Wall Street’s AI Invasion — What It Means for Everyone in Financial Services

A 241-year-old bank just deployed 134 “digital employees” that work 24/7, don't take sick days, and don't have names. They work alongside 48,000 humans—and the humans are being trained to build more of them.

That bank is BNY Mellon, and what's happening there is a blueprint for every financial institution on the planet. But it's also a warning for anyone working in the industry who thinks this wave won't reach them.

The Numbers That Should Get Your Attention

BNY Mellon is spending $3.8 billion a year on technology—roughly 19% of revenue, the highest proportion among its large-bank peers. Their internal AI platform, Eliza, has 125+ production AI solutions running. 98% of their 52,000-person workforce has completed GenAI training. And 20,000 employees—not IT staff, but accountants, lawyers, HR, and operations people—are actively building their own AI agents.

As Sean Boyce puts it: “They're not experimenting anymore. They've empowered what they call their ‘builders’—20,000 of them—to create their own agents for various use cases throughout the company. This isn't a pilot. This is full deployment.”

And BNY isn't alone. Goldman Sachs has embedded AI into account reconciliation and KYC processes. JP Morgan Chase has been running extensive internal AI training programs. Across the industry, only 2% of financial institutions report no AI usage at all, according to Finastra's 2026 State of the Nation report.

The Career Ladder Is Flattening

The most immediate impact for workers isn't the technology itself—it's how it's reshaping the career path. The traditional ladder—intern to junior to mid-level to senior—is compressing from both ends.

Entry-level positions are shrinking because AI handles the routine work that juniors used to cut their teeth on. And middle management is getting squeezed because the coordination, reporting, and project management layers that justified those roles are increasingly automated.

But here's the twist that most people miss. James Perkins sees the apprenticeship model flipping entirely: “The senior individual is now more reliant on that junior person to understand and learn AI. The senior has all the experience and strategic understanding. The junior has the technology exposure. I think that's where they have an in.”

The generation that grew up digital-first—always on devices, always connected—has a genuine advantage. Not in domain expertise, but in technology comfort that their senior counterparts need.

The Reversed Apprenticeship

This is a concept worth sitting with: the traditional mentorship flow is reversing. Senior professionals have decades of industry knowledge, relationship networks, and strategic judgment. But they often struggle with the technology that's reshaping their work. Meanwhile, newer entrants lack domain depth but navigate AI tools intuitively.

The smart play for both sides is obvious. Juniors who pair technology fluency with genuine curiosity about the business become invaluable. Seniors who embrace learning from their younger colleagues—rather than viewing it as a threat—position themselves to amplify their existing expertise rather than be replaced by it.

Why Banks Are Leading the Charge

Financial services isn't just adopting AI—it's leading the adoption curve across industries. The reason comes down to three factors.

First, data density. Banks sit on massive troves of customer, transaction, and compliance data that AI is purpose-built to process. As James notes: “Big banks have everything you could think about—customer data, transaction data, family profiles, who they share their cards with. Going through all that data has been a challenge for years. Now with AI, they can use it to their advantage.”

Second, investor pressure. Research shows 97% of investors are currently penalizing firms that fail to upskill their workers on AI. That's not a preference—it's an active penalty. When your investors are punishing you for not adopting AI, the urgency becomes existential.

Third, the fintech lesson. A decade ago, banks scrambled to keep up with nimble fintech startups. Many tried acquisitions and struggled with integration. AI gives them a chance to leapfrog—processing the data they already have rather than trying to absorb outside innovation.

The Regulation and Compliance Disruption

One of the least-discussed but most significant impacts is on compliance and regulatory teams. Every financial institution has dedicated regulatory and compliance staff. AI now knows every state-specific regulation, every federal law, and how international law applies—instantly.

This doesn't mean compliance professionals disappear overnight. But the role changes fundamentally. The value shifts from knowing the rules to applying judgment about how rules interact with specific business contexts—exactly the kind of nuanced, ambiguous work that AI still struggles with.

The Acquisition Wave Is Coming

James raises a prediction most people aren't thinking about: a flood of acquisitions over the next two to three years. Large enterprises will buy smaller companies that have already built AI-powered solutions, partly to avoid IP risks of building their own, and partly because AI has dramatically shortened integration timelines.

What used to take two to three years—absorbing a company's technology, people, and infrastructure—can now happen much faster. For anyone building AI-powered tools or services in financial services, this creates a genuine opportunity. You don't need to build a massive company. You need to demonstrate capabilities that a larger institution would want to acquire.

What This Means for You

If you're in financial services right now, the playbook is clear: get AI-literate immediately. Not “I've heard of ChatGPT” literate—hands-on, building-things literate. BNY isn't asking its employees to understand AI conceptually. They're asking 20,000 of them to build agents.

If you're looking to break into the industry, lead with technology skills and pair them with genuine business curiosity. The reversed apprenticeship means your AI fluency is a currency that senior leaders need.

And if you've been displaced or are between roles, consider building something. It's never been easier to launch a one-person company with AI filling the gaps. Several of our coaching clients have turned layoffs into startups—using AI to handle the functions they'd normally need a team for.

Sources