---
type: Note
id: 2534f5bb07e9
title: 'An AI operating model for a bank: ten decisions to make before the twentieth
  use case'
description: 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.
tags:
- playbook
generated:
  by: seed-import/1.0
  at: '2026-10-17T19:25:45+00:00'
  body_hash: sha256:73790ec2feb3582b6095aad06a89b00204c5dcdca46d18bbe907edf64b6cdfed
sources:
- id: s1
  resource: /raw/20261017-192545-an-ai-operating-model-for-a-bank-ten.md
  title: 'An AI operating model for a bank: ten decisions to make before the twentieth
    use case'
kind: Playbook
status: stable
stale_after: '2027-04-17T00:00:00Z'
classification: public
owner: human:muthu
canonical_essay: https://muthub.org/playbooks/ai-operating-model-for-a-bank
verified:
  by: human:muthu
  at: '2026-10-18T02:01:22Z'
---

# 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.[^s1]

# 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

# Read the original

The playbook and its self-assessment are on muthub.org: [An AI operating model for a bank: ten decisions to make before the twentieth use case](https://muthub.org/playbooks/ai-operating-model-for-a-bank).

[^s1]: An AI operating model for a bank: ten decisions to make before the twentieth use case, muthub.org, reviewed 2026-10-16.