This article was originally published on Forbes.com by Bill Harris, Founder and CEO of evergreen.ai, on March 27, 2026. Read the original article here.
Technology revolutions lead to a fork in the road: Do you use new tools to do what you already do, only cheaper? Or do you use it to do something customers never imagined? Artificial intelligence is forcing us to consider a trade-off between short-term results and long-term competitive position, especially in the financial sector.
For banks and wealth management firms, AI’s most compelling promise is cost reduction. Bank of America’s virtual assistant, Erica, now handles roughly 2 million customer conversations daily, which has resulted in lower call volume to the IT service desk. JPMorganChase execs also report gains related to the use of AI. Boston Consulting Group projects that AI could unlock more than $370 billion in annual profit for retail banks by 2030, and several mega-banks are adopting the tech at a breakneck pace.
Efficiency, however, has limits. Internal emails from Bank of America employees show how the company struggled to use Nvidia’s enterprise AI software. Employees complained they were handed a powerful system with little structural support for integrating it into daily work.
The deeper issue is philosophical. Incumbent institutions tend to start with an existing process—for example, a call center fielding 50,000 inquiries a day—and ask how AI can reduce the cost of handling inquiries. The customer’s experience is largely unchanged: Call, wait, talk to a bot or a human and hang up. The interaction may be faster and cheaper per unit, but it is recognizably the same interaction it was five years ago. Sometimes, the quality actually declines, as when Klarna cut its workforce almost in half and then faced customer backlash.
Agile, early-stage fintech companies approach the problem from the opposite direction. Instead of asking how to trim costs in existing processes, they ask what the customer actually wants, then build the experience from scratch around what AI makes newly possible.
The difference is architectural. An AI-native platform can combine multiple technical capabilities simultaneously available to the intelligence engine in real time: vetted financial and tax knowledge bases, live connections to external data resources, quantitative tools for personal financial planning, secure access to each individual’s financial data and conversation history, a reasoning and compliance layer that helps verify every recommendation, and multimodal presentation blending text, visualization, voice and interactive video. None of these capabilities is impossible for a large bank to assemble, but layering them onto legacy infrastructure is vastly harder than weaving them together at inception.
The potential payoff is a qualitatively different relationship with the customer. AI can often deliver better-researched, more quantitative and more personalized answers to individual questions faster and at a lower cost than human representatives. A client wondering whether a Roth conversion makes sense does not have to wait to schedule a meeting with their advisor or wade through a generic blog post. Some AI engines can pull the client’s financial and tax data, model multiple scenarios, factor in current law and present a clear recommendation within seconds, all while documenting the reasoning trail for compliance review.
At the leading edge of this reimagined experience, emerging technology now enables interactive “digital twins” of real advisors that can humanize the conversation in ways that text-based chat cannot. The simple act of speaking with a responsive, virtual advisor can make the interaction feel more realistic and familiar. As with fiction or movies, some people quickly forget that the experience is man-made and embrace what Coleridge famously called the “willing suspension of disbelief.”
The two approaches are not mutually exclusive, but they do reflect fundamentally different theories of value. Cost-reduction strategies improve margins on existing revenue streams. Innovation strategies create new revenue streams by attracting customers who were previously underserved because the economics were unsustainable.
Large companies investing billions in AI-powered efficiency can capture real savings. And startups using AI to reimagine the customer experience can better capture the next generation of clients. Financial firms of all sizes face a strategic decision: Do the old thing cheaper or the new thing better?
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.
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