Large banks are aggressively integrating AI to transform operations and drive efficiency
If you’re a bank executive and you’re not already planning how AI fits into your infrastructure, you’re late. The big players are moving, and fast. Institutions like BNY Mellon and Bank of America have declared their AI ambitions openly, with strategy, capital, and execution all visible. This isn’t about checking a box or running proof-of-concepts. This is about redesigning the way global finance operates. Top financial institutions are no longer asking whether AI tools work, they’re asking how quickly they can bring them to scale.
We’re not only talking about back-office automation. AI is now incorporated at the heart of banking operations, from customer support to risk modeling. At BNY Mellon, CEO Robin Vince pointed to their AI platform, Eliza, and its integration with Google Cloud’s Gemini Enterprise. They’re also scaling their work with OpenAI. This setup allows them to inject more intelligence directly into decision-making systems. It’s a shift toward systems that don’t just perform tasks but understand context and improve outcomes.
Bank of America, under CEO Brian Moynihan, has put hundreds of millions of dollars into AI, with 20 projects live. That level of investment shows how serious this has become. It’s no longer a speculative play, it’s fundamental to staying competitive. Whether AI is deployed in software development to write cleaner, more secure code, or in customer service to resolve inquiries faster and more accurately, the result is the same, fewer errors, faster output, stronger performance.
AI doesn’t just help at the edge. It changes the core. And when it’s integrated correctly, the compounding impact across business units becomes obvious very quickly. But it’s not just about deploying large language models. It’s about aligning them with strategic priorities and embedding them across systems in a way that scales with the business. That’s where many firms will struggle, but those who solve it will leap ahead.
McKinsey’s Global Banking Annual Review in 2025 backed this up. They estimate AI could cut operational costs across banking by up to 20%. That’s not marginal, it’s game-changing. Accenture’s 2026 trend report shared similar signals: banks already using AI are reporting real improvements in critical areas like risk management, engineering, and customer experience. The industry is entering a consolidation phase, where proven use cases are being scaled company-wide.
This AI acceleration isn’t just for tech teams. It matters across HR, compliance, marketing, and client services. Executives need to focus on enablement and infrastructure readiness. Because if your people aren’t prepared to work with AI, the system won’t evolve. As Michael Abbott from Accenture put it, “Generative AI is going to impact and change the way work gets done in every single place. But that work won’t change until it’s enabled.”
Right now, the firms winning are the ones where C-suite leadership is already thinking like engineers, solving problems, removing friction, and using AI where it can compound real value. Rebuilding these organizations around intelligence isn’t optional. It’s now the standard.
Executive confidence and measured progress signal a broad, transformative approach to AI adoption in banking
AI in banking is no longer experimental. It’s entering execution mode. What we’re seeing across top firms is a shift in tone, from curiosity to conviction. Citigroup, Morgan Stanley, and Goldman Sachs aren’t talking about theoretical benefits anymore, they’re deploying AI across the most complex functions inside their organizations. Their CEOs are clear: this is how scale happens.
Jane Fraser, CEO of Citigroup, is applying AI across more than 50 of the firm’s largest operational processes. These touch everything from client onboarding to loan underwriting. That’s not surface-level effort, it’s one of the deepest operational integrations we’ve seen to date. The focus is clear: use AI to simplify processes, reduce internal complexity, and enhance overall speed and accuracy.
At Morgan Stanley, CEO Ted Pick has made it clear that the firm’s future performance hinges on AI integration across both internal systems and client-facing platforms. He described their momentum accurately, confidence is growing every quarter, grounded in real results. This is what serious transformation looks like when backed by a clear enterprise strategy.
Goldman Sachs is also moving. CEO David Solomon outlined the company’s AI efforts targeting six core operations. This isn’t casual experimentation, they’re reframing key processes to make them more efficient and flexible. Solomon explained that these changes would free up capacity for growth initiatives. And while details were limited in this quarter’s summary, he made it clear that further updates will follow as the impact scales into 2026.
The real shift isn’t just in capabilities, it’s in mindset. These leaders aren’t just applying AI tools; they’re creating conditions where AI can deliver long-term execution advantages. That means integrating AI into the business model, and aligning internal teams across functions to support it. This isn’t something you delegate to IT or data science divisions. This is owned at the top.
Michael Abbott at Accenture stated the same: for banks to fully leverage AI, internal enablement has to be a priority. That means ensuring every function, marketing, legal, HR, front office, knows how to use, adapt, and collaborate with AI systems. Abbott made a useful point: the future won’t shift until the workforce is fully enabled. Tools alone don’t transform systems. Capability must match intention.
Data supports this trajectory. McKinsey’s 2025 Global Banking Annual Review forecasts that fully integrated AI across financial institutions could refactor cost structures, reducing operating expenses by up to 20%. That’s highly material at enterprise scale. And Accenture’s Top Banking Trends for 2026 confirms the shift is already underway, payoff is showing up in systems engineering, risk operations, and customer management.
Strong AI execution requires clarity from the top, and the firms succeeding now are those with leadership that understands timing matters. Early groundwork is behind them. What happens in 2026 will be determined by how well these companies scale what they’ve already started. For C-suite teams across the industry, that means treating AI not as a side initiative, but as a primary driver of future returns.
Key executive takeaways
- AI is now central to bank operations: Leading banks like BNY Mellon and Bank of America are embedding AI into core systems to streamline processes, improve risk controls, and enhance customer service. Leaders should prioritize strategic tech partnerships to scale AI across enterprise platforms.
- Confidence drives deeper adoption: With promising early results, banks including Citigroup, Morgan Stanley, and Goldman Sachs are scaling AI into high-impact operational areas. C-suite leaders should align cross-functional teams for full AI enablement to unlock long-term efficiencies and cost reduction.
