AI operating model checklist for CDOs and CTOs
Speed comes from ownership and clarity, not more tools.
Most AI programmes stall for predictable reasons.
Use cases are unclear, ownership is split, governance is bolted on late, and deployment is treated as an afterthought.
This checklist is a practical way to spot gaps before they become delivery problems.
1) Ownership
- Is there a single accountable owner for AI outcomes?
- Who decides what gets built and what gets paused?
- Who owns incidents, rollbacks, and retraining decisions?
2) Use case discipline
- Can each use case be expressed as a measurable outcome?
- Do you have a prioritisation framework that leadership agrees on?
- Do you have clear “stop rules” for work that is not landing value?
3) Deployment pathway
- Can you describe the path from prototype to production in one page?
- Is the engineering owner clear?
- Do you have monitoring, alerting, and auditability built into the plan?
4) Governance that enables delivery
- Do teams know what is non negotiable (privacy, security, access)?
- Are approvals clear and timeboxed?
- Is governance owned, or is it a shared worry?
5) Capability design
- Do you know whether you need product leadership, modelling, platform, or MLOps first?
- Are job specs aligned to reality and authority?
- Do reporting lines support outcomes, not just org charts?
If you want a quick sanity check, this is exactly the sort of conversation I run before a retained search.
It is light touch, practical, and it usually saves weeks.
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