Artificial Intelligence has moved from research labs into boardrooms, but too often it is surrounded by hype, inflated promises, and misguided investments. Many organizations rush to “do something with AI” without considering trust, data confidentiality, or the actual problems they are trying to solve.
Where ChatGPT cowboys ride the hype, we need a clear look at AI:
It is a pattern recognition machine. It has no brain. We still need to use our own. It does not replace humans, it complements them. Why we should start thinking in feedback loops.
Artificial Intelligence does not decide Europe’s future. Human decisions do. In this talk, I show what you can do differently starting tomorrow: which skills to build, how to choose AI tools, and how to reduce dependencies. No moral finger-wagging, just straight talk.
Imagine it is 2046. And your most important conversation is not with a human. What remains of autonomy, responsibility, and purpose? A look into the AI future, in three scenarios.
AI is everywhere, between hype, hope, and knee-jerk reactions. But engineering is more than output: responsibility, systems thinking, and decision quality matter.
What This Talk Covers # In this talk, Romano Roth, Chief AI Officer at Zühlke, shows where AI already delivers reliable value in engineering today, where it fails, and what new skills and ways of working are emerging. With concrete examples from everyday practice.
Banks spend 93% of their AI budget on technology. Only 7% goes to people. And then they wonder why 95% see no impact on their P&L.
On March 31, 2026, I gave a keynote at the Zühlke Banking Talk in Schlieren on why the future of AI is “cybernetic” and what it takes to become an AI-native bank. The evening brought together perspectives from academia, a major German bank, and practitioners from Swiss financial institutions.
How to build adaptive, AI-native organizations where people, technology, data, and AI models work together efficiently.
$30 to 40 billion invested in generative AI globally. 95% of companies see no impact on their P&L. Only 5% create real value. Companies spend 93% on technology and only 7% on people. What is missing is not the next tool. What is missing is the ability to steer and adapt.
It is not the technology that is under pressure, but its integration into everyday business operations. Budgets are tightening, regulation is taking hold, and boards are demanding robust results rather than more roadmaps. The era of non-committal pilot projects is ending.
Summary # The NZZ article examines how tech giants like Meta, OpenAI, and Nvidia are now pouring billions into AI agents after already spending massive amounts on large language models. Meta acquired Moltbook (a “social network for AI bots”) and spent $2 billion on Manus. OpenAI acquired Openclaw for its autonomous AI agents. Critics see this as a “flight forward,” with companies investing even more money to solve the problem of poor returns on their LLM investments.
Although AI has moved fast, many organisations haven’t. Most leaders we speak to aren’t short on ideas, proofs of concept, or vendor demos. The challenge is turning AI into something repeatable, a capability you can trust, scale, and steer without creating new risks or bottlenecks.
To stay competitive, companies need a radical upgrade. The Cybernetic Enterprise transforms organizations into learning systems that adapt faster than their market changes.
2026 will be the year when the wheat is separated from the chaff in AI: it’s not the better model that makes the difference, but the ability to deliver impact under real-world conditions. What decision-makers need to know and do now so that AI evolves from experiment to true partner.
