AI & Automation
How do we know if we are ready for AI?
Readiness is less about technology than about decisions: whether your data supports the use case, whether the process is defined enough to automate, who reviews output, and what happens when the system is wrong. Teams that cannot answer the last two are not ready, regardless of their tooling.
The practical test is to pick one process and ask: how much volume does it carry, how variable are the inputs, and what does an error cost? High volume, bounded variation and cheap errors is where to start. Low volume with expensive errors is where pilots go to die.
A readiness assessment costs materially less than a failed pilot, and its most valuable output is often the list of things we recommend you do not automate.
Commercial · Updated 2026-08-11
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