AI in Banking: Raw, Messy and Necessary Work of Changing How a Bank Operates

▤ Summary

The article explains how a regional bank is trying to embed AI across its operations instead of limiting it to isolated pilots. It highlights the biggest blockers to successful deployment: messy data, legacy infrastructure, and insufficient training. It also shows that banks need strong governance and human oversight because errors can spread quickly at scale. The piece argues that AI should improve customer experience, simplify routine work, and deliver measurable business outcomes. Overall, it frames AI adoption in banking as a broad operating-model change rather than a simple technology upgrade.

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