AI readiness starts with a workflow, not a model shortlist. The candidate process should have accessible inputs, a defined owner and an observable result. If nobody can explain what happens after a wrong output, automation is premature.

The service outline at https://ai-software-development.net offers context for planning an assessment. A discovery phase can map data permissions, integration constraints and the manual path that already works. <a href="https://ai-software-development.net">AI readiness planning</a> is useful when framing those questions.

Keep the first test narrow enough to compare model behavior with the current process. <a href=https://ai-software-development.net>AI discovery services</a> can then support a discussion about scope. A workflow with unstable rules or missing source data may need process repair before any model work begins.