Artificial intelligence is everywhere. On LinkedIn, on TikTok, in corporate strategies and in political debates. At the same time, uncertainty is growing: Is AI a productivity booster, a miracle weapon – or a job killer?
In a podcast episode of Scavenger AI, Maximilian Hahnenkamp talks with Irina Gebauer, Managing Director at Syntea+ and part of the IU Group, about exactly these areas of tension: AI hype, fake news, competence gaps – and why we urgently need to learn to work confidently with AI.
From Media Competence to AI Competence
Irina Gebauer's path into the topic of AI is anything but linear. Journalism, acting, product management in software companies – and yet a common thread runs through her stations: an understanding of how technology changes our behavior.
It became clear early on that technological development is progressing faster than societal competence. What was once described as a "Digital Divide" is now manifesting itself in a new form:
Not everyone can understand, use, or critically assess AI – even though it is already part of everyday life.
This is exactly where modern AI competence comes into play: not as purely technical knowledge but as an extension of classical media competence.
Why AI is Often Overrated – and Misunderstood at the Same Time
A central problem in the current debate: AI is almost exclusively equated with generative AI. ChatGPT, Midjourney, or Copilot dominate perceptions – while it is forgotten that AI has existed for decades and has many forms.
This leads to two extremes:
AI as a panacea that companies just need to "implement"
AI as a threat that replaces jobs
Both views are too simplistic. AI is neither all-knowing nor objective. It is a reflection of its training data, the underlying decisions, and the prompts that people give it.
Confusion arises where marketing narratives, hype, and a lack of basic understanding meet.
Fake News, Algorithms, and the New Attention Economy
Things become particularly critical where AI intersects with social media. Content today is prioritized not by truthfulness but by engagement. Emotional, visual, and confirmatory content spreads faster than verified facts.
Irina Gebauer describes two levels that come into play:
The Psychological Level
Emotions such as fear, curiosity, or indignation
Confirmation bias: We believe what supports our opinion
Visual content appears "real" because we see it
The Algorithmic Level
Personalized feeds reinforce existing beliefs
Engagement supersedes fidelity to facts
Information overload ("Flood the Zone") creates confusion instead of clarity
The result: Fake news, deepfakes, and manipulated content are becoming increasingly difficult to recognize – with real consequences for society, politics, and even financial markets.
Four Dimensions of Modern AI Competence
To address this development, more than just tool implementations are needed. Based on international frameworks (including UNESCO, OECD), four central competence dimensions can be defined:
Understanding
Fundamental knowledge of how AI systems work – without being a developer themselves.Applying
Using AI purposefully, responsibly, and contextually.Evaluating
Critically questioning AI-generated content: source, purpose, distortion, reliability.Designing
Actively using AI, sharing knowledge and ethically reflecting on further development.
Critical thinking is not replaced by AI – but rather becomes more important than ever.
Newskilling instead of Upskilling: Learning in an AI-Shaped Work Environment
In the context of companies, Irina Gebauer consciously distinguishes between upskilling and newskilling.
Upskilling deepens existing skills
Newskilling supplements them with new competencies that become necessary due to technological changes
AI does not require a complete relearning of professions – but new ways of thinking, new work routines, and new forms of collaboration between humans and machines.
How Synthia Integrates AI Competence into Everyday Work
With Synthia, Syntea+ pursues an implicit approach: learning not as a course but as part of daily work.
The system:
adapts to the roles, goals, and use cases of the users
builds competence profiles in the background
supports prompts, fact-checks, and assessments
enables knowledge exchange and re-use of results within the team
This creates a combination of competence building, knowledge management, and efficiency increase – without additional entry barriers.
Common Mistakes in Implementing AI in Companies
A recurring misunderstanding:
AI is implemented – and is expected to work "on its own".
In practice, it becomes clear:
lack of utilization due to uncertainty or fear
lack of role models in management
no clear structures for learning and exchange
Successful companies actively accompany AI: with internal formats, experimentation space, clear communication – and the message that AI is not a job risk, but a tool.
European Skepticism – Strength or Blockade?
Skepticism is not a disadvantage. It protects against blind technological faith.
It becomes problematic where it leads to stagnation.
In international comparison, it becomes clear:
USA: eagerness to experiment and tolerance for mistakes
China: pragmatism and rapid scaling
Europe: quality, regulation, data protection
Europe, in particular, could successfully shape AI if regulatory strength is combined with more courage to experiment.
How AI Will Change Learning and Work in the Next Five Years
The outlook is clear:
AI will be integrated into work processes so seamlessly that the question will no longer be whether it is used but how it is utilized.
Further education will be more:
implicit
personalized
taking place directly in the work context
Not as a substitute for humans or education – but as a multiplier of individual abilities.
A Possible Future Headline
"AI competence will become compulsory in schools – and sustainably change our society for the better."
A vision that makes it clear:
It is not technology that determines our future – but our ability to competently engage with it.