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Articles tagged governance
18 articles about governance.
AI literacy: what the AI Act asks of employees, management and supervisors
AI literacy is knowing what AI can do, what it can't and where the risks are. What the AI Act still requires after the Digital Omnibus, and how to approach it by role.
Stop rules for AI: set your exit criteria before go-live
Stop rules are the criteria, set in advance, for pausing or stopping an AI application. Which criteria you need, who decides and how to actually switch it off.
NIS2 and the Dutch Cybersecurity Act: what your data and AI layer must be able to prove
The Dutch Cybersecurity Act (NIS2) has applied since 15 August 2026. What it requires, who is in scope and what your data platform and AI must be able to demonstrate.
The AI Act in the Netherlands: which law applies and who supervises
The AI Act applies directly. The Dutch implementation act sets who supervises: ten authorities, with the AP and RDI coordinating.
What is the EU AI Act? What applies now and what applies from December 2027
The AI Act regulates AI by risk. Which obligations already apply, what the Digital Omnibus postponed, and what your organisation needs to arrange now.
Five questions a supervisory board can ask about AI every quarter
Overseeing AI takes no technical knowledge, just the right questions. Five quarterly questions for the supervisory board, what a good answer looks like and when to probe further.
AI governance for executive and supervisory boards: value, ethics and stop rules
AI governance is the agreement on what AI may do, who is accountable and when you stop. A three-question framework for boards and supervisors.
Why a semantic layer isn't enough for AI
A semantic layer defines your metrics. AI needs meaning that is governed, reaches the operation, and stays yours.
What is master data, and why does your reporting depend on it?
Customer, item, location and supplier: the data that shows up everywhere. When it is wrong, no report above it is right.
Sensitive data in Power BI: row-level, object-level and the layer beneath
Who may see which number is an agreement, not a report setting. How to enforce it in Power BI, and where the report stops.
What is a data platform? The layers, the choices, the pitfalls
A data platform is not a tool you switch on. It is the layer where your data comes together, gains meaning and becomes reusable. Here is what that layer looks like.
Connect once, not once per agent
Every agent you add has to be identified, authorised, networked and trusted, and then kept that way. You solve that once, centrally, or you turn it into a permanent job that grows with every agent you run.
From your critical Excel file to the Datahub company brain
Start with your critical Excel file in Datahub. Capture definitions and source references and prove the company brain’s value before expanding the platform.
An agent's bill is set before it runs
A customer interaction that cost four cents in 2023 now costs about $1.20. The token bill is the visible part; what an agent really costs is decided by the foundation it runs on.
What does AI Native mean?
AI Native does not mean you bought AI. It means your organization is set up so intelligence can do real work in it: definitions captured, data reliable, decisions owned.
AI is not a miracle. It is craftsmanship.
AI does not fix chaos. What works is the order: processes first, then definitions, then trustworthy data. Only then does AI become the brain at the heart of your organization.
What is a data mesh?
A data mesh puts ownership of data with the domains doing the work instead of one central team. What that buys you, and what to settle first.
What is data architecture?
Data architecture is the layout that decides where data arrives, where meaning is recorded and where answers come from. Four layers, and the choices that recur.
Start with one decision that has to get better
Pick a recurring question where teams lose time or contradict each other. Make the meaning behind it explicit and bring it straight into daily operations.