For finance leaders still trying to move AI beyond pilots, the core metrics are noteworthy: revenue rose to about $31.6 billion, net income to $9.1 billion, and EPS to $1.21, all while the bank continued returning capital to shareholders and leaning into tech investment. Borthwick argues that this combination—earnings growth, improving efficiency, and ongoing reinvestment—is what a scaled AI program should look like when it hits the P&L.
“New AI capabilities now allow more than 200,000 of our employees to work more effectively, and they’ve helped contribute to producing a 59% efficiency ratio, a roughly 360 basis point improvement from last year,” he said.
When I asked Borthwick how he’s defining an “AI efficiency ratio,” he didn’t offer a new formula so much as a reframing of a familiar one: the bank’s efficiency ratio—expenses divided by revenue. When that number is going down, that means the investments you’re making—your expenses—are paying off in terms of higher revenue, he said. “So when we go from a 63% efficiency ratio last year to 59% this year, it shows we’re making progress against our goals,” Borthwick said. “It is the simplest way to think about what it costs to generate $1 of revenue.”
