When an AI tool becomes an integral part of authoring workflows – checking terminology, simplifying warnings or suggesting effective structures, for example – it quickly becomes clear: this support is much more than just a technical add-on. AI automates routine tasks, increases efficiency and frees up time for core technical writing tasks. Tasks that were once tedious and error-prone can now be handled more efficiently, opening up new possibilities for technical documentation. The developments of recent years show: AI has arrived in practice. While around a third of technical authors had no experience with AI a year ago, today significantly more teams are using AI regularly – the proportion without any exposure has dropped to 10 – 20%. We gathered these figures during our webinar "KI in der Technischen Redaktion – Praxischeck und Zukunftsausblick" (in German).
Associations with AI are now much more positive and concrete: reducing workload, heightened efficiency, saving time, assistance with phrasing and quality assurance are at the forefront. Data protection and costs remain topics of concern but are less dominant than before.
However, successful AI integration is not automatic: it requires a structured strategy, technical and organisational adjustments and involvement of all stakeholders. The requirements of technical authoring – accuracy, clarity, completeness and being up-to-date – remain unchanged. AI can support in fulfilling these requirements but cannot fully automate them. So what does reasonable level of integration look like – one that is neither overwhelming nor superficial?
Implementation strategy: Start small, set realistic goals
The introduction of AI in technical authoring is most successful with a clear, step-by-step approach. Studies and practical experience show that many AI projects fail because they are too ambitious or lack structure. The key is to start with a few, clearly defined use cases and test them in a targeted manner. Our own experience confirms this: those who start small can quickly assess where AI truly adds value and where adjustments are needed. Below you find a list of potential starting points as examples.
Recommended starting points:
- Automated text simplification (e.g. for warnings)
- Structure suggestions for documents
- Terminology checks
- Duplicate and metadata analysis
- Summarising content
- Checking against editorial guidelines
These tasks can be assessed quickly and provide immediate value. A test phase of four to six weeks is often sufficient for determining where AI genuinely helps and where further enhancements are needed. It is crucial to set realistic goals: What should AI improve in the first month? Which tasks are genuinely made easier? What is deliberately left out?
It is also advisable to introduce clear guidelines, even for seemingly small projects. A structured approach prevents the technical writing team from getting lost in a multitude of tools or wasting time on experiments that bring no real benefit.
Key guidelines:
- Clear rules for data usage and data protection (especially with public AI applications)
- Assign responsibilities for reviewing AI output
- Document experiences and evaluate use cases
The trend is clear: AI usage is becoming more tangible, use cases are more specific and concerns are diminishing – a positive sign for acceptance and practical success.
Interfaces to content management systems: How to achieve seamless integration
Once initial experience with AI has been gained, the question soon arises: how can these new tools be sensibly and sustainably embedded into existing systems? AI must be integrated into established workflows so that content is not created outside the system and then laboriously imported. Some tools relevant to technical authoring already offer AI integrations.
Typical applications of AI in content management systems:
- Automated assignment and checking of metadata
- Suggestions for structuring and modularising content
- Terminology checks and standardisation of terms
- Duplicate detection and quality control
- Content classification (important for migration)
- Machine translation as a standard, meaningfully integrated into translation workflows
Many content management systems now offer plug-ins, API connections or user-friendly ways of adopting AI results in a controlled manner.
Content delivery portals also benefit: AI-powered chatbots summarise topics and search results, answer support queries, improve search, offer on-the-fly translations and enable ticket deflection.
A central concept is Retrieval Augmented Generation (RAG): the AI uses additional information – such as existing user manuals in a knowledge base – for more precise and traceable answers. This improves context and source referencing in responses, thereby heightening traceability and accuracy.
It remains important: the content management system sets the framework. AI provides suggestions, ideas and simplifications – but it does not replace module logic, version control or approvals. The final decision on whether to adopt AI suggestions remains with the technical writing team.
The team at the centre: How to gain acceptance
Despite all the technology, people remain the most important success factor. The introduction of AI is not just a technical but above all a social and organisational challenge. Acceptance within the team is crucial to success. Developments over the past year show: the mood is much more positive, use cases are more tangible and concerns are decreasing. Nevertheless, data protection, quality and job security remain important issues.
Success factors for team integration:
- Open communication about goals, expectations and the limitations of AI
- Taking concerns seriously, especially regarding data protection and job security
- Clearly defining roles and responsibilities (e.g. who reviews AI outputs? Who develops prompts?)
- Providing training and workshops with practical examples for further development
- Developing a prompt guide for standardising AI use and achieving consistent results
The role of the technical writer is evolving: new roles and tasks around AI and information modelling are emerging. This development is not a threat but an opportunity for greater quality, consistency and new possibilities in technical authoring.
How to successfully integrate AI into your technical authoring – Step by step:
Involve the team:
Regularly share knowledge within the team to understand different perspectives and use cases. Training on AI strategy is also useful at this stage.
Identify use cases:
Analyse your authoring processes and determine specific tasks where AI can provide meaningful support (e.g. automatic text generation, translations, quality checks).
Define objectives:
Set realistic, measurable goals for AI use – such as time savings, quality improvement or better user experience. Also define rules for data protection and data usage here, and determine who reviews the AI results.
Select suitable AI tools:
Evaluate various AI solutions and select those that can be optimally integrated into your existing systems and workflows.
Start with a pilot phase:
Initially introduce AI in a clearly defined area and test the results in practice.
Measure and optimise results:
Monitor the defined objectives and continuously adapt processes and AI tools to fully realise the potential.
Conclusion: Step by step towards an AI-supported authoring workflow
The successful introduction of AI in technical authoring is based on a gradual, practice-oriented approach. Start with clearly defined use cases and realistic goals, sensibly integrate AI tools into existing systems and actively involve your team in the process. AI is a valuable tool that supports authoring, streamlines processes and improves quality – but it does not replace the expertise of your technical writing team.
