BNY uses the term “digital employees” to describe AI agents that work inside the same systems, workflows, and audit trails as human staff. These agents run on BNY’s internal AI platform, Eliza, which is built partly on Microsoft Azure. Instead of sitting on the sidelines as a separate tool, digital employees:

• Have unique IDs, credentials, supervisors, and reporting lines—just like regular employees.
• Log every action in an audit trail, so their work is fully traceable and reviewable.
• Operate within a secure, credentialed infrastructure with identity management and encryption.

BNY uses these agents for repetitive, rules-based tasks that run around the clock, such as payment validations inside regulated financial systems. This allows the bank to keep human judgment and accountability at the center, while letting AI handle high-volume execution. For a bank that clears most US Treasury bills and safeguards $59.4 trillion in assets for institutions worldwide, this setup helps ensure that AI is observable, controllable, and auditable before it ever touches live, high-stakes processes.

BNY is using AI to reimagine how work is structured, not simply to cut costs. Historically, the organization looked like a pyramid: a wide base of people doing repetitive processing work, with fewer employees focused on analysis and strategy. As digital employees take on more of the routine tasks, BNY expects that shape to move toward a “diamond”:

• The base of repetitive work is handled by AI agents.
• The middle layer of analytical and creative roles expands.
• Strategic leadership at the top continues to make the key decisions.

BNY is explicit that the goal is to reshape the organization, not shrink it. Even as it adds more agents, the bank still plans to hire thousands of human employees every year. As routine processing shifts to digital employees, human roles move:

• From processing to problem-solving.
• From execution to strategy.
• From clearing queues to benchmarking clients and designing new agents.

In other words, the organization becomes more of a learning system, where intelligence compounds over time and people are freed up to focus on higher-value work.

BNY tracks the impact of AI not just through ROI, but through time saved, errors reduced, and capacity freed for higher-value work. In one key area—manual payment validations—the bank reports that:

• Time per validation dropped from 5–6 minutes to under 30 seconds.
• Open investigations fell from about 5,000 to fewer than 1,000.
• Capacity was freed up for analytical work across time zones, rather than just queue-clearing.

These gains are supported by a significant commitment to change: BNY is investing nearly 19% of its annual revenue in transformation, including secure infrastructure, AI governance, and workforce evolution. BNY also emphasizes that every AI action is traceable. Audit trails do double duty: they satisfy regulatory and risk requirements, and they also serve as learning signals to improve both agents and processes over time. Overall, the bank measures momentum—how quickly it can redesign work and move people into more strategic roles—rather than looking only at short-term cost savings.