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BBVA

Customer Service Automation · Document Processing

BBVA wanted to scale safe, responsible generative AI across the bank so frontline and specialist teams could solve knowledge-heavy tasks faster, including answering high volumes of client legal questions, accelerating company credit analysis from many document sources, and extracting insight from large volumes of customer feedback.

3,000 ChatGPT Enterprise licenses
700 are available on BBVA GPT Store
83 percent of these users use the tool on a weekly basis
less than 24 hours

Reported impact

Business outcome

BBVA achieved broad internal adoption of generative AI, with most licensed users incorporating it into weekly work, thousands of custom GPTs created, faster legal responses for clients, quicker credit-analysis preparation, and improved ability to derive customer-service insights from survey data.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

BBVA rolled out ChatGPT Enterprise across the organization and used it as the shared generative AI platform for employees to build task-specific GPTs. The implementation distributed 3,000 enterprise licenses and allowed teams across countries to create custom assistants close to their business processes. Within that environment, BBVA published around 700 reusable GPTs in its internal BBVA GPT Store. Concrete implementations include the Retail Banking Legal Assistant GPT, which uses documentation maintained by a nine-lawyer advisory team to answer client legal questions routed through branch managers; the Credit Analysis Pro GPT, which pulls relevant information from annual reports, sustainability evaluations, and media documentation to support credit risk assessments; and the Client Experience Assistant GPT in Mexico, which processes thousands of satisfaction surveys to generate conclusions and improvement actions for customer service. BBVA also embedded legal, compliance, and IT security oversight into adoption.

Data flow

Legal documentation from BBVA's advisory team is used by the Retail Banking Legal Assistant GPT to answer client questions for branch managers; annual reports, sustainability evaluations, and media documentation feed Credit Analysis Pro GPT for analyst review; satisfaction surveys feed Client Experience Assistant GPT for service-improvement recommendations. These GPTs are built and accessed through ChatGPT Enterprise, with many shared through BBVA GPT Store.

Solution architecture

5 components · 1 layer
  1. Serving
    • ChatGPT Enterprise Provides the bank-wide generative AI platform where employees use and build custom GPTs for operational tasks.
    • BBVA GPT Store Distributes reusable GPTs internally so users across the organization can access high-interest assistants.
    • Retail Banking Legal Assistant GPT Uses the advisory team's documentation to help answer client legal questions received through branch managers.
    • Credit Analysis Pro GPT Extracts relevant company data from multiple document sources and compares companies with sector peers for credit analysts.
    • Client Experience Assistant GPT Processes satisfaction survey data to summarize conclusions and recommend actions to improve customer service.

Architecture clues

  • About 700 GPTs were published in BBVA GPT Store for organization-wide reuse.
  • BBVA says legal, compliance, and IT security teams were involved to ensure safe and responsible use.
  • Credit risk analysts monitor generative AI results to prevent errors.
  • Employees across Uruguay, Mexico, and Türkiye can build solutions simultaneously.
  • Initial rollout started in May 2024.

Evidence from the source

"700 are available on BBVA GPT Store."
"83 percent of these users use the tool on a weekly basis, and they have created nearly 3,000 GPTs."
"The GPT was trained to respond to these questions with all the documentation from this team of nine attorneys, thus making it possible to provide faster, more thorough responses to clients, many of them in less than 24 hours."
"The bank started to distribute 3,000 ChatGPT Enterprise licenses in May 2024 at all levels of the organization."
"This GPT automates the extraction of the most relevant data on a company and compares it to its peers in the sector."
"a GPT that processes thousands of satisfaction surveys, extracting the main conclusions and proposing steps to continuously improve customer service"
Transparency Evidence & methodology 100/100 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 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
    2 metrics 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
    6 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/bbva-sparks-a-wave-of-innovation-among-its-employees-with-the-deployment-of-chatgpt-enterprise/
    Source type
    Customer-published story
    Published
    Nov 20, 2024
    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.