OpenAI Financial Services Europe & UK

BBVA

Enterprise Knowledge Search · IT Operations Automation

BBVA needed to move AI from pilot projects into secure, enterprise-wide daily use so teams across the bank could improve productivity, decision quality, and customer outcomes without creating shadow-AI risk.

20,000+ Custom GPTs created
3 hours saved per employee per week
80%+ efficiency improvements in workflow tests
83% weekly active usage

Reported impact

Business outcome

BBVA saved employees about three hours per week, reached 83% weekly active usage, created more than 20,000 custom GPTs, and proved that a governed AI rollout can drive strong adoption and workflow improvements across a global bank.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

BBVA made ChatGPT a bank-wide operating capability by pairing rapid rollout with governance, leadership training, and secure experimentation. The bank first deployed access to thousands of employees, then quickly expanded as usage proved sticky, while involving security, legal, and compliance as launch partners rather than downstream reviewers. ChatGPT became the shared surface where employees create and reuse custom GPTs, access safe experimentation space, and adapt workflows close to the work instead of waiting on central teams. One example is an internal assistant in Peru that helps employees answer queries far faster than before, showing how local teams can turn the platform into business-specific tools. The program also included hands-on enablement for 250 senior leaders and a deliberate strategy to replace shadow AI with trusted, approved environments, allowing thousands of GPTs to be created and reused across the bank under the broader AI roadmap known as The Eight.

Data flow

Employees work inside ChatGPT instead of unmanaged tools, create Custom GPTs close to their teams, and share successful patterns across the bank. Local assistants such as the Peru workflow pull internal context into approved AI surfaces, then return faster answers and reusable productivity gains under governance and training programs.

Solution architecture

3 components · 2 layers
  1. Serving
    • ChatGPT Provides the secure enterprise interface employees use to experiment, draft, analyze, and share AI workflows.
    • internal assistant Handles employee queries in Peru and demonstrates how local teams can turn the platform into high-impact business tools.
  2. Orchestration
    • Custom GPTs Package specialized workflows that teams can create and reuse across the bank.

Architecture clues

  • 250 senior leaders received hands-on training
  • Program intentionally replaced shadow AI with safe, approved environments
  • Security, legal, and compliance aligned from the start
  • Thousands of Custom GPTs were created by employees

Evidence from the source

"20,000+ Custom GPTs created across the bank"
"In Peru, one internally built assistant is now used by more than 3,000 employees"
"Instead of shadow AI... we gave them a platform that was safe so they could start experimenting"
"We created this atmosphere of being in a safe place to learn and to use AI."
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
    4 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-2025/
    Source type
    Vendor-published customer story
    Published
    Nov 6, 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

Date evidence

  • Published on source page Nov 6, 2025 · 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.