Where AI Readiness Actually Breaks

Teams often begin by asking which model or vendor to choose. In practice, the first breakpoints usually appear much earlier: unclear ownership, fragmented knowledge, and workflows that already depend on invisible manual coordination.

An AI initiative becomes more credible when it is scoped around a specific operating problem. That means understanding who needs the output, what system owns the source data, how exceptions are handled, and where quality review belongs.

Readiness is less about whether a team is 'innovative' and more about whether the business can support repeatable execution. The right starting point is usually narrower, more operational, and more measurable than people expect.