaks the program first.

That's the situation poimi is built for. We're a legacy-to-Workday data conversion offering, tool plus services, focused on taking the conversion lane off the customer team so design, parallel testing, and client-side decisions get the time they need. The tool is in production on client engagements today. Lawson is the deepest lane and the lead pillar in poimi's source library; the same framework also handles UKG, PeopleSoft, NetSuite, and other source systems.

Senior practitioners reading this already know how these go. The hard part isn't data quality — it's how Lawson's logic translates to Workday's object model. A generalist conversion tool, AI or otherwise, gets the easy 60% and stalls. The remaining 40% is what burns weekends and slips dates.

If you're earlier in the evaluation and the open question is which delivery model fits a team without bandwidth for cleanup, that buyer's-side framework lives on a separate page: Lawson-to-Workday conversion with limited HRIS bandwidth.

what Lawson-to-Workday actually requires

Lawson resolves data at runtime. Workday stores it as objects. poimi was built around that gap.

A Lawson production environment isn't just the core HR, payroll, and finance databases. It's those plus fifteen or twenty years of custom logic and one-off extracts on top, and values that only exist when a specific cycle runs. Pull a raw employee extract on a Tuesday and you won't see what a Lawson user saw on screen Monday afternoon. Generalist tools assume a column is a column. That's where they break.

poimi's framework is built around that reality:

• Effective-dated history is rebuilt against Workday's HCM object model. The hard cases are the ones where Lawson's date logic doesn't translate cleanly: mid-period changes, retroactive adjustments, terminations later reversed. Workday stores history. Lawson reconstructs it. The mapping isn't one-to-one.
• Parallel payroll reconciliation comes out of the same run as the load, not as a separate post-load check. That's how parallel cycles close on schedule instead of slipping two more weeks.

The tool reads from Lawson directly when the customer allows it. When direct access isn't available, it works from extracts (spreadsheet exports, CSVs, or whatever the source allows). Transformations and business-rule validations run inline, and the output is in the formats deployment partners and Workday consume: DGWs, EIBs, and Advanced Loads.

how the AI layer works

The AI layer sits inside the conversion path, not on top of it. It handles the data drudgery that used to eat your team's expert hours: catching defects before they hit a load attempt, triaging the ones that do, and finding patterns that get missed when defects are worked one at a time.

What it does:

• Reads the project's defect log (JIRA, Smartsheet, similar trackers) and builds context from prior tickets, resolutions, and recurring failure patterns on this engagement and adjacent ones.
• Runs proactive validation against incoming source data, flagging cases that fit known defect patterns before they're written into a DGW or EIB.
• Triages incoming defects: clusters them, suggests a likely root cause, and pulls the prior ticket that resolved a similar one.
• Drafts technical context for the people who are going to work the defect, so your already-strapped team members start with something like "this looks like the cycle-based deduction case we hit two cycles back, here's where the calc lives" instead of starting cold.

What it doesn't do:

• Client data isn't used for model training, full stop. It won't be tomorrow either. The wall is structural, not a policy that could change later.
• The AI doesn't replace the experts. It does the lookup and pattern-matching work that used to eat the first few hours of an expert's day.

security posture

Walled-off client data and a no-model-training commitment aren't differentiators anymore. They're the floor for any conversion tool an enterprise HRIS lead will let near a Lawson production environment.

In poimi's case that means client environments are isolated. Data doesn't flow into shared model training. The prompts and embeddings the AI layer uses are scoped to the client's engagement. Any third-party model traffic runs against zero-retention, no-training endpoints, with the configuration documented per engagement. We document this so it can be audited.

where poimi sits in the category

The Workday data conversion category currently splits into roughly three approaches.

Generalist AI Workday tooling. New entrants in this category lead with AI-accelerated workflows, walled-off client data, and no-model-training commitments. That's the same positioning poimi holds. What they're missing, publicly, is depth in any specific legacy system. The marketing copy is source-agnostic, and so is the execution. That's fine for the easy 60% of a conversion. It isn't fine for the cycle-based pay calc that nobody documented in 2009.

Legacy ETL platforms. The volume players here have real Lawson lineage; they were doing Lawson-to-Workday before it was a category. Their tooling was designed before LLM-driven validation was a credible option, and their public messaging still leads with consultant hours and accelerator templates rather than what AI inside the conversion path actually changes. The incumbency is real. The integration of the new layer has been slow.

Manual scripts. A meaningful share of Lawson conversions still get done with the SI partner's own internal scripts plus one-off extracts written by the client's HRIS team. That holds together on small populations and a few modules. It doesn't hold together on a multi-module, multi-source, parallel-payroll cutover at enterprise scale.

poimi sits in a different spot. AI-accelerated conversion tooling, with the Lawson depth the generalist category doesn't have. Productized accelerator instead of staffing augmentation. Zero-model-training as a hard architectural posture, which the legacy ETL category hasn't fully gotten to.

the rest of the source library

Lawson is poimi's deepest lane and the lead pillar in the source library. The same framework handles UKG, PeopleSoft, NetSuite, and other Workday-bound migrations. Same conversion path, same AI layer, same DGW, EIB, and Advanced Load outputs.

talk through your conversion

If you're an SI partner principal staffing a Lawson-to-Workday conversion, or an HRIS lead running one, send a note. Tell us where you are in the program, which modules are in scope, what's on your team's plate, and what isn't getting the time it needs. We'll come back with whether and how poimi fits.