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Experience atOmnicom Group

Driving financial systems and automation across global teams.

Builder ofEnterprise AI Systems

AI agents, audit workflows, and decision intelligence at scale.

Based inNew York City

Building systems for enterprises around the world.

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All enterprise work

Controls & assurance case study

UAT, compliance, and release assurance.

A structured delivery discipline for test cases, issue triage, evidence capture, access review, and accountable release decisions.

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

Business problem

What people needed to solve.

Enterprise finance changes become risky when test evidence, issue ownership, and compliance checks sit outside the release process and get reconstructed after go-live. Regulators and assurance teams increasingly expect the same discipline applied to AI-assisted work as to any other change in an audited reporting environment.

Decision frame

Questions the work needed to answer.

  • What has been tested?
  • Which issue remains open and who owns it?
  • What is the evidence for production readiness?
Interactive operating flowSelect any node for context or ask the AI guide.

Selected Operating layer

Triage + retest + sign-off

Ran structured test scenarios with accountable owners, tracked defects through remediation and retesting, and aligned access review and compliance checks to the delivery process. Open issues and exceptions stayed visible before sign-off so evidence supported the release decision rather than a later reconstruction.

Implementation

From the business problem to a working operating model.

Ran structured test scenarios with accountable owners, tracked defects through remediation and retesting, and aligned access review and compliance checks to the delivery process. Open issues and exceptions stayed visible before sign-off so evidence supported the release decision rather than a later reconstruction.

  1. 01

    Defined test scenarios and accountable owners.

  2. 02

    Tracked defects, remediation, retesting, and release evidence.

  3. 03

    Aligned access review and compliance checks to the delivery process.

Controls, approvals, and delivery constraints

The work around the work.

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

  • Open issues and exceptions are visible before sign-off.
  • Evidence supports release decisions rather than post-release reconstruction.
  • No issue logs, control findings, or compliance records are shown publicly.

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.

PCAOB

PCAOB staff shares observations from outreach on use of generative AI in audits and financial reporting

Reports that firms using AI in audit and reporting stress strong supervision and controls over data privacy and reliability, which is the governance bar this release process is built to meet.

Capgemini

World Quality Report 2025: AI adoption surges in quality engineering, but enterprise-level scaling remains elusive

Finds test case design and requirements refinement now lead AI adoption in QA, with data privacy, reliability, and governance the top barriers to enterprise scale, arguing for a hybrid human-plus-AI approach.

Grant Thornton

The power of AI in efficient SOX compliance

Advocates automated evidence capture and explicit auto-clear versus human-review criteria while keeping control owners accountable for the final decision, the same sign-off model used for release readiness.

Public-safe outcome

Release decisions backed by test evidence, named owners, and accountable sign-off, with issue logs and compliance records kept out of the public example.