Fractional CAIO: AI leadership you can defend
I work as a part-time Chief AI Officer for leadership teams that are already using AI, or about to, and need someone accountable for the decisions behind it. That means setting priorities, deciding who can approve what, checking that agents stay inside their authority, and staying hands-on until the work ships. That last part is the forward-deployed engineering.
Who it's for
- Leadership teams with AI pilots or agents running and no one clearly owning the risk.
- Companies weighing AI vendors, copilots or agent platforms and wanting a straight answer.
- Teams that need governance that helps delivery move instead of slowing it down.
What you get (confirmed in a written scope)
- A prioritized AI use-case register.
- An authority map: who can suggest, execute and approve, and what proof travels with each action.
- Vendor and workflow reviews.
- Risk and evidence gaps, with what to fix first.
- An executive review cadence.
- Hands-on build support where it's agreed.
How it starts
Most engagements start with the AI Trust Audit. It starts with a 90-minute scoping session, then a 10-day audit that ends with a prioritized action plan. Fractional CAIO support continues from there, on a scope we agree in writing.
Proof
I run my own company on an AI agent system, WWAIOS™, and I publish what goes wrong.
Questions
What's the difference between a fractional CAIO and a consultant?
A consultant delivers a report. A fractional CAIO owns AI decisions with your leadership on an ongoing, part-time basis, under a written scope.
Do you also build?
Yes. Forward-deployed engineering means I work inside your workflows until the controls actually run. Implementation is scoped separately.
Where do you work?
Remote, plus in person by arrangement. Kansas and Missouri come first, then Nebraska, Oklahoma, Arkansas, Texas and Colorado.