This article first appeared in American Banker magazine in its ‘Bank Think’ section on Friday 28th June 2024.
AI was the talk of the American Banker Digital Banking conference held this week in Boca Raton, Florida (where McKinsey was knowledge partner). The buzz is easy to understand, given its potential to boost productivity and lift so many elements of banking today to a new level. In the context of several sectoral headwinds, for CIOs and C-suites thinking about digital and AI, the challenges are compounded by three major factors: the need to demonstrate ROI on past technology investments, differentiate the bank from competitors, and achieve success in their existing transformation efforts.
To date, our industry’s record at these is uneven at best. Demonstrating the ROI on tech investments is not simple—indeed, for the industry, higher revenue remains very strongly correlated with more manual work. If tech were truly resulting in automation, we should be seeing significant returns to scale, but these appear absent from the data. Factor in that a lot of the spending has been on infrastructure modernization and risk management, which does not necessarily generate revenue. Moreover, few firms have managed to distinguish themselves from the competition when one considers a variety of financial and investor measures. Additionally, our research finds that a mere 30 percent of digital transformation initiatives have fully succeeded.[1]
Thus, the big challenge for banks in the months and years ahead is how to deliver material outcomes from their spending on tech, including AI. That means not just delivering products on time and on budget, but to do so in a way that gets measurable value out of it—by generating revenue, taking out cost, or tangibly improving risk management, among other benefits.
Going beyond the technology
A key insight that our research has found is that capturing value from technology and AI requires taking actions beyond just those domains. For example, in surveys we have conducted, 60 percent of executives cite skill gaps as an obstacle that they have had to address in their digital transformations, and 70 percent say they faced fundamental resistance to change. Similarly, our research has shown that there are five paths to beating long-term market TSR. However, many banks’ technology portfolios are not aligned with those types of drivers. With “change the bank” spend (initiatives that aren’t just about “run the bank” maintenance), being often significantly less than half a bank’s tech spend, it is not surprising that business leaders don’t see technology investments generating topline growth or reducing expenses.
Our McKinsey colleagues recently published a book called Rewired that highlights the importance of going beyond the tech itself to address issues such as where to play, clarity of roles and responsibilities, how to break down organizational silos, ways to create a compelling vision for investors and stakeholders, and how to motivate people and drive change with urgency. One rule of thumb across successful institutions we have seen is that for every dollar invested in technology, it takes another dollar to be invested in this kind of strategic organizational, cultural, and change management to be assured of the value.
Against this background, AI now looms ever larger for banks. But to capture any meaningful value from AI, the actions need to reach much further than just building sophisticated models. For example, at some institutions, even the process of validating machine learning or AI models can stretch to as long as two years. While there are often good reasons for this duration, in many cases relooking at these processes can compress the time taken while preserving the risk management (and sometimes even enhancing it).
Three questions for banking leaders
Over the last 15 years, banks have seen many trends that held the promise to change their business, like lean, agile, robotic process automation, core platform modernization, or cloud. Consequently, many institutions are still on those journeys. And now they are faced with taking on AI. At this week’s conference and from our work with clients, it is becoming clear how smarter and more effective deployment of tech and AI holds the promise to change the odds in favor of banks. Many institutions already have several pilots running.
Yet as this new “wonder” technology takes hold, the big lesson of past technology implementations needs to be kept in mind—namely that the impact of tech will need to be captured outside the CIO’s office. To that end, we see three critical sets of questions for banking leaders as they head home from this week’s gathering in Florida:
- First, can you objectively identify areas where tech and AI can generate the most business value in your context such as reducing risk, introducing new revenue, and cutting cost)?
- Second, are you materially reallocating your spending toward those areas or are you being incremental and “peanut buttering” investments?
- Finally, beyond the tech or AI model deliverable, do you have a change management formula in place that goes “beyond the CIO’s office” and benefits from your past lessons from other similar epochal programs (some of which may very well still be in flight)?
As our colleagues have recently written, ironically, the secret to successfully deploying AI technology and turning the high hopes for AI into reality is never just technology.
Ido Segev is a senior partner in McKinsey’s Boston office, and Vik Sohoni is a senior partner in the Chicago office.
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[1] "Losing from day one: Why even successful transformations fall short,” McKinsey & Company December 7, 2021