BNY Implemented AI Strategy in 2025

The bank utilized AI training and digital employees to drive financial growth throughout the year.

Updated on Oct. 1, 2026 in Remote Work

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BNY launched an aggressive AI-focused technology strategy throughout 2025. The initiative significantly increased internal efficiency and contributed to a rise in total revenue and net income.

Why it matters

The implementation aimed to unify previously siloed bank operations under a horizontal management structure. By utilizing these new capabilities, the bank sought to fundamentally improve how its daily work gets done.

BNY reported $20.1 billion in revenue and $5.6 billion in net income for 2025, representing increases of 8% and 23% respectively over the previous year. The bank also deployed over 150 digital employees and reached a 99% training adoption rate among staff.

The players

Leigh-Ann Russell

She is the former Chief Technology Officer of BP who directed the AI strategy at BNY.

BNY

It is a global financial services company that underwent a major technological transition in 2025.

The details

Leigh-Ann Russell led the strategy, incorporating vibe coding to allow staff to build software using plain language rather than traditional syntax. Consequently, over 60% of the bank's software code is now built with AI assistance, while digital employees actively identify and fix vulnerabilities within bank systems.

In 2024, BNY generated $18.6 billion in revenue and $4.5 billion in net income.
At the start of 2025, 20% of employees utilized the Eliza AI platform.
By mid-2025, 99% of BNY employees had completed Eliza training.
Financial data from 2025 reflected the impact of the completed AI implementation.
Projections in Q1 2026 suggested continued growth for the bank.

Market Landscape

BNY's strategy follows the broader documented trend of using generative AI agents to replace legacy banking workflows. The bank's aggressive 2025 pivot marks a definitive example of how large financial institutions are moving from pilot programs to full-scale internal integration of AI agents.

The shift toward AI-assisted code and digital employees suggests that clients may experience faster service and more secure banking infrastructure. These operational improvements are designed to streamline internal processes that ultimately affect how customers interact with the bank's digital tools.

The takeaway

The success of the bank's strategy highlights how plain-language coding tools can bridge the gap between non-technical staff and complex software development. Companies looking to emulate this model should prioritize comprehensive workforce training to ensure high adoption rates of internal AI platforms.