Most AI failures are not technical. They are organizational.

Mostly because organizations invest heavily in AI models, data, and tools. But they underinvest in structures, decision rights, and coordination.

This creates a silent risk.

According to recent 2025 industry reports, more than 50 percent of AI initiatives stall after pilot stage because governance is fragmented across IT, legal, and business units [1].

At the same time, academic studies confirm that AI value depends less on algorithms and more on how decisions around AI are made, monitored, and owned [2], [3].

Therefore, I can say that:

AI governance is not a control layer added at the end.
It is a design choice embedded in the organization.

What does that mean in practice?

• Clear ownership across the AI lifecycle

• Alignment between strategy, data, and operations

• Governance routines that evolve as systems learn

• Continuous oversight, not one-time approval

Without this, AI becomes unstable.

Risk grows. Trust drops. Value disappears.

For organizations, this matters now more than ever.

With the EU AI Act, governance gaps will turn into legal and financial exposure.

At Centralink, we help leaders translate AI governance research into practical operating models.

Not theory. Not checklists.
Structures that work under real pressure.

If your AI system makes a wrong decision today, who owns it tomorrow?

Contact info@centralink.nl for a free consulting session.

References

[1] McKinsey & Company. (2025). The state of AI governance and risk.

[2] Janssen, M., et al. (2022). Governing artificial intelligence: Organizational and institutional challenges. Information Systems Frontiers, 24, 1–16.

[3] Deloitte. (2025). Responsible AI governance in practice.