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Huntington Bank targets to achieve 10% to 15% cost reductions and 10% to 15% revenue increases through targeted uses of LLMs and agentic AI; finds GenAI is perfect for customer service exception handling

September 12, 2025 //  by Finnovate

Huntington Bank’s Chief Financial Officer Zach Wasserman has made it a mission to achieve a return on the bank’s generative AI investments. It’s on track to achieve 10% to 15% cost reductions and 10% to 15% revenue increases through targeted uses of large language models and agentic AI, he told American Banker in an interview. “I think leaders need to find a way to balance the urgency [of keeping up or staying ahead in the AI race] with making sure they actually have an ROI and that the technology is usable by the organization,” Wasserman said. The bank’s renewed focus on return on AI comes two months after MIT released a report that found that 95% of businesses are achieving zero return on generative AI. “That’s a sobering statistic and it reflects something that we should already know: AI implementation isn’t plug-and-play,” Theo Lau, founder of Unconventional Ventures and author of the book “Banking on (Artificial) intelligence” wrote in a blog Wednesday. “But I’d also push back on the narrative of failure. What exactly are we measuring? And what is the time frame that we are giving ourselves to experiment and gauge the technology? Are we using the right metrics?” Wasserman, who was put in charge of AI at Huntington Bank late last year, noted that most investments don’t pay off in the early stages. “We’re really trying to be focused, in a disciplined way, understanding what objectives we’re trying to get out of any one of these projects, and then holding accountabilities to actually generate that return,” he said. Sumeet Chabria, CEO of consultancy ThoughtLinks, told American Banker that overall, banks are doing better than “the 95% no-ROI headline.” Many first-wave generative AI investments did miss ROI targets. “However, banks were experimenting,” Chabria said.

“Pilots were opportunistic, run in silos and were not integrated well

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