Large organizations are already putting more structure around AI consumption. The Wall Street Journal reported that TIAA uses token limits based on employee role and workload, with additional consumption subject to approval. The company is also trying to distinguish valuable, compute-intensive work from activity that simply consumes more compute. Its longer-term direction is more intelligent routing, including matching a task to an appropriate model.
Read MoreSmaller banks are often told they are behind on enterprise AI. That is true, but only up to a point. In practice, many of them already run on a more standardized operating base than the largest banks, because so much of their work moves through SaaS and core banking platforms that quietly shape how the bank operates.
Read MoreThe irony is hard to miss. SR 26-2 leaves each bank to determine how agentic AI should be governed through its own risk framework and architecture. Inside the workflow, the agent is doing its own version of that: resolving what its inputs mean before it acts. hat is where the hardest problem now sits: not in the model output or the execution record, but in the reasoning layer, where operational meaning forms before action.
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