Tens of thousands of software engineers at JPMorgan Chase have significantly improved their productivity, completing projects 10% to 20% faster through the use of an innovative coding assistant tool developed in-house. Lori Beer, the global chief information officer, shared these insights ahead of the bank’s internal DevUp conference, which aims to bring together leading engineers from around the world.

According to Beer, the enhanced efficiency of software engineers presents “a great opportunity” for JPMorgan to reallocate its talent to more complex tasks. This shift is especially important in light of the bank’s substantial $17 billion technology budget earmarked for 2024.

The technical workforce at JPMorgan currently boasts around 63,000 employees, with approximately one-third working in India. This represents about 21% of the bank’s total global headcount, indicating the significant role of technology in JPMorgan’s operations.

Beer emphasized that these efficiency gains from the coding assistant will allow the bank’s Indian centers to focus more on high-value projects, particularly those involving artificial intelligence and data analytics.

As part of its ongoing commitment to AI, JPMorgan has identified approximately 450 potential use cases where artificial intelligence can be integrated. CEO Jamie Dimon, optimistic about future AI initiatives, expects this number to increase to 1,000 applications by the following year.

Beer noted that the bank is particularly focused on leveraging AI to enhance profitability across its various business lines. She stated, “If we continue to articulate that it’s not just an incremental shift with AI, but we’re transforming and creating value,” the initiative can be deemed a success.

In previous remarks, JPMorgan’s president, Daniel Pinto, indicated that implementing AI could potentially add between $1 billion to $1.5 billion in value for the bank. This underscores the significant economic impact projected from AI advancements.

Beer expressed that the true measure of success for these AI initiatives would not solely hinge on the number of use cases implemented. Instead, she emphasized the importance of achieving substantive outcomes, stating, “If I don’t pass those bars of true outcomes, it doesn’t matter how many use cases I enabled in production.”

Reflecting on hiring trends, Beer commented, “We’ve sort of passed our high growth time,” highlighting a shift in the bank’s focus towards optimizing its existing workforce and projects.

Looking ahead, she acknowledged, “There’s so much productivity and opportunity as we think about a world with AI.” She suggested that optimization efforts will continue as the bank seeks to maximize its technological footprint.

In conclusion, the implementation of the coding assistant tool has empowered JPMorgan Chase’s engineers to boost efficiency, paving the way for increased focus on high-value AI projects and driving meaningful outcomes for the bank’s future.