Around 70% of global new value is already generated by digitally driven business models. Data is refined, products adapt in real time, and the experience replaces the one-time purchase experience with a lasting relationship between humans and machines.

Software-defined manufacturing decouples production logic from hardware, while large action models translate signals and intentions directly into actions, right down to automated robotics on the shop floor. The result is adaptive systems capable of learning that automate tasks that previously required human interaction.

It is not the lowest wage that matters, but the question of where information can be used most intelligently. Companies are monetizing sensor data, bringing internal QA software to market as a product, and networking to form ecosystems: a genuine realignment of industrial competitiveness.

​​AI is becoming the universal language of industry: language→code, language→legal text, and language→machine commands (LAMs) without any “translation losses.” What used to be reserved for experts is now just a prompt away.​

Teams are bridging disciplinary boundaries and moving faster from idea to prototype: requirements, designs, validation – processes that used to take months are now compressed into days. This unlocks hidden knowledge treasures from corporate silos and makes even sluggish industries agile.

In “manufacturing-as-a-service” and open factory scenarios, companies and their software teams share their code. This requires clear rules for quality, IP protection, and collaboration – in short, an Industry 5.0 etiquette that combines technology and cultural updates.

When every answer is just a keystroke away, there is a risk of losing deep reflection. Constantly delegating to models turns people into passive consumers of algorithmic solutions, with noticeable losses in problem-solving and creativity.

Generative models average out masses of data, which is both their strength and their limitation. Without conscious countermeasures, average logic eats away at edges, idiosyncrasies, and truly disruptive approaches. Companies need spaces where non-mainstream thinking is explicitly encouraged.

AI-free oases and touch-intensive activities become refuges for creative thinking and empathetic compassion. Nursing professions, for example, still have a very low probability of automation. Empathy remains “robot-proof” and gains in value.

Long-term studies show that EQ is four times more important than IQ for professional success. With AI as an “intelligence commodity,” the need for empathy, sense-making, and relationship building is increasing—skills that cannot be automated.

Leadership is shifting from controlling to coaching, staging a “we” and translating purpose into everyday life. The 4 I's (Idealized Influence, Intellectual Stimulation, Inspirational Motivation, Individualized Consideration) provide the framework for human-centered leadership in the AI age.

When expertise is democratized, the value of common rules increases. Companies are professionalizing “interpersonal skills”: conversation techniques, feedback, conflict resolution – so that efficiency gains are not lost to new bureaucratic friction.

The regulation sets global standards: risk classes, audit obligations, and a digital CE path. Critics warn of bureaucracy, but the GDPR effect is becoming apparent – international regulators are already evaluating which passages they will adapt to professionalize their own markets.

From open model and data platforms to new foundation models and billion-dollar initiatives, Europe's AI machinery is gaining traction beyond pure cloud dependencies. The decisive factor is the combination of research depth, industry proximity, and capital.

Thousands of hidden champions spread value creation more broadly and reduce cluster risks. “Data trust” is becoming a commodity – for example, when clinics prefer auditable EU clouds. And with dual-use innovations, Europe is showing what “democracy by design” can look like in security-critical domains.