Bank of America’s AI bet is starting to pay off, its CFO says

Good morning. Bank of America’s latest quarter offers an example of whether large‑scale AI investment can translate into hard operating results.

The big bank reported its Q2 earnings on Tuesday, which included double‑digit profit growth, a 59% efficiency ratio, and roughly 17% return on tangible common equity. CFO Alastair Borthwick increasingly frames those gains as evidence of AI‑enabled productivity rather than just cost‑cutting and rate tailwinds.

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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.”

But it still leaves open the question that many CFOs are wrestling with: how precisely to isolate AI’s contribution from traditional metrics like net interest income and cost discipline.

Borthwick pointed to a combination of growth and productivity across business segments to make his case. In consumer banking, he said that with revenues up 5% and continued cost management, Holly O’Neill, president of consumer, retail and preferred banking, and her team generated their fifth consecutive quarter of positive operating leverage. They notched a 51% efficiency ratio and a 29% return on capital. The business grew earnings 10%, contributing $3.3 billion to the company’s bottom line, while deploying new AI capabilities and investing in its financial center network, Borthwick explained.

For CFOs looking to benchmark their own AI programs, that’s one template: using automation to support front‑line staff and branch operations, then watching whether operating leverage and returns hold up as you keep investing.

In consumer banking specifically, the most relevant internal AI is Erica for Employees, because it supports staff handling consumer service, branch support and operations behind the scenes. Rather than replacing roles outright, tools like Erica triage routine requests and surface information faster, so people spend more time on exceptions and higher‑value work. If that pattern scales, it suggests the efficiency gains Borthwick is touting may be less about one‑off headcount actions and more about gradually changing how much human labor the bank needs per dollar of revenue.

A focus on the U.S. middle market

Borthwick also said on the call that the U.S. middle market remains a key strategic priority, and here the AI story links directly to growth. On Monday, the bank announced multiple senior hires in regional investment banking. Since its 2016 launch, that business has expanded to more than 200 bankers working across 26 U.S. cities, he said.

The latest hires add senior expertise in Austin, Boston, Charlotte, Chicago, Detroit, Minneapolis, New York, San Francisco and West Palm Beach, reporting to Neil Kell and Samardh Kumar, co‑heads of regional investment banking. The team collaborates closely with global commercial bankers, Merrill, the private bank and the local market organization. In effect, the bank is spending part of its efficiency dividend on expensive front‑office talent, betting that an AI‑enabled core allows it to keep margins healthy while pushing for more fee income in the middle market.

For CFOs benchmarking AI, BofA’s Q2 suggests three lenses: watch how the efficiency ratio moves as AI scales, build a scorecard that tracks costs, cycle times and human‑in‑the‑loop behavior and decide what share of productivity gains you’ll reinvest in growth talent versus keep as margin.

BofA’s bet is that AI‑driven productivity can lift returns while it keeps investing; investors will be watching closely to see if the story holds up over the coming quarters.

Sheryl Estrada
[email protected]

This story was originally featured on Fortune.com

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