Generali's Advanced Level provides system developers with masterclasses led by external experts for safe, compliant AI development. Access is gated by formal assessments.
DBS Future Tech Academy (launched Jan 2021) equips the bank's ~5,000-strong tech workforce with applied skills including AI/ML, site reliability engineering, cloud and cybersecurity.
INECO provides advanced modules for technical teams and has delivered specific training to employees involved in AI design, deployment or supervision.
Palantir provides in-depth AI literacy resources for engineers using AI daily and customised resources for employees in customer-facing configuration of AI systems.
Kaspersky provides specialised programmes for technical experts and developers, and dedicated training on Guidelines for Secure Development and Deployment of AI Systems.
OpenSky provides applied labs for engineers and analysts covering evaluation and guardrails, data handling, integration patterns and reliable prompt design.
Criteo's AI Bootcamp trains technical employees by skill level on foundations, responsible AI, Criteo's AI stack and real AdTech use cases, followed by a supervised project.
Collibra trains technical teams on effective deployment of AI tools and engineers on how to stress test and monitor Collibra's generative AI products.
Brian Armstrong stated publicly that ~40% of daily code at Coinbase is AI-generated and set a >50% target by October. Source is Armstrong's X post.
Marco Argenti (CIO) said Goldman is seeing up to 20% developer efficiency gains from AI-assisted coding, with a Devin pilot underway.
JPMorgan is upskilling software engineers to build scalable systems using agents and LLM components. In 2025 employees could use its proprietary LLM to help write annual performance reviews.
JPMorgan launched LLM Suite (proprietary internal GenAI assistant) in early 2024, reaching 200,000 users within eight months.
Re-engineers how software gets built so AI becomes part of the operating model, not just an individual productivity boost; code production, review, testing and delivery are meaningfully restructured around it.
AI fundamentally changes how they engineer: they default to AI-first approaches where appropriate and have built workflows, tooling or practices that improve output beyond just themselves.
Uses AI regularly across implementation, debugging, testing and documentation, with concrete examples of better quality, speed or leverage. Shows real tool and model literacy.
Uses AI as a lightweight assist inside a mostly unchanged workflow; helps with snippets, debugging or summarization but does not materially change how they design, build, test or ship.