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OpenAI Software / SaaS Global

Oracle

Document Processing · IT Operations Automation

The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and assesses the talent pool across relevant locations. The tool arms the recruiter with valuable information that they need going into a...

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"We’ve gone from zero to a hundred," says Jan Ackerman, Senior Vice President and Global Head of Talent Acquisition at Oracle. "Now we’re able to sit down and prep for about 15 to 20 minutes using...
The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and assesses the talent...

Reported impact

Business outcome

"We’ve gone from zero to a hundred," says Jan Ackerman, Senior Vice President and Global Head of Talent Acquisition at Oracle. "Now we’re able to sit down and prep for about 15 to 20 minutes using the tool that we’ve built using [ChatGPT] Work."

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and assesses the talent pool across relevant locations. The tool arms the recruiter with valuable information that they need going into a conversation with the hiring manager, information that previously took 2–4 days to compile. Across recruiting, engineering, and operations, Oracle turns specialist knowledge into fast, repeatable workflows with ChatGPT Work and Codex. Over a hundred thousand employees across Oracle are using ChatGPT Work and Codex, from talent acquisition to Oracle Applications Lab to the IT organization.

Data flow

Across recruiting, engineering, and operations, Oracle turns specialist knowledge into fast, repeatable workflows with ChatGPT Work and Codex.

Solution architecture

2 components · 2 layers
  1. Compute
    • Codex Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
  2. Orchestration
    • ChatGPT Work Turns repeatable business processes into AI-assisted workflows with shared context and actions.

Architecture clues

  • Across recruiting, engineering, and operations, Oracle turns specialist knowledge into fast, repeatable workflows with ChatGPT Work and Codex.
  • Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
  • Over a hundred thousand employees across Oracle are using ChatGPT Work and Codex, from talent acquisition to Oracle Applications Lab to the IT organization.
  • The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and...
  • Turns repeatable business processes into AI-assisted workflows with shared context and actions.

Evidence from the source

"Now we’re able to sit down and prep for about 15 to 20 minutes using the tool that we’ve built using [ChatGPT] Work."
"We’ve gone from zero to a hundred,"
Across recruiting, engineering, and operations, Oracle turns specialist knowledge into fast, repeatable workflows with ChatGPT Work and Codex.
The talent acquisition team used ChatGPT Work to build a talent market intelligence tool that takes a job description, researches comparable roles, benchmarks compensation, and...
Transparency Evidence & methodology 80/100 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 value80/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    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

    Oracle customer story

    First-party
    https://openai.com/index/oracle/
    Source type
    Vendor-published customer story
    Published
    May 7, 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 May 7, 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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