Generative AI emerged prominently in early 2023, displaying notably positive outcomes and introducing potential new risks for organisations worldwide. Despite potential challenges, leaders in the banking sector seem open-minded in this regard. According to a recent McKinsey forum on Generative AI, two-thirds of senior digital and analytics leaders believed this technology would fundamentally alter their business practices. The crucial queries for banking institutions revolve around the most effective utilisation of gen AI, its strategic implementation, and ensuring widespread adoption within their organisational frameworks.
McKinsey Global Institute’s estimation suggests that across 63 analysed use cases, generative AI could contribute between $2.6 trillion to $4.4 trillion annually in value to industries worldwide. In particular, banking stands to gain significantly, with an estimated annual potential of $200 billion to $340 billion, constituting 9 to 15 percent of operating profits. This growth is largely attributed to heightened productivity. The economic impact is anticipated to positively affect all segments and functions within the banking sector, with the corporate and retail sectors expected to experience the most substantial absolute gains, approximately $56 billion and $54 billion, respectively.
For banks aiming to use this valuable technology, scaling up AI generation is, in some ways, similar to any other significant change—it requires traditional skills in managing change, alignment, and support from senior leadership upfront, accountability from business units for outcomes, focusing on valuable applications, setting clear goals, and more. However, in other aspects, scaling up AI generation is unlike anything most leaders have encountered before.
There are several reasons why scaling up AI generation is different. Firstly, it’s about the scale of the task and its related consequences. Similar to how smartphones triggered a whole range of businesses and new business models, AI generation is bringing into relevance the complete set of advanced analytics capabilities and uses. Leadership teams are suddenly realising the potential of AI. Almost immediately, banking leaders navigate through unfamiliar terms like reinforcement learning and convolutional neural networks. However, scaling up AI generation requires more than just understanding new terminology—management teams need to decode and evaluate the various potential paths AI generation could create and strategically adapt to position themselves advantageously.
The second reason is that scaling up AI generation complicates a working dynamic that most financial institutions thought they had resolved. Just as banks believed they were finally bridging the gap between business and technology (e.g., through agile methods, cloud computing, and changes in the product operating model), analytics and data emerged as a crucial third aspect requiring coordination. While analytics in banks had been relatively focused and often centrally governed, AI generation has highlighted that data and analytics will need to support every stage of the value chain significantly. Business leaders must engage more deeply with analytics colleagues and align their sometimes conflicting priorities. Based on our experience, this shift is still a work in progress for most banks, and their operational models are continuously evolving.
