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AI bank of the future needs the right talent, culture, and organizational design

McKinsey & Company | Brant Carson, Abhishek Chakravarty, Kristy Koh, and Renny Thomas | May 18 2021

AI Bank of the future - AI bank of the future needs the right talent, culture, and organizational design

Technology alone cannot define a successful AI bank; the AI bank of the future also needs an operating model that brings together the right talent, culture, and organizational design.

As we noted at the beginning of this series on the AI bank of the future, disruptive AI technologies can dramatically improve banks’ performance in four key areas: higher profits, at-scale personalization, smart omnichannel experiences, and rapid innovation cycles. The stakes could not be higher, and success requires a holistic transformation spanning all layers of the organization’s capability stack.

Our previous articles have focused on the capability stack’s technology layers: reimagined engagement, 1 AI-powered decision making, 2 and modern core technology and data infrastructure. 3 Leveraging these capabilities to create value requires an operating model combining structure, talent, culture, and ways of working to synchronize all layers of the stack. Synchronizing these layers is not easy. Any organization undertaking an AI-bank transformation must determine how to structure the organization so that its people interact and leverage tools and capabilities to deliver value for each customer at scale. In this article, we take a closer look at the need for a platform operating model, the categories and scope of operating models, and the building blocks of effective models.

The heart of an AI bank is always-on customer interaction

The need to change a bank’s operating model arises from a combination of external and internal circumstances. Externally, as consumers and businesses increasingly rely on AI technologies in daily life, banks are shifting the foundation of their business models from products to experiences. In other words, as many traditional banking products become embedded—or even “invisible”—within beyond-the-bank journeys, experiences become the more salient element of a customer’s relationship with the bank.

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This shift involves a rapid increase in the number of customer interactions, and at the same time, the revenue associated with each interaction is declining. This is a fundamental change:

just a few years ago, customers conducted business with the bank by visiting a branch once or twice a month; more recently, they would conduct transactions several times each week through the bank website; now many customers interact with their bank daily through their mobile banking app, and often several times a day through wearable devices. In short, banks and their customers now have an interconnected, always-on relationship.

Circumstances within the bank are changing as well—albeit at a slower pace, due largely to the complexity of legacy technology and operating models coupled with the steadily rising cost of maintaining and upgrading IT infrastructure. Siloed structures also hamper organizations’ ability to transform themselves. Decision making at traditional banks is typically slow and cumbersome, and ineffective prioritization (done at too high a level without understanding underlying resource contentions) results in frequent project delays and cost overruns. Insufficient domain expertise and blurred accountability—particularly between business units and technology teams—too often cause new solutions to fall short of customer expectations. What is more, multiple systems perform similar functions, and the increasing complexity of IT architecture with a proliferation of applications weakens system resilience and stability and increases risk when changes are made.

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The widening divide between fast-evolving customer expectations and inertia within the bank reinforces silos and weakens the bank’s ability to respond to the demands of the new machine age. The challenge for leaders is to shift the organization from this siloed structure to a radically flattened network of platforms.

Platforms focus on delivering business solutions

Today, banks that recognize the value of AI and technology enabling better customer and business experience are moving steadily toward a platform operating model, leveling command-and-control structures to speed decision making and bring people together in teams relentlessly focused on delivering solutions that customers value. In this agile approach, each platform can be thought of as a collection of software and hardware assets, funding, and talent that together provide a specific capability. While some platforms, such as those for retail mortgages, deliver business-technology solutions to serve internal or external clients, others enable other platforms with shared services and support functions (for example, payments and core banking). Each platform is largely self-contained in producing business and technology outcomes and autonomous in prioritizing its work to meet strategic goals within clearly defined guardrails, such as common standards, finance, and risk control.

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