Knowledge Foundation

An AI operating model for a bank: ten decisions to make before the twentieth use case

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Summary of a playbook on muthub.org

Read the full playbook on muthub.org →

Reviewed by Muthu · Playbook · Last verified Oct 18, 2026 · Drafted Oct 17, 2026 by seed-import/1.0 · Re-check by Apr 17, 2027 · 1 source

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Overview

How a regulated bank should organize, fund, govern and measure AI so that the first few wins turn into a portfolio instead of a pile of pilots.1

Points

  1. Name one accountable leader and a federated model
  2. Run a portfolio, not a pile of pilots
  3. Build the platform before the twentieth use case
  4. Tier use cases by risk and set controls by tier
  5. Bring model risk in at design, not at launch
  6. Treat evaluation as a product
  7. Give knowledge an owner
  8. Plan for the bad day
  9. Invest in fluency, not only tools
  10. Measure outcomes and cost per task

Sources

  1. An AI operating model for a bank: ten decisions to make before the twentieth use case · published Oct 16, 2026

Footnotes

  1. An AI operating model for a bank: ten decisions to make before the twentieth use case, muthub.org, reviewed 2026-10-16. ↩

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