The banking sector is recognizing the potential of generative artificial intelligence (Gen-AI), according to a study by McKinsey. Executives in the industry believe that Gen-AI will fundamentally change the way they do business. However, scaling these projects presents new challenges. Banks need to determine how and where to effectively use AI and ensure that the applications are fully adopted and scaled within their organizations.Scaling Gen-AI in banks requires similar skills to other scaling efforts, including change management and top-level management support. However, there are factors that make scaling Gen-AI different, such as the scope of the task and its implications. Gen-AI makes advanced analytical capabilities and applications relevant, similar to how smartphones catalyzed an entire ecosystem of companies and business models.Bank managers must navigate through unfamiliar terms like “reinforcement learning” and “convolutional neural networks.” But scaling Gen-AI goes beyond learning new terms. Management teams must understand the potential paths Gen-AI could take, strategically adapt, and position themselves accordingly.Traditionally, analytics in banks have been relatively focused and centrally controlled. However, Gen-AI shows that data and analytics need to support every step of the value chain more strongly. Management must engage more intensively with colleagues in analytics and align their priorities.The pace of change is high, posing challenges for slower-moving companies. The proliferation of AI also brings unique talent challenges, particularly for banks with fewer AI experts. These banks must enhance their capabilities through training and hiring.The McKinsey Global Institute estimates that Gen-AI could create an annual value of $2.6 trillion to $4.4 trillion worldwide. For the banking sector, there is a potential annual value of $200 billion to $340 billion, primarily through productivity gains. The largest absolute gains will be in corporate banking.

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