The EU AI Act is here, and it shifts AI from a "research project" to a regulated component. For engineering teams, this means new requirements for data lineage, explainability, and risk logging.
Beyond the "Black Box"
The core of the regulation is transparency. High-risk AI systems (like those in HR, Credit Scoring, or Critical Infrastructure) can no longer be black boxes. You must be able to explain why the model made a decision.
Engineering Implications
This changes how we build:
- Data Lineage: We must track the provenance of every training dataset.
- RAG vs Fine-Tuning: For many enterprises, Retrieval-Augmented Generation (RAG) is safer than fine-tuning because the source of truth (the retrieved document) is explicit and auditable.
- Human-in-the-loop: Workflows must be designed to allow human override of AI decisions.
Preparing for 2026
Start by auditing your current AI pilots. Classify them by risk level under the Act. Most chatbots are "Limited Risk" but still require transparency notices ("You are talking to an AI").