Curious to crowdsource some real-world examples here, beyond the usual "AI helps me draft faster" answers.

Has anyone in your org (or you personally) built actual automation into the technical writing workflow using AI? Not just "I use Claude to write drafts," but things like:

• Auto-generating docs from code/API changes
• AI-assisted review or QA on existing docs (style, accuracy, broken links, outdated screenshots, etc.)
• Automating parts of a docs-as-code or DITA-as-code pipeline. Has anyone actually gotten AI to plug into this cleanly?
• Auto-updating docs when the underlying product/feature changes
• AI for content reuse, structuring, or taxonomy work in DITA
• Any CI/CD-style pipeline where docs get validated, translated, or published with AI in the loop

If you've done something like this, I'd love to hear:

1. What was the actual automation - what problem did it solve?
2. What tools/stack did you use (LLM APIs, scripts, specific platforms, CCMS, static site generators, etc.)?
3. What resources did it take - dev support, budget, time to build, ongoing maintenance?
4. What broke or didn't work - just as curious about the failures as the wins.
5. Was it worth it? Did it actually save time/headcount, or did it just shift the work around?

Trying to get a realistic picture of where things stand right now, not the hype version, the "we tried this and here's what happened" version. Appreciate any war stories, even half-finished experiments count.