AI Boosts Bank Productivity, but Revenue Generation Remains a Challenge

AI's Impact on Banking Productivity

Major banks are increasingly adopting artificial intelligence (AI) to boost productivity across various operations. At the Reuters Next conference in New York, industry leaders highlighted the significant potential of AI in enhancing efficiency, particularly in coding and daily tasks 1

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Goldman Sachs CEO David Solomon emphasized the potential productivity gains in coding:

"We have 11,000 engineers. We do an enormous amount of coding. If we can increase with these tools their coding productivity by 20 or 30%, it's a huge tailwind for us." 1

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Similarly, BNY Mellon CEO Robin Vince reported widespread AI adoption within the bank:

"We have thousands of people at BNY who are now enabled to be able to build and commission agents to be able to help them with their daily tasks." 2

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Current Applications of AI in Banking

Banks are currently applying AI in several areas:

1. Virtual assistants for clients
2. Tools for human resources, risk, compliance, and finance
3. Wealth management product development

BMO Financial Group has seen significant time savings in report generation. Kristin Milchanowski, Chief AI and Data Officer at BMO, noted:

"AI has been most useful in tasks such as shrinking the time BMO's equities teams need to produce reports - an important part of many investment banks' offerings - from more than four hours a day to less than one, leaving the analysts free to do more creative tasks." 3

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Challenges in Monetizing AI

Despite these productivity gains, banks are struggling to generate direct revenue from AI investments. Milchanowski, appointed to her role at BMO in October, candidly addressed this issue:

"The hype cycle brought a lot of positive attention to this space. I am chief AI officer now because there was a little bit of a hype cycle. I do believe that people thought it was going to impact the revenue or have a cost takeout different from what the effect actually has been. We're not seeing revenue-generating activity." 1

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Future Prospects and Use Cases

As banks continue to explore AI applications, industry experts emphasize the importance of identifying specific use cases. Milchanowski highlighted potential future applications:

1. Optimizing trades
2. Generating clients

The banking sector remains optimistic about AI's potential, but recognizes the need for more targeted strategies to translate productivity gains into revenue growth 3

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