For financial services, tomorrow is actually today

“There are three exponential technologies that will transform everything you’re doing,” said Shanker Ramamurthy, IBM Managing Partner for Global Banking & Financial Markets, addressing a packed house at the Financial Services Leadership Exchange, held last week at the Yale Club of New York. “I think of them in terms of ‘yesterday and today,’ ‘today and tomorrow,’ and ‘tomorrow and the day after,” he continued. “Yesterday and today is hybrid cloud. Today and tomorrow is all about AI and generative AI. And tomorrow and the day after is about quantum.” Are financial services firms ready? Based on talks by Ramamurthy and other speakers at the event, it would seem these firms haven’t much time for an existential debate, because tomorrow is actually today.

“What do the winners need to do to thrive in this new era of AI and generative AI?” he asked. Up until the last decade or so, explained Ramamurthy, the normal practice for many enterprises was that “the [executive team] would lay down a business strategy, and then the technology would come along to support that strategy.” Today, though, it’s an ambidextrous world: “The combination of these exponential technologies are not just informing the business model; technology is actually shaping and driving the business model.”

The term “exponential” is often used in everyday parlance as a hyperbolic synonym for “a lot,” but when it comes to AI and Quantum, said IBM experts at the Exchange, the growth of these technologies is literally exponential—not just in terms of capacity, but in cost-effectiveness.

Ramamurthy cited a recent episode of the podcast Decoder with Nilay Patel, in which IBM CEO Arvind Krishna explained that accelerated cost reduction in AI technology is almost inevitable, based on empirical patterns we’ve all witnessed. Moore’s law—the phenomenon that the number of transistors on an integrated circuit will double every two years or so with minimal rise in cost—has been borne out, Krishna noted, most recently in the last five-year arc of semiconductor technology. (In fact, Moore’s law has been consistently accurate since the coinage of the term in 1965.)

“Are the amounts of capital being committed (for AI) up in the trillions? That’s absolutely true,” said Krishna on the podcast. “But over a five-year arc, we’ll probably get a 10x advantage in pure semiconductor capability. Then you’ll get 10x on the design side and 10x from the software side. You put those three tens together, that’s 1000x cheaper.”

Ramamurthy elaborated, “How are we going to take advantage of that?” Time is of the essence, with cost being a pressing worry amongst most banking professionals, he said. “Banking CEOs are looking to achieve three objectives: growing revenue, optimizing for cost and managing for risk and regulatory compliance. And by the way, we all know that cost-income ratios in banking have been stubbornly stuck. In the U.S., it’s close to 62%.”

“Managing for these three competing objectives is extraordinarily hard,” he said, “and every few decades, almost like clockwork, we see banking crises pop up all around the world. Typically, it’s because one of these three tends to be suboptimally focused on.”

So, what is a financial services leader to do?

“Banks in mature markets have a problem,” he said. “They’ve been investing in technology for decades, but it’s complex and expensive—as are the processes and business models. The technology is the servant of the business.” How does one adapt this seemingly unwieldy legacy of the past? “We must re-imagine, not re-engineer, how we conceive of the front, middle and back offices,” he explained. In the traditional model, “about two-thirds of the people, processes and technology tend to be focused on the middle and back office, because of their extraordinary complexity. I called that yesterday’s banking model. Tomorrow’s banking model is going to flip this equation on its head.”

What that entails, he said, is “re-imagining what these exponential technologies can do, so we can focus on what really matters—the customers, the ecosystems and the partnerships. It’s about platforms that transition from yesterday to tomorrow. It’s a multi-year journey.”

For most of the past two decades, said Ramamurthy, IBM CXO studies had shown that financial services were ahead of the curve in terms of early adoption of technology. The advent of AI and gen AI changed that. “This is the first time since doing this study that technology has been adopted much more aggressively outside financial services than within,” he observed. The reasons for this are several: concerns about risk and regulations, as well as AI hallucinations. So, some banks have been cautious in their approach to this new technology. “In the early days, I was seeing a lot more back-to-front automation,” he said, and banks were taking baby steps. “Banks thought, ‘I’m not sure how the generative AI is going to work, because it can hallucinate. Let me do projects that only impact employees, and not the customer—HR, finance, procurement and other back-office operations.” Only then, according to this approach, would banks use AI for the middle office, “and then very thoughtfully and gingerly I expose my customers to it,” he said.

The strategy of the future, he said, has to be “simultaneously back to front, and front to back.” The approach of yesterday is “plus-AI,” he said; in other words, seeing where AI tools can be shoehorned into the existing systems. But the future, he said, is AI-first—what IBM sometimes calls AI-plus. What this entails is “asking how you can use technology and agentic capability in the context of generative AI to imagine the art of the possible, and then how to bring humans into the loop.” The difference between plus-AI and AI-plus is significant, he said. “The economic value climbs substantially. If you add a bit of AI to your re-engineering, that can increase productivity by, say, 10-12%. As you move up to orchestrating multi-agency processes with AI, that can unlock something like a 60-75% increase in productivity. And depending on the bank’s risk tolerance and how aggressively it looks to move forward, you’re going to see a combination of these.”

These exponential technologies, noted Ramamurthy, “are progressing at a rate we’ve never seen before with any technology—not just in living memory, but perhaps since the start of the industrial revolution.” That formidable reality, he said, represents both an opportunity and a challenge. “It forces us to confront three questions,” he said.

“First, is our strategy ambitious enough?” Maybe not, he said, given that technology has surpassed the limitations of what our biology allows us to comprehend. “Evolution has designed the human mind only for linear change, not exponential change,” he said. “Let’s wrap our head around that. Every doubling [of computing power] gives us more capability than all of computing since the dawn of computing.”

Because exponential technologies are advancing faster than humans can intuitively plan, in other words, financial services leaders must consider whether they’re thinking boldly enough about what is possible. He gave an example: “With these exponential technologies put together, you can use generative AI to write quantum math code and solve problems in the next frontier.”

The second question, he said, is “Are we executing fast enough? Particularly for large institutions with extraordinary complexity, that’s a powerful question.” And the third question: “Do we have what it takes to transform from a people and business standpoint, and from a capability standpoint? When I have my work sessions with CXOs from banks around the world, almost every team says it has work to do on these questions. The threats are daunting, but the opportunity is extraordinary. I’m confident that we can master this challenge and unlock an incredible amount of value over the coming five years.”

Meanwhile, quantum computing technology is maturing so quickly that it’s important for financial services firms to get started, said Dr. Scott Crowder, Vice President of IBM Quantum Adoption and Business Development, at the Exchange.
 
IBM is responsible for setting that fast pace. “Nine years ago, IBM put a five-qubit quantum computer on the cloud and let other people program it,” said Crowder. A qubit is a quantum bit, the basic unit of information in a quantum computer. IBM has deployed over 85 systems since that first five-qubit system, according to Crowder, and now maintains a fleet entirely comprising quantum computers with more than 100 qubits, a scale beyond what classical computers can access with brute-force techniques.
 
Contrary to popular belief, said Crowder, advancing computation is about more than building faster, bigger computers. Quantum computers give algorithm developers access to mathematical operations that are innately more complex than those of classical computers, he added. That math gives the potential to solve differential equations more efficiently, for example, which could have important financial services applications.
 
What makes today a critical moment for the financial industry is the speed that quantum is maturing, said Crowder, adding that quantum advantage—the moment at which quantum outperforms any known method that relies on classical computing alone—is expected sometime in the next 12 months. Developers across the field are actively publishing and benchmarking quantum advantage candidates as we speak with the help of IBM Quantum hardware and software tools.
 
IBM Quantum aims to deploy the world’s first large-scale, fault-tolerant quantum computer by 2029, the company announced in June 2025. That computer is expected to run programs with 100,000,000 quantum operations on 200 qubits, giving access to a powerful suite of algorithms far beyond the abilities of today’s computers. Representing the computational state of an IBM Quantum Starling would require the memory of more than a quindecillion (10^48) of the world’s most powerful supercomputers, according to the announcement.
 
Crowder said that his advice to financial services firms was to begin building their teams of quantum practitioners. “If you want to differentiate your business, have the team start to explore the algorithmic approaches and interlock them within the lines of business. Because my experience is that it’s the combination of understanding quantum and the business that is really the algorithmic secret sauce.”

Vanguard, the pioneering investment advisory giant, is a case study in the importance of quantum literacy in financial services firms. In a joint IBM-Vanguard study announced in September 2025, researchers explored how quantum computing can construct optimized portfolios under real-world constraints. The study tackled the conundrum of how to balance risk and return amidst complicated and shifting variables such as transaction costs, regulatory limits and how small changes affect the overall portfolio.

As Jon Cambras, Vanguard’s Head of Emerging Technology Research, told the crowd of financial leaders at the Exchange, “With a basket of 100 bonds, if each of those bonds has five variables attached, that’s five to the 100th power, which is about the number of atoms in the observable universe. You’ll never get there with a classical computer.” He explained how Vanguard got ahead of the curve in 2023 and started exploring quantum technology’s financial use cases. “Sometimes you can play catch-up with technologies, and sometimes you can’t. Vanguard realized that quantum was one where we didn’t want to wake up one day to realize that quantum had arrived, and we didn’t know how to use the algorithms, and we didn’t know where to start. So we wanted to start the journey early.”