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Experience atOmnicom Group

Driving financial systems and automation across global teams.

Builder ofEnterprise AI Systems

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Based inNew York City

Building systems for enterprises around the world.

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Finance operations case study

Working capital decisioning.

A finance operating view that connects receivables, payables, cash, and working-capital signals to the questions operators need to answer.

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Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Working-capital decisions usually arrive after receivables, payables, cash, and inventory data has fragmented across ERP and operational systems, so operators cannot quickly tell a real cash problem from a timing or data-quality artifact. Finance needs a reviewable view that names the decision owner and the evidence behind each action.

Decision frame

Questions the work needed to answer.

  • Where is cash tied up and why?
  • Which aging, payment, or inventory movements need intervention?
  • What is the decision owner and what evidence supports the action?
Interactive operating flowSelect any node for context or ask the AI guide.

Selected Operating layer

Aging + exception review

Connected finance and operating inputs into a common working-capital view with recurring exception, aging, and scenario workflows, and structured commentary so leaders can move from a headline number to its contributing detail. Reconciliation checkpoints and visible assumptions sit ahead of any management-review use.

Implementation

From the business problem to a working operating model.

Connected finance and operating inputs into a common working-capital view with recurring exception, aging, and scenario workflows, and structured commentary so leaders can move from a headline number to its contributing detail. Reconciliation checkpoints and visible assumptions sit ahead of any management-review use.

  1. 01

    Mapped source data into common working-capital views.

  2. 02

    Created an exception-oriented workflow for movement, aging, and scenario review.

  3. 03

    Structured commentary so finance leaders can move from headline to contributing detail.

Controls, approvals, and delivery constraints

The work around the work.

Enterprise systems change only when data, access, testing, ownership, and evidence move together.

  • Reconciliation checkpoints were required before information was used for management review.
  • Definitions, timing, and scenario assumptions stayed visible to avoid false precision.
  • Access and release decisions followed the existing finance control model.

Prior art

How leading teams approach this.

Public references for the same class of problem, so this work can be read against how other organizations are building it.

Microsoft

Streamlining finance cash collection at Microsoft with AI

Microsoft Global Treasury unified SAP and Dynamics 365 data and layered AI to predict late payments, match cash to invoices, and prioritize collector workloads, the same receivables-to-cash decisioning this case study structures.

Ramp

Ramp Introduces AI Agents to Automate Finance Operations

Ramp's OpenAI-powered agents run first-pass review of spend transactions, flag items needing a human, and keep a clear audit trail, an example of exception-oriented finance-operations workflow with a named owner for escalations.

Public-safe outcome

Illustrative of a repeatable decision process that turns working-capital data into owned actions with a clear owner and audit path rather than a passive monthly report.