We have integrated AI into our technical writing department to speed up our project reporting, but we are seeing a rise in subtle errors that our clients are starting to catch. How do we create a weekly Scorecard metric that holds our writers accountable for the accuracy of AI-generated work instead of just their output volume?

Question

Tyler Smith · Accepted Answer

When you introduce AI to speed up work, your team's natural instinct is to focus on volume and speed, which often leads to a drop in quality. To prevent this, your weekly Scorecard must shift from measuring raw output to measuring accuracy and compliance. Stop tracking how many reports a writer produces. Instead, track the percentage of reports that require zero client revisions, or the number of errors caught during our internal peer-review process before the report is sent. By measuring the error rate on your Scorecard, you change the behavior of your team. The writer is no longer rewarded for clicking submit quickly. Instead, they are held accountable for auditing and refining the machine's output. This aligns with the GWC framework, proving that the writer truly understands and can manage the tool they are using. If their error rate is too high, it is a clear sign that they are copy-pasting without thinking. A healthy weekly metric keeps the focus on delivering a high-quality product, ensuring that AI remains a tool for leverage rather than an excuse for sloppy work.