The EU AI Act for business: what it is and how to prepare

What is the EU AI Act? The first European regulation on AI

The EU AI Act is the European regulation that sets common rules for placing AI systems on the market and using them within the European Union. It is often called the first complete legal framework dedicated to AI: where rules like the GDPR govern personal data, the AI Act focuses on artificial intelligence systems themselves.

Its core idea is a risk-based approach. The regulation does not treat all AI the same way: the more a system can affect people’s safety or rights, the stricter the obligations. A spam filter and a system that assesses access to credit do not pose the same risks, and the rules reflect that.

Another key principle is transparency: where required, people should know when they are interacting with an AI system, or when content has been generated or manipulated by artificial intelligence.

The obligations the AI Act introduces are graduated and apply on a staggered timeline: different categories of rules become applicable at different times. So rather than memorising a single date, companies are better off understanding which category their systems fall into and preparing accordingly.

Who does the AI Act apply to? Roles and risk tiers

The AI Act has a broad reach. In general terms it concerns those who develop and make AI systems available (providers) and those who use them in a professional context (deployers). So a company that does not build AI but uses it in its processes still falls within scope.

The regulation can also reach beyond the borders of the EU: it may concern operators established elsewhere when the outputs of their AI systems are used within the Union. This is why many international organisations consider it relevant regardless of where they are based.

At the heart of the AI Act is the classification of systems by risk level. In simplified terms, a few tiers can be distinguished.

Unacceptable risk: certain AI practices, seen as a clear threat to people’s rights, are prohibited.

High risk: systems used in sensitive areas — for example in relation to employment, essential services, or safety — are subject to the strictest obligations, such as risk management, data quality, documentation, human oversight, and traceability.

Limited risk: here transparency obligations matter most, such as informing people that they are interacting with an AI system.

Minimal risk: this covers most everyday AI systems, for which obligations are light.

There are also specific rules for general-purpose AI models (the large models behind many applications), especially on transparency. Because classification depends on actual use, the first step for a company is to understand which categories its use cases fall into — an analysis best carried out with compliance and, where needed, legal support.

The AI Act: what your company should start doing now to prepare

Even without knowing every technical detail, a company can start building the right foundations now. Most of the preparation work is, at heart, good AI governance.

Inventory the AI you use. The starting point is to map where and how artificial intelligence is already present in your processes: which systems, for which purposes, with which data, and from which providers. You cannot govern what you do not know you have.

Classify use cases by risk. Once mapped, systems should be assessed by their potential impact on people. This helps focus attention on the most sensitive uses.

Define roles and governance. Establish who is responsible for AI adoption, how new use cases are approved, and which internal policies govern them.

Ensure human oversight. For the most sensitive uses, people must be able to understand, review, and — if necessary — correct or stop decisions made with the support of AI. Human control is a recurring principle in the regulation.

Document and keep records. Being able to show how a system works, with which data and with which results, is central: logs, traceability of runs, and technical documentation are valuable allies.

Protect data. Much of AI runs on data, often personal: data protection and GDPR compliance remain a pillar, as the next section shows.

The AI Act and GDPR: how the two frameworks fit together

The AI Act does not replace the GDPR: the two frameworks coexist and complement each other. If an AI system processes personal data, the GDPR still applies in full, with its principles of lawfulness, minimisation, transparency, and accountability.

Put simply: the GDPR governs how personal data is processed; the AI Act governs how AI systems are designed and used. A high-risk AI system that processes personal data will therefore need to satisfy both sets of rules.

For companies this is good news more than a problem: much of the work already done for the GDPR — data governance, impact assessments, records of processing, access control — forms a solid basis for preparing for the AI Act too. Those who have set up data protection well start ahead.

The common thread is the same: know which data you use, for which purposes, with which safeguards — and be able to demonstrate it.

How an enterprise AI platform helps you prepare for the AI Act

No tool, on its own, makes a company “AI-Act compliant”: compliance depends on how the whole organisation governs AI. What a well-designed enterprise AI platform can do is help you prepare, making it easier to put the principles the regulation calls for into practice. This is where Alomana sits: the AI operating system for autonomous companies.

Data that stays under control. With Alomana each customer runs a dedicated single-tenant instance, hosted in the European Union: data never touches shared infrastructure and is never used to train models. On top of that sit ISO 27001 certification and GDPR compliance — the same foundations that also help you prepare for the AI Act.

Traceability of every run. Every operator run is recorded in audit logs: you can reconstruct what the system did, with which data, and with what result. This traceability is exactly the kind of documentation AI governance calls for.

Human oversight built in. Alomana builds operators, not copilots: they run the work end-to-end, but with a person in the loop for the exceptions, approvals, and decisions that matter. Human control is part of how it works, not an add-on.

Traceable, reviewable results. Alomana logs every run end-to-end — input, output, tools used, and user — so any result can be reviewed and traced back to its origin. A concrete help when you need to show how a system behaves.

In short, a platform like this does not “certify” you, but it puts you in a position to prepare: governed data, traceable runs, people in the loop, and verifiable outputs.

Choosing AI tooling with the AI Act in mind

If you are evaluating AI tools to adopt in your company, the AI Act offers a useful lens for asking vendors the right questions: preparation depends in large part on technology choices.

Where does my data live, and how is it isolated? Favour solutions with EU hosting and single-tenant isolation, where your data does not feed third-party models.

Is my data used to train models? The answer should be no — and it should be in writing.

What traceability does the tool offer? Look for audit logs and the ability to reconstruct every run: they are essential for documentation and review.

How is human control provided for? Check that people can supervise, approve, and intervene, especially in the most sensitive uses.

What security and compliance guarantees does the vendor offer? Certifications such as ISO 27001 and GDPR compliance are a good indicator of maturity.

Choosing tools that build in these principles does not automatically make you compliant, but it reduces friction: starting from a platform designed for governed data, traceable runs, and human oversight makes it far easier to prepare as obligations become applicable.