BNY Mellon, with $52 trillion under management, isn’t just keeping pace with Wall Street’s AI revolution—it’s leading it. In 2023, the bank launched Eliza, an enterprise AI platform built on GPT-4 and hosted in Azure, to weave artificial intelligence into every corner of its operations (Cloud Wars). Under CEO Robin Vince, BNY Mellon signed a multi-year partnership with OpenAI, not to replace jobs, but to supercharge efficiency and client service (TIME).
Today, every one of BNY’s 50,000+ employees can tap Eliza for instant answers on internal procedures and workflow shortcuts (American Banker). Over half the workforce uses Eliza regularly, and thousands have been trained to build custom AI agents for specialized tasks (Cloud Wars; American Banker). This all-in approach has made BNY Mellon a model for AI adoption in finance.
The bank’s mantra: AI is for everyone, not just engineers.
“AI is reshaping the landscape of financial services and helping us to move quicker, be more efficient and resilient,” says CIO Leigh-Ann Russell (American Banker).
Leadership’s focus is clear: empower every employee to be “a little more productive each and every day” (Cloud Wars). In 2024, a sweeping upskilling initiative trained tens of thousands on AI tools, fueling rapid adoption across all departments (Cloud Wars). The result: over 40 AI solutions in production, from risk anomaly detection to predictive analytics for client trades (Cloud Wars; American Banker). Nowhere is this more visible than in BNY’s software development teams.
BNY Mellon’s developers have embraced AI co-pilots as essential teammates. In 2024, the bank rolled out GitHub Copilot—an AI pair programmer that suggests code in real time. Adoption was immediate: over 80% of BNY’s developers now use Copilot daily to write code faster (American Banker).
Copilot is more than autocomplete—it generates boilerplate, unit tests, and even full functions based on context (Medium). Developers call it “coding with a superpower.” One engineer shared, “I wrote a full Express.js API in half the time. It even added comments!” (Medium). These assistants handle repetitive work and nudge coders toward best practices, cutting down on trivial bugs (Medium).
Beyond the IDE, developers use ChatGPT-like assistants for design, debugging, and knowledge sharing. Eliza, now enhanced with OpenAI’s API, lets engineers ask natural-language questions and get instant, secure guidance (American Banker). This mirrors GitHub’s Copilot Chat, which provides real-time troubleshooting and remediation (GitHub Blog). The impact is clear: less time searching documentation, more time in the creative flow. A GitHub study found 88% of developers felt more focused and less frustrated using an AI chat assistant (GitHub Blog).
BNY Mellon is also pioneering the use of AI as virtual team members. The bank has deployed dozens of AI-powered “digital workers”—autonomous agents with their own logins—assigned to tasks like scanning code for vulnerabilities and validating payment instructions (LinkedIn). These agents run continuously under human oversight, proactively flagging issues. Plans are underway to embed such agents into collaboration tools like Microsoft Teams, making them virtual colleagues who can join stand-ups or alert project managers to critical bugs. “Digital workers have arrived in banking,” notes one industry observer, emphasizing that BNY’s AI agents work autonomously yet securely, each confined to specific teams (LinkedIn). Routine but crucial tasks—security checks, code quality enforcement, test data generation—are now offloaded to AI, freeing developers for higher-level design and innovation.
The results are measurable. With AI co-pilots, tasks that once took a full workday now finish in hours. One multinational bank saw code writing time drop from 8 hours to just 2—a 75% reduction—after integrating Copilot (Microsoft Blog). BNY Mellon’s experience is similar. “With GitHub Copilot, our developers stay in the flow state and keep momentum instead of clawing through code libraries or documentation,” says Johnathan Burket, engineering manager, who reported a 25% increase in developer velocity (GitHub Blog). AI-generated code is now standard in nearly every new application, saving time and effort (Microsoft Customer Story). BNY tracks metrics like efficiency gains, cost savings, and code quality improvements (American Banker).
Research backs this up. Developers using Copilot complete coding tasks 55% faster on average (GitHub Blog), with Copilot generating up to 46% of code in enabled files. Critically, speed doesn’t sacrifice quality: developers using Copilot Chat produced better code quality and more thorough reviews (GitHub Blog), and 85% felt more confident in their code (GitHub Blog). BNY’s own AI agents further enhance security, autonomously scanning for bugs or vulnerabilities (LinkedIn).
Developer experience is up, too. Surveys show 70% of engineers believe AI coding tools provide a competitive advantage (Joyk), and 75% feel more fulfilled in their jobs when using Copilot (GitHub Blog). At BNY Mellon, developers are learning new languages and frameworks faster, spending less time debugging, and more time solving real business problems (Medium). The result: teams that are more productive, proficient, and satisfied.
BNY Mellon’s transformation is as much about people as technology. Early on, leadership saw that successful AI adoption required rethinking team roles, training, and governance. The bank created an internal AI Hub and, in 2024, trained over 22,000 employees—from frontline bankers to senior developers (Cloud Wars). This investment demystified AI and empowered staff to experiment. Notably, about 15% of those trained are now building their own AI agents (American Banker).
Leadership consistently frames AI as an augmentation tool, not a replacement. CEO Robin Vince is clear: the goal is to “boost efficiency and client service rather than cut jobs” (TIME). Leigh-Ann Russell reinforces that AI enables everyone to be more productive—not just the tech-savvy (American Banker). This people-first approach is key to high adoption rates.
Strong governance underpins BNY’s AI strategy. In a regulated industry, security is non-negotiable. All employees access AI through secure, managed environments—Eliza on the intranet and enterprise-grade OpenAI services (American Banker). Data stays within approved channels, and models are fine-tuned to the bank’s needs. Other major banks, including JPMorgan and Goldman Sachs, are following suit with private AI portals (American Banker). Clear guardrails—defining who can use which tools, on what data, and how outputs are validated—mitigate risk and enable rapid, responsible AI-driven development (Cloud Wars). The result: a steady, sustainable rollout that drives value without chaos.
BNY Mellon’s journey offers actionable lessons for any organization aiming to harness AI in software development:
1. Start early and scale strategically.
Pilot AI pair programmers now—early adopters see compounding productivity gains (Fintech Futures; American Banker). The AI landscape is evolving fast—those who start now will win.
2. Invest in upskilling your teams.
AI tools are only as effective as their users. BNY’s broad upskilling program created internal champions and maximized adoption (Cloud Wars).
3. Build on secure, managed AI environments.
Avoid public AI tools for sensitive work. Use enterprise-grade platforms and private sandboxes to ensure compliance and data security (American Banker).
4. Make AI a true collaborator, not a threat.
Position AI as a colleague. Celebrate wins, encourage learning, and reinforce that AI augments human expertise (TIME; LinkedIn).
5. Measure, iterate, and refine.
Track efficiency, quality, and satisfaction metrics to prove ROI and continuously improve (American Banker). The tech and use cases will keep evolving—so should your approach.
Stay current on the fast-moving AI landscape and update your policies as needed. While concerns about code quality and security remain, the evidence is clear: when used responsibly, AI co-pilots can improve both code robustness and developer productivity (GitHub Blog). Due diligence—like peer review of AI-generated code—remains essential.
BNY Mellon proves that even the most traditional organizations can unlock radical productivity by empowering their people with AI. Developers now code with AI sidekicks at their keyboards and digital agents watching their backs—delivering software faster, with higher quality, and more creative energy. The takeaway: banking on AI is no longer optional for teams that want to stay competitive. Teams leveraging AI co-pilots are achieving gains once thought impossible—sometimes shaving days or weeks off development cycles (Microsoft Blog).
Those who hesitate risk falling behind as competitors benefit from an “AI multiplier” effect (GitHub Blog). By following BNY Mellon’s lead—investing in tools, people, and governance—any team can start harnessing AI’s potential. The age of AI-augmented development is here. It’s time to co-pilot your way to new heights.
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