In global B2B companies, marketing teams face an enormous challenge: How can content be brought to diverse markets efficiently and consistently – while at the same time meeting increasing demands for personalisation, speed and compliance? The answer lies in a centralised, AI-supported content flow that combines efficiency, control and relevance. But what does it take for content automation to really work? And how can complex organisational structures be taken into account?
Fragmented processes, high costs
In international companies, content production is often organised in a decentralised manner. While brand management and central campaign strategy are based at headquarters, implementation takes place in the national subsidiaries – with their own tools, workflows and interpretations. The result: non-transparent processes, inconsistent brand messages and high manual effort.
The problem is particularly acute in regulated industries such as medical technology: different target groups (doctors, administrators, technicians) require highly specific content that is legally compliant yet individually relevant.
A centralised, AI-powered content flow
The key to scalability lies in automating content processes without compromising quality and precision. Successful approaches combine three elements:
They form the basis for consistent content creation across national borders.
Generative AI in content production: personalisation through data integration
Large language models (LLMs) take over the automated creation of texts based on structured prompts. Quality assurance is ideally carried out by a second validation LLM to ensure stylistic, content-related and regulatory requirements are met.
A customer data platform (CDP) can be used to integrate role profiles, interests and user behaviour. This not only ensures that content is produced efficiently, but also that it is highly personalised.
Success factors for scalable content automation
For AI-supported content processes to function in complex B2B structures, more than just technology is required. The following success factors are crucial:
Best practice: ZEISS as a pioneer in content automation
One company that has successfully implemented this approach is ZEISS. As part of an AI initiative, an automated content flow was established there that works via a prompt generator system, LLMs for text creation and validation, and a CDP for personalisation. The result: 78% cost savings per campaign while increasing consistency and target group relevance. ZEISS thus demonstrates how content processes can be scaled efficiently in regulated B2B environments – without compromising quality.
Conclusion: AI scales content – but only with process discipline
AI alone cannot solve content challenges. Only the combination of centralised control, clean databases and the targeted use of generative AI makes content automation scalable in global B2B organisations. Those who strategically leverage efficiency gains can not only save costs, but also sustainably improve the customer experience in an increasingly demanding market environment.
In the whitepaper "Road to Agentic AI" Dr. Jochen Tham, Head of Digital Customer Experience at ZEISS, shares further insights into strategy, challenges and lessons learned regarding the use of AI in global content processes.