AI Delivery Audit
Red flags in an AI delivery audit
The signs that a workflow is not ready for automation yet.
By JirakJ
6 min read
I do not read this as a tooling problem first. I read it as a sign that the company wants automation before clarifying rules, sources and ownership.
If the output cannot be rejected, improved or handed off, it is not a delivery system yet. That is why the early work should be concrete enough that buyers preparing for AI automation work can argue with it.
The boardroom version
The boardroom version is simple: the company is paying for repeated work because the company wants automation before clarifying rules, sources and ownership. That is a margin problem before it is a technology problem.
The operating version
The operating version is just as direct: look for unclear owners, conflicting sources, no quality bar and no escalation path. Make the work visible enough that a non-specialist can follow the handoff.
The standard
A audit red-flag checklist is the minimum standard I would want before calling this mature. Otherwise the process still lives in somebody's head.
The upside
Red flags help teams avoid building the wrong thing too early. That upside is easier to defend than a generic claim about AI productivity.
Monday morning checklist
- • List the sources the workflow is allowed to trust and the sources it should ignore.
- • Write down the artifact that would make the work reviewable: in this case, a audit red-flag checklist.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
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