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OpenAI Financial Services Global

BBVA

Customer Service Automation · Enterprise Knowledge Search · Risk Management

BBVA needed a secure, governed way to embed AI across a global bank so customer experiences, risk analysis, operations, and employee productivity could all improve without sacrificing regulatory discipline.

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20,000+ GPTs created across the organization
250 senior leaders trained
70%+ monthly active usage
Up to 80% efficiency gains in selected workflows

Reported impact

Business outcome

BBVA achieved large-scale employee adoption, measurable time savings, major efficiency gains in selected workflows, and a bank-wide structure for turning AI into proactive customer, risk, and operational capabilities rather than isolated experiments.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

BBVA moved from an initial ChatGPT Enterprise pilot to a strategic alliance with OpenAI centered on "The Eight," its roadmap for redesigning banking end to end. The bank first proved secure employee adoption with a deployment to 3,000 users, then expanded access to more than 100,000 employees across legal, risk, engineering, operations, finance, marketing, and customer service under shared governance built by security, legal, compliance, and technology teams. On top of the enterprise workspace, employees created more than 20,000 custom GPTs, including Credit Analysis Pro GPT for unstructured risk signals, a Retail Banking Legal Assistant GPT for branch inquiries, and a Client Experience Assistant GPT for survey analysis. BBVA also created AI champions and "wizards" to spread hands-on expertise, turning AI from a point tool into a coordinated operating layer that helps the bank anticipate client needs, accelerate internal decisions, and scale knowledge across regions.

Data flow

Employees submit legal, risk, customer-service, and operational tasks into ChatGPT Enterprise and department-specific GPTs. Those tools draw on internal knowledge sources and unstructured documents, then return reviewed outputs within BBVA's governance framework so insights and decisions can scale across markets.

Solution architecture

4 components · 1 layer
  1. Serving
    • ChatGPT Enterprise Provides the secure enterprise-wide workspace used across regulated banking functions.
    • Credit Analysis Pro GPT Automates extraction and analysis of unstructured data for credit-risk assessments.
    • Retail Banking Legal Assistant GPT Drafts responses to high-volume legal inquiries from branch managers using internal sources.
    • Client Experience Assistant GPT Analyzes open-ended survey feedback and recommends customer-experience actions at scale.

Architecture clues

  • AI champions network and AI "wizards" support enablement at scale
  • Custom GPTs are used for risk, legal, client-experience, and internal decision workflows
  • Security, legal, compliance, and technology teams aligned governance from the start
  • The Eight serves as BBVA's AI transformation roadmap across banking functions

Evidence from the source

"By the end of 2025, that collaboration had evolved into a broader strategic alliance centered around "The Eight""
"Today more than 100,000 employees globally use ChatGPT Enterprise"
"employees have created more than 20,000 GPTs across the organization"
"reduced average query handling times from approximately 7.5 minutes to around 1 minute"
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

    BBVA customer story

    First-party
    https://openai.com/index/bbva/
    Source type
    Vendor-published customer story
    Published
    Jun 11, 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 Jun 11, 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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