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OpenAI Commercial and Professional Services Asia-Pacific & Oceania

Gilbert + Tobin

Document Processing · Enterprise Knowledge Search · Security and Compliance

Gilbert + Tobin needed to improve research, drafting, analysis, and operational workflows across a law firm without compromising client confidentiality, governance requirements, or human accountability.

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87% active usage among enabled ChatGPT users
One day of manual work avoided preparing audit reports across 300 entities
Recruitment research workflow reduced from approximately four hours to about 20 minutes
Selected conflict, KYC, AML, and related checks reduced from three to eight hours down to five minutes

Reported impact

Business outcome

Gilbert + Tobin more than doubled typical tool adoption, compressed operational workflows from hours to minutes, avoided large batches of manual work, and created a governance-first template for AI adoption inside a law firm.

  • Compliance
  • Productivity Improvement
  • Scalability

Solution

Gilbert + Tobin started with ChatGPT Enterprise in operations teams, then expanded into marketing, business development, recruitment, finance, technology, and legal use cases once governance was in place. The rollout combined CEO-led sponsorship, enablement, approved-task guidance, data-processing controls, and Australian data residency so employees could use AI within the firm's confidentiality requirements. Teams now use ChatGPT for pitch synthesis, recruitment research, data extraction, analysis, deep research, documentation, and pressure testing via a custom GPT built from approved CEO materials. Codex extends the same model from assistance into execution, helping produce audit reports across 300 entities, validate file sets, build a Python application from specifications, and accelerate selected conflict, KYC, AML, and related checks before human review. This lets the firm standardize operational intelligence while preserving professional judgment over every final deliverable.

Data flow

Employees submit research, drafting, compliance, and operational tasks into ChatGPT Enterprise or Codex within governed access controls. Outputs can pull from approved organizational context or specifications, then move through human review and sign-off before they affect client or business processes.

Solution architecture

4 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Supports governed research, drafting, analysis, and operational productivity across firm functions.
    • custom GPT Acts as a controlled digital twin for executive pressure-testing and other approved workflows.
  2. Orchestration
    • Codex Executes multi-step operational work such as audit preparation, file processing, and compliance workflows.
    • ChatGPT Work Represents the next phase of broader connected context and workflow execution.

Architecture clues

  • AI rollout used approved-task guidance, access controls, and Australian data residency
  • Codex executes irregular multistep tasks under human review
  • Custom GPT was built from approved CEO materials for pressure-testing ideas
  • Technology team also used Codex for an AWS monitoring watchtower

Evidence from the source

"87% of enabled seats were active"
"Codex can take AI from being a helper to being a doer."
"Moving to an OpenAI environment with Australian data residency gave Gilbert + Tobin greater confidence"
"selected checks that previously took up to eight hours can now be completed in minutes"
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Gilbert + Tobin customer story

    First-party
    https://openai.com/index/gilbert-tobin/
    Source type
    Vendor-published customer story
    Published
    Oct 8, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 8, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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