Big banks broaden tech talent pool to scale AI
Top banks are expanding their AI bench and turning that headcount into shipped products. According to Evident, AI talent at major financial firms grew 25%, with Bank of America, Capital One, Citigroup, JPMorgan Chase and Wells Fargo out in front.
The results show up in output: the 10 banks with the largest AI teams reported nearly twice as many use cases as the rest. AI roles grew at five times the pace of overall hiring, pushing the sector's AI workforce toward 90,000 people.
Why this matters for product leaders
- Talent density correlates with velocity: bigger AI teams shipped nearly 2x more use cases.
- Execution beats strategy slides: firms that built data and cloud foundations are now scaling delivery with specialized roles.
- Product sits closer to the business: "Banks are hiring more aggressively for AI expertise to sit alongside the heads of business," said Alexandra Mousavizadeh, co-founder and co-CEO of Evident.
How leading banks are staffing for AI
Hiring is broad, not just research. Firms are adding model risk, product management, data engineering, and software implementation to keep models useful, safe and in production.
- Capital One added 2,200+ AI professionals, boosted by its Discover merger, and recruited Chief Scientist and Head of Enterprise AI Prem Natarajan from Amazon.
- Citi rolled out AI platforms, agentic capabilities and coding tools, hired Head of AI Shobhit Varshney from IBM, named Dipendra Malhotra to lead wealth tech, and appointed Tim Ryan to head technology and business enablement.
- Banks are also pulling senior technical leaders from Big Tech - chief architects, CTOs, CIOs and chief data officers - to accelerate buildouts.
Training is scaling too. Banks with public employee AI programs rose to 38 (from 32), and 33 of 50 offer specific training for senior leadership.
Org design that ships AI products
- Cross-functional pods: pair AI PMs with line-of-business owners, data engineering, and model risk early. Keep review cycles inside the sprint.
- Platform-first: standardize model hosting, feature stores, prompt/memory patterns, and observability to reuse components across use cases.
- Model risk as a product: treat policy, testing and monitoring as user journeys. Align to frameworks such as the NIST AI Risk Management Framework for clearer controls and faster approvals. NIST AI RMF
- Use-case portfolio: rank by value, data readiness, compliance complexity and integration effort. Kill low-signal pilots fast.
What the data signals
- AI hiring intensity predicts output: the top-10 talent holders showed almost 2x use cases vs. peers.
- Headcount mix matters: model risk + product + engineering is the shipping combo, not research alone.
- Leadership alignment reduces friction: 33 banks now train senior leaders on AI, improving decision speed and budget clarity.
Action plan for product teams this quarter
- Run a skills inventory: AI PMs, applied scientists, data engineers, MLOps, and model risk. Fill the gaps that block deployment, not just experimentation.
- Stand up an AI review path: define PRD templates for AI features, red-teaming steps, and success metrics (precision/recall, latency, deflection, NPS, cost per call).
- Adopt a common stack: pick your feature store, model gateway, evaluation suite, and observability tools; publish golden paths for teams.
- Instrument from day one: human-in-the-loop feedback, prompt/version tracking, data lineage, and rollback procedures.
- Upskill at scale: give ICs hands-on labs and leaders decision frameworks. Start with short, role-based programs and certify completion.
Notes on the leaders
JPMorgan Chase leads in AI talent size, with Capital One close behind after integrating Discover's team. Citi is building momentum with platform work and senior hires across AI, wealth tech and enablement roles.
Across the board, banks are tapping "West Coast thinking" - AI PMs and architects from Meta, Google and OpenAI - to speed product cycles and bring stronger product/market fit discipline into regulated environments.
Further reading
- Evident's analysis of AI maturity and hiring trends: Evident Insights
Level up your team
- Role-based AI upskilling for product and engineering: Courses by job
- Curated AI tools for finance use cases: AI tools for finance
