Published 08/05/2026
Written by
Dr. Chantal Spleiss
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Summary #
AI excels in closed systems; the real world, though, is open. When real-world data is analyzed through high-speed correlation but without anchoring the outputs in context, data driven decision-making is at risk. Humanity eliminated the "Jack of all Trades" — the integrator — and is now replicating that fragmented, multi-expert culture into AI, emphasizing silos while trying to escape them.
A possible solution is to embed an integrator directly into the architecture: a meta-cognitive core that links specialized agents, questions their assumptions, reframes problems, and acknowledges the boundaries of its own box. Such an "AI Jack of all Trades" would not only improve the reliability of current systems but could open the door to superintelligence.
With the ability of meta-cognition comes a danger as well as a choice. This is the blueprint, the warning, and the invitation to build such systems with humility and awareness because: what can be done, will be done. How we do it — and who we become in the process — is still ours to shape.
1. Are experts “mostly harmless”? #
According to an MIT report in 2025[1], a staggering 95% of AI pilots do not generate revenue updating numbers of around 50% (Gartner)[2] and 80% (RAND)[3] reported in 2024. Conclusion: AI is not improving on ROI across industries despite the theoretical development of artificial intelligence itself is proceeding fast and the hype goes on. Where is the gap?
The gap is between AI performance in closed systems (like math) versus AI performance in open systems (like the “real world”).
AI is a master of high-speed correlation and pattern recognition. In closed systems with a clear set of rules, AI already outperforms human capabilities. But companies reside in the “real world”, in the messy, chaotic, dynamic “real world”. For AI to be reliably predictive in an open environment, it needs to anchor the correlations in context. And it needs “common sense”.
The gap has its root in the misconception that AI performing stunningly well in closed systems, but is not designed to be reliable in open environments [Figure 1]!
Figure 1: Agentic AI in closed versus open environments
Why do we need AI to perform reliable in open systems – the domain of (originally) human competence?
Most companies are fragmented and organized in silos. Process optimization. Sounds familiar? The goal was to break workflows into clear, measurable tasks with defined boundaries. It's efficient and quantifiable. Success is measurable. Failure can be tracked down. All good?
No, because we fired the person whose job it was to connect those tasks: the “Jack of all Trades”. Now a customer gets handed from silo to silo while everyone repeats, "I'm just doing my job." Remember Asterix’ and Obelix’ A38 adventure? That’s process optimization ad ultimum, but without integration. Orchestration coordinates outputs. Integration changes the process landscape[4]. And that's exactly why most AI pilots fail: they lack integration.
The integrator, the “Jack of all Trades” needs to know enough about each silo to facilitate seamless integration. “Jack” needs to envision and drive the big picture while still being able to talk to experts as well as the business and grasp all their unique requirements for a productive solution. Now, we are betting on AI to solve the integration challenge.
Currently, human and artificial systems base their input on data analytics coming from silos. This is called “data driven decisions”. No creativity. No intuition. Even though “data driven decisions” are deterministic, trackable, compliant, they might not be as harmless as they appear... Some companies already are realizing that business decisions based on data alone are not working. But they also realize that it is difficult to move beyond the concept — not because the process can't be adapted, but because the culture needs to be changed.
*“Culture eats strategy for breakfast” *(Peter Ducker)
Our culture rewarded experts for decades. Thinking out of the box has mostly manifested in arts and hardly found its way into the corporate environment. To build an AI that is able to connect the dots, we must acknowledge the box, its limits and use meta-cognition to generate integrated, useful and safe solutions that are grounded in the context of the “real world”.
But where is meta-cognition located in the control plane of Agentic AI and what is its primary goal [Figure 2]?
Figure 2: Agentic AI - Layers of Control
2. Life, the Universe and Everything: Nature’s Answer #
Nature has been working on the concept of integration for billions of years - and solved it through communication and collaboration. Nature cultivates highly specialized experts – but it connects the dots.
The journey from a single cell to a highly developed multi-cell organism started with communication between single-cell organisms. Chemical signaling allowed individual cells to sense their neighbors, synchronize their behavior, and act as a coordinated whole. It allowed for cooperation and is the very basis on which evolution has been building complex organisms. The human brain, for example, is a highly specialized group of expert systems – but: they are governed by a meta-reasoning function. Let’s call this function “Jack” for now. “Jack” doesn’t know everything but enough to cross-examine, weigh confidence, reframe (if indicated), and: hesitate to evaluate the consequence of different strategies within the given context.
Let this sink in. The leap from isolated cells to the human brain didn’t come from better cells; it came from better communication and collaboration. The integrator was the innovation and allowed the evolution of highly specialized experts functioning within an organism [Figure 3].