Deposit pricing tools have come a long way, but there’s a disconnect between what they produce and action that hits the market. Generative AI, handled properly, can accelerate implementation measurably. When applied to deposit-optimizing technology, generative AI can significantly accelerate decision-making and time to market for deposit pricing refinements. This reduces the effort needed to interpret results, and present findings in straightforward language with supporting data assets that virtually any responsible party in the organization can act on. Such accessibility can allow the results of scenario-based queries to be delivered to a decision-making audience within hours. A deposit pricing manager at a large regional bank might be tasked by the head of retail banking to model a pricing optimization to grow money market account balances by 70% while minimizing overall interest expense — perhaps to fund the bank’s expected lending demand. A typical optimized rate grid might contain tens of thousands of pricing cells — combinations of product features and customer attributes such as geography, balance tier and depth of relationship with the bank. The output to the deposit pricing manager would look like pricing, margins and expected balances across those numerous cells, requiring significant further analysis and distillation to provide to the head of retail to put into effect as the bank’s product offerings. But with AI, the output can be executive- and execution-ready.