Marcus Belke
CEO of 2B Advice GmbH, driving innovation in privacy compliance and risk management and leading the development of Ailance, the next-generation compliance platform.
The debate about artificial intelligence is often conducted at the wrong level. Much of the discussion still revolves around the question of whether AI is „really“ intelligent, whether generative AI only works with probabilities or whether machines can ever achieve a form of thinking comparable to that of humans. For companies, supervision, Compliance and management is not the decisive question.
The crucial question is: When does a system deliver equivalent or better results than a human being in a clearly defined task area?
The debate needs a precise term for precisely this point: Equivalent intelligence.
When is equivalent intelligence present?
Equivalent intelligence is when an AI system consistently delivers equivalent or better results than a human in a defined task area. Not philosophical. Not romantically. But functional, operational and verifiable.
This shifts the AI debate to the point where it becomes relevant for companies in the first place. Not anymore: What is intelligence in the metaphysical sense? But rather: Where has it already reached as a result?
This is not a theoretical point. The widespread objection that generative AI is „just statistics“ does not hold water. Human perception, language, judgment and memory also work under uncertainty, with heuristics, approximations and error correction. The statistical nature of machine systems is therefore not a counter-argument against intelligence, but merely describes how they work.
The real dividing line between humans and AI lies elsewhere: not only in intelligence, but also in autonomy, normative evaluation and responsibility.
People set their own goals. They decide what is important. They prioritize from their own perspective. They live with the consequences of their decisions. AI can already analyze, structure, formulate, classify, compare and derive recommendations in many areas. But it does not set its own legitimate goals. It bears no responsibility. And it has no biographical or normative self-commitment.
This is the real consequence for AI Governance.
As soon as a system Equivalent intelligence you no longer just regulate software. You regulate delegated judgment.
This is where traditional governance approaches fall short. Anyone who only treats AI as a technical tool underestimates its organizational impact. Those who only talk about ethics or awareness miss the operational reality. In companies, powerful AI systems have long influenced what is considered plausible, which risks become visible, which priorities are set and which decisions are prepared.
AI governance thus becomes a question of AI compliance, AI risk management and organizational control capability.
Governance becomes mandatory for AI use
Today, companies not only face the challenge of using AI, but also of documenting, evaluating, approving and continuously monitoring it in a structured manner. It is precisely this direction that is being reinforced by regulation. Since February 2, 2025, the bans on certain AI practices and AI literacy obligations have already been in force. Governance rules and obligations for general-purpose AI models have been in force since August 2, 2025. From August 2, 2026, the majority of the AI Act rules will become applicable; transparency obligations and the requirements for many high-risk AI systems will then apply in practice.
Anyone who still treats AI governance as a later formality is mistaken. Governance is no longer a documentation exercise. It is a prerequisite for using AI in the organization in a secure, scalable and auditable way.
This is precisely where a specialized AI Governance Platform on. The correct answer to Equivalent intelligence is not an abstract concern, but an operational structure. Companies need a Central AI registera Inventory of all AI applications, a resilient Risk classification, Model cards, Role-based approval workflows, DPIA trigger, Audit Trails, Review cycles and a Compliance Dashboard for management, Data protection, security and specialist departments. It is precisely these elements that Ailance describes as the core of its AI governance approach.
This is more than just product functionality. It is the actual organizational answer to powerful AI.
Because where Equivalent intelligence loose policies, Excel lists or e-mail shares are no longer sufficient. This is exactly what your internal positioning is all about: ad hoc solutions with Excel, SharePoint or wiki pages are not consistent, not enforceable, not scalable and not auditable. Ailance therefore explicitly positions itself as a modular AI governance solution with a lifecycle focus instead of a generic GRC add-on.
Reading tip: Successfully implementing AI governance
What points must AI governance in the company include?
So the crucial point is this: AI governance must clearly separate intelligence, autonomy and responsibility.
First: Intelligence concerns the quality of cognitive performance.
When a system evaluates use cases in a structured way and recognizes risks, Documentation prepared or decisions pre-structured, it can already be Equivalent intelligence have achieved in some areas.
Secondly: Autonomy concerns the ability to set goals and determine priorities legitimately.
This level still lies with humans and the organization. Even a very powerful AI system does not have the right to control itself.
Thirdly: Responsibility concerns the attribution of the consequences.
Because AI itself has no responsibility, human responsibility must be made more organizationally rigorous, not less. This is precisely why AI governance needs clear roles, defined approvals, escalation logic and audit-proof evidence.
The practical consequence is harsh: a „human in the loop“ is not enough if this human only nods at the AI output. Then human control is no longer control, but liability decoration. Good AI governance therefore not only asks whether there is a human in the process, but whether this human is actually superior to the system in the specific control function.
This results in a new benchmark for companies:
Not anymore: Is this just an AI tool?
But rather: Where has the system already Equivalent intelligence achieved - and what governance must follow from this?
A specialized solution is needed for precisely this operational translation. Ailance describes the right building blocks here: AI Use Case Register, Risk assessment and classification, Model cards, Automated authorizations, Monitoring, Re-audits, DPIA automation, Integration with data protection, risk and IT governance processes and auditable reports. This is not aimed at abstract sets of rules, but at the controllable management of the entire AI life cycle - from the idea to the review.
The strategic value of equivalent intelligence
This also creates a clear market positioning:
Not governance „via Excel“.
Not abstract policies without enforcement.
Non-generic GRC tools without AI-specific risk models.
But AI governance by design.
This is precisely where the strategic value lies. AI governance will not become more important in the coming years primarily because machines are becoming „human“. But because they will be used in more and more Equivalent intelligence without being the bearer of responsibility themselves.
The stronger the Equivalent intelligence, the greater the duty of governance.
The central sentence is therefore:
AI governance really begins where AI no longer just delivers efficiency, but Equivalent intelligence - and thus delegated judgment.
Those who understand this are not just building a tool landscape. They build a resilient system for EU AI Act Compliance, AI risk management, model governance, Audit Readiness and responsible innovation.
And this is precisely where modern corporate management must start.
Marcus Belke is CEO of 2B Advice as well as a lawyer and IT expert for data protection and digital Compliance. He writes regularly about AI governance, GDPR compliance and risk management. You can find out more about him on his Author profile page.
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