Short answer
Data can be pristine and a company can still be nowhere close to ready — readiness isn't a property of your database, it's a property of your organization. Check decision rights, workflow ownership, feedback loops, and incentives before you scope an AI project.
A data checklist says nothing about who's authorized to act on a model's output, whether a process will actually change, or what happens when the tool is wrong. Companies routinely pass the data checklist and still stall.
If a tool produces a recommendation, someone specific needs authority to act on it without escalating every time. Self-check: can you name the individual role that will act on this system's output tomorrow?
AI only creates value when it replaces or reshapes a real workflow step, not when it's bolted onto an unchanged process. Self-check: can you point to the specific step that disappears or shortens, and who owns redesigning it?
Readiness means there's a defined path for flagging a bad output, and that the people expected to change behavior are also the people whose goals or metrics change. Without both, adoption quietly fails regardless of tool quality.
It's most valuable before you scope a project, but it also explains why an existing AI or automation initiative isn't gaining adoption.
That's not a reason to abandon the initiative — it's the actual scope of work. Sometimes the highest-value engagement is clarifying decision rights and redesigning a workflow before building anything.