From documents to smart data. How digitalization and AI are transforming the role of technical communicators
Not long ago, the role of technical communicators was clearly defined: they created structured texts, explained complex topics, and delivered final documents at the end of development.
Today, the picture looks very different. Digitalization, connected products, and artificial intelligence have rewritten the rules. Content is no longer limited to static documents but lives in dynamic information spaces that adapt to context, audience, and system. AI chatbots generate texts in multiple languages and for diverse audiences, while also taking over routine tasks. With interfaces and cloud-based systems on the rise, many activities that once required manual effort can now be fully automated.
Drivers of change
The transformation of technical communication is driven by multiple factors:
- Digitalization turns information from static documents into dynamic system content.
- Smart, highly variant products demand flexible, individualized information solutions.
- User expectations call for always-available, context-sensitive support – whether in instruction manuals, apps, or portals.
- Legal requirements necessitate precise, traceable, and version-secure documentation.
- Artificial intelligence takes over routine tasks and unlocks new possibilities for information processing.
In this environment, technical communicators are evolving into information managers and information architects. Their responsibilities now include systematically structuring content, linking and managing content systems, planning publications strategically, ensuring legal compliance, and maintaining consistent corporate communication.
as the key
Smart content – or intelligent information – is the backbone of today’s information landscape. It makes content findable within the organization and usable across systems. Instead of being locked in linear documents, it is built on modular information architectures.
Modular content enriched with metadata can be tailored to specific products and delivered according to context across channels such as web portals, chatbots, or traditional documents. The same content building blocks can be flexibly combined to meet diverse requirements — regulatory, product-specific, linguistic, or audience-oriented. This way, identical modules can power both a classic operating manual and integrated help within a software application.
Metadata modeling builds the bridge between humans and machines. It allows information modules to be located, connected, and delivered in context – both during content management and in the final delivery.
The connection between the information world of technical communication, with metadata for audiences, information types, and documents, and the product world, with metadata for functions, components, and product structures, is becoming increasingly crucial. With growing product variability, product metadata can no longer be maintained separately in authoring systems. Instead, it must be supplied from upstream systems such as PIM, ERP, or PLM.
Enterprise content instead of technical documentation
Just as enterprise content is moving out of silos and becoming interconnected, technical documentation is also becoming less isolated. It is now a core part of enterprise communication, and of the broader content a company publishes, both internally and externally.
In times of skilled labor shortages, information must go beyond static documents that require users to search actively. Companies need to provide self-service learning content across multiple channels. This not only boosts online visibility but also reduces the need to directly interact with employees (e.g. support, service, and sales). Interaction with the company continues though, only through digital channels.
For technical communicators, this transformation is creating new areas of expertise:
- Data and content modeling. Systematically structuring content and data
- Product knowledge. Placing content in the right context and making product structures usable in documentation
- Process knowledge. Understanding and shaping enterprise-wide content processes
- System integration and interfaces. Enabling information use across system boundaries
- Automation and AI. Automating manual processes through rule-based or AI-supported methods
- Knowledge management. Delivering product knowledge across channels, formats, and audiences
Technical communication as a hub
Today, technical communication sits at the interface of development, IT, marketing, training, and users. The skills in highest demand include:
- Communicating as peers with development, product management, and product data management
- Understanding technical data structures and applying content modeling
- Thinking systemically and working across functions
Designing modular information architectures no longer is a “nice to have,” but a true competitive advantage.
AI in everyday technical communication
Artificial intelligence supports but does not replace technical communication. It takes over routine tasks such as suggesting text, ensuring consistency, and applying formatting automatically.
Humans remain responsible for content, quality, and data protection. A key competence in this context is prompt engineering: to use AI effectively, one must be able to guide it precisely.
Learn more: Whitepaper "Artificial intelligence in technical communication" by Ulrike Parson – Applications, opportunities and risks, and the future of our profession.
New Roles in technical communication
The transformation of technical communication is creating new roles that manage information flow, organize data, and leverage AI efficiently:
Information architect
Plans and structures content in a modular and context-sensitive way. The goal is a scalable architecture that enables reusability and ensures a consistent user experience. A central foundation is semantic modeling: content is systematically linked with metadata and knowledge structures—essential for clear information flows and intelligent usage scenarios.
Read also: Information architect for technical communication – an increasingly important job profile
Content engineer and knowledge engineer
Content engineers and knowledge engineers implement the designed architecture. They are responsible for modularizing content, enriching it with metadata, and automating cross-channel delivery.
The content engineer focuses on the structured preparation and reuse of content. The knowledge engineer adds expertise in areas such as data integration and interlinking.
The interaction of these two roles is necessary for advanced applications such as filtering, personalization, and digital twins. Together, they implement user-centered solutions based on .
Read also:
Wearing different hats and speaking the same language. Interview with Lukas Jetzig, knowledge engineer and technical communicator at parson
Knowledge manager
Focuses on shaping processes around knowledge within the company: How is knowledge captured, distributed, and maintained, both internally and externally? This places the organizational dimension in the spotlight. With the growing demand for self-learning resources, knowledge management is experiencing a revival. Topics such as AI, ontologies, and digital twins establish knowledge management as a bridge between technical documentation, product knowledge, and corporate knowledge.
Conclusion and outlook
The core task of technical communication – making complex knowledge understandable and usable – remains unchanged. What is changing are the methods, tools, and roles. New focal points include:
- Information architecture
- Metadata management
- System architecture and process knowledge
- AI integration
- Development of robust content strategies
Those who invest in these competencies today are actively shaping the future of technical communication. This ensures that the discipline remains strategically relevant, indispensable, and future-proof, even in an automated world.