Artificial intelligence (AI) is currently a hot topic in almost every professional magazine, at conferences and in social networks. Despite the hype, however, many technical documentation teams are still uncertain: What productive uses are there for AI technical documentation? Do I need to completely change the way I work?
Technical writers often face the challenge of incorporating AI into their processes without compromising safety and quality. AI does not need to transform your entire workflow overnight. Small, low-risk tests provide a practical and safe starting point. With clearly defined use cases, such as text creation, translation, terminology harmonisation or creation of tables of contents, you can gain initial experience. General oversight, data protection, and compliance always remain in the hands of the technical writing team. With the right mindset and targeted applications, AI can be introduced step by step as a supportive tool.
Many technical writers know the situation: There is great interest in AI, but no particular place to start. The sheer number of tools, technical terms, and concerns about incorrect results prevent many from getting started. At the same time, there is a certain amount of pressure: those who do not begin to use AI in their daily work now may risk being left behind.
If these thoughts sound familiar, you are not alone. Especially in technical documentation with its particular requirements for clarity, up-to-date content and compliance with guidelines, AI may at first appear to be a black box. But an easy entry is possible — without changing everything about the way you work.
Before you search for the “right” AI tool, it is important to define recurring use cases where AI can support the documentation process. AI is an assistant, not a replacement. It takes on tasks, helps with routine activities and can relieve you of repetitive work, but it always requires you to review and approve what it created.
Start with small, manageable goals and test AI applications with neutral or fictional sample data. This allows you to gain confidence in using AI without touching production data or sensitive information.
The following practical examples for applying AI show how you can gradually test it out in your daily technical writing work and gain valuable insights for your own workflows:
1. Text Creation and Editing
Ask an AI to write a safety warning following the SAFE principle for a fictitious product. Analyse which aspects of the answer are convincing and where it lacks expertise. The more precise your prompt (your task description), the closer the results will fit your requirements.
2. Creating Outlines and Tables of Contents
Ask AI to create an outline for an operating manual. Check: Is the structure logical? Could you already use parts of the results? Where does the structure need to be refined?
3. Question Catalogues for Research Interviews
Ask AI to generate a set of questions for an interview, for example with someone from engineering. This will give you ideas for your own research, but it will also quickly reveal the limits of AI, such as its tendency to hallucinate (i.e., invent pieces of information).
4. Translations and Terminology
Use AI-based translations and review all results carefully. This allows you to assess terminological consistency and linguistic quality and systematically improve them.
Important: All results generated by AI must be checked and approved. No one should reuse AI-generated content unchecked – this is especially true for safety-critical texts.
Many technical writers are uncertain: What happens to the data I put into an AI? As a basic rule, do not enter confidential or personal data in the first step. Only use fictitious examples for initial trials and check whether the AI tool is compliant with data protection regulations (e.g., by using GDPR-compliant settings and European hosting providers).
Responsibility for all published content remains with you. AI can support and accelerate processes – but quality assurance remains firmly in human hands.
To ensure a secure and structured start, it is advisable to set up and document a clear, traceable process.
Getting started with AI is more sustainable when the technical writing team learns together. Talk about the topic openly and involve your colleagues early on. You could, for example:
The best way to start working with AI is through clearly defined, manageable tasks. It makes little sense to change all established processes at once. Instead, it is advisable to start with small pilot projects and low-risk tests to understand the functionality and added value of AI tools in your own workflow.
It is essential that all AI-generated content is always carefully checked and adapted to editorial standards. Technical documentation remains a task where expertise, control, and quality assurance are central. Those who proceed systematically and keep an eye on ongoing developments can successfully establish AI as a support tool in technical documentation.
In short:
AI provides valuable input – but final editorial responsibility always remains with humans.