OpenAI Media and Entertainment United States

Fanatics Betting and Gaming

Document Processing · IT Operations Automation

Fanatics Betting and Gaming needed to reduce manual finance work so its team could spend less time collecting information and more time on strategy, decision-making, and higher-impact operational planning.

saving us around 18 hours of monthly work

Reported impact

Business outcome

Fanatics Betting and Gaming saved manual finance time, reduced effort in the monthly close process, and created a repeatable operating model for AI-driven analysis inside the function.

  • Productivity Improvement
  • Speed to Market

Solution

Fanatics Betting and Gaming built a structured ChatGPT adoption program inside finance so AI could automate repetitive work instead of remaining a one-off experiment. The team formed an AI automation task force, gathered process ideas from staff, trained every finance team member on ChatGPT, and ran a GPT-athon with data scientists to build custom GPTs around specific workflow opportunities. One result is VendorID GPT, which automates vendor identification and contract summarization, while other uses focus on faster data analysis, cohort analysis, trend plotting, and closing-the-books work. Operational inputs such as vendor documents, contracts, and financial datasets flow into ChatGPT or specialized GPTs, and the resulting summaries and insights are used directly by the finance team and the CFO. That combination of enablement plus workflow-specific assistants turns ChatGPT into a daily finance partner rather than an ad hoc productivity tool.

Data flow

Vendor documents, contracts, and financial datasets are sent into ChatGPT or finance-specific GPTs, which return summaries, identified issues, and analytical views that the team uses to close books, communicate performance, and plan next steps.

Solution architecture

3 components · 1 layer
  1. Serving
    • ChatGPT Provides the everyday AI workspace the finance team uses for analysis, insight gathering, and workflow acceleration.
    • VendorID GPT Automates vendor identification and contract summarization for the finance organization.
    • custom GPTs Support targeted finance workflows that emerged from the GPT-athon and task-force roadmap.

Architecture clues

  • A GPT-athon paired teams with data scientists to build workflow-specific GPTs
  • The adoption model is managed through an AI automation task force and roadmap
  • The rollout included basic training for every finance team member

Evidence from the source

Once training was complete, we organized a GPT-athon
VendorID GPT... automates vendor identification and contract summarization, saving us around 18 hours of monthly work
We formed an AI automation task force
We've reduced the time it takes us to close our books
Transparency Evidence & methodology 85/100 reference value · 94% confidence · 3 sources

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 value85/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 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 transparency9/10
    3 source references 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

    Fanatics Betting and Gaming customer story

    First-party
    https://openai.com/index/fanatics-betting-gaming-andrea-ellis/
    Source type
    Vendor-published customer story
    Published
    Dec 11, 2024
    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
  2. Fanatics

    Powering the Future of Fandom

    First-party
    https://www.fanaticsinc.com/about-fanatics/

    With a team of more than 22,000 employees (including our Lids business) across 80+ locations worldwide.

    Source type
    Official company website
    Supports
    • Company size
  3. Fanatics

    Contact Us - Fanatics Inc

    First-party
    https://www.fanaticsinc.com/contact-us

    Jacksonville, FL — 8100 NATIONS WAY, JACKSONVILLE, FL 32256.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Dec 11, 2024 · OpenAI · First-party
    Open date source
Not separately sourced Industry. Treat these normalized attributes as lower-confidence until a dedicated source is added.