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BBVA

From employee pilots to an enterprise-wide generative AI adoption system
First-party enterprise source Banking Enterprise deployment Deep case | Key delivery chain is substantially documented Evidence level A Enterprise-wide scale
A Evidence level
BBVA drives AI adoption through talent
Publisher: BBVA · First-party transformation review · Direct case-level source
Claim origin: Company management disclosure · Independent verification: No · Accessed: 2026-09-19
Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.
Deep case | Key delivery chain is substantially documented 12 / 12
Business context2 / 2
Transformation workflow2 / 2
Technical workflow2 / 2
Human roles & governance2 / 2
Measured outcomes2 / 2
Source traceability2 / 2
All six dimensions meet the current completeness threshold.
BBVA evolved from a 3,000-license pilot into enterprise-wide access to two AI platforms, an 8,000+ use-case funnel, and an internal Wizards network. The distinguishing feature is the operating model for adoption and governance—not a single model.
Business problem

With more than 125,000 employees across multiple countries, BBVA needed to turn generative AI from a limited license pilot into a governed, reusable enterprise capability. The bank also had to manage use-case discovery, legal and compliance review, employee skills, and output quality—not merely count activated accounts.

Solution

BBVA first distributed 3,000 ChatGPT Enterprise licenses across organizational levels, allowing frontline employees to build custom GPTs under joint legal, compliance, and IT-security governance. It later expanded access to ChatGPT and Gemini, created a funnel of more than 8,000 active use cases and about 700 strategic candidates, and built an internal network of roughly 750 “Wizards,” formal training, and a 90,000-person community of practice.

Technical architecture & production workflow
Step 01
Enterprise access to ChatGPT Enterprise and Gemini
→
Step 02
Custom GPTs and employee assistants connected to governed bank knowledge
→
Step 03
Knowledge base of 2,500+ HR, process, benefits, and payroll documents
→
Step 04
Portfolio funnel covering 8,000+ active and about 700 strategic use cases
→
Step 05
Review by legal, compliance, IT security, and domain professionals
→
Step 06
Wizards network, training, community of practice, adoption, and time-saved measurement
Key technology & infrastructure components
ChatGPT EnterpriseGeminiCustom GPTsEnterprise knowledge baseUse-case portfolio managementLegal, compliance, and security governanceInternal Wizards network
Human roles & accountability

Employees closest to each workflow identify opportunities and review outputs. Credit analysts monitor model errors; a nine-lawyer team maintains legal knowledge; legal, compliance, and IT security define usage boundaries; and internal Wizards coach teams and spread working practices.

FDE delivery actions
  • Give employees at multiple levels direct access so real workflow opportunities surface
  • Publish high-value employee-built GPTs in an internal store
  • Separate active experiments from strategically important use cases
  • Involve legal, compliance, security, and domain reviewers
  • Use champions, training, and community support to sustain adoption
Reusable delivery patterns
  • Enterprise-wide tool access needs a use-case funnel, not only login metrics
  • Frontline employees are often best positioned to identify workflow opportunities
  • An internal champion network can shorten the organizational learning curve
  • Regulated workflows must keep professional judgment and error monitoring with people
Business outcomes & delivery results

BBVA reports that more than half of its workforce uses generative AI weekly. The bank has identified more than 8,000 active use cases, about 700 of strategic importance, and estimates roughly three hours saved per employee per week. Its employee assistant handles more than 34,000 monthly queries over a knowledge base of 2,500+ documents.

Evidence boundaries & verification notes
  • The roughly three hours per week figure is BBVA’s estimate for repetitive-task savings, not an audited per-employee productivity measure.
  • The 8,000+ figure covers active use cases; about 700 are considered strategically important or high potential.
Primary source: BBVA drives AI adoption through talent ↗ Additional sources: BBVA sparks a wave of innovation with ChatGPT Enterprise ↗ Additional sources: OpenAI customer story: BBVA ↗
Traceable does not mean independently audited
Open primary public source
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