What Can Tech Writers Do in the Age Of AI?

This article is a companion piece to our article, “Will AI Replace Technical Writers?“. That article laid out the foundation for how the job market and role of technical writers are shifting in an AI-first world. Now, we zoom in to analyze the new division of technical writer workflows.

We know that AI won’t replace technical writers, but what does that relationship actually look like? Despite the fears of being replaced, technical writers that have adopted AI into their daily work declare high levels of satisfaction. 67% say AI has made their documentation better. 78% report AI helps them complete documentation workflows faster.

Technical writers willing to embrace AI as an ally will outperform those who avoid this technology. That being said, AI isn’t a catch-all solution that can replace any documentation work. Knowing what tasks to automate is a challenge. This article breaks down real-world examples of which content workflows are best suited to be managed by AI and which must stay under human control.

What Can AI Do in Technical Writing Today?

According to Fluid Topics’ Head of Product Knowledge, Rémi Bove, “Technical writers can significantly benefit from AI technology to improve their workflows, boost efficiency, and generate top-notch content.” Technical writers report that top tasks where they use AI are drafting (62%), brainstorming (58%), proofreading (58%), and style guide matching (50%). The specific AI use cases for documentation depend on what kind of AI they use. The top use cases for Generative AI include:

• Brainstorming documentation ideas: Suggest new topics, headings, and use cases to include in content.
• Summarizing research and SME interviews: Condense notes, meeting transcripts, and SME interviews into concise summaries.
• Creating technical documentation outlines: Create outlines of lengthy documentation without starting from scratch.
• Creating multimedia content for technical documentation: Generate blocks of code, diagrams, images, and videos to accompany documentation.
• Translating technical documentation: Automatically translate content into multiple languages while maintaining technical accuracy and consistent terminology for inclusive, accessible content.
• Optimizing and marking up technical content: Use tools like Markup AI, Congree UCC, and oXygen Positron AI Assistant to maintain content adherence to internal styles guides and compliance requirements.

Alternatively, the top use cases for agentic AI include:

• Automating tech doc workflows: Monitor code changes in development environments, extract relevant updates, draft new documentation versions, send them to SMEs for review, and track approval through any project management tool.
• Creating continuous user feedback loops: Continuously monitor support tickets, community discussions, and documentation analytics to identify knowledge gaps, create content improvement tickets, draft updates in the CCMS, and submit the changes for review.
• Automating release note creation: Monitor ticketing solutions like Jira for new information. Then, initiate a release note generation process, fetch changelog data from GitHub, add product images and diagrams, generate the release note, and publish it to multiple channels.

Integrating AI into content workflows will increase employee productivity, accelerate time-to-publication, and improve user-writer feedback loops.

There are now many different tools available to seamlessly integrate AI workflows into content operations. To stay competitive and keep pace with new innovations, technical writers should be aware of the main categories of AI tools available to execute the above use cases with ease.

• AI answer engines
• AI content creation and editing tools
• AI writing optimization and governance tools
• AI code assistants
• AI-powered tech writing and content publishing tools

What Unique Value Do Human Technical Writers Bring?

AI can support technical writers in many ways, but it still falls short where human expertise matters most. Technical writing requires more than generating accurate, well-structured content: it demands context, judgment, empathy, and a deep understanding of users and their needs. These distinctly human strengths make technical writers essential, even as AI becomes more capable.

Rémi elaborates on this, explaining that “writing often necessitates collaboration with various stakeholders…[which] involves conducting effective interviews, holding stakeholder meetings, and ensuring all feedback is appropriately integrated into the final content.” He then continues, explaining why this poses a problem for AI. “AI can automate repetitive tasks, enhance content quality, and streamline various aspects of the technical writing process. However, it falls short in replicating the specialized skills and contextual understanding of experienced technical writers.“

Even for basic AI-generated text, tech writers need to draft prompts to get the desired outputs. AI regurgitates and reworks existing content, but humans are innovative and creative. Yes, it is becoming more autonomous with agentic reasoning, but even agentic workflows require prompts, limits, and human oversight. By leaning into the strengths of humanity, technical writers will continue to have the upper hand against AI applications.

What Shouldn’t AI Do?

In the world of product knowledge, AI solutions require technical writers to provide AI-ready, authoritative source material. Once the documentation is available, AI tools use it to generate accurate, relevant outputs or manage workflows. Yet, the value of human expertise isn’t limited to the initial text. Human oversight and reasoning remain essential throughout content operations. Here are some actions that AI should not manage or attempt without keeping a human in the loop:

• Approve regulatory and legal workflows
• Create content for complex products and processes
• Determine implicit user needs and pain points
• Manage situations where empathy is required
• Make judgement calls or ethics decisions
• Run independent quality assurance and accuracy checks
• Approve final releases

AI is not a product expert, and it can’t replace one. It only knows the information easily found in available documentation. While AI tools can produce plausible content, humans must verify the accuracy of these outputs. Finally, AI may easily confuse product versions, user roles, and environments unless constraints are explicitly defined in the prompt.

Conclusion

AI assistance and automation are valuable tools for technical writers to move faster and focus on human-essential tasks. AI will take on specific tasks to increase productivity and improve documentation quality, but it won’t replace writers.

Looking ahead, Rémi left us with a piece of wisdom. “Staying updated with AI advancements and industry trends is crucial. [Technical writers] can gain hands-on experience with the latest AI tools by participating in personal or open-source projects. These projects should show their ability to integrate AI into the documentation process. They should highlight [the writer’s] capability to improve content with advanced technologies.“