OpenAI Media and Entertainment North America

ATV Big Air Tour

IT Operations Automation

ATV Big Air Tour needed a way for a two-person leadership team to keep event information accurate, manage merchandise, and improve discoverability without adding a larger operations or marketing staff.

1,223% increase in OpenAI search and user-bot hits
3 days to 3 hours for merchandise inventorying and reorder
7 hours saved per week on event listing reviews
Nearly 26 tour dates across the United States

Reported impact

Business outcome

ATV Big Air Tour cut event-listing review time from about eight hours to one hour per week, reduced inventory and reordering work from two to three days to two to three hours, and dramatically increased AI-search visibility while keeping a two-person team competitive.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

ATV Big Air Tour turned ChatGPT Work into an operating assistant for marketing, merchandising, and AI-search visibility. The team created a scheduled daily workflow that scans priority event-listing sites, flags inconsistent dates or ticket details, suggests the right publication contacts, and drafts correction emails so the founders can keep nearly 26 tour dates accurate across the web. They also use ChatGPT Work as a multimodal inventory assistant by uploading merchandise photos, letting the system organize the catalog, generate a spreadsheet and visual inventory site, and recommend reorder quantities before the founders make final adjustments. A third automation audits the company website for AI-search readability, surfacing issues such as missing FAQ structure and suggesting changes that improve discoverability in ChatGPT-style search. Together these workflows converted repetitive coordination work into repeatable digital operations for a very small team.

Data flow

Event listings, website content, and merchandise photos are fed into ChatGPT Work. It audits listings, drafts correction emails, structures inventory into spreadsheets and reorder plans, and reviews the website for AI-search discoverability before Larissa approves and sends the outputs.

Solution architecture

1 components · 1 layer
  1. Orchestration
    • ChatGPT Work Runs scheduled event audits, image-based inventory analysis, and AI-search optimization workflows for the two-person team.

Architecture clues

  • AEO automation audits site structure for AI-powered search engines and assistants
  • Daily scheduled briefing checks event listings and drafts correction emails
  • Inventory workflow starts from uploaded merchandise photos

Evidence from the source

"ChatGPT Work runs this report for me every morning."
"She started by taking photos of ATV Big Air Tour's merchandise and uploading them to ChatGPT Work."
"The entire process from inventorying to reordering took just two to three hours, down from two to three days."
"Website analytics showed OpenAI search and user-bot hits rising from 183 to 2,421"
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

    ATV Big Air Tour customer story

    First-party
    https://openai.com/index/atv-big-air-tour/
    Source type
    Vendor-published customer story
    Published
    Sep 2, 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 Sep 2, 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.