Building an organization that uses generative AI responsibly, productively, and at scale is an ongoing challenge. The potential risks are great, which is why risk must be part of the design process, not a post-launch filter. Data quality is key. Before the models, prompts, and prototypes comes well-prepared data. Without structured, explainable, high-quality data, even the most advanced AI systems can be prone to hallucination—delivering flawed or misleading results. The long-term benefits of this breakthrough technology require financial institutions to maintain human oversight and embrace transparency. This is not a set-it-and-forget-it type of experiment.

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