BNY ignores tokenmaxxing, measures AI by business outcomes instead

BNY Mellon's CFO Dermot McDonogh recently spoke with Fortune about the bank's AI strategy and why it ignored one of enterprise AI's trendiest metrics. "Tokenmaxxing" had become a status symbol at some major tech companies, where engineers were urged to climb leaderboards by burning more AI tokens—a practice that critics argued distorted priorities and exposed a disconnect between AI spending and actual productivity gains. McDonogh said the topic never took root at BNY: "It's not something we spend any time talking about," he noted, pointing out that token costs are "modest within modest" relative to the firm's broader engineering budget. Even as the trend gained external momentum, BNY's leadership viewed it as a distraction from more meaningful measures of value.

Instead, BNY pursued an early and deliberate AI strategy. Since ChatGPT's emergence, the bank spent several years building an internal, model-agnostic platform and forging partnerships across hyperscalers and model providers. CEO-level commitment and a focus on cultural adoption proved equally important. "There's been a demystification," McDonogh said. "People don't feel insecure about AI. That's a really important cultural point." The approach enabled BNY to scale AI without fixating on cost per query. Internally, systems automatically route tasks to the appropriate models, ensuring efficiency without requiring employees to manually optimize prompts. "I couldn't tell you how many prompts we did last week," McDonogh said. "I'm focused more on outcomes."

Those outcomes are increasingly measurable. In the first quarter of 2026, more than 40% of BNY's code was authored by AI, rising to roughly 50% more recently. AI is embedded across operations: about half of annual account plans are drafted with AI, 25% of client onboarding is AI-supported, and roughly 70% of restricted-party payment screening is reviewed by AI. The impact shows up in financial metrics: revenue per employee rose from $338,000 in 2022 to $401,000 in 2025, while pre-tax income per employee increased from $99,000 to $143,000 over the same period. McDonogh frames these gains less as cost savings and more as capacity creation. "We haven't reduced the footprint, but it's allowed us to do more with the footprint that we have," he said.

To track progress, BNY measures AI impact across core workflows—including innovating, prospecting, onboarding, transacting, and streamlining—while continuously building out its internal "Eliza" platform, which serves as a firm-wide context layer improving over time as it ingests more data and use cases. Employee adoption is structured: staff progress through three levels of AI proficiency, culminating in a "pioneer" designation that requires formal training and testing. Access to more advanced models is gated by expertise, reinforcing both quality and accountability. Within finance specifically, AI is reshaping core processes like regulatory reporting, balance sheet analytics, predictive modeling, and earnings preparation, where it helps synthesize analyst expectations and anticipate investor questions. For McDonogh, the takeaway is straightforward: AI productivity is not about how much you use, but how effectively it changes what an organization can do.

The tokenmaxxing trend emerged as a status symbol at major tech firms, where engineers were pressured to climb leaderboards by consuming more AI tokens—a practice critics argue misaligned incentives and masked a gap between AI spending and real productivity gains. BNY Mellon took the opposite path. Rather than adopt external benchmarks, the bank's leadership, led by CEO commitment and CFO Dermot McDonogh, built an internal LLM-agnostic platform and invested years in cultural adoption after ChatGPT emerged. The bank was deliberate: it routed tasks to appropriate models without requiring employees to manually optimize prompts, and it measured success by business outcomes, not token volume.

That discipline produced tangible financial results. Revenue per employee climbed from $338,000 in 2022 to $401,000 in 2025, a 18.6% gain over three years; pre-tax income per employee jumped from $99,000 to $143,000 over the same span—a 44.4% increase. These gains reflect what McDonogh calls "capacity creation": the bank did not shrink its workforce, but AI allowed it to accomplish more with the same headcount. The platform, called "Eliza," serves as a firm-wide context layer, improving as it ingests additional data and use cases. BNY's structured approach to employee adoption—progressing staff through proficiency levels to a "pioneer" designation requiring formal training—reinforces both quality outcomes and organizational accountability, setting it apart from the undisciplined token-chasing culture that emerged elsewhere.

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