The 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.
Read MoreOver the past two years, enterprises have rapidly adopted AI copilots, chatbots, and assistants across support, IT, and customer-facing workflows. Early results have been promising. Teams report faster answers, reduced manual effort, and incremental productivity gains.
Read MoreThe volume of headlines around AI is staggering. In boardrooms around the country, it's being hailed as the most revolutionary technology since the Internet. But while leaders are fascinated by the promise of artificial intelligence, few are seeing significant returns on their investments. In fact, many AI initiatives fail. However, there are exceptions. Companies such as Mars Wrigley, Colgate Palmolive, Turbo Tax and Pega Systems are putting AI into action.
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