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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. AI readiness planning is useful when framing those questions.

Keep the first test narrow enough to compare model behavior with the current process. AI discovery services 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.