OpenAI Financial Services Global Company newsroom

BBVA

Customer Service Automation

BBVA wanted to improve customer service, give retail and business managers AI-based support, and raise employee productivity by embedding generative AI into digital channels and internal work processes.

85 percent of them use the license on a regular basis
Eighty percent claim to have saved at least two hours of work a week
around a thousand have scalability potential
average of 11% in Spain and up to 20% in Mexico

Reported impact

Business outcome

BBVA reports better customer interaction through a more conversational virtual assistant, AI support for managers serving retail and business clients, and measurable employee time savings from ChatGPT Enterprise adoption.

  • Customer Experience
  • Productivity Improvement

Solution

BBVA describes a multi-front AI implementation that combines customer-facing generative AI with employee productivity tooling. For customers, the bank feeds Blue with customer financial context plus its catalog of products, services and banking transactions so the assistant can answer natural-language questions, return personalized financial information with empathy, and execute selected account and card actions in BBVA's digital channels. The same language-model pattern is being extended to retail and business managers as an AI support layer for faster query resolution and more tailored guidance. Internally, BBVA signed an agreement with OpenAI to deploy ChatGPT Enterprise across the group, with a Global AI Adoption area handling access and an AI Factory developing and scaling use cases. The source does not name a separate storage stack, but it clearly shows AI embedded in both employee workflows and customer service surfaces.

Data flow

Customer financial data and BBVA's catalog of products, services and banking transactions are used by Blue to generate natural-language answers, personalized financial information, and selected account or card actions in the bank's digital channels. Separately, BBVA distributes ChatGPT Enterprise to employees through its Global AI Adoption area for internal use cases that the Data area's AI Factory helps develop and scale.

Solution architecture

2 components · 1 layer
  1. Serving
    • ChatGPT Enterprise Provides generative AI assistance to BBVA employees for internal productivity use cases across the bank.
    • Blue Acts as BBVA's generative-AI virtual assistant, answering natural-language banking questions and handling selected account and card transactions.

Architecture clues

  • BBVA created a Global AI Adoption area to broaden employee access to AI tools.
  • BBVA says the Data area includes its own AI Factory to develop AI-based products and services.
  • Blue is able to answer upward of 3,000 questions posed by users.
  • Blue's new generative AI model launched with more than 150 queries and transactions.
  • ChatGPT Enterprise had more than 3,300 users across the bank at the time of publication.

Evidence from the source

"Eighty percent claim to have saved at least two hours of work a week by using the tool"
"In 2024, the bank was one of the first European financial institutions to sign an agreement with OpenAI to hand out ChatGPT Enterprise licenses among its employees."
"Its new conversational capabilities allow customers to interact with Blue using natural language, receive personalized information about their finances delivered with empathy, and carry out certain account and card-related transactions."
"The new model has been launched with more than 150 queries and transactions and is able to answer upward of 3,000 questions posed by users."
"The use of ChatGPT Enterprise is already saving BBVA employees an average of two hours of work per week."
"Users who rely on these tools have succeeded in growing their bank balances by an average of 11% in Spain and up to 20% in Mexico"
"the evolution of its virtual assistant Blue to feature generative AI"
Transparency Evidence & methodology 90/100 reference value · 94% 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 value90/100
  • 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 scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 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. BBVA

    BBVA customer story

    First-party
    https://www.bbva.com/en/innovation/alvaro-martin-bbva-ai-is-changing-the-way-we-work-and-serve-our-customers/
    Source type
    Customer-published story
    Published
    Mar 5, 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

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