What a LatAm SAP Cutover Taught Me About Finance Systems

Evidence label: Experience. This note draws on my work leading regional finance teams and an SAP implementation across Latin America while at Sony Pictures Entertainment. It does not disclose confidential cutover plans, customer data, or employer metrics. Scope details that cannot be verified publicly stay qualitative.

The real work was not the configuration workbook

The workbook mattered. What decided whether close survived cutover was whether process ownership, review checkpoints, and definitions matched how teams actually closed and reported—not only how the template said they should.

Three failure modes showed up repeatedly in systems change of this kind:

1. Definition drift — the same labeled metric meant different things in the legacy close pack and the new system extract.
2. Owner vacuum — a control existed on paper without a named accountable person in the region.
3. Cutover theater — rehearsal covered happy-path posting and skipped exception queues, timing differences, and bilingual handoffs.

What I would still do the same way

• Map the operating process before debating screens: who prepares, who reviews, who signs, and what evidence they need.
• Force a joint glossary for material metrics before go-live. If lineage cannot be stated in one sentence, AI or reporting automation will inherit the ambiguity later—see Data Lineage Is a Practical AI Bottleneck.
• Rehearse exception paths, not only the standard journal.
• Keep bilingual communication explicit when regions operate across English and Spanish stakeholders; silent translation gaps become control gaps.

What this does not prove

This is not a claim about SAP module lists, country counts, headcount, or budget. Those figures belong in a verified CV update when they can be disclosed. It is also not a production AI outcome. The transferable pattern for forward-deployed work is the same sequence I use on prototypes today: discover the control boundary, make the checkable path visible, then narrow the technical scope.

Public software evidence for that pattern lives in the statement and variance workbenches—labeled Prototype, not Experience.

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For career context, see About and the CV. For a guided reading path on finance × AI controls, see Start Here.