Axel Springer and Humboldt University launch a 17-week AI certificate for media staff

You have probably sat through a vendor demo that promised to automate your workflow with a single prompt. The tool works until the edge case arrives, and you realise you cannot explain why it failed. Axel Springer wants its people to do more than click through interfaces. The publisher's Axel Springer Academy of Journalism & Technology has partnered with Humboldt University of Berlin, through the Humboldt-Innovation Academy, on a 17-week certificate programme called AI Manager: AI Skills for Media & Publishing Professionals. It is aimed at employees in Berlin and that teaches the technology beneath the tools and asks participants to design their own AI use cases from their day-to-day work.

What the programme covers

The curriculum combines computer science, machine learning and IT law from Humboldt University faculty with project work anchored in participants' roles at Axel Springer. The syllabus spans machine learning fundamentals, generative AI, agentic AI and the legal and ethical questions that follow deployment. Participants visit Microsoft, the Google AI Center and the German Research Center for Artificial Intelligence to see how research institutions and technology companies approach the same problems.

Ekaterina Schneider, Chief Learning Officer at the Axel Springer Academy of Journalism & Technology, says that "AI skills will increasingly determine whether we shape the future of the media industry ourselves or simply react to change. At Axel Springer, we want to lead in digital and AI-supported journalism. To do that, we need colleagues who can do more than use individual tools. They need to rethink processes, make informed decisions, and develop their own solutions."

The programme awards five ECTS credits and a joint certificate from Humboldt University of Berlin and the Axel Springer Academy. Participants develop their own AI use cases from their day-to-day work and apply what they learn directly to real projects.

Why academic depth matters for media

Niels Pinkwart, Professor of Computer Science Education at Humboldt University of Berlin, argues that responsible use requires understanding how the systems work, where they excel and where they break. "That is exactly the kind of understanding we want to provide, from the fundamentals of machine learning to generative and agentic AI, as well as legal and ethical considerations," he says. "What is particularly valuable to us is that participants can immediately put what they learn into practice through their own use cases. This turns abstract knowledge into practical expertise, while giving us as a university valuable insights into what effective AI education looks like in practice."

The partnership reflects a broader shift in corporate learning. Vendors sell convenience; universities teach first principles. When the two combine, employees gain the conceptual scaffolding to evaluate new tools, spot failure modes and design solutions that fit their organisation's constraints. Media companies face a particular challenge: the technology that can accelerate research and production also introduces risks around accuracy, bias and intellectual property. A journalist who understands how a large language model generates text can assess when to trust its output and when to verify every claim.

What this means for L&D

The Axel Springer Academy now combines emerging-talent training with upskilling and AI enablement for current employees. The Academy also offers an innovation tour focused on AI, media and digital transformation to external companies. The AI Manager certificate is the newest piece of that portfolio, alongside hands-on AI training for Axel Springer staff.

Humboldt-Innovation Academy delivers certificate courses, workshops, campus tours and customised programmes that connect businesses, public-sector organisations and society to academic expertise. The model creates a feedback loop: practitioners bring real problems to the university, and researchers gain insight into how their work translates outside the lab.

For learning and development leaders, the programme offers a template. Pair academic rigour with applied project work. Insist that participants build something they will use next week. Measure success not by completion rates but by the quality of the use cases that emerge. When employees understand the technology beneath the interface, they can do more than follow a workflow. They can redesign it.

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Sources:

• Axel Springer, 2026