The EU Artificial Intelligence Act (AI Act) is no longer theoretical. As compliance timelines begin to tighten, every organization building or deploying AI systems needs to prepare for one thing: scrutiny. Whether it’s a regulator, a notified body, or an internal auditor, your reviewers will expect to see clear evidence that you understand where your AI systems fall under the Act — and that you can prove compliance, not just claim it. This article turns the AI Act’s requirements into a practical inspection checklist you can actually use from day one.

Business Intelligence (BI) promised data-driven decision-making — yet most organizations are still stuck in the dashboard trap. Endless KPIs, no context, and too much latency between insight and action. Decision Intelligence (DI) is the next step: connecting data, models, and human judgment into a measurable decision flow. In 90 days, you can go from scattered dashboards to decisions you can trace, audit, and improve.

You can’t govern what you can’t trust. Yet many organizations still treat data quality as a technical afterthought — until an auditor, partner, or regulator asks for proof. Under the EU AI Act and GDPR, “trustworthy AI” starts with trustworthy data. Data quality (DQ) is not a nice-to-have; it’s an auditable control. And with a structured scorecard, you can not only measure it — but show evidence that it’s improving.

The EU AI Act is built on a simple idea: governance is not a document — it’s a daily practice. But most organizations still treat compliance as a project to finish, not a process to run. If your AI governance doesn’t fit into your operational rhythm — product releases, data updates, incident handling — it won’t hold up to review. The goal is not more paperwork, but evidence that someone owns every duty, every day.