The program was structured around a single target operating model for finance and supply chain, with country-level exceptions documented and bounded — not invented mid-build. Steering combined business leadership with independent technical QA so integrator milestones could not substitute for outcome proof.

Cutover waves were sequenced by legal entity and materiality, not by vendor convenience. Data migration, reconciliation, and hypercare were budgeted as first-class workstreams, which is how the organization held close quality while compressing calendar time.

Post go-live, the same governance forum owned benefits tracking against the original business case — so the consolidation stayed accountable after the consultants left.

Anonymized. Client name, logos, and identifying systems are withheld under confidentiality. Metrics and narrative reflect the completed engagement.

We mapped procurement touchpoints where human effort was pure routing, exception handling, or policy checks — then introduced agentic flows behind the same UI the category teams already trusted. The architecture assumed imperfect data and noisy integrations; reliability was designed in, not promised after a data lake project.

Vendor and contract logic stayed in governed systems of record; the AI layer surfaced recommendations and draft actions with audit trails suitable for internal controls review.

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Executive steering owned scope trade-offs in writing — what was deferred, what was cut, and what operational risk each choice carried. That discipline is what kept UAT from becoming a negotiation over whether “good enough” was good enough.

Independent QA on integration contracts and milestone evidence prevented the classic pattern where activity milestones masquerade as outcome readiness.

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Requirements were locked before scripted demos. Reference checks included organizations the vendors did not provide — matched for scale, regulatory load, and implementation complexity.

The final board pack tied every major score to a documented requirement and a validated data point, which is what made the decision defensible when implementation friction appeared (as it always does).

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Models were trained on operational telemetry the client already owned; the integration layer was designed so TMS upgrades would not automatically break downstream scoring pipelines.

Operations leadership defined “good” in units of service reliability and cost-to-serve — not model accuracy alone — which is how the program stayed anchored to P&L outcomes.

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The intervention started with a no-politics view of where the program actually was — not where status reports said it was. That baseline drove a credible re-baseline of scope, dates, and quality gates.

Decision rights were re-seated with executives who could absorb trade-offs, and SI deliverables were tied to evidence of fit, not milestone theater.

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Wave planning treated manufacturing and distribution calendars as hard constraints — finance modernization had to ride the business rhythm, not the reverse.

Parallel run and reconciliation standards were defined up front so “ready” could not be renegotiated under schedule pressure in the final month.

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