Since last October, my team has been exploring how AI can support technical writing and automate repetitive tasks. It’s been a powerful tool when paired with careful validation.

One of our biggest wins last year was building a custom assistant chain that turns feature-focused PDFs into user–focused topics:

• 𝗨𝘀𝗲𝗿 𝗦𝘁𝗼𝗿𝘆 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 𝗚𝗣𝗧: Reverse-engineered product documentation into user stories we didn’t have.
• 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: Drafted overview topics in a specified format.
• 𝗦𝗵𝗼𝗿𝘁 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗖𝗿𝗲𝗮𝘁𝗼𝗿: Created concise overviews.
• 𝗦𝘁𝘆𝗹𝗲 𝗚𝘂𝗶𝗱𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: Checked tone and consistency.

The result: Complex documentation transformed into clear, reusable, user–centered content.

We’ve also used AI to:

• Run a transcript through ChatGPT, validate with subject matter experts (SMEs), and refine the content into an article for our client community and a guide for documentation updates.
• Build custom scripts to automate manual work, such as mapping topics.
• Create an agent that searches our Confluence site to get answers faster.
• Summarize text, draft glossaries, create SMART goals, identify errors in data tables, condense slide content, suggest ways to display complex information and create templates, draft release notes, help write Jira queries, and experiment with agents that connect to other systems to automate even more work.

We’re not stopping there. We also maintain a list of future ideas we’re excited to try, from accessibility reviews and reusable content discovery to persona-based rewrites, smarter metadata, and deeper automation across our systems.

AI doesn’t replace the work of a technical writer. It extends it. The real value comes from how we guide it and how rigorously we validate outcomes at every step.

How is your team experimenting with AI in documentation?

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