Why Tech Writers Should Use The Content-First + AI Readiness Scorecard

Tech writers are being dragged into AI conversations whether they volunteered or not.

One minute we’re trying to fix a broken procedure, standardize terminology, or get one stubborn SME to stop rewriting our approved interface labels. The next minute, someone in leadership is asking whether our content is “AI-ready,” as if that were a setting buried in a dropdown menu somewhere between Export and Panic.

That is why a tool like the Content-First + AI Readiness Scorecard is worth a look. The brainchild of Sarah Johnson, Founder and Strategic Consultant at Content-first Design, the scorecard gives us a way to assess whether our organization is actually ready for AI or not. It helps us spot weak governance, shaky workflows, missing structure, poor feedback loops, and the usual fantasy that better output will somehow emerge from messy inputs.

It also gives us a better way to talk with leadership. Instead of muttering about metadata until the room goes glassy-eyed, we can point to gaps in readiness, consistency, ownership, and review. That shifts the conversation from technical whining to business risk.

For technical writers, that matters. The scorecard frames AI readiness as a content systems issue — which is our turf. It helps us find workflow gaps, make the case for structured content and cross-functional involvement, identify training needs, and ask better questions before bad content starts scaling with machine-speed confidence.

No, it won’t fix governance by itself. It won’t make leadership care. It won’t stop random prompt shenanigans from spreading through the building like office glitter. But it can help us see what’s broken, name it clearly, and start a smarter conversation.

A downloadable (and fillable) PDF of the Content-First + AI Readiness Scorecard can be found at the end of this post.

Section 01: Framework And Process Maturity

This part of the scorecard checks whether our organization has a real, repeatable way to get content in shape before we dump it into design systems, publishing workflows, or AI experiences and pretend that was a strategy. It asks whether we have an actual framework, whether we use it in day-to-day work, whether we catch message problems early, whether we understand our roles, whether our testing improves our standards and training data, and whether leadership actually reviews the results.

In plain language, this section is asking: Do we have a real process, or are people mostly winging it and hoping AI won’t notice?