Das habe ich jetzt geändert und das Modell direkter zugänglich gemacht, natürlich einschließlich einer Erläuterung. Hier findet ihr es nun (hoffentlich) ganz einfach.

Das KI-Kompetenz-Audit entstand im Rahmen des Projekts KI-Campus Hub Baden-Württemberg, ein Projekt unter Leitung des Stifterverbandes mit Förderung der Dieter Schwarz Stiftung. Entwickelt wurde das Audit vom KI-Team der Bildungsforschung an der DHBW Heilbronn.

Probiert es doch auch einmal aus! Neben der grafischen Auswertung gibt es noch eine ausführliche schriftliche Bewertung mit Hinweisen auf passende Weiterbildungsmöglichkeiten aus dem Angebot des KI Campus. Der KI Campus Baden-Württemberg ist eine komplett kostenfreie Lernplattform mit einem vielfältigen und hochwertigen Angebot an Webinaren.

Übrigens: Das KI-Kompetenz-Audit gibt es auch für Unternehmen/Organisationen: Wie hoch ist die KI-Kompetenz meines Unternehmens?

1. The disappearing ‚first steps‘
   For decades, organisations relied on a relatively stable pattern: Entry-level employees handled rather routine tasks like research, documentation, data preparation etc. Doing this, over time, they developed judgment, contextual understanding, and professional intuition.
   Thus, this was never just “simple work.” It was learning infrastructure.
   Today, this infrastructure is eroding. Recent studies (e.g. McKinsey, Revelio, Stanford) show that precisely these tasks are increasingly being taken over by AI systems and that the demand for entry-level roles is declining sharply. While demand for experienced workers is holding steady or even increasing.
2. A fundamental shift
   The challenge is not losing jobs. The challenge is not getting in. Entry-level roles are becoming fewer, more demanding, and less “forgiving”.
   In other words: The threshold for becoming productive is rising.
3. From learning-by-doing to learning-by-design?
   Traditionally, organisations relied on a simple mechanism: People learn through doing – even if the work is imperfect. AI disrupts this mechanism in two ways:
   Task removal: The “practice field” disappears.
   Task delegation: Instead of doing the work, juniors supervise AI output.
   And here lies a paradox. Experimental research shows that heavy reliance on AI can actually impair skill development, particularly for novices: weaker conceptual understanding, reduced ability to diagnose errors, and less deep learning.
   So we are asking early-career professionals to start at a higher level with fewer opportunities to learn while relying on tools that may undermine their learning.
4. The missing piece: Seniors because they work at home
   If entry-level work becomes more complex, the obvious answer is: more support from experienced colleagues. But this is exactly where the second major trend comes into play: remote and hybrid work.
   Research (e.g. Emma Harrington, University of Virginia and again Emma) shows:
   Remote work makes it harder to train and mentor junior employees. Physical proximity significantly increases feedback and learning. Career progression slows when mentoring is reduced.
   There is even evidence suggesting that a large share of rising youth unemployment can be linked to the spread of remote work.
   From a knowledge perspective, this is not surprising. Learning in organisations is not just about formal training, explicit knowledge, and documented processes. It is about informal feedback, observation, asking “quick questions”, being pulled into conversations. In short: tacit knowledge transfer. And tacit knowledge does not travel well over Teams.
5. Double disruption
   If we combine both developments, a problematic dynamic emerges:
   AI removes beginner tasks, raises entry requirements, speeds up workflows. Remote work removes informal learning, reduces access to mentors, slows down capability building.
6. A Knowledge Management blind spot
   From a KM perspective, this should ring alarm bells. Many organisations are currently focusing on AI tools, automation, efficiency gains, and knowledge repositories.
   But they are overlooking more fundamental questions: How do people develop the capability to work with knowledge in the first place? Where do juniors practice? Where do they make mistakes? Where do they receive feedback? Where do they observe experts at work?If these mechanisms are not redesigned, we risk creating a generation of workers who are highly tool-enabled but insufficiently experienced.
7. Rethinking the entry phase
   Based on the emerging evidence, three areas seem critical:
   Deliberate learning design (structured learning tasks, well designed progression paths, combination of AI use with reflection)
   Reinvestment in mentoring with structured check-ins, conscious feedback loops, and visible expert work.
   Hybrid thinking (not just hybrid working), which means that if proximity matters for learning, then presence must be intentional. Thus, not “come to the office sometimes” but „come together when learning happens“.

So – who should be worried now?

In the last post, the answer was: It depends on the tasks. For early-career professionals, the answer becomes sharper: You should be concerned if your learning depends on routine tasks, passive observation, and implicit guidance. Because all three are under pressure.

Final thought

The first blog post asked: Who is afraid of AI? Who should be?
This follow-up suggests a different question: Who will still be able to learn how to work? Because the real long-term risk is not job loss. It is the quiet erosion of the pathways that create expertise. And that is, at its core, a knowledge management challenge.

If you follow current debates, you might think that everyone should be afraid of AI.
Or no one. Or only “the others.”

A recent policy brief by the Kiel Institute for the World Economy takes a more differentiated – and refreshingly evidence-based – view.

Here some insights from a knowledge management perspective:

1. Exposure is not the same as replacement:
   One of the most important clarifications in the study is also one that is often missing in discussions: “Exposure to AI … indicates the potential applicability of AI to an occupation’s tasks — it is not inherently a measure of substitution or complementarity.”
   In other words:
   Just because AI can do parts of your work does not mean it will replace you.
   For knowledge work, this is a crucial distinction. Even in organisations where AI is already deeply embedded it does not necessarily reduce headcount. Instead, we see shifts in how work is done and what kind of knowledge becomes critical.
2. Surprise(?): High-skilled knowledge workers are most exposed
   The study shows a pattern that is different from previous economic transformations: Highly cognitive, non-physical, low-social-interaction jobs (e.g. analysts, developers, translators) are most exposed to AI. Whereas, jobs requiring manual skills or intensive interpersonal interaction are least exposed.
   It is not the “low-skilled” jobs that are immediately most affected – it is classic knowledge work.
   From a KM perspective, these are precisely the domains where structured knowledge, explicit processes, and codified expertise already exist – making them easier for AI to engage with. Or following Willkes definition of knowledge work it is the knowledge-intense work (not the actual knowledge work).
3. No (immediate) job loss – but skill shifts
   The empirical results across Denmark, Portugal, and Sweden are remarkably consistent:
   – No significant negative effect on total employment
   – Clear shift towards higher-skilled work (“skill upgrading”)
   Firms exposed to AI “raise their high-to-low skill employment ratios” and reallocate toward high-skill white-collar jobs.
   This fits closely with what many of us observe in practice:
   Routine documentation is automated, analytical work is accelerated. But interpretation, integration, and contextualisation become more important.
   Or, in KM terms: We are moving from information processing to meaning-making (from explicit to tacit).
4. The overlooked dimension: social and tacit knowledge
   A particularly interesting methodological choice in the study is the inclusion of social skills as a counterweight to AI exposure:
   Social tasks are assumed to be more difficult to automate. This aligns strongly with long-standing KM insights:
   Tacit knowledge is hard to codify.
   Social interaction is key to knowledge creation (think SECI…).
   Context and trust cannot easily be replicated by systems.
   If you connect this to earlier discussions on this blog (e.g. on knowledge transfer during offboarding or peer-based formats like Peer Assist), a clear conclusion emerges:
   What protects jobs is not the absence of AI—but the presence of social, contextual, and experiential knowledge work.
5. Not all AI is the same – and that matters
   Another important nuance in the study: Different AI subdomains have different effects. In particular: Language modelling, speech recognition, reading comprehension show positive associations with employment across all skill groups.
   This is highly relevant for organisations currently introducing generative AI: These systems are not only automation tools – they are collaboration technologies. They amplify communication, documentation, and coordination – core KM processes, if used correctyl and not as a Google 2.0.

What does this mean for knowledge management?

If we step back, the findings point to a shift in the KM agenda.

From:

• Capturing knowledge
• Structuring information
• Reducing redundancy

To:

• Enabling skill development
• Supporting AI-augmented workflows
• Strengthening social knowledge processes

Or more provocatively:
The real risk is not that AI replaces knowledge workers.
The real risk is that organisations fail to redefine knowledge work.

So, who should be afraid?

The answer from the study is clear: It depends on the tasks you perform.

Be concerned if your work is:

• Highly standardised
• Easily codified
• Low in social interaction

Be optimistic if your work involves:

• Interpretation and judgment
• Cross-context integration
• Collaboration and sensemaking

However, AI does not primarily eliminate work. It reshapes the skill profile of work. Which is not a surprise, is it?
But what are the implications for knowledge management?

• We need better ways to develop and transfer higher-order skills.
• We need to redesign knowledge processes for human–AI collaboration.
• And we need to take tacit and social knowledge more seriously than ever.

Because in the end, the question is: Who is prepared to work differently with knowledge?

And another question that arises from this is: What does this mean for young people entering the workforce?
To be continued.

Maximilian Wendt ist dabei kreativ übers Ziel hinausgeschossen und hat anstelle einer einfachen Visualisierung gleich ein Modell für Wissensmanagement erstellt: sein Münchner Haus des Wissens (München, weil sich im oberen Stockwerk Anklänge an das Münchner Modell nach Reinmann finden). Andere Inspirationsquellen von Max finden sich im kleinen einleitenden Reim.

Auch dieses wunderbare Arbeitsergebnis dieser kleinen Studienaufgabe wollte ich euch nicht vorenthalten. Ich finde es sehr inspirierend. Vielen Dank, Max!

In diesem Semester haben Nils Weber und Maximilian Fetzer gemeinsam das Konzept der Unlearning Organisation visualisiert, und zwar im Gegensatz zur Forgetting Organisation. Das ist ihnen sehr gut gelungen, weshalb ich ihre Visualisierung hier gerne teile. Und weshalb ich nun auch gar nichts weiter zu den Konzepten Unlearning und Forgetting sage, weil sich dies aus dem Bild einfach erschließt.

Vielen Dank für die Erlaubnis zur Veröffentlichung, Nils und Max!

These principles span the entire drug‑development lifecycle: preclinical research, clinical trials, and manufacturing. This expansion is not only regulatory in nature—it also has important implications for how organisations learn, govern knowledge, and stabilise expertise around AI.

Human‑centric by design: making responsibility explicit
The emphasis on human‑centric and ethical design reframes AI as a socio‑technical system rather than a purely technical artefact. Human oversight is no longer assumed; it is a design requirement.
From a KM perspective, this matters because it forces organisations to make responsibility, judgement, and decision authority explicit—and therefore learnable. Tacit assumptions about “what the system does” or “who decides in the end” no longer suffice. AI design becomes a vehicle for clarifying roles, expectations, and accountability structures that are central to organisational knowledge.

AI systems as part of the GxP (Good Practices in Pharma) knowledge landscape
The explicit requirement for GxP compliance positions AI systems firmly within the organisation’s regulated knowledge infrastructure.
For AI‑enabled computerized systems, e.g. analytical decision‑support systems, this implies:

• structured data governance as a shared organisational memory (not just a technical safeguard)
• quality management across the entire AI lifecycle, turning development, deployment, monitoring, and change into learning loops rather than isolated events

In KM terms, AI is treated as institutionalised knowledge—codified, governed, audited, and continuously maintained.

Proportionate validation as organisational sense‑making
The call for risk‑based, proportionate validation once again supports a move away from schematic compliance towards contextual judgement.
This aligns closely with organisational learning: validation becomes an ongoing process of sense‑making about risk, impact, and uncertainty, rather than a checklist exercise. Different AI systems require different depths of scrutiny—not because standards are weakened, but because learning is situated.

Performance beyond metrics: learning in use
By extending performance evaluation beyond isolated model metrics to include human–AI interaction, the principles acknowledge an old KM insight: systems only reveal their quality in practice.
Interpretability and explainability are not technical luxuries; they are prerequisites for:

• shared understanding
• justified trust
• reflective use

An AI system that cannot be meaningfully explained cannot become part of an organisation’s collective knowledge—no matter how accurate it is.

Plain language and monitoring: sustaining knowledge over time
Two further aspects strengthen the learning dimension.
First, the requirement for plain‑language communication treats understanding as a quality attribute. Knowledge about AI functionality, limits, and changes must be accessible—not only to experts, but to users and, where relevant, patients.
Second, the focus on continuous monitoring and data drift reinforces the idea that AI systems are never “finished”. They evolve with their context. Managing them therefore means learning over time, detecting change, and deliberately updating both models and organisational understanding.

My conclusion:
Seen through a KM lens, the EMA–FDA principles do more than regulate AI. They provide a framework for embedding AI into organisational learning structures – through transparency, lifecycle thinking, and explicit responsibility. Reliable AI, in this sense, is not primarily a technical achievement. It is the outcome of organisations that are able to learn about their systems, their data, and their own practices—continuously and collectively.

Whilst many companies are still experimenting with prototypes, the medical sector has already established a strict framework for the safe use of these technologies: Good Machine Learning Practice (GMLP).
These ten principles, developed by leading regulatory bodies such as the FDA (US Food and Drug Administration) and the IMDRF (International Medical Device Regulators Forum), provide a blueprint for robust and trustworthy AI systems, not only in the regulated field of medical technology.

Here are the key GMLP principles:

1. Interdisciplinary teams rather than siloed solutions
   An AI model is only as good as the understanding of its intended purpose. GMLP requires experts from various disciplines to collaborate throughout the entire lifecycle. Engineers, data specialists and the actual end-users must work together to define the clinical or business benefits the AI is intended to deliver, as well as the risks involved.
2. Sound software engineering and security
   AI is software – and should be treated as such. This means consistently applying best practices in software engineering, cybersecurity and risk management. A methodical design process ensures that decisions are traceable and that the integrity and authenticity of the data are maintained.
3. Data quality and representativeness
   A common mistake in AI projects is a ‘bias’ in the data. GMLP stipulates that datasets must reflect the actual target population. If, for example, only data from a specific user group is used, the model will fail in the real world. Careful data governance is key here.
4. Strict separation of training and test data
   To objectively evaluate a model’s performance, the training and test datasets must be strictly independent of one another. This prevents ‘label leakage’ – a phenomenon whereby the model ‘guesses’ the outcome based on hidden clues in the training data, rather than learning genuine patterns. Only through independent testing can it be demonstrated that the AI also works with new, unknown data.
5. Suitability of reference standards
   Use of the best available (clinical) methods for generating reference data
6. Tailored model design
   The model design must be suited to the available data and the intended use in order to minimise risks such as overfitting
7. Focus on human-AI interaction
   In practice, AI rarely operates entirely independently. GMLP therefore focuses on the interaction between humans and machines. It is essential to ensure that users can interpret the AI’s outputs correctly and that no dangerous over-reliance on the system develops. Transparency and clear information about the system’s limitations are essential for this.
8. (Clinically) Relevant test conditions
   Testing is carried out under conditions that simulate real-world clinical use
9. Clear user information
   Users are provided with transparent information about the system’s capabilities, limitations and updates/re-training.
10. Control does not stop after roll-out
    An ML model is not a static product. GMLP requires continuous monitoring of performance in real-world use (‘post-market monitoring’). This allows performance declines caused by changing data patterns (dataset drift) to be detected at an early stage and risks associated with retraining the model to be managed.

At first glance, applying these principles may seem like a lot of work. However, it is just necessary if we should build trust in AI, isn’t it? And for me the main message is: „It is the controlled data quality, stupid“.

Thank you very much to all the attendees for the valuable feedback that I will take back to our working group, which is constantly updating the catalogue. The next version is on the way!

Als Erwachsene können wir entscheiden, welche geistigen Prozesse wir an eine KI auslagern und wo wir uns bewusst dieser Faulheit widersetzen. Dazu nutzen wir unseren kritischen Verstand. Doch wie steht es um Kinder und Jugendliche, deren Mut sich des eigenen Verstandes zu bedienen – das Kantsche ’sapere aude‘ – gar nicht erst entwickelt wird, weil sie die anstrengende Reibung, die ein entsprechender Lernprozess bedeutet, vermeiden können?

Und was macht diese Entwicklung mit unserer Gesellschaft?

Unsere liberalen Demokratien beruhen auf mündigen Bürgern und Bürgerinnen, wie sie die Philosophen der Aufklärung beschreiben. Also Menschen, die sich ihres Verstandes bedienen und die Meinungen kritisch hinterfragen, auch ihre eigene. Wer nicht selbst denkt, wird alles glauben.

Und das, was wir da denken lassen, wird immer homogener, denn schließlich beruht generative KI auf Statistik und Wahrscheinlichkeit. „In großem Maßstab wird das, was als statistisches Musterlernen beginnt, zu einer generativen Kraft, die zentrale Tendenzen bevorzugt (…)“, so der Informatiker Zhivar Sourati.

Das Risiko, vor dem unsere Gesellschaften stehen, ist nicht die imaginäre Super-KI, die irgendwann die Weltherrschaft an sich reißt. Es ist unser selbst gewählter Weg in eine vor-aufklärerische Unmündigkeit – aus schierer Bequemlichkeit. Warum nur muss ich seit einiger Zeit immer wieder an den Animationsfilm Wall-E denken?

Vielleicht etwas pathetisch, aber ich ende mit einem hochaktuellen 300 Jahre alten Zitat:
„Aufklärung ist der Ausgang des Menschen aus seiner selbstverschuldeten Unmündigkeit. Unmündigkeit ist das Unvermögen, sich seines Verstandes ohne Leitung eines anderen zu bedienen. Selbstverschuldet ist diese Unmündigkeit, wenn die Ursache derselben nicht am Mangel des Verstandes, sondern der Entschließung und des Mutes liegt, sich seiner ohne Leitung eines anderen zu bedienen. Sapere aude! Habe Mut, dich deines eigenen Verstandes zu bedienen! ist also der Wahlspruch der Aufklärung.“