What an AI-ready team needs before automation

Most "AI readiness" conversations jump straight to models and tooling. The teams that actually ship useful automation usually spend more time on two less exciting things first: workflow clarity and data ownership.

Workflow clarity means being able to describe, step by step, how a decision or task currently gets made by a person — including the judgment calls that don't show up in the documented process. Automation projects that skip this step tend to automate the wrong 80% of the workflow.

Data ownership means someone is accountable for the accuracy and freshness of the inputs the automation depends on. Without a clear owner, data quality degrades quietly, and the automation's output degrades with it — often before anyone notices.

On talent: the strongest AI-adoption teams pair people who understand the domain (what "correct" actually looks like) with engineers who understand the implementation. Neither is sufficient alone — domain experts without technical partners produce automation that doesn't ship, and engineers without domain partners produce automation nobody trusts.

Getting these fundamentals in place before investing heavily in tooling tends to produce automation that survives contact with real usage, rather than a promising demo that quietly stalls.