OpenAI Media and Entertainment North America

San Antonio Spurs

Enterprise Knowledge Search · Marketing Personalization

The Spurs wanted to scale fan engagement, internal operations, and leadership programs across new international markets without losing the club's culture or overloading staff with manual analysis and content work.

94% of users report greater confidence with LLM tools
AI fluency increased from 14% to over 85%
Employees save over 1,800 hours each month
GPT solutions now engage fans in Spanish and French

Reported impact

Business outcome

The Spurs saved large amounts of staff time, broadened multilingual fan engagement, raised employee confidence with AI, and built a repeatable adoption model that ties GPT creation directly to operational and community impact.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

The San Antonio Spurs deployed ChatGPT Enterprise with a people-first rollout that emphasized fluency, cultural fit, and employee-led experimentation rather than usage quotas. After a 150-person pilot, training, onboarding guides, and hackathons helped teams across data analytics, community engagement, partnerships, learning and development, and retail build GPTs for concrete needs. Fan Voice GPT summarizes thousands of post-game comments in minutes, Transformative Leadership GPT localizes leadership programming, Counterfeit Merchandise GPT flags suspicious resale listings against official inventory, Spurs Culture GPT packages a 50-part docuseries into a reusable knowledge base, and other GPTs translate sponsorship and feedback data into action. The same adoption model helped the club expand fan engagement in Spanish and French. By making employees the builders and keeping goals ahead of tools, the Spurs created an internal AI platform that scales both business impact and team culture.

Data flow

Fan comments, sponsorship metrics, leadership materials, resale listings, and cultural documentation flow into team-built GPTs within ChatGPT Enterprise. Those GPTs return summarized insights, localized content, counterfeit signals, and reusable knowledge that staff use to improve fan, partner, and internal experiences.

Solution architecture

6 components · 1 layer
  1. Serving
    • ChatGPT Enterprise Provides the enterprise workspace that teams use to build and apply their own GPT workflows.
    • Fan Voice GPT Summarizes post-game fan sentiment so teams can quickly spot strengths and issues.
    • Transformative Leadership GPT Tailors leadership workshop content to local cultures and audiences.
    • Counterfeit Merchandise GPT Flags suspicious resale listings by comparing them with official Spurs inventory data.
    • Spurs Culture GPT Turns the club's long-form cultural materials into reusable organizational knowledge.
    • Partnership Insights GPT Converts sponsorship data into actionable insights for partners.

Architecture clues

  • Cultural assets such as a 50-part docuseries are turned into reusable context for everyday work
  • Employees build domain-specific GPTs instead of relying on a central AI team for every workflow
  • People-first rollout used pilots, in-person training, onboarding guides, and hackathons

Evidence from the source

"AI fluency jumped from 14% to more than 85%"
"ChatGPT Enterprise helps the Spurs save 1,800 staff hours a month"
"Fan Voice GPT ... scans thousands of post-game comments"
"GPT solutions now engage fans in Spanish and French"
Transparency Evidence & methodology 100/100 reference value · 90% confidence · 2 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 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 confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 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

    San Antonio Spurs customer story

    First-party
    https://openai.com/index/san-antonio-spurs/
    Source type
    Vendor-published customer story
    Published
    May 7, 2025
    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. LeadIQ

    San Antonio Spurs Employee Directory, Headcount & Staff | LeadIQ

    Independent secondary
    https://leadiq.com/c/san-antonio-spurs/5a1d97a72300005300863aa2/employee-directory

    LeadIQ says San Antonio Spurs has approximately 201 employees as of July 2026, and the Organization JSON-LD lists numberOfEmployees value 201.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.