AI readiness is often framed as a technology upgrade. In reality, it is an operations redesign. The gap between “we have a model” and “we have a reliable outcome” is created by workflows, decision rights, and governance.
Most organizations already have the systems of record: CRMs, ERPs, ticketing tools. The missing piece is the system of work — how requests are triaged, how exceptions get approved, and how humans know when to intervene. AI fails when it is layered on top of invisible work.
Operational readiness starts by mapping where work actually happens. Which teams touch the same request? Where are the handoffs? Where does the decision get deferred? These are the sources of latency. If you can compress them, AI becomes a catalyst instead of a risk.
When leadership starts with operations, the AI conversation changes. You stop debating models and begin targeting workflows. You can articulate the decision ladder, set escalation thresholds, and define an audit trail. The technology then fits into an operating model that already makes sense.
The result is not “automation.” It is controlled compression: fewer steps, clearer ownership, and a governance structure that holds under volume.