A practical look at how AI is changing the way we write, review, and think about documentation.
From Consumer to Collaborator: How AI Supports Documentation Teams
A practical look at how AI is changing the way we write, review, and think about documentation.
This article is part of our series on how documentation is evolving to serve both human readers and AI systems like search, chatbots, and LLMs.
Series overview:
Writing for Humans and Machines: Why Docs Now Serve Two Audiences
From Consumer to Collaborator: How AI Supports Documentation Teams (you’re here)
10 Ways to Make Your Docs AI-Friendly
In the first two articles of this series, we explored how documentation serves two audiences - humans and machines - and how good structure helps both.
In this article, we show how the relationship evolves. The same AI tools that consume and summarize your documentation are beginning to support the process of creating it.
AI is becoming part of the documentation workflow: an assistant that helps writers plan, edit, and improve content. AI can accelerate the routine work of technical writing, allowing humans to focus on judgment, empathy, and context, the skills that remain uniquely human.
AI can support documentation teams across the entire content lifecycle. Here are a few areas where it already makes a difference.
Writers can use AI to summarize complex topics, extract key information from technical specs, or generate outlines for new documentation sets. This helps teams ramp up quickly on unfamiliar technology or domain areas.
AI is increasingly capable of producing initial drafts for structured or repetitive content, such as release notes or task-based instructions. This gives writers a starting point they can refine rather than a blank page.
Language models are powerful at spotting tone inconsistencies, long sentences, or unclear phrasing. Tools like EkLine can review clarity and structure in real time, helping maintain a consistent, human-friendly voice.
When moving large volumes of legacy documentation into new templates or topic types, AI can help detect and reformat repetitive patterns.
For example, AI tools can rewrite legacy HTML or Word docs into clean Markdown with consistent structure.
AI can analyze user comments, support tickets, and search data to identify what people struggle to find. This helps teams plan new documentation or update existing topics.
Each of these use cases speeds up production and strengthens quality when paired with human review and domain expertise.
Several recent resources highlight how AI is being integrated into documentation workflows:
MadCap software’s guide AI for Technical Writers explains how integrated AI tools help teams draft, edit, and localize content faster while maintaining consistency across products.
Scout shares practical prompt strategies for rewriting technical documentation, improving flow and tone without losing accuracy.
Kapa.ai demonstrates how structured docs feed intelligent support systems, showing how high-quality content can train AI to deliver better answers.
At platformOS, we are seeing similar benefits. Our DocsKit framework and EkLine assistant work together to support clarity and structure while helping writers focus on accuracy and intent. AI complements our modular approach by flagging inconsistencies, detecting missing steps, and suggesting more appropriate phrasing.
As useful as AI has become, it is not a substitute for human expertise.
AI models can speed up tasks, but they lack understanding of audience needs, brand tone, and emotional nuance.
Writers must remain the decision-makers, curating, verifying, and refining what AI suggests.
Good documentation is still an act of empathy. AI can recommend better phrasing, but it cannot judge whether that phrasing builds trust with the reader. It can summarize release notes, but it cannot decide which change matters most to users. Editorial review remains essential for ensuring factual accuracy, accessibility, and quality.
A balanced workflow combines automation with thoughtful human review. As we often say in our team, AI can catch what we miss, but only humans decide what belongs.
At platformOS, we approach AI as a tool to help augment our team. AI is a collaborator within a structured, human-led workflow. AI is NOT a replacement for HI (human intelligence).
DocsKit provides the foundation through predefined content types inspired by DITA principles: task, concept, reference, and more. This gives AI tools predictable structure to analyze and improve.
EkLine assists during authoring, reviewing tone, clarity, and structure to help maintain consistency.
For large migrations, we sometimes use prompt-based rewriting to standardize formatting across legacy content.
On the documentation site for the Washington D.C. Department of Buildings, for example, AI-assisted review helped us detect structural gaps, refine tone, and ensure consistency across multiple contributors.
The result is documentation that is easier to navigate, faster to maintain, and more accessible to both humans and AI-powered search.
If you are considering adding AI tools to your documentation process, these principles can help guide you:
Start with strong structure. Clear templates and topic types give AI a better framework to work with.
Use AI for acceleration, not authorship. Let it handle repetitive or time-consuming tasks.
Always review and verify. Treat AI output as a draft, not a final product.
Keep a human voice. Prioritize empathy and clarity over algorithmic efficiency.
Close the loop. Gather team feedback to refine prompts and improve future results.
When used with care, AI becomes part of a healthy documentation ecosystem, an assistant that extends, augments, enhances, rather than replaces, human skill.
In the next article, we’ll bring the entire Writing Docs for AI series together with an actionable checklist: 10 Ways to Make Your Docs AI-Friendly.
It will summarize the practices we’ve discussed so far and offer a clear roadmap for teams ready to make their documentation truly AI-ready.
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