AI readiness is an infrastructure problem, not a model problem
Most organizations stall on AI because their data, identity and process layers cannot support it — not because the models are insufficient.
The bottleneck moved
Frontier model capability has outpaced the readiness of the systems around it. The constraint on enterprise AI is rarely reasoning quality; it is whether an organization can supply verified context, enforce access boundaries and route outputs into a process that someone owns.
When a pilot fails, the post-mortem usually surfaces the same three findings: the data was not modelled, the permissions were not expressible, and no one had defined what a correct output looked like.
What readiness actually requires
A readable data layer with agreed definitions. An identity layer that can answer who is allowed to see what. A process layer where AI output has a defined destination and reviewer. Without these, an AI deployment is a demo with a production URL.
The organizations moving fastest are not the ones with the largest model budgets. They are the ones that treated data architecture and access governance as prerequisites rather than follow-up work.
A pragmatic sequence
Start with one workflow where the cost of manual handling is measurable and the correctness criteria are explicit. Instrument it. Ground the model in a narrow, verified corpus. Keep the human approval gate until evaluation data justifies removing it.
This sequence is unglamorous and it consistently outperforms a broad rollout with no measurement layer beneath it.